Cassava dreg as replacement of corn in goat kid diets.

PubMed

Ferraz, Lucíola Vilarim; Guim, Adriana; Véras, Robson Magno Liberal; de Carvalho, Francisco Fernando Ramos; de Freitas, Marciela Thais Dino

2018-02-01

The effects of corn replacement by cassava dreg in diets of crossbred goat kids were evaluated. We tested the impacts of 0, 33, 66 and 100% replacement on intake, digestibility, feeding behaviour, performance and carcass characteristics. Thirty-six goat kids, aged between 4 and 5 months and with initial body weights of 17.61 ± 1.98 kg, were used in a completely randomised design. Analysis of regression revealed a negative linear effect on neutral detergent fibre (NDF) intake and a positive linear effect on non-fibrous carbohydrates (NFC) and hydrocyanic acids (HCN) intake. Cassava dreg use had a positive linear effect on organic matter digestibility and non-fibrous carbohydrates. Based on our results, cassava dreg use did not negatively impact animal performance, feeding behaviour and carcass characteristics, suggesting that it may replace corn up to 100% in the diets of confined goat kids.

Relationship of negative self-schemas and attachment styles with appearance schemas.

PubMed

Ledoux, Tracey; Winterowd, Carrie; Richardson, Tamara; Clark, Julie Dorton

2010-06-01

The purpose was to test, among women, the relationship between negative self-schemas and styles of attachment with men and women and two types of appearance investment (Self-evaluative and Motivational Salience). Predominantly Caucasian undergraduate women (N=194) completed a modified version of the Relationship Questionnaire, the Young Schema Questionnaire-Short Form, and the Appearance Schemas Inventory-Revised. Linear multiple regression analyses were conducted with Motivational Salience and Self-evaluative Salience of appearance serving as dependent variables and relevant demographic variables, negative self-schemas, and styles of attachment to men serving as independent variables. Styles of attachment to women were not entered into these regression models because Pearson correlations indicated they were not related to either dependent variable. Self-evaluative Salience of appearance was related to impaired autonomy and performance negative self-schema and the preoccupation style of attachment with men, while Motivational Salience of appearance was related only to the preoccupation style of attachment with men. 2010 Elsevier Ltd. All rights reserved.

Prediction of cotton resistance to Helicoverpa armigera based on the percent (+)-gossypol in mature seed

USDA-ARS?s Scientific Manuscript database

Various Uzbek commercial varieties were grown in the field and these were exposed to cotton bollworm (Helicoverpa armigera) larvae. A significant negative correlation coefficient (r = -0.89) and linear regression (Y = 109.69-5.26X) was observed between the concentration of (+)-gossypol in cotton se...

Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations

NASA Astrophysics Data System (ADS)

Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang

2018-02-01

To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.

Field Demonstration Report Applied Innovative Technologies for Characterization of Nitrocellulose- and Nitroglycerine Contaminated Buildings and Soils, Rev 1

DTIC Science & Technology

2007-01-05

positive / false negatives. The quantitative on-site methods were evaluated using linear regression analysis and relative percent difference (RPD) comparison...Conclusion ...............................................................................................3-9 3.2 Quantitative Analysis Using CRREL...3-37 3.3 Quantitative Analysis for NG by GC/TID.........................................................3-38 3.3.1 Introduction

The Relationship between Religious Coping and Self-Care Behaviors in Iranian Medical Students.

PubMed

Sharif Nia, Hamid; Pahlevan Sharif, Saeed; Goudarzian, Amir Hossein; Allen, Kelly A; Jamali, Saman; Heydari Gorji, Mohammad Ali

2017-12-01

In recent years, researchers have identified that coping strategies are an important contributor to an individual's life satisfaction and ability to manage stress. The positive relationship between religious copings, specifically, with physical and mental health has also been identified in some studies. Spirituality and religion have been discussed rigorously in research, but very few studies exist on religious coping. The aim of this study was to determine the relationship between religious coping methods (i.e., positive and negative religious coping) and self-care behaviors in Iranian medical students. This study used a cross-sectional design of 335 randomly selected students from Mazandaran University of Medical Sciences, Iran. A data collection tool comprised of the standard questionnaire of religious coping methods and questionnaire of self-care behaviors assessment was utilized. Data were analyzed using a two-sample t test assuming equal variances. Adjusted linear regression was used to evaluate the independent association of religious copings with self-care. Adjusted linear regression model indicated an independent significant association between positive (b = 4.616, 95% CI 4.234-4.999) and negative (b = -3.726, 95% CI -4.311 to -3.141) religious coping with self-care behaviors. Findings showed a linear relationship between religious coping and self-care behaviors. Further research with larger sample sizes in diverse populations is recommended.

Modeling workplace bullying using catastrophe theory.

PubMed

Escartin, J; Ceja, L; Navarro, J; Zapf, D

2013-10-01

Workplace bullying is defined as negative behaviors directed at organizational members or their work context that occur regularly and repeatedly over a period of time. Employees' perceptions of psychosocial safety climate, workplace bullying victimization, and workplace bullying perpetration were assessed within a sample of nearly 5,000 workers. Linear and nonlinear approaches were applied in order to model both continuous and sudden changes in workplace bullying. More specifically, the present study examines whether a nonlinear dynamical systems model (i.e., a cusp catastrophe model) is superior to the linear combination of variables for predicting the effect of psychosocial safety climate and workplace bullying victimization on workplace bullying perpetration. According to the AICc, and BIC indices, the linear regression model fits the data better than the cusp catastrophe model. The study concludes that some phenomena, especially unhealthy behaviors at work (like workplace bullying), may be better studied using linear approaches as opposed to nonlinear dynamical systems models. This can be explained through the healthy variability hypothesis, which argues that positive organizational behavior is likely to present nonlinear behavior, while a decrease in such variability may indicate the occurrence of negative behaviors at work.

Socio-economic factors associated with infant mortality in Italy: an ecological study.

PubMed

Dallolio, Laura; Di Gregori, Valentina; Lenzi, Jacopo; Franchino, Giuseppe; Calugi, Simona; Domenighetti, Gianfranco; Fantini, Maria Pia

2012-08-16

One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Associations between infant mortality rates in the 20 Italian regions (2006-2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15-64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = -0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = -0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels.

Measurement of pediatric regional cerebral blood flow from 6 months to 15 years of age in a clinical population.

PubMed

Carsin-Vu, Aline; Corouge, Isabelle; Commowick, Olivier; Bouzillé, Guillaume; Barillot, Christian; Ferré, Jean-Christophe; Proisy, Maia

2018-04-01

To investigate changes in cerebral blood flow (CBF) in gray matter (GM) between 6 months and 15 years of age and to provide CBF values for the brain, GM, white matter (WM), hemispheres and lobes. Between 2013 and 2016, we retrospectively included all clinical MRI examinations with arterial spin labeling (ASL). We excluded subjects with a condition potentially affecting brain perfusion. For each subject, mean values of CBF in the brain, GM, WM, hemispheres and lobes were calculated. GM CBF was fitted using linear, quadratic and cubic polynomial regression against age. Regression models were compared with Akaike's information criterion (AIC), and Likelihood Ratio tests. 84 children were included (44 females/40 males). Mean CBF values were 64.2 ± 13.8 mL/100 g/min in GM, and 29.3 ± 10.0 mL/100 g/min in WM. The best-fit model of brain perfusion was the cubic polynomial function (AIC = 672.7, versus respectively AIC = 673.9 and AIC = 674.1 with the linear negative function and the quadratic polynomial function). A statistically significant difference between the tested models demonstrating the superiority of the quadratic (p = 0.18) or cubic polynomial model (p = 0.06), over the negative linear regression model was not found. No effect of general anesthesia (p = 0.34) or of gender (p = 0.16) was found. we provided values for ASL CBF in the brain, GM, WM, hemispheres, and lobes over a wide pediatric age range, approximately showing inverted U-shaped changes in GM perfusion over the course of childhood. Copyright © 2018 Elsevier B.V. All rights reserved.

Changing perception: facial reanimation surgery improves attractiveness and decreases negative facial perception.

PubMed

Dey, Jacob K; Ishii, Masaru; Boahene, Kofi D O; Byrne, Patrick J; Ishii, Lisa E

2014-01-01

Determine the effect of facial reanimation surgery on observer-graded attractiveness and negative facial perception of patients with facial paralysis. Randomized controlled experiment. Ninety observers viewed images of paralyzed faces, smiling and in repose, before and after reanimation surgery, as well as normal comparison faces. Observers rated the attractiveness of each face and characterized the paralyzed faces by rating severity, disfigured/bothersome, and importance to repair. Iterated factor analysis indicated these highly correlated variables measure a common domain, so they were combined to create the disfigured, important to repair, bothersome, severity (DIBS) factor score. Mixed effects linear regression determined the effect of facial reanimation surgery on attractiveness and DIBS score. Facial paralysis induces an attractiveness penalty of 2.51 on a 10-point scale for faces in repose and 3.38 for smiling faces. Mixed effects linear regression showed that reanimation surgery improved attractiveness for faces both in repose and smiling by 0.84 (95% confidence interval [CI]: 0.67, 1.01) and 1.24 (95% CI: 1.07, 1.42) respectively. Planned hypothesis tests confirmed statistically significant differences in attractiveness ratings between postoperative and normal faces, indicating attractiveness was not completely normalized. Regression analysis also showed that reanimation surgery decreased DIBS by 0.807 (95% CI: 0.704, 0.911) for faces in repose and 0.989 (95% CI: 0.886, 1.093), an entire standard deviation, for smiling faces. Facial reanimation surgery increases attractiveness and decreases negative facial perception of patients with facial paralysis. These data emphasize the need to optimize reanimation surgery to restore not only function, but also symmetry and cosmesis to improve facial perception and patient quality of life. © 2013 The American Laryngological, Rhinological and Otological Society, Inc.

Community psychiatry: results of a public opinion survey.

PubMed

Lauber, Christoph; Nordt, Carlos; Haker, Helene; Falcato, Luis; Rössler, Wulf

2006-05-01

Mental health authorities must know the public's attitude to community psychiatry when planning community mental health services. However, previous studies have only investigated the impact of demographic variables on the attitude to community psychiatry. To assess the influence of psychological and sociological parameters on the public opinion of community psychiatry in Switzerland. Linear regression analyses of the results of a public opinion survey on a representative population sample in Switzerland (n = 1737). Most respondents have positive attitudes to community psychiatry. In the regression analysis (R2 adjusted = 21.2%), negative emotions towards mentally ill people as depicted in the vignette, great social distance, a positive attitude to restrictions, negative stereotypes, high rigidity and no participation in community activities significantly influenced negative attitudes to community psychiatry. Additionally, other parameters, e.g. contact with mentally ill people and the nationality of the interviewee, have a significant influence. In planning psychiatric community services, general individual traits and emotive issues should be considered because they influence the response towards community psychiatry facilities in the host community.

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

FPGA implementation of predictive degradation model for engine oil lifetime

NASA Astrophysics Data System (ADS)

Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

2018-03-01

This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

Influence of air humidity and the distance from the source on negative air ion concentration in indoor air.

PubMed

Wu, Chih Cheng; Lee, Grace W M; Yang, Shinhao; Yu, Kuo-Pin; Lou, Chia Ling

2006-10-15

Although negative air ionizer is commonly used for indoor air cleaning, few studies examine the concentration gradient of negative air ion (NAI) in indoor environments. This study investigated the concentration gradient of NAI at various relative humidities and distances form the source in indoor air. The NAI was generated by single-electrode negative electric discharge; the discharge was kept at dark discharge and 30.0 kV. The NAI concentrations were measured at various distances (10-900 cm) from the discharge electrode in order to identify the distribution of NAI in an indoor environment. The profile of NAI concentration was monitored at different relative humidities (38.1-73.6% RH) and room temperatures (25.2+/-1.4 degrees C). Experimental results indicate that the influence of relative humidity on the concentration gradient of NAI was complicated. There were four trends for the relationship between NAI concentration and relative humidity at different distances from the discharge electrode. The changes of NAI concentration with an increase in relative humidity at different distances were quite steady (10-30 cm), strongly declining (70-360 cm), approaching stability (420-450 cm) and moderately increasing (560-900 cm). Additionally, the regression analysis of NAI concentrations and distances from the discharge electrode indicated a logarithmic linear (log-linear) relationship; the distance of log-linear tendency (lambda) decreased with an increase in relative humidity such that the log-linear distance of 38.1% RH was 2.9 times that of 73.6% RH. Moreover, an empirical curve fit based on this study for the concentration gradient of NAI generated by negative electric discharge in indoor air was developed for estimating the NAI concentration at different relative humidities and distances from the source of electric discharge.

The role of sense of coherence and physical activity in positive and negative affect of Turkish adolescents.

PubMed

Oztekin, Ceyda; Tezer, Esin

2009-01-01

This study investigated the role of sense of coherence and total physical activity in positive and negative affect. Participants were 376 (169 female, 206 male, and 1 missing value) student volunteers from different faculties of Middle East Technical University. Three questionnaires: Sense of Coherence Scale (SOC), Physical Activity Assessment Questionnaire (PAAQ), and Positive and Negative Affect Schedule (PANAS) were administered to the students together with the demographic information sheet. Two separate stepwise multiple linear regression analyses were conducted to examine the predictive power of sense of coherence and total physical activity on positive and negative affect scores. Results revealed that both sense of coherence and total physical activity predicted the positive affect whereas only the sense of coherence predicted the negative affect on university students. Findings are discussed in light of sense of coherence, physical activity, and positive and negative affect literature.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Exposure to preeclampsia in utero affects growth from birth to late childhood dependent on child’s sex and severity of exposure: Follow-up of a nested case-control study

PubMed Central

Øymar, Knut; Eide, Geir Egil; Forman, Michele R.; Júlíusson, Pétur Benedikt

2017-01-01

Background and objective An adverse intrauterine environment may affect offspring growth and development. Our aim was to explore whether preeclampsia (PE) exposure in utero influences growth from birth to 13 years. Methods In a nested case-control study, 229 children were exposed to PE (mild/moderate: n = 164, severe: n = 54) and 385 were unexposed. Length/height and weight were abstracted from records at birth, 3 and 6 months, 1 and 4 years, and measured along with waist circumference and skinfolds at follow-up at 11/12 (girls/boys) and 13 years (both sexes). Associations between PE and z-scores for growth were analyzed by multiple linear and fractional polynomial regression with adjustment for potential confounders. Results In boys, exposure to mild/moderate PE was positively associated with linear growth after 0.5 years, but severe PE was negatively associated with linear growth in all ages. In girls, both exposure to mild/moderate and severe PE were negatively associated with linear growth. Exposure to PE was negatively associated with weight and body mass index (BMI) during infancy, but positively associated with weight and BMI thereafter, except that boys exposed to severe PE consistently had a lower weight and BMI compared to the unexposed. Exposure to severe PE only was positively associated with waist-to-height ratio at 11/12 (girls/boys) and 13 years (both sexes). Conclusions From birth to adolescence, linear growth, weight and BMI trajectories differed between the sexes by severity of exposure to PE. In general, PE exposure was negatively associated with linear growth, while in girls; positive associations with weight and BMI were observed. This underlines fetal life as a particularly sensitive period affecting subsequent growth and this may have implications for targeted approaches for healthy growth and development. PMID:28486480

Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Emotional exhaustion and cognitive performance in apparently healthy teachers: a longitudinal multi-source study.

PubMed

Feuerhahn, Nicolas; Stamov-Roßnagel, Christian; Wolfram, Maren; Bellingrath, Silja; Kudielka, Brigitte M

2013-10-01

We investigate how emotional exhaustion (EE), the core component of burnout, relates to cognitive performance, job performance and health. Cognitive performance was assessed by self-rated cognitive stress symptoms, self-rated and peer-rated cognitive impairments in everyday tasks and a neuropsychological test of learning and memory (LGT-3); job performance and physical health were gauged by self-reports. Cross-sectional linear regression analyses in a sample of 100 teachers confirm that EE is negatively related to cognitive performance as assessed by self-rating and peer-rating as well as neuropsychological testing (all p < .05). Longitudinal linear regression analyses confirm similar trends (p < .10) for self-rated and peer-rated cognitive performance. Executive control deficits might explain impaired cognitive performance in EE. In longitudinal analyses, EE also significantly predicts physical health. Contrary to our expectations, EE does not affect job performance. When reversed causation is tested, none of the outcome variables at Time 1 predict EE at Time 2. This speaks against cognitive dysfunctioning serving as a vulnerability factor for exhaustion. In sum, results underpin the negative consequences of EE for cognitive performance and health, which are relevant for individuals and organizations alike. In this way, findings might contribute to the understanding of the burnout syndrome. Copyright © 2012 John Wiley & Sons, Ltd.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change.

PubMed

Schut, Antonius G T; Ivits, Eva; Conijn, Jacob G; Ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982-2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17-36% of all productive areas depending on the NDVI metric used. For only 1-2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity.

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach.

PubMed

Helbich, Marco; Klein, Nadja; Roberts, Hannah; Hagedoorn, Paulien; Groenewegen, Peter P

2018-06-20

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was: a) to assess antidepressant prescription rates in relation to green space, and b) to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15%, 50%, and 85% quantiles to estimate green space-prescription rate correlations, controlling for physical activity levels, socio-demographics, urbanicity, etc. RESULTS: The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28% needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79%. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies. Copyright © 2018 Elsevier Inc. All rights reserved.

A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.

PubMed

Kilian, Reinhold; Matschinger, Herbert; Löeffler, Walter; Roick, Christiane; Angermeyer, Matthias C

2002-03-01

Transformation of the dependent cost variable is often used to solve the problems of heteroscedasticity and skewness in linear ordinary least square regression of health service cost data. However, transformation may cause difficulties in the interpretation of regression coefficients and the retransformation of predicted values. The study compares the advantages and disadvantages of different methods to estimate regression based cost functions using data on the annual costs of schizophrenia treatment. Annual costs of psychiatric service use and clinical and socio-demographic characteristics of the patients were assessed for a sample of 254 patients with a diagnosis of schizophrenia (ICD-10 F 20.0) living in Leipzig. The clinical characteristics of the participants were assessed by means of the BPRS 4.0, the GAF, and the CAN for service needs. Quality of life was measured by WHOQOL-BREF. A linear OLS regression model with non-parametric standard errors, a log-transformed OLS model and a generalized linear model with a log-link and a gamma distribution were used to estimate service costs. For the estimation of robust non-parametric standard errors, the variance estimator by White and a bootstrap estimator based on 2000 replications were employed. Models were evaluated by the comparison of the R2 and the root mean squared error (RMSE). RMSE of the log-transformed OLS model was computed with three different methods of bias-correction. The 95% confidence intervals for the differences between the RMSE were computed by means of bootstrapping. A split-sample-cross-validation procedure was used to forecast the costs for the one half of the sample on the basis of a regression equation computed for the other half of the sample. All three methods showed significant positive influences of psychiatric symptoms and met psychiatric service needs on service costs. Only the log- transformed OLS model showed a significant negative impact of age, and only the GLM shows a significant negative influences of employment status and partnership on costs. All three models provided a R2 of about.31. The Residuals of the linear OLS model revealed significant deviances from normality and homoscedasticity. The residuals of the log-transformed model are normally distributed but still heteroscedastic. The linear OLS model provided the lowest prediction error and the best forecast of the dependent cost variable. The log-transformed model provided the lowest RMSE if the heteroscedastic bias correction was used. The RMSE of the GLM with a log link and a gamma distribution was higher than those of the linear OLS model and the log-transformed OLS model. The difference between the RMSE of the linear OLS model and that of the log-transformed OLS model without bias correction was significant at the 95% level. As result of the cross-validation procedure, the linear OLS model provided the lowest RMSE followed by the log-transformed OLS model with a heteroscedastic bias correction. The GLM showed the weakest model fit again. None of the differences between the RMSE resulting form the cross- validation procedure were found to be significant. The comparison of the fit indices of the different regression models revealed that the linear OLS model provided a better fit than the log-transformed model and the GLM, but the differences between the models RMSE were not significant. Due to the small number of cases in the study the lack of significance does not sufficiently proof that the differences between the RSME for the different models are zero and the superiority of the linear OLS model can not be generalized. The lack of significant differences among the alternative estimators may reflect a lack of sample size adequate to detect important differences among the estimators employed. Further studies with larger case number are necessary to confirm the results. Specification of an adequate regression models requires a careful examination of the characteristics of the data. Estimation of standard errors and confidence intervals by nonparametric methods which are robust against deviations from the normal distribution and the homoscedasticity of residuals are suitable alternatives to the transformation of the skew distributed dependent variable. Further studies with more adequate case numbers are needed to confirm the results.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach

PubMed Central

Clough-Gorr, Kerri; Zwahlen, Marcel

2015-01-01

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest. PMID:26339544

Southern Indian Ocean SST as a modulator for the progression of Indian summer monsoon

NASA Astrophysics Data System (ADS)

Shahi, Namendra Kumar; Rai, Shailendra; Mishra, Nishant

2018-01-01

This study explores the possibility of southern Indian Ocean (SIO) sea surface temperature (SST) as a modulator for the early phase of Indian summer monsoon and its possible physical mechanism. A dipole-like structure is obtained from the empirical orthogonal function (EOF) analysis which is similar to an Indian Ocean subtropical dipole (IOSD) found earlier. A subtropical dipole index (SDI) is defined based on the SST anomaly over the positive and negative poles. The regression map of rainfall over India in the month of June corresponding to the SDI during 1983-2013 shows negative patterns along the Western Ghats and Central India. However, the regression pattern is insignificant during 1952-1982. The multiple linear regression models and partial correlation analysis also indicate that the SDI acts as a dominant factor to influence the rainfall over India in the month of June during 1983-2013. The similar result is also obtained with the help of composite rainfall over the land points of India in the month of June for positive (negative) SDI events. It is also observed that the positive (negative) SDI delays (early) the onset dates of Indian monsoon over Kerala during the time domain of our study. The study is further extended to identify the physical mechanism of this impact, and it is found that the heating (cooling) in the region covering SDI changes the circulation pattern in the SIO and hence impacts the progression of monsoon in India.

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

Negative trust and depression among female sex workers in Western China: The mediating role of thwarted belongingness.

PubMed

Chen, Hong; Li, Xu; Li, Bingbing; Huang, Ailong

2017-10-01

Female sex workers are at high risk for depression in China but they are understudied and underserved. Based on cognitive models of depression, dysfunctional beliefs about oneself and others may act as vulnerability factors for depression. However, the association between negative trust and depression is still under debate. The present study aimed to test the hypothesis that negative trust positively relates to depression through thwarted belongingness among female sex workers. Four hundred and fifty-seven participants completed measures of negative trust, thwarted belongingness, and depression. Stepwise multiple linear regression analyses showed that both negative trust and thwarted belongingness significantly positively predicted depression, and thwarted belongingness was positively predicted by negative trust. The results from the mediation analysis suggest that thwarted belongingness might be an underlying mechanism linking negative trust and depression. Psychological interventions could focus on helping female sex workers form and strengthen meaningful social connectedness (the behavioral/observable indicators of the constructs of thwarted belongingness). Copyright © 2017. Published by Elsevier B.V.

Socio-economic factors associated with infant mortality in Italy: an ecological study

PubMed Central

2012-01-01

Introduction One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Methods Associations between infant mortality rates in the 20 Italian regions (2006–2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15–64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. Results The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = −0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = −0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). Conclusions In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels. PMID:22898293

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water.

PubMed

Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil

2015-12-07

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.

Coronary artery calcium distributions in older persons in the AGES-Reykjavik study

PubMed Central

Gudmundsson, Elias Freyr; Gudnason, Vilmundur; Sigurdsson, Sigurdur; Launer, Lenore J.; Harris, Tamara B.; Aspelund, Thor

2013-01-01

Coronary Artery Calcium (CAC) is a sign of advanced atherosclerosis and an independent risk factor for cardiac events. Here, we describe CAC-distributions in an unselected aged population and compare modelling methods to characterize CAC-distribution. CAC is difficult to model because it has a skewed and zero inflated distribution with over-dispersion. Data are from the AGES-Reykjavik sample, a large population based study [2002-2006] in Iceland of 5,764 persons aged 66-96 years. Linear regressions using logarithmic- and Box-Cox transformations on CAC+1, quantile regression and a Zero-Inflated Negative Binomial model (ZINB) were applied. Methods were compared visually and with the PRESS-statistic, R2 and number of detected associations with concurrently measured variables. There were pronounced differences in CAC according to sex, age, history of coronary events and presence of plaque in the carotid artery. Associations with conventional coronary artery disease (CAD) risk factors varied between the sexes. The ZINB model provided the best results with respect to the PRESS-statistic, R2, and predicted proportion of zero scores. The ZINB model detected similar numbers of associations as the linear regression on ln(CAC+1) and usually with the same risk factors. PMID:22990371

A Systematic Review and Meta-Regression Analysis of Lung Cancer Risk and Inorganic Arsenic in Drinking Water

PubMed Central

Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil

2015-01-01

High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190

[Effects of climate and grazing on the vegetation cover change in Xilinguole League of Inner Mongolia, North China].

PubMed

Wang, Hai-Mei; Li, Zheng-Hai; Wang, Zhen

2013-01-01

Based on the monthly temperature and precipitation data of 15 meteorological stations and the statistical data of livestock density in Xilinguole League in 1981-2007, and by using ArcGIS, this paper analyzed the spatial distribution of the climate aridity and livestock density in the League, and in combining with the ten-day data of the normalized difference vegetation index (NDVI) in 1981-2007, the driving factors of the vegetation cover change in the League were discussed. In the study period, there was a satisfactory linear regression relationship between the climate aridity and the vegetation coverage. The NDVI and the livestock density had a favorable binomial regression relationship. With the increase of NDVI, the livestock density increased first and decreased then. The vegetation coverage had a complex linear relationship with livestock density and climate aridity. The NDVI had a positive correlation with climate aridity, but a negative correlation with livestock density. Compared with livestock density, climate aridity had far greater effects on the NDVI.

Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048

Peripheral Refraction, Peripheral Eye Length, and Retinal Shape in Myopia.

PubMed

Verkicharla, Pavan K; Suheimat, Marwan; Schmid, Katrina L; Atchison, David A

2016-09-01

To investigate how peripheral refraction and peripheral eye length are related to retinal shape. Relative peripheral refraction (RPR) and relative peripheral eye length (RPEL) were determined in 36 young adults (M +0.75D to -5.25D) along horizontal and vertical visual field meridians out to ±35° and ±30°, respectively. Retinal shape was determined in terms of vertex radius of curvature Rv, asphericity Q, and equivalent radius of curvature REq using a partial coherence interferometry method involving peripheral eye lengths and model eye raytracing. Second-order polynomial fits were applied to RPR and RPEL as functions of visual field position. Linear regressions were determined for the fits' second order coefficients and for retinal shape estimates as functions of central spherical refraction. Linear regressions investigated relationships of RPR and RPEL with retinal shape estimates. Peripheral refraction, peripheral eye lengths, and retinal shapes were significantly affected by meridian and refraction. More positive (hyperopic) relative peripheral refraction, more negative RPELs, and steeper retinas were found along the horizontal than along the vertical meridian and in myopes than in emmetropes. RPR and RPEL, as represented by their second-order fit coefficients, correlated significantly with retinal shape represented by REq. Effects of meridian and refraction on RPR and RPEL patterns are consistent with effects on retinal shape. Patterns derived from one of these predict the others: more positive (hyperopic) RPR predicts more negative RPEL and steeper retinas, more negative RPEL predicts more positive relative peripheral refraction and steeper retinas, and steeper retinas derived from peripheral eye lengths predict more positive RPR.

Inverse Association between Air Pressure and Rheumatoid Arthritis Synovitis

PubMed Central

Furu, Moritoshi; Nakabo, Shuichiro; Ohmura, Koichiro; Nakashima, Ran; Imura, Yoshitaka; Yukawa, Naoichiro; Yoshifuji, Hajime; Matsuda, Fumihiko; Ito, Hiromu; Fujii, Takao; Mimori, Tsuneyo

2014-01-01

Rheumatoid arthritis (RA) is a bone destructive autoimmune disease. Many patients with RA recognize fluctuations of their joint synovitis according to changes of air pressure, but the correlations between them have never been addressed in large-scale association studies. To address this point we recruited large-scale assessments of RA activity in a Japanese population, and performed an association analysis. Here, a total of 23,064 assessments of RA activity from 2,131 patients were obtained from the KURAMA (Kyoto University Rheumatoid Arthritis Management Alliance) database. Detailed correlations between air pressure and joint swelling or tenderness were analyzed separately for each of the 326 patients with more than 20 assessments to regulate intra-patient correlations. Association studies were also performed for seven consecutive days to identify the strongest correlations. Standardized multiple linear regression analysis was performed to evaluate independent influences from other meteorological factors. As a result, components of composite measures for RA disease activity revealed suggestive negative associations with air pressure. The 326 patients displayed significant negative mean correlations between air pressure and swellings or the sum of swellings and tenderness (p = 0.00068 and 0.00011, respectively). Among the seven consecutive days, the most significant mean negative correlations were observed for air pressure three days before evaluations of RA synovitis (p = 1.7×10−7, 0.00027, and 8.3×10−8, for swellings, tenderness and the sum of them, respectively). Standardized multiple linear regression analysis revealed these associations were independent from humidity and temperature. Our findings suggest that air pressure is inversely associated with synovitis in patients with RA. PMID:24454853

The moderation of resilience on the negative effect of pain on depression and post-traumatic growth in individuals with spinal cord injury.

PubMed

Min, Jung-Ah; Lee, Chang-Uk; Hwang, Sung-Il; Shin, Jung-In; Lee, Bum-Suk; Han, Sang-Hoon; Ju, Hye-In; Lee, Cha-Yeon; Lee, Chul; Chae, Jeong-Ho

2014-01-01

To determine the moderating effect of resilience on the negative effects of chronic pain on depression and post-traumatic growth. Community-dwelling individuals with SCI (n = 37) were recruited at short-term admission for yearly regular health examination. Participants completed self-rating standardized questionnaires measuring pain, resilience, depression and post-traumatic growth. Hierarchical linear regression analysis was performed to identify the moderating effect of resilience on the relationships of pain with depression and post-traumatic growth after controlling for relevant covariates. In the regression model of depression, the effect of pain severity on depression was decreased (β was changed from 0.47 to 0.33) after entering resilience into the model. In the final model, both pain and resilience were significant independent predictors for depression (β = 0.33, p = 0.038 and β = -0.47, p = 0.012, respectively). In the regression model of post-traumatic growth, the effect of pain severity became insignificant after entering resilience into the model. In the final model, resilience was a significant predictor (β = 0.51, p = 0.016). Resilience potentially mitigated the negative effects of pain. Moreover, it independently contributed to reduced depression and greater post-traumatic growth. Our findings suggest that resilience might provide a potential target for intervention in SCI individuals.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Hostility and social support explain physical activity beyond negative affect among young men, but not women, in college.

PubMed

Maier, Karl J; James, Ashley E

2014-01-01

We examined social support as a moderator of cynical hostility in relation to physical activity and body mass index among college students (n = 859; M = 18.71 years (SD = 1.22); 60% women, 84% White). After controlling for negative affect in hierarchical linear regression models, greater hostility was associated with lesser physical activity among those with low social support, as expected. Greater hostility was also associated with greater physical activity among those high in social support, ps < .05. Effects were observed for men only. Hostility and social support were unrelated to body mass index, ps > .05. Young men with a hostile disposition and low social support may be at risk for a sedentary lifestyle for reasons other than negative affect.

Converting positive and negative symptom scores between PANSS and SAPS/SANS.

PubMed

van Erp, Theo G M; Preda, Adrian; Nguyen, Dana; Faziola, Lawrence; Turner, Jessica; Bustillo, Juan; Belger, Aysenil; Lim, Kelvin O; McEwen, Sarah; Voyvodic, James; Mathalon, Daniel H; Ford, Judith; Potkin, Steven G; Fbirn

2014-01-01

The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25 years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two-hundred-and-five schizophrenia patients [mean age±SD=39.5±11.6, 156 males] were assessed with the SANS, SAPS, and PANSS. Pearson's correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs=0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This study's conversion equations, implemented at http:/converteasy.org, may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales. Copyright © 2013 Elsevier B.V. All rights reserved.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

[Correlation research on contents of podophyllotoxin and total lignans in Sinopodophyllum hexandrum and ecological factors].

PubMed

Li, Min; Zhong, Guo-yue; Wu, Ao-lin; Zhang, Shou-wen; Jiang, Wei; Liang, Jian

2015-05-01

To explore the correlation between the ecological factors and the contents of podophyllotoxin and total lignans in root and rhizome of Sinopodophyllum hexandrum, podophyllotoxin in 87 samples (from 5 provinces) was determined by HPLC and total lignans by UV. A correlation and regression analysis was made by software SPSS 16.0 in combination with ecological factors (terrain, soil and climate). The content determination results showed a great difference between podophyllotoxin and total lignans, attaining 1.001%-6.230% and 5.350%-16.34%, respective. The correlation and regression analysis by SPSS showed a positive linear correlation between their contents, strong positive correlation between their contents, latitude and annual average rainfall within the sampling area, weak negative correlation with pH value and organic material in soil, weaker and stronger positive correlations with soil potassium, weak negative correlation with slope and annual average temperature and weaker positive correlation between the podophyllotoxin content and soil potassium.

Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.

PubMed

Hasani Sangani, Mohammad; Jabbarian Amiri, Bahman; Alizadeh Shabani, Afshin; Sakieh, Yousef; Ashrafi, Sohrab

2015-04-01

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

[Health expenditures, income inequality, and the marginalization index in Mexico's health system].

PubMed

Pinzón Florez, Carlos Eduardo; Reveiz, Ludovic; Idrovo, Alvaro J; Reyes Morales, Hortensia

2014-01-01

Evaluate the effect of the relationship among public health expenditures, income inequality, and the marginalization index on maternal and child mortality in Mexico, to determine the effect of these factors on health system performance from a technical efficiency perspective. An ecological study of 32 Mexican states. Correlations were estimated between maternal and infant mortality and public health expenditures in total per capita, federal per capita, and state per capita for the years 2000, 2005, and 2010 (Gini coefficient and marginalization index). Linear regressions were used to explore the association of these variables with health indicators in the state systems. Negative correlations were observed for the marginalization index and Gini coefficient with regard to life expectancy at birth (-0.62 and -0.28 respectively). Furthermore, there was a positive correlation of 0.59 between the marginalization index and infant mortality (P <0.05). Multiple linear regression models revealed a negative effect of the marginalization index and Gini coefficient on health out-comes. Federal funding had a positive effect on system performance in terms of health indicators. Health system reform in Mexico has had a positive impact on the country's health indicators; federal financial investment seems to be effective in this regard. Social determinants have an important effect on health system performance, and analysis using multisectoral and multidisciplinary approaches are needed in addressing them.

Force required for correcting the deformity of pectus carinatum and related multivariate analysis.

PubMed

Chen, Chenghao; Zeng, Qi; Li, Zhongzhi; Zhang, Na; Yu, Jie

2017-12-24

To measure the force required for correcting pectus carinatum to the desired position and investigate the correlations of the required force with patients' gender, age, deformity type, severity and body mass index (BMI). A total of 125 patients with pectus carinatum were enrolled in the study from August 2013 to August 2016. Their gender, age, deformity type, severity and BMI were recorded. A chest wall compressor was used to measure the force required for correcting the chest wall deformity. Multivariate linear regression was used for data analysis. Among the 125 patients, 112 were males and 13 were females. Their mean age was 13.7±1.5 years old, mean Haller index was 2.1±0.2, and mean BMI was 17.4±1.8 kg/m 2 . Multivariate linear regression analysis showed that the desirable force for correcting chest wall deformity was not correlated with gender and deformity type, but positively correlated with age and BMI and negatively correlated with Haller index. The desirable force measured for correcting chest wall deformities of patients with pectus carinatum positively correlates with age and BMI and negatively correlates with Haller index. The study provides valuable information for future improvement of implanted bar, bar fixation technique, and personalized surgery. Retrospective study. Level 3-4. Copyright © 2018. Published by Elsevier Inc.

Higher direct bilirubin levels during mid-pregnancy are associated with lower risk of gestational diabetes mellitus.

PubMed

Liu, Chaoqun; Zhong, Chunrong; Zhou, Xuezhen; Chen, Renjuan; Wu, Jiangyue; Wang, Weiye; Li, Xiating; Ding, Huisi; Guo, Yanfang; Gao, Qin; Hu, Xingwen; Xiong, Guoping; Yang, Xuefeng; Hao, Liping; Xiao, Mei; Yang, Nianhong

2017-01-01

Bilirubin concentrations have been recently reported to be negatively associated with type 2 diabetes mellitus. We examined the association between bilirubin concentrations and gestational diabetes mellitus. In a prospective cohort study, 2969 pregnant women were recruited prior to 16 weeks of gestation and were followed up until delivery. The value of bilirubin was tested and oral glucose tolerance test was conducted to screen gestational diabetes mellitus. The relationship between serum bilirubin concentration and gestational weeks was studied by two-piecewise linear regression. A subsample of 1135 participants with serum bilirubin test during 16-18 weeks gestation was conducted to research the association between serum bilirubin levels and risk of gestational diabetes mellitus by logistic regression. Gestational diabetes mellitus developed in 8.5 % of the participants (223 of 2969). Two-piecewise linear regression analyses demonstrated that the levels of bilirubin decreased with gestational week up to the turning point 23 and after that point, levels of bilirubin were increased slightly. In multiple logistic regression analysis, the relative risk of developing gestational diabetes mellitus was lower in the highest tertile of direct bilirubin than that in the lowest tertile (RR 0.60; 95 % CI, 0.35-0.89). The results suggested that women with higher serum direct bilirubin levels during the second trimester of pregnancy have lower risk for development of gestational diabetes mellitus.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Predicting Reactive Intermediate Quantum Yields from Dissolved Organic Matter Photolysis Using Optical Properties and Antioxidant Capacity.

PubMed

Mckay, Garrett; Huang, Wenxi; Romera-Castillo, Cristina; Crouch, Jenna E; Rosario-Ortiz, Fernando L; Jaffé, Rudolf

2017-05-16

The antioxidant capacity and formation of photochemically produced reactive intermediates (RI) was studied for water samples collected from the Florida Everglades with different spatial (marsh versus estuarine) and temporal (wet versus dry season) characteristics. Measured RI included triplet excited states of dissolved organic matter ( 3 DOM*), singlet oxygen ( 1 O 2 ), and the hydroxyl radical ( • OH). Single and multiple linear regression modeling were performed using a broad range of extrinsic (to predict RI formation rates, R RI ) and intrinsic (to predict RI quantum yields, Φ RI ) parameters. Multiple linear regression models consistently led to better predictions of R RI and Φ RI for our data set but poor prediction of Φ RI for a previously published data set,1 probably because the predictors are intercorrelated (Pearson's r > 0.5). Single linear regression models were built with data compiled from previously published studies (n ≈ 120) in which E2:E3, S, and Φ RI values were measured, which revealed a high degree of similarity between RI-optical property relationships across DOM samples of diverse sources. This study reveals that • OH formation is, in general, decoupled from 3 DOM* and 1 O 2 formation, providing supporting evidence that 3 DOM* is not a • OH precursor. Finally, Φ RI for 1 O 2 and 3 DOM* correlated negatively with antioxidant activity (a surrogate for electron donating capacity) for the collected samples, which is consistent with intramolecular oxidation of DOM moieties by 3 DOM*.

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

The effects of climate change on harp seals (Pagophilus groenlandicus).

PubMed

Johnston, David W; Bowers, Matthew T; Friedlaender, Ari S; Lavigne, David M

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data.

The Effects of Climate Change on Harp Seals (Pagophilus groenlandicus)

PubMed Central

Johnston, David W.; Bowers, Matthew T.; Friedlaender, Ari S.; Lavigne, David M.

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data. PMID:22238591

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London.

PubMed

Halonen, Jaana I; Blangiardo, Marta; Toledano, Mireille B; Fecht, Daniela; Gulliver, John; Ghosh, Rebecca; Anderson, H Ross; Beevers, Sean D; Dajnak, David; Kelly, Frank J; Wilkinson, Paul; Tonne, Cathryn

2016-01-01

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Factors Impacting Online Ratings for Otolaryngologists.

PubMed

Calixto, Nathaniel E; Chiao, Whitney; Durr, Megan L; Jiang, Nancy

2018-06-01

To identify factors associated with online patient ratings and comments for a nationwide sample of otolaryngologists. Ratings, demographic information, and written comments were obtained for a random sample of otolaryngologists from HealthGrades.com and Vitals.com . Online Presence Score (OPS) was based on 10 criteria, including professional website and social media profiles. Regression analyses identified factors associated with increased rating. We evaluated for correlations between OPS and other attributes with star rating and used chi-square tests to evaluate content differences between positive and negative comments. On linear regression, increased OPS was associated with higher ratings on HealthGrades and Vitals; higher ratings were also associated with younger age on Vitals and less experience on HealthGrades. However, detailed correlation studies showed weak correlation between OPS and rating; age and graduation year also showed low correlation with ratings. Negative comments more likely focused on surgeon-independent factors or poor bedside manner. Though younger otolaryngologists with greater online presence tend to have higher ratings, weak correlations suggest that age and online presence have only a small impact on the content found on ratings websites. While most written comments are positive, deficiencies in bedside manner or other physician-independent factors tend to elicit negative comments.

Acoustic radiation force impulse elastography in evaluation of triple-negative breast cancer: A preliminary experience.

PubMed

Wan, Jing; Wu, Rong; Yao, Minghua; Xu, Guang; Liu, Hui; Pu, Huan; Xiang, Lihua; Zhang, Shupin

2018-05-19

To assess the elastographic features of triple-negative breast cancers and evaluate the diagnostic value of acoustic radiation force impulse imaging (ARFI) for the characterization of triple-negative breast cancers. This study analyzed data from 234 women with breast cancer. Patients were categorized into three groups; 1) triple-negative breast cancers (n = 48); 2) ER-positive tumors (n = 128) and 3) HER2-positive tumors (n = 58). Mean tumor stiffness was evaluated by virtual touch tissue imaging (VTI) and virtual touch tissue quantification (VTQ) and quantified as both qualitative scores (1-5) and shear wave velocity (SWV) (m/s). The relationship between mean SWV and tumor parameters, including tumor size, tumor type, histologic grade and lymph node status, were investigated using multiple linear regression. Triple-negative tumor were more likely to have a large invasive size (p = 0.002), high histological grade (p < 0.001), lymph node involvement (p = 0.022) and strong ki-67 expression (p < 0.001). The highest mean SWV value were recorded in triple-negative tumors (7.36 m/s±1.83), followed by HER2+ tumors (6.65 m/s±2.26) and ER+ tumors (6.60 m/s±2.35) (p = 0.122). Triple-negative tumors were also associated with increased stiffness than ER+ tumors and HER2+ tumors (p = 0.016), as measured by qualitative VTI scores. Tumor size was independently associated with mean SWV value on adjusted regression (p < 0.001). Triple-negative breast cancer is associated with high stiffness scores and SWV in ARFI. The latter may be considered a useful complementary tool in evaluation of triple-negative breast cancer.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Plasma apelin levels, blood pressure and cardiovascular risk factors in a coastal Chinese population.

PubMed

Zhu, Pengli; Huang, Feng; Lin, Fan; Yuan, Yin; Chen, Falin; Li, Qiaowei

2013-11-01

To describe the relationship of plasma apelin levels with blood pressure in a coastal Chinese population. This cross-sectional study included a total of 1031 subjects from the coastal areas of China. One-way analysis of variance (ANOVA) and linear trend test, Pearson's correlation analysis, as well as multivariate linear regression analysis were used to evaluate the association between plasma apelin levels and blood pressure. Plasma apelin levels dropped with increasing quartiles of systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial blood pressure (MABP) (all P<0.001). SBP, DBP, and MABP values decreased as the apelin levels increased within the quartiles. After adjusting for age and gender, the significant differences in SBP, DBP, and MABP between the groups within the apelin quartiles remained (all P<0.05). A significant negative correlation between SBP, DBP, as well as MABP and apelin levels was observed (all P<0.01); even after adjusting for cardiovascular confounding factors, this negative correlation remained (all P<0.001). A negative correlation between plasma apelin levels and blood pressure was found in this 1000-population-based epidemiological study. Apelin may become a potential therapeutic target of anti-hypertensive treatment.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

An introduction to using Bayesian linear regression with clinical data.

PubMed

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Attachment style and readiness for psychotherapy among psychiatric outpatients.

PubMed

Kealy, David; Tsai, Michelle; Ogrodniczuk, John S

2017-06-01

Ninety-two adults attending outpatient mental health services completed measures of attachment style and readiness to engage in psychotherapy. Correlation and linear regression analyses found anxious attachment to be positively associated with treatment-seeking distress and found avoidant attachment to be negatively associated with openness to personal disclosure in the therapy relationship. Insecure attachment may influence prospective patients' readiness for psychotherapy. Patients with an avoidant attachment style may need assistance in preparing for the relational aspects of psychotherapy. © 2016 The British Psychological Society.

Analyzing prospective teachers' images of scientists using positive, negative and stereotypical images of scientists

NASA Astrophysics Data System (ADS)

Subramaniam, Karthigeyan; Esprívalo Harrell, Pamela; Wojnowski, David

2013-04-01

Background and purpose : This study details the use of a conceptual framework to analyze prospective teachers' images of scientists to reveal their context-specific conceptions of scientists. The conceptual framework consists of context-specific conceptions related to positive, stereotypical and negative images of scientists as detailed in the literature on the images, role and work of scientists. Sample, design and method : One hundred and ninety-six drawings of scientists, generated by prospective teachers, were analyzed using the Draw-A-Scientist-Test Checklist (DAST-C), a binary linear regression and the conceptual framework. Results : The results of the binary linear regression analysis revealed a statistically significant difference for two DAST-C elements: ethnicity differences with regard to drawing a scientist who was Caucasian and gender differences for indications of danger. Analysis using the conceptual framework helped to categorize the same drawings into positive, stereotypical, negative and composite images of a scientist. Conclusions : The conceptual framework revealed that drawings were focused on the physical appearance of the scientist, and to a lesser extent on the equipment, location and science-related practices that provided the context of a scientist's role and work. Implications for teacher educators include the need to understand that there is a need to provide tools, like the conceptual framework used in this study, to help prospective teachers to confront and engage with their multidimensional perspectives of scientists in light of the current trends on perceiving and valuing scientists. In addition, teacher educators need to use the conceptual framework, which yields qualitative perspectives about drawings, together with the DAST-C, which yields quantitative measure for drawings, to help prospective teachers to gain a holistic outlook on their drawings of scientists.

Fever Is Associated with Reduced, Hypothermia with Increased Mortality in Septic Patients: A Meta-Analysis of Clinical Trials

PubMed Central

Rumbus, Zoltan; Matics, Robert; Hegyi, Peter; Zsiboras, Csaba; Szabo, Imre; Illes, Anita; Petervari, Erika; Balasko, Marta; Marta, Katalin; Miko, Alexandra; Parniczky, Andrea; Tenk, Judit; Rostas, Ildiko; Solymar, Margit

2017-01-01

Background Sepsis is usually accompanied by changes of body temperature (Tb), but whether fever and hypothermia predict mortality equally or differently is not fully clarified. We aimed to find an association between Tb and mortality in septic patients with meta-analysis of clinical trials. Methods We searched the PubMed, EMBASE, and Cochrane Controlled Trials Registry databases (from inception to February 2016). Human studies reporting Tb and mortality of patients with sepsis were included in the analyses. Average Tb with SEM and mortality rate of septic patient groups were extracted by two authors independently. Results Forty-two studies reported Tb and mortality ratios in septic patients (n = 10,834). Pearson correlation analysis revealed weak negative linear correlation (R2 = 0.2794) between Tb and mortality. With forest plot analysis, we found a 22.2% (CI, 19.2–25.5) mortality rate in septic patients with fever (Tb > 38.0°C), which was higher, 31.2% (CI, 25.7–37.3), in normothermic patients, and it was the highest, 47.3% (CI, 38.9–55.7), in hypothermic patients (Tb < 36.0°C). Meta-regression analysis showed strong negative linear correlation between Tb and mortality rate (regression coefficient: -0.4318; P < 0.001). Mean Tb of the patients was higher in the lowest mortality quartile than in the highest: 38.1°C (CI, 37.9–38.4) vs 37.1°C (CI, 36.7–37.4). Conclusions Deep Tb shows negative correlation with the clinical outcome in sepsis. Fever predicts lower, while hypothermia higher mortality rates compared with normal Tb. Septic patients with the lowest (< 25%) chance of mortality have higher Tb than those with the highest chance (> 75%). PMID:28081244

Fever Is Associated with Reduced, Hypothermia with Increased Mortality in Septic Patients: A Meta-Analysis of Clinical Trials.

PubMed

Rumbus, Zoltan; Matics, Robert; Hegyi, Peter; Zsiboras, Csaba; Szabo, Imre; Illes, Anita; Petervari, Erika; Balasko, Marta; Marta, Katalin; Miko, Alexandra; Parniczky, Andrea; Tenk, Judit; Rostas, Ildiko; Solymar, Margit; Garami, Andras

2017-01-01

Sepsis is usually accompanied by changes of body temperature (Tb), but whether fever and hypothermia predict mortality equally or differently is not fully clarified. We aimed to find an association between Tb and mortality in septic patients with meta-analysis of clinical trials. We searched the PubMed, EMBASE, and Cochrane Controlled Trials Registry databases (from inception to February 2016). Human studies reporting Tb and mortality of patients with sepsis were included in the analyses. Average Tb with SEM and mortality rate of septic patient groups were extracted by two authors independently. Forty-two studies reported Tb and mortality ratios in septic patients (n = 10,834). Pearson correlation analysis revealed weak negative linear correlation (R2 = 0.2794) between Tb and mortality. With forest plot analysis, we found a 22.2% (CI, 19.2-25.5) mortality rate in septic patients with fever (Tb > 38.0°C), which was higher, 31.2% (CI, 25.7-37.3), in normothermic patients, and it was the highest, 47.3% (CI, 38.9-55.7), in hypothermic patients (Tb < 36.0°C). Meta-regression analysis showed strong negative linear correlation between Tb and mortality rate (regression coefficient: -0.4318; P < 0.001). Mean Tb of the patients was higher in the lowest mortality quartile than in the highest: 38.1°C (CI, 37.9-38.4) vs 37.1°C (CI, 36.7-37.4). Deep Tb shows negative correlation with the clinical outcome in sepsis. Fever predicts lower, while hypothermia higher mortality rates compared with normal Tb. Septic patients with the lowest (< 25%) chance of mortality have higher Tb than those with the highest chance (> 75%).

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change

PubMed Central

Schut, Antonius G. T.; Ivits, Eva; Conijn, Jacob G.; ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982–2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17–36% of all productive areas depending on the NDVI metric used. For only 1–2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity. PMID:26466347

Negative correlation between altitudes and oxygen isotope ratios of seeds: exploring its applicability to assess vertical seed dispersal.

PubMed

Naoe, Shoji; Tayasu, Ichiro; Masaki, Takashi; Koike, Shinsuke

2016-10-01

Vertical seed dispersal, which plays a key role in plant escape and/or expansion under climate change, was recently evaluated for the first time using negative correlation between altitudes and oxygen isotope ratio of seeds. Although this method is innovative, its applicability to other plants is unknown. To explore the applicability of the method, we regressed altitudes on δ 18 O of seeds of five woody species constituting three families in temperate forests in central Japan. Because climatic factors, including temperature and precipitation that influence δ 18 O of plant materials, demonstrate intensive seasonal fluctuation in the temperate zone, we also evaluated the effect of fruiting season of each species on δ 18 O of seeds using generalized linear mixed models (GLMM). Negative correlation between altitudes and δ 18 O of seeds was found in four of five species tested. The slope of regression lines tended to be lower in late-fruiting species. The GLMM analysis revealed that altitudes and date of fruiting peak negatively affected δ 18 O of seeds. These results indicate that the estimation of vertical seed dispersal using δ 18 O of seeds can be applicable for various species, not just confined to specific taxa, by identifying the altitudes of plants that produced seeds. The results also suggest that the regression line between altitudes and δ 18 O of seeds is rather species specific and that vertical seed dispersal in late-fruiting species is estimated at a low resolution due to their small regression slopes. A future study on the identification of environmental factors and plant traits that cause a difference in δ 18 O of seeds, combined with an improvement of analysis, will lead to effective evaluation of vertical seed dispersal in various species and thereby promote our understanding about the mechanism and ecological functions of vertical seed dispersal.

Association of lifestyle with serum lipid levels: a study of middle-aged Japanese men.

PubMed

Nakanishi, N; Tatara, K; Nakamura, K; Suzuki, K

2000-07-01

Cross-sectional associations between lifestyle and serum lipid levels were examined in 1591 Japanese male office workers aged 35 to 59 years in Osaka, Japan. From multiple linear regression analyses, significant correlates with low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and Log triglyceride levels and the ratio of LDL cholesterol to HDL cholesterol were, in the order of relative importance: BMI, alcohol intake (negative) and age for LDL cholesterol level; BMI (negative), cigarette smoking (negative), alcohol intake, consideration for nutritional balance, hours of brisk walking, hours of walking at an ordinary pace and physical exercise for HDL cholesterol level; BMI, cigarette smoking, consideration for nutritional balance (negative), hours of work (negative), alcohol intake and coffee drinking (negative) for Log triglyceride level; and BMI, alcohol intake (negative), cigarette smoking, consideration for nutritional balance (negative), age, hours of brisk walking (negative) and the frequency of snack intake between meals for the ratio of LDL cholesterol to HDL cholesterol. Our data suggest that obesity, cigarette smoking and snack intake between meals are atherogenic whereas alcohol consumption, consideration for nutritional balance and walking long hours, especially at a brisk pace, are anti-atherogenic in middle-aged Japanese men.

Genomic prediction based on data from three layer lines using non-linear regression models.

PubMed

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

Digital Image Restoration Under a Regression Model - The Unconstrained, Linear Equality and Inequality Constrained Approaches

DTIC Science & Technology

1974-01-01

REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Who Will Win?: Predicting the Presidential Election Using Linear Regression

ERIC Educational Resources Information Center

Lamb, John H.

2007-01-01

This article outlines a linear regression activity that engages learners, uses technology, and fosters cooperation. Students generated least-squares linear regression equations using TI-83 Plus[TM] graphing calculators, Microsoft[C] Excel, and paper-and-pencil calculations using derived normal equations to predict the 2004 presidential election.…

Daily commuting to work is not associated with variables of health.

PubMed

Mauss, Daniel; Jarczok, Marc N; Fischer, Joachim E

2016-01-01

Commuting to work is thought to have a negative impact on employee health. We tested the association of work commute and different variables of health in German industrial employees. Self-rated variables of an industrial cohort (n = 3805; 78.9 % male) including absenteeism, presenteeism and indices reflecting stress and well-being were assessed by a questionnaire. Fasting blood samples, heart-rate variability and anthropometric data were collected. Commuting was grouped into one of four categories: 0-19.9, 20-44.9, 45-59.9, ≥60 min travelling one way to work. Bivariate associations between commuting and all variables under study were calculated. Linear regression models tested this association further, controlling for potential confounders. Commuting was positively correlated with waist circumference and inversely with triglycerides. These associations did not remain statistically significant in linear regression models controlling for age, gender, marital status, and shiftwork. No other association with variables of physical, psychological, or mental health and well-being could be found. The results indicate that commuting to work has no significant impact on well-being and health of German industrial employees.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Maternal heterozygosity and progeny fitness association in an inbred Scots pine population.

PubMed

Abrahamsson, S; Ahlinder, J; Waldmann, P; García-Gil, M R

2013-03-01

Associations between heterozygosity and fitness traits have typically been investigated in populations characterized by low levels of inbreeding. We investigated the associations between standardized multilocus heterozygosity (stMLH) in mother trees (obtained from12 nuclear microsatellite markers) and five fitness traits measured in progenies from an inbred Scots pine population. The traits studied were proportion of sound seed, mean seed weight, germination rate, mean family height of one-year old seedlings under greenhouse conditions (GH) and mean family height of three-year old seedlings under field conditions (FH). The relatively high average inbreeding coefficient (F) in the population under study corresponds to a mixture of trees with different levels of co-ancestry, potentially resulting from a recent bottleneck. We used both frequentist and Bayesian methods of polynomial regression to investigate the presence of linear and non-linear relations between stMLH and each of the fitness traits. No significant associations were found for any of the traits except for GH, which displayed negative linear effect with stMLH. Negative HFC for GH could potentially be explained by the effect of heterosis caused by mating of two inbred mother trees (Lippman and Zamir 2006), or outbreeding depression at the most heterozygote trees and its negative impact on the fitness of the progeny, while their simultaneous action is also possible (Lynch. 1991). However,since this effect wasn't detected for FH, we cannot either rule out that the greenhouse conditions introduce artificial effects that disappear under more realistic field conditions.

Impact of sociodemographic variables on executive functions.

PubMed

Campanholo, Kenia Repiso; Boa, Izadora Nogueira Fonte; Hodroj, Flávia Cristina da Silva Araujo; Guerra, Glaucia Rosana Benute; Miotto, Eliane Correa; de Lucia, Mara Cristina Souza

2017-01-01

Executive functions (EFs) regulate human behavior and allow individuals to interact and act in the world. EFs are sensitive to sociodemographic variables such as age, which promotes their decline, and to others that can exert a neuroprotective effect. To assess the predictive role of education, occupation and family income on decline in executive functions among a sample with a wide age range. A total of 925 participants aged 18-89 years with 1-28 years' education were submitted to assessment of executive functions using the Card Sorting Test (CST), Phonemic Verbal Fluency (FAS) Task and Semantic Verbal Fluency (SVF) Task. Data on income, occupation and educational level were collected for the sample. The data were analyzed using Linear Regression, as well as Pearson's and Spearman's Correlation. Age showed a significant negative correlation (p<0.001) with performance on the CST, FAS and SVF, whereas education, income and occupation were positively associated (p<0.001) with the tasks applied. After application of the multivariate linear regression model, a significant positive relationship with the FAS was maintained only for education (p<0.001) and income (p<0.001). The negative relationship of age (p<0.001) and positive relationship of both education (p<0.001) and income (p<0.001and p=0.003) were evident on the CST and SVF. Educational level and income positively influenced participants' results on executive function tests, attenuating expected decline for age. However, no relationship was found between occupation and the cognitive variables investigated.

Impact of sociodemographic variables on executive functions

PubMed Central

Campanholo, Kenia Repiso; Boa, Izadora Nogueira Fonte; Hodroj, Flávia Cristina da Silva Araujo; Guerra, Glaucia Rosana Benute; Miotto, Eliane Correa; de Lucia, Mara Cristina Souza

2017-01-01

Executive functions (EFs) regulate human behavior and allow individuals to interact and act in the world. EFs are sensitive to sociodemographic variables such as age, which promotes their decline, and to others that can exert a neuroprotective effect. Objective To assess the predictive role of education, occupation and family income on decline in executive functions among a sample with a wide age range. Methods A total of 925 participants aged 18-89 years with 1-28 years' education were submitted to assessment of executive functions using the Card Sorting Test (CST), Phonemic Verbal Fluency (FAS) Task and Semantic Verbal Fluency (SVF) Task. Data on income, occupation and educational level were collected for the sample. The data were analyzed using Linear Regression, as well as Pearson's and Spearman's Correlation. Results Age showed a significant negative correlation (p<0.001) with performance on the CST, FAS and SVF, whereas education, income and occupation were positively associated (p<0.001) with the tasks applied. After application of the multivariate linear regression model, a significant positive relationship with the FAS was maintained only for education (p<0.001) and income (p<0.001). The negative relationship of age (p<0.001) and positive relationship of both education (p<0.001) and income (p<0.001and p=0.003) were evident on the CST and SVF. Conclusion Educational level and income positively influenced participants' results on executive function tests, attenuating expected decline for age. However, no relationship was found between occupation and the cognitive variables investigated. PMID:29213495

Ultra-endurance sports have no negative impact on indices of arterial stiffness.

PubMed

Radtke, Thomas; Schmidt-Trucksäss, Arno; Brugger, Nicolas; Schäfer, Daniela; Saner, Hugo; Wilhelm, Matthias

2014-01-01

Marathon running has been linked with higher arterial stiffness. Blood pressure is a major contributor to pulse wave velocity (PWV). We examined indices of arterial stiffness with a blood pressure-independent method in marathon runners and ultra-endurance athletes. Male normotensive amateur runners were allocated to three groups according to former participation in competitions: group I (recreational athletes), group II (marathon runners) and group III (ultra-endurance athletes). Indices of arterial stiffness were measured with a non-invasive device (VaSera VS-1500N, Fukuda Denshi, Japan) to determine the cardio-ankle vascular index (CAVI, primary endpoint) and brachial-ankle PWV (baPWV). Lifetime training hours were calculated. Cumulative competitions were expressed as marathon equivalents. Linear regression analysis was performed to determine predictors for CAVI and baPWV. Measurements of arterial stiffness were performed in 51 subjects (mean age 44.6 ± 1.2 years): group I (n = 16), group II (n = 19) and group III (n = 16). No between-group differences existed in age, anthropometric characteristics and resting BP. CAVI and baPWV were comparable between all groups (P = 0.604 and P = 0.947, respectively). In linear regression analysis, age was the only independent predictor for CAVI (R(2) = 0.239, β = 0.455, P = 0.001). Systolic BP was significantly associated with baPWV (R(2) = 0.225, β = 0.403, P = 0.004). In middle-aged normotensive athletes marathon running and ultra-endurance sports had no negative impact on arterial stiffness.

Depressive symptoms, smoking, drinking, and quality of life among head and neck cancer patients.

PubMed

Duffy, Sonia A; Ronis, David L; Valenstein, Marcia; Fowler, Karen E; Lambert, Michael T; Bishop, Carol; Terrell, Jeffrey E

2007-01-01

The authors examined the relationship between depressive symptoms, smoking, problem drinking, and quality of life among 973 head and neck cancer patients who were surveyed and had their charts audited. Forty-six percent screened positive for depressive symptoms, 30% smoked, and 16% screened positive for problem drinking. Controlling for clinical and demographic variables, linear-regression analyses showed that depressive symptoms had a strong negative association with all 12 quality-of-life scales; smoking had a negative association on all but one of the quality-of-life scales; and problem drinking was not associated with any of the quality-of-life scales. Interventions targeting depression, smoking, and problem drinking need to be integrated into oncology clinics.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

[Ultrasonic measurements of fetal thalamus, caudate nucleus and lenticular nucleus in prenatal diagnosis].

PubMed

Yang, Ruiqi; Wang, Fei; Zhang, Jialing; Zhu, Chonglei; Fan, Limei

2015-05-19

To establish the reference values of thalamus, caudate nucleus and lenticular nucleus diameters through fetal thalamic transverse section. A total of 265 fetuses at our hospital were randomly selected from November 2012 to August 2014. And the transverse and length diameters of thalamus, caudate nucleus and lenticular nucleus were measured. SPSS 19.0 statistical software was used to calculate the regression curve of fetal diameter changes and gestational weeks of pregnancy. P < 0.05 was considered as having statistical significance. The linear regression equation of fetal thalamic length diameter and gestational week was: Y = 0.051X+0.201, R = 0.876, linear regression equation of thalamic transverse diameter and fetal gestational week was: Y = 0.031X+0.229, R = 0.817, linear regression equation of fetal head of caudate nucleus length diameter and gestational age was: Y = 0.033X+0.101, R = 0.722, linear regression equation of fetal head of caudate nucleus transverse diameter and gestational week was: R = 0.025 - 0.046, R = 0.711, linear regression equation of fetal lentiform nucleus length diameter and gestational week was: Y = 0.046+0.229, R = 0.765, linear regression equation of fetal lentiform nucleus diameter and gestational week was: Y = 0.025 - 0.05, R = 0.772. Ultrasonic measurement of diameter of fetal thalamus caudate nucleus, and lenticular nucleus through thalamic transverse section is simple and convenient. And measurements increase with fetal gestational weeks and there is linear regression relationship between them.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Interrelation and independence of positive and negative psychological constructs in predicting general treatment adherence in coronary artery patients - Results from the THORESCI study.

PubMed

van Montfort, Eveline; Denollet, Johan; Widdershoven, Jos; Kupper, Nina

2016-09-01

In cardiac patients, positive psychological factors have been associated with improved medical and psychological outcomes. The current study examined the interrelation between and independence of multiple positive and negative psychological constructs. Furthermore, the potential added predictive value of positive psychological functioning regarding the prediction of patients' treatment adherence and participation in cardiac rehabilitation (CR) was investigated. 409 percutaneous coronary intervention (PCI) patients were included (mean age = 65.6 ± 9.5; 78% male). Self-report questionnaires were administered one month post-PCI. Positive psychological constructs included positive affect (GMS) and optimism (LOT-R); negative constructs were depression (PHQ-9, BDI), anxiety (GAD-7) and negative affect (GMS). Six months post-PCI self-reported general adherence (MOS) and CR participation were determined. Factor Analysis (Oblimin rotation) revealed two components (r = − 0.56), reflecting positive and negative psychological constructs. Linear regression analyses showed that in unadjusted analyses both optimism and positive affect were associated with better general treatment adherence at six months (p < 0.05). In adjusted analyses, optimism's predictive values remained, independent of sex, age, PCI indication, depression and anxiety. Univariate logistic regression analysis showed that in patients with a cardiac history, positive affect was significantly associated with CR participation. After controlling for multiple covariates, this relation was no longer significant. Positive and negative constructs should be considered as two distinct dimensions. Positive psychological constructs (i.e. optimism) may be of incremental value to negative psychological constructs in predicting patients' treatment adherence. A more complete view of a patients' psychological functioning will open new avenues for treatment. Additional research is needed to investigate the relationship between positive psychological factors and other cardiac outcomes, such as cardiac events and mortality.

Orthogonal Regression: A Teaching Perspective

ERIC Educational Resources Information Center

Carr, James R.

2012-01-01

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Multifactor analysis and simulation of the surface runoff and soil infiltration at different slope gradients

NASA Astrophysics Data System (ADS)

Huang, J.; Kang, Q.; Yang, J. X.; Jin, P. W.

2017-08-01

The surface runoff and soil infiltration exert significant influence on soil erosion. The effects of slope gradient/length (SG/SL), individual rainfall amount/intensity (IRA/IRI), vegetation cover (VC) and antecedent soil moisture (ASM) on the runoff depth (RD) and soil infiltration (INF) were evaluated in a series of natural rainfall experiments in the South of China. RD is found to correlate positively with IRA, IRI, and ASM factors and negatively with SG and VC. RD decreased followed by its increase with SG and ASM, it increased with a further decrease with SL, exhibited a linear growth with IRA and IRI, and exponential drop with VC. Meanwhile, INF exhibits a positive correlation with SL, IRA and IRI and VC, and a negative one with SG and ASM. INF was going up and then down with SG, linearly rising with SL, IRA and IRI, increasing by a logit function with VC, and linearly falling with ASM. The VC level above 60% can effectively lower the surface runoff and significantly enhance soil infiltration. Two RD and INF prediction models, accounting for the above six factors, were constructed using the multiple nonlinear regression method. The verification of those models disclosed a high Nash-Sutcliffe coefficient and low root-mean-square error, demonstrating good predictability of both models.

Assessment of ecologic regression in the study of lung cancer and indoor radon.

PubMed

Stidley, C A; Samet, J M

1994-02-01

Ecologic regression studies conducted to assess the cancer risk of indoor radon to the general population are subject to methodological limitations, and they have given seemingly contradictory results. The authors use simulations to examine the effects of two major methodological problems that affect these studies: measurement error and misspecification of the risk model. In a simulation study of the effect of measurement error caused by the sampling process used to estimate radon exposure for a geographic unit, both the effect of radon and the standard error of the effect estimate were underestimated, with greater bias for smaller sample sizes. In another simulation study, which addressed the consequences of uncontrolled confounding by cigarette smoking, even small negative correlations between county geometric mean annual radon exposure and the proportion of smokers resulted in negative average estimates of the radon effect. A third study considered consequences of using simple linear ecologic models when the true underlying model relation between lung cancer and radon exposure is nonlinear. These examples quantify potential biases and demonstrate the limitations of estimating risks from ecologic studies of lung cancer and indoor radon.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Baseline social amotivation predicts 1-year functioning in UHR subjects: A validation and prospective investigation.

PubMed

Lam, Max; Abdul Rashid, Nur Amirah; Lee, Sara-Ann; Lim, Jeanette; Foussias, George; Fervaha, Gagan; Ruhrman, Stephan; Remington, Gary; Lee, Jimmy

2015-12-01

Social amotivation and diminished expression have been reported to underlie negative symptomatology in schizophrenia. In the current study we sought to establish and validate these negative symptom domains in a large cohort of schizophrenia subjects (n=887) and individuals who are deemed to be Ultra-High Risk (UHR) for psychosis. Confirmatory factor analysis conducted on PANSS item domains demonstrate that the dual negative symptom domains exist in schizophrenia and UHR subjects. We further sought to examine if these negative symptom domains were associated with functioning in UHR subjects. Linear regression analyses confirmed that social amotivation predicted functioning in UHR subjects prospectively at 1 year follow up. Results suggest that the association between social amotivation and functioning is generalisable beyond schizophrenia populations to those who are at-risk of developing psychosis. Social amotivation may be an important dimensional clinical construct to be studied across a range of psychiatric conditions. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Perceptions of academic skills of children diagnosed with ADHD.

PubMed

Eisenberg, Daniel; Schneider, Helen

2007-05-01

This study investigates how the academic skills of children diagnosed with ADHD are perceived by teachers, parents, and the children themselves. The authors analyze data collected for third graders in spring 2002 in the nationally representative Early Childhood Longitudinal Survey. They use linear regressions to estimate independent associations between perceptions of academic abilities and parent-reported ADHD diagnoses, controlling for scores on standardized reading and math tests, assessments of externalizing behaviors, and other factors. Results show that for ADHD-diagnosed girls compared to other girls, both parents' and teachers' perceptions are substantially more negative. For ADHD-diagnosed boys, the differentials are also negative but less pronounced. Self-perceptions are not significantly different by ADHD status, except for boys' more negative self-perceptions related to math. Given the potentially damaging effects of these negative perceptions and expectations on self-esteem, motivation, and performance, efforts may be needed to bring perceptions of ADHD children more in line with the abilities they demonstrate on objective assessments. (J. of Att. Dis. 2007; 10(4) 390-397).

Factors Predicting Internalized Stigma Among Men Who Have Sex with Men Living with HIV in Beijing, China.

PubMed

Xu, Xiaohua; Sheng, Yu; Khoshnood, Kaveh; Clark, Kirsty

Internalized stigma in people living with HIV is associated with negative outcomes including sexual risk behaviors and depression. Little research has focused on internalized stigma in men who have sex with men living with HIV (MSMLWH) in China. We measured internalized stigma and examined its potential predictors in a sample of 277 MSMLWH from two infectious disease specialist hospitals in Beijing, China. Descriptive analysis showed an intermediate high level of internalized stigma in these men. Multiple linear regression revealed that higher levels of stereotypes, negative affect, older age, lower levels of mastery, and limited information and emotional support were significant predictors of internalized stigma. Cognitive reconstruction interventions should be developed to change negative stereotypes and reduce internalized stigma, and information and emotional support should be provided to develop mastery, foster coping skills for internalized stigma, and alleviate negative affect. MSMLWH of older ages need more attention in stigma reduction programs. Copyright © 2016 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

[Income inequality, corruption, and life expectancy at birth in Mexico].

PubMed

Idrovo, Alvaro Javier

2005-01-01

To ascertain if the effect of income inequality on life expectancy at birth in Mexico is mediated by corruption, used as a proxy of social capital. An ecological study was carried out with the 32 Mexican federative entities. Global and by sex correlations between life expectancy at birth were estimated by federative entity with the Gini coefficient, the Corruption and Good Government Index, the percentage of Catholics, and the percentage of the population speaking indigenous language. Robust linear regressions, with and without instrumental variables, were used to explore if corruption acts as intermediate variable in the studied relationship. Negative correlations with Spearman's rho near to -0.60 (p < 0.05) and greater than -0.66 (p < 0.05) between life expectancy at birth, the Gini coefficient and the population speaking indigenous language, respectively, were observed. Moreover, the Corruption and Good Government Index correlated with men's life expectancy at birth with Spearman's rho -0.3592 (p < 0.05). Regressions with instruments were more consistent than conventional ones and they show a strong negative effect (p < 0.05) of income inequality on life expectancy at birth. This effect was greater among men. The findings suggest a negative effect of income inequality on life expectancy at birth in Mexico, mediated by corruption levels and other related cultural factors.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Correlation Of Deviance In Arterial Oxygenation With Severity Of Chronic Liver Disease.

PubMed

Shaukat, Al-Aman; Zar, Adnan; Zuhaid, Muhammad; Afridi, Safa Saadat

2016-01-01

Hepatitis B and C related chronic liver diseases have led to development of a serious threat to the people of South Asia. The main aim of this study was to evaluate the correlation of magnitude of arterial deoxygention to the severity of liver disease. It was a hospital based cross-sectional descriptive study, carried out in the Medical Department of Khyber Teaching Hospital Peshawar. All in all 115 patients were assessed for the severity of the liver diseases and were correlated with arterial deoxygenation using linear regression models. Male to female ratio was 1.5:1. Males infected with hepatitis B, hepatitis C and both were 9, 60 and 1, while females suffered from hepatitis B, Hepatitis C and both were 2, 42 and 1 respectively. The linear relationship between A-a DO2 with severity of liver disease showed positive correlation while PO2 showed negative correlation with severity of liver disease. There was a positive correlation between A-a DO2 and severity of liver diseases while PO2 and severity of liver diseases showed negative correlation.

Quality of life in breast cancer patients--a quantile regression analysis.

PubMed

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Negative psychological aspects and survival in lung cancer patients.

PubMed

Nakaya, Naoki; Saito-Nakaya, Kumi; Akechi, Tatsuo; Kuriyama, Shinichi; Inagaki, Masatoshi; Kikuchi, Nobutaka; Nagai, Kanji; Tsugane, Shoichiro; Nishiwaki, Yutaka; Tsuji, Ichiro; Uchitomi, Yosuke

2008-05-01

We conducted a prospective cohort study in Japan to investigate associations between negative psychological aspects and cancer survival. Between July 1999 and July 2004, a total of 1178 lung cancer patients were enrolled. The questionnaire asked about socioeconomic variables, smoking status, clinical symptoms, and psychological aspects after diagnosis. Negative psychological aspects were assessed for the subscales of helplessness/hopelessness and depression. Clinical stage, performance status (PS), and histologic type were obtained from medical charts. The subjects were followed up until December 2004, and 686 had died. A Cox regression model was used to estimate the hazards ratio (HR) of all-cause mortality. After adjustment for socioeconomic variables and smoking status in addition to sex, age, and histologic type, both helplessness/hopelessness and depression subscales showed significant linear positive associations with the risk of mortality (p for trend<0.001 for both). However, after adjustment for clinical state variables in addition to sex, age, and histologic type, these significant linear positive associations were no longer observed (p for trend=0.41 and 0.26, respectively). Our data supported the hypothesis that the association between helplessness/hopelessness and depression and the risk of mortality among lung cancer patients was largely confounded by clinical state variables including clinical stage, PS, and clinical symptoms. (c) 2007 John Wiley & Sons, Ltd.

Use of probabilistic weights to enhance linear regression myoelectric control

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2015-12-01

Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Study of process variables associated with manufacturing hermetically-sealed nickel-cadmium cells

NASA Technical Reports Server (NTRS)

Miller, L.; Doan, D. J.; Carr, E. S.

1971-01-01

A program to determine and study the critical process variables associated with the manufacture of aerospace, hermetically-sealed, nickel-cadmium cells is described. The determination and study of the process variables associated with the positive and negative plaque impregnation/polarization process are emphasized. The experimental data resulting from the implementation of fractional factorial design experiments are analyzed by means of a linear multiple regression analysis technique. This analysis permits the selection of preferred levels for certain process variables to achieve desirable impregnated plaque characteristics.

Developing a dengue forecast model using machine learning: A case study in China.

PubMed

Guo, Pi; Liu, Tao; Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-10-01

In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

Examining the interplay among negative emotionality, cognitive functioning, and attention deficit/hyperactivity disorder symptom severity.

PubMed

Healey, Dione M; Marks, David J; Halperin, Jeffrey M

2011-05-01

Cognition and emotion, traditionally thought of as largely distinct, have recently begun to be conceptualized as dynamically linked processes that interact to influence functioning. This study investigated the moderating effects of cognitive functioning on the relationship between negative emotionality and attention deficit/hyperactivity disorder (ADHD) symptom severity. A total of 216 (140 hyperactive/inattentive; 76 typically developing) preschoolers aged 3-4 years were administered a neuropsychological test battery (i.e., NEPSY). To avoid method bias, child negative emotionality was rated by teachers (Temperament Assessment Battery for Children-Revised), and parents rated symptom severity on the ADHD Rating Scale (ADHD-RS-IV). Hierarchical Linear Regression analyses revealed that both negative emotionality and Perceptual-Motor & Executive Functions accounted for significant unique variance in ADHD symptom severity. Significant interactions indicated that when negative emotionality is low, but not high, neuropsychological functioning accounts for significant variability in ADHD symptoms, with lower functioning predicting more symptoms. Emotional and neuropsychological functioning, both individually and in combination, play a significant role in the expression of ADHD symptom severity.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Prediction of erodibility in Oxisols using iron oxides, soil color and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques, José, Jr.

2015-04-01

The prediction of erodibility using indirect methods such as diffuse reflectance spectroscopy could facilitate the characterization of the spatial variability in large areas and optimize implementation of conservation practices. The aim of this study was to evaluate the prediction of interrill erodibility (Ki) and rill erodibility (Kr) by means of iron oxides content and soil color using multiple linear regression and diffuse reflectance spectroscopy (DRS) using regression analysis by least squares partial (PLSR). The soils were collected from three geomorphic surfaces and analyzed for chemical, physical and mineralogical properties, plus scanned in the spectral range from the visible and infrared. Maps of spatial distribution of Ki and Kr were built with the values calculated by the calibrated models that obtained the best accuracy using geostatistics. Interrill-rill erodibility presented negative correlation with iron extracted by dithionite-citrate-bicarbonate, hematite, and chroma, confirming the influence of iron oxides in soil structural stability. Hematite and hue were the attributes that most contributed in calibration models by multiple linear regression for the prediction of Ki (R2 = 0.55) and Kr (R2 = 0.53). The diffuse reflectance spectroscopy via PLSR allowed to predict Interrill-rill erodibility with high accuracy (R2adj = 0.76, 0.81 respectively and RPD> 2.0) in the range of the visible spectrum (380-800 nm) and the characterization of the spatial variability of these attributes by geostatistics.

Five-year change in morale is associated with negative life events in very old age.

PubMed

Näsman, Marina; Niklasson, Johan; Saarela, Jan; Nygård, Mikael; Olofsson, Birgitta; Conradsson, Mia; Lövheim, Hugo; Gustafson, Yngve; Nyqvist, Fredrica

2017-10-27

The objectives were to study changes in morale in individuals 85 years and older, and to assess the effect of negative life events on morale over a five-year follow-up period. The present study is based on longitudinal data from the Umeå85+/GERDA-study, including individuals 85 years and older at baseline (n = 204). Morale was measured with the Philadelphia Geriatric Center Morale Scale (PGCMS). Negative life events were assessed using an index including 13 negative life events occurring during the follow-up period. Linear regression was used for the multivariate analyses. The majority of the sample (69.1%) had no significant changes in morale during the five-year follow-up. However, the accumulation of negative life events was significantly associated with a greater decrease in PGCMS. A higher baseline PGCMS score did not attenuate the adverse effect negative life events had on morale. Morale seemed to be mainly stable in a five-year follow-up of very old people. It seems, nonetheless, that individuals are affected by negative life events, regardless of level of morale. Preventing negative life events and supporting individuals who experience multiple negative life events could have important implications for the care of very old people.

A positive take on schizophrenia negative symptom scales: Converting scores between the SANS, NSA and SDS.

PubMed

Preda, Adrian; Nguyen, Dana D; Bustillo, Juan R; Belger, Aysenil; O'Leary, Daniel S; McEwen, Sarah; Ling, Shichun; Faziola, Lawrence; Mathalon, Daniel H; Ford, Judith M; Potkin, Steven G; van Erp, Theo G M

2018-06-20

To provide quantitative conversions between commonly used scales for the assessment of negative symptoms in schizophrenia. Linear regression analyses generated conversion equations between symptom scores from the Scale for the Assessment of Negative Symptoms (SANS), the Schedule for the Deficit Syndrome (SDS), the Positive and Negative Syndrome Scale (PANSS), or the Negative Symptoms Assessment (NSA) based on a cross sectional sample of 176 individuals with schizophrenia. Intraclass correlations assessed the rating conversion accuracy based on a separate sub-sample of 29 patients who took part in the initial study as well as an independent sample of 28 additional subjects with schizophrenia. Between-scale negative symptom ratings were moderately to highly correlated (r = 0.73-0.91). Intraclass correlations between the original negative symptom rating scores and those obtained via using the conversion equations were in the range of 0.61-0.79. While there is a degree of non-overlap, several negative symptoms scores reflect measures of similar constructs and may be reliably converted between some scales. The conversion equations are provided at http://www.converteasy.org and may be used for meta- and mega-analyses that examine negative symptoms. Copyright © 2018 Elsevier B.V. All rights reserved.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

Linearity versus Nonlinearity of Offspring-Parent Regression: An Experimental Study of Drosophila Melanogaster

PubMed Central

Gimelfarb, A.; Willis, J. H.

1994-01-01

An experiment was conducted to investigate the offspring-parent regression for three quantitative traits (weight, abdominal bristles and wing length) in Drosophila melanogaster. Linear and polynomial models were fitted for the regressions of a character in offspring on both parents. It is demonstrated that responses by the characters to selection predicted by the nonlinear regressions may differ substantially from those predicted by the linear regressions. This is true even, and especially, if selection is weak. The realized heritability for a character under selection is shown to be determined not only by the offspring-parent regression but also by the distribution of the character and by the form and strength of selection. PMID:7828818

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

PubMed

Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C

2014-03-01

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Stratospheric Ozone Trends and Variability as Seen by SCIAMACHY from 2002 to 2012

NASA Technical Reports Server (NTRS)

Gebhardt, C.; Rozanov, A.; Hommel, R.; Weber, M.; Bovensmann, H.; Burrows, J. P.; Degenstein, D.; Froidevaux, L.; Thompson, A. M.

2014-01-01

Vertical profiles of the rate of linear change (trend) in the altitude range 15-50 km are determined from decadal O3 time series obtained from SCIAMACHY/ENVISAT measurements in limb-viewing geometry. The trends are calculated by using a multivariate linear regression. Seasonal variations, the quasi-biennial oscillation, signatures of the solar cycle and the El Nino-Southern Oscillation are accounted for in the regression. The time range of trend calculation is August 2002-April 2012. A focus for analysis are the zonal bands of 20 deg N - 20 deg S (tropics), 60 - 50 deg N, and 50 - 60 deg S (midlatitudes). In the tropics, positive trends of up to 5% per decade between 20 and 30 km and negative trends of up to 10% per decade between 30 and 38 km are identified. Positive O3 trends of around 5% per decade are found in the upper stratosphere in the tropics and at midlatitudes. Comparisons between SCIAMACHY and EOS MLS show reasonable agreement both in the tropics and at midlatitudes for most altitudes. In the tropics, measurements from OSIRIS/Odin and SHADOZ are also analysed. These yield rates of linear change of O3 similar to those from SCIAMACHY. However, the trends from SCIAMACHY near 34 km in the tropics are larger than MLS and OSIRIS by a factor of around two.

Pre-natal exposures to cocaine and alcohol and physical growth patterns to age 8 years

PubMed Central

Lumeng, Julie C.; Cabral, Howard J.; Gannon, Katherine; Heeren, Timothy; Frank, Deborah A.

2007-01-01

Two hundred and two primarily African American/Caribbean children (classified by maternal report and infant meconium as 38 heavier, 74 lighter and 89 not cocaine-exposed) were measured repeatedly from birth to age 8 years to assess whether there is an independent effect of prenatal cocaine exposure on physical growth patterns. Children with fetal alcohol syndrome identifiable at birth were excluded. At birth, cocaine and alcohol exposures were significantly and independently associated with lower weight, length and head circumference in cross-sectional multiple regression analyses. The relationship over time of pre-natal exposures to weight, height, and head circumference was then examined by multiple linear regression using mixed linear models including covariates: child’s gestational age, gender, ethnicity, age at assessment, current caregiver, birth mother’s use of alcohol, marijuana and tobacco during the pregnancy and pre-pregnancy weight (for child’s weight) and height (for child’s height and head circumference). The cocaine effects did not persist beyond infancy in piecewise linear mixed models, but a significant and independent negative effect of pre-natal alcohol exposure persisted for weight, height, and head circumference. Catch-up growth in cocaine-exposed infants occurred primarily by 6 months of age for all growth parameters, with some small fluctuations in growth rates in the preschool age range but no detectable differences between heavier versus unexposed nor lighter versus unexposed thereafter. PMID:17412558

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

An Expert System for the Evaluation of Cost Models

DTIC Science & Technology

1990-09-01

contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John

Advanced Statistical Analyses to Reduce Inconsistency of Bond Strength Data.

PubMed

Minamino, T; Mine, A; Shintani, A; Higashi, M; Kawaguchi-Uemura, A; Kabetani, T; Hagino, R; Imai, D; Tajiri, Y; Matsumoto, M; Yatani, H

2017-11-01

This study was designed to clarify the interrelationship of factors that affect the value of microtensile bond strength (µTBS), focusing on nondestructive testing by which information of the specimens can be stored and quantified. µTBS test specimens were prepared from 10 noncarious human molars. Six factors of µTBS test specimens were evaluated: presence of voids at the interface, X-ray absorption coefficient of resin, X-ray absorption coefficient of dentin, length of dentin part, size of adhesion area, and individual differences of teeth. All specimens were observed nondestructively by optical coherence tomography and micro-computed tomography before µTBS testing. After µTBS testing, the effect of these factors on µTBS data was analyzed by the general linear model, linear mixed effects regression model, and nonlinear regression model with 95% confidence intervals. By the general linear model, a significant difference in individual differences of teeth was observed ( P < 0.001). A significantly positive correlation was shown between µTBS and length of dentin part ( P < 0.001); however, there was no significant nonlinearity ( P = 0.157). Moreover, a significantly negative correlation was observed between µTBS and size of adhesion area ( P = 0.001), with significant nonlinearity ( P = 0.014). No correlation was observed between µTBS and X-ray absorption coefficient of resin ( P = 0.147), and there was no significant nonlinearity ( P = 0.089). Additionally, a significantly positive correlation was observed between µTBS and X-ray absorption coefficient of dentin ( P = 0.022), with significant nonlinearity ( P = 0.036). A significant difference was also observed between the presence and absence of voids by linear mixed effects regression analysis. Our results showed correlations between various parameters of tooth specimens and µTBS data. To evaluate the performance of the adhesive more precisely, the effect of tooth variability and a method to reduce variation in bond strength values should also be considered.

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

PubMed

Luque-Fernandez, Miguel Angel; Belot, Aurélien; Quaresma, Manuela; Maringe, Camille; Coleman, Michel P; Rachet, Bernard

2016-10-01

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

Social relationships and longitudinal changes in body mass index and waist circumference: the coronary artery risk development in young adults study.

PubMed

Kershaw, Kiarri N; Hankinson, Arlene L; Liu, Kiang; Reis, Jared P; Lewis, Cora E; Loria, Catherine M; Carnethon, Mercedes R

2014-03-01

Few studies have examined longitudinal associations between close social relationships and weight change. Using data from 3,074 participants in the Coronary Artery Risk Development in Young Adults Study who were examined in 2000, 2005, and 2010 (at ages 33-45 years in 2000), we estimated separate logistic regression random-effects models to assess whether patterns of exposure to supportive and negative relationships were associated with 10% or greater increases in body mass index (BMI) (weight (kg)/height (m)(2)) and waist circumference. Linear regression random-effects modeling was used to examine associations of social relationships with mean changes in BMI and waist circumference. Participants with persistently high supportive relationships were significantly less likely to increase their BMI values and waist circumference by 10% or greater compared with those with persistently low supportive relationships after adjustment for sociodemographic characteristics, baseline BMI/waist circumference, depressive symptoms, and health behaviors. Persistently high negative relationships were associated with higher likelihood of 10% or greater increases in waist circumference (odds ratio = 1.62, 95% confidence interval: 1.15, 2.29) and marginally higher BMI increases (odds ratio = 1.50, 95% confidence interval: 1.00, 2.24) compared with participants with persistently low negative relationships. Increasingly negative relationships were associated with increases in waist circumference only. These findings suggest that supportive relationships may minimize weight gain, and that adverse relationships may contribute to weight gain, particularly via central fat accumulation.

Social Relationships and Longitudinal Changes in Body Mass Index and Waist Circumference: The Coronary Artery Risk Development in Young Adults Study

PubMed Central

Kershaw, Kiarri N.; Hankinson, Arlene L.; Liu, Kiang; Reis, Jared P.; Lewis, Cora E.; Loria, Catherine M.; Carnethon, Mercedes R.

2014-01-01

Few studies have examined longitudinal associations between close social relationships and weight change. Using data from 3,074 participants in the Coronary Artery Risk Development in Young Adults Study who were examined in 2000, 2005, and 2010 (at ages 33–45 years in 2000), we estimated separate logistic regression random-effects models to assess whether patterns of exposure to supportive and negative relationships were associated with 10% or greater increases in body mass index (BMI) (weight (kg)/height (m)2) and waist circumference. Linear regression random-effects modeling was used to examine associations of social relationships with mean changes in BMI and waist circumference. Participants with persistently high supportive relationships were significantly less likely to increase their BMI values and waist circumference by 10% or greater compared with those with persistently low supportive relationships after adjustment for sociodemographic characteristics, baseline BMI/waist circumference, depressive symptoms, and health behaviors. Persistently high negative relationships were associated with higher likelihood of 10% or greater increases in waist circumference (odds ratio = 1.62, 95% confidence interval: 1.15, 2.29) and marginally higher BMI increases (odds ratio = 1.50, 95% confidence interval: 1.00, 2.24) compared with participants with persistently low negative relationships. Increasingly negative relationships were associated with increases in waist circumference only. These findings suggest that supportive relationships may minimize weight gain, and that adverse relationships may contribute to weight gain, particularly via central fat accumulation. PMID:24389018

Posttraumatic stress symptoms in police staff 12-18 months after the Canterbury earthquakes.

PubMed

Surgenor, Lois J; Snell, Deborah L; Dorahy, Martin J

2015-04-01

Understanding posttraumatic stress disorder (PTSD) symptoms in police first-responders is an underdeveloped field. Using a cross-sectional survey, this study investigated demographic and occupational characteristics, coping resources and processes, along with first-responder roles and consequences 18 months following a disaster. Hierarchical linear regression (N = 576) showed that greater symptom levels were significantly positively associated with negative emotional coping (β = .31), a communications role (β = .08) and distress following exposure to resource losses (β = .14), grotesque scenes (β = .21), personal harm (β = .14), and concern for significant others (β = .17). Optimism alone was negatively associated (β = -.15), with the overall model being a modest fit (adjusted R(2) = .39). The findings highlight variables for further study in police. Copyright © 2015 International Society for Traumatic Stress Studies.

General functioning predicts reward and punishment learning in schizophrenia.

PubMed

Somlai, Zsuzsanna; Moustafa, Ahmed A; Kéri, Szabolcs; Myers, Catherine E; Gluck, Mark A

2011-04-01

Previous studies investigating feedback-driven reinforcement learning in patients with schizophrenia have provided mixed results. In this study, we explored the clinical predictors of reward and punishment learning using a probabilistic classification learning task. Patients with schizophrenia (n=40) performed similarly to healthy controls (n=30) on the classification learning task. However, more severe negative and general symptoms were associated with lower reward-learning performance, whereas poorer general psychosocial functioning was correlated with both lower reward- and punishment-learning performances. Multiple linear regression analyses indicated that general psychosocial functioning was the only significant predictor of reinforcement learning performance when education, antipsychotic dose, and positive, negative and general symptoms were included in the analysis. These results suggest a close relationship between reinforcement learning and general psychosocial functioning in schizophrenia. Published by Elsevier B.V.

Compound Identification Using Penalized Linear Regression on Metabolomics

PubMed Central

Liu, Ruiqi; Wu, Dongfeng; Zhang, Xiang; Kim, Seongho

2014-01-01

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study. PMID:27212894

Social anxiety and negative early life events in university students.

PubMed

Binelli, Cynthia; Ortiz, Ana; Muñiz, Armando; Gelabert, Estel; Ferraz, Liliana; S Filho, Alaor; Crippa, José Alexandre S; Nardi, Antonio E; Subirà, Susana; Martín-Santos, Rocío

2012-06-01

There is substantial evidence regarding the impact of negative life events during childhood on the aetiology of psychiatric disorders. We examined the association between negative early life events and social anxiety in a sample of 571 Spanish University students. In a cross-sectional survey conducted in 2007, we collected data through a semistructured questionnaire of sociodemographic variables, personal and family psychiatric history, and substance abuse. We assessed the five early negative life events: (i) the loss of someone close, (ii) emotional abuse, (iii) physical abuse, (iv) family violence, and (v) sexual abuse. All participants completed the Liebowitz Social Anxiety Scale. Mean (SD) age was 21 (4.5), 75% female, LSAS score was 40 (DP = 22), 14.2% had a psychiatric family history and 50.6% had negative life events during childhood. Linear regression analyses, after controlling for age, gender, and family psychiatric history, showed a positive association between family violence and social score (p = 0.03). None of the remaining stressors produced a significant increase in LSAS score (p > 0.05). University students with high levels of social anxiety presented higher prevalence of negative early life events. Thus, childhood family violence could be a risk factor for social anxiety in such a population.

The relationship between thermal environments and clothing insulation for elderly individuals in Shanghai, China.

PubMed

Jiao, Yu; Yu, Hang; Wang, Tian; An, Yusong; Yu, Yifan

2017-12-01

The relationship between thermal environmental parameters and clothing insulation is an important element in improving thermal comfort for the elderly. A field study was conducted on the indoor, transition space, and outdoor thermal environments of 17 elderly facilities in Shanghai, China. A random questionnaire survey was used to gather data from 672 valid samples. A statistical analysis of the data was conducted, and multiple linear regression models were established to quantify the relationships between clothing insulation, respondent age, indoor air temperature, and indoor relative humidity. Results indicated that the average thermal insulation of winter and summer clothing is 1.38 clo and 0.44 clo, respectively, for elderly men and 1.39 clo and 0.45 clo, respectively, for elderly women. It was also found that the thermal insulation of winter clothing is linearly correlated with age, and that there were seasonal differences in the relationship between clothing insulation and the environment. During winter, the clothing insulation is negatively correlated only with indoor temperature parameters (air temperature and operative temperature) for elderly males, while it is negatively correlated with indoor temperature parameters as well as transition space and outdoor air temperature for elderly females. In summer, clothing insulation for both elderly males and females is negatively correlated with outdoor temperature, as well as indoor temperature parameters (air temperature and operative temperature). The thermal insulation of summer clothing is also negatively correlated with transitional space temperature for males. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian Correction for Misclassification in Multilevel Count Data Models.

PubMed

Nelson, Tyler; Song, Joon Jin; Chin, Yoo-Mi; Stamey, James D

2018-01-01

Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

Control Variate Selection for Multiresponse Simulation.

DTIC Science & Technology

1987-05-01

M. H. Knuter, Applied Linear Regression Mfodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F., Probability, Allyn and Bacon...1982. Neter, J., V. Wasserman, and M. H. Knuter, Applied Linear Regression .fodels, Richard D. Erwin, Inc., Homewood, Illinois, 1983. Neuts, Marcel F...Aspects of J%,ultivariate Statistical Theory, John Wiley and Sons, New York, New York, 1982. dY Neter, J., W. Wasserman, and M. H. Knuter, Applied Linear Regression Mfodels

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer.

PubMed

Ross, David E; Ochs, Alfred L; Tate, David F; Tokac, Umit; Seabaugh, John; Abildskov, Tracy J; Bigler, Erin D

2018-05-30

NeuroQuant ® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Depression-related differences in lean body mass distribution from National Health and Nutrition Examination Survey 2005-2006.

PubMed

Li, Ying; Meng, Lu; Li, Yue; Sato, Yasuto

2014-03-01

Although the association between depression and body composition has been widely discussed, the effects of depression on lean body mass (LBM) are unclear. The present study aimed to investigate the association of depression with LBM. The study included 2406 participants aged 18-69 years. The sex and body mass index (BMI) stratified analysis of covariance was performed to compare total LBM and percentage LBM (%LBM) in subjects with different depression score levels. Multiple linear regression analysis was conducted to estimate the association between depression score and serum albumin level. An analysis of covariance stratified by sex showed that participants with moderate-to-severe depression had significantly decreased total LBM and total and regional %LBM in men, except for total LBM and percentage gynoid LBM, which was observed in women. In the BMI stratified analysis of covariance, depression was significantly associated with decreased total and regional %LBM and with increased total and regional percentage fat body mass. In people with BMI≥25kg/m(2), the associations between depression or depressive syndrome and LBM, and total and regional %LBM are stronger compared to those with BMI<25kg/m(2). Multiple linear regression analysis showed that depression score was significantly negatively associated with serum albumin level. This is a cross-sectional study based on a general population, some information about clinical diagnosis and medication use is not available. Depression had a significant negative association with LBM and serum albumin level. Copyright © 2014 Elsevier B.V. All rights reserved.

Numerical Investigation of the Residual Stress Distribution of Flat-Faced and Convexly Curved Tablets Using the Finite Element Method.

PubMed

Otoguro, Saori; Hayashi, Yoshihiro; Miura, Takahiro; Uehara, Naoto; Utsumi, Shunichi; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The stress distribution of tablets after compression was simulated using a finite element method, where the powder was defined by the Drucker-Prager cap model. The effect of tablet shape, identified by the surface curvature, on the residual stress distribution was investigated. In flat-faced tablets, weak positive shear stress remained from the top and bottom die walls toward the center of the tablet. In the case of the convexly curved tablet, strong positive shear stress remained on the upper side and in the intermediate part between the die wall and the center of the tablet. In the case of x-axial stress, negative values were observed for all tablets, suggesting that the x-axial force always acts from the die wall toward the center of the tablet. In the flat tablet, negative x-axial stress remained from the upper edge to the center bottom. The x-axial stress distribution differed between the flat and convexly curved tablets. Weak stress remained in the y-axial direction of the flat tablet, whereas an upward force remained at the center of the convexly curved tablet. By employing multiple linear regression analysis, the mechanical properties of the tablets were predicted accurately as functions of their residual stress distribution. However, the multiple linear regression prediction of the dissolution parameters of acetaminophen, used here as a model drug, was limited, suggesting that the dissolution of active ingredients is not a simple process; further investigation is needed to enable accurate predictions of dissolution parameters.

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

Disorganized Symptoms Predicted Worse Functioning Outcome in Schizophrenia Patients with Established Illness.

PubMed

Ortiz, Bruno Bertolucci; Gadelha, Ary; Higuchi, Cinthia Hiroko; Noto, Cristiano; Medeiros, Daiane; Pitta, José Cássio do Nascimento; de Araújo Filho, Gerardo Maria; Hallak, Jaime Eduardo Cecílio; Bressan, Rodrigo Affonseca

Most patients with schizophrenia will have subsequent relapses of the disorder, with continuous impairments in functioning. However, evidence is lacking on how symptoms influence functioning at different phases of the disease. This study aims to investigate the relationship between symptom dimensions and functioning at different phases: acute exacerbation, nonremission and remission. Patients with schizophrenia were grouped into acutely ill (n=89), not remitted (n=89), and remitted (n=69). Three exploratory stepwise linear regression analyses were performed for each phase of schizophrenia, in which the five PANSS factors and demographic variables were entered as the independent variables and the total Global Assessment of Functioning Scale (GAF) score was entered as the dependent variable. An additional exploratory stepwise logistic regression analysis was performed to predict subsequent remission at discharge in the inpatient population. The Disorganized factor was the most significant predictor for acutely ill patients (p<0.001), while the Hostility factor was the most significant for not-remitted patients and the Negative factor was the most significant for remitted patients (p=0.001 and p<0.001, respectively). In the logistic regression, the Disorganized factor score presented a significant negative association with remission (p=0.007). Higher disorganization symptoms showed the greatest impact in functioning at acute phase, and prevented patients from achieving remission, suggesting it may be a marker of symptom severity and worse outcome in schizophrenia.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION

EPA Science Inventory

We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...

Status of tuberculosis-related stigma and associated factors: a cross-sectional study in central China.

PubMed

Yin, Xiaoxv; Yan, Shijiao; Tong, Yeqing; Peng, Xin; Yang, Tingting; Lu, Zuxun; Gong, Yanhong

2018-02-01

Tuberculosis (TB) poses a significant challenge to public health worldwide. Stigma is a major obstacle to TB control by leading to delay in diagnosis and treatment non-adherence. This study aimed to evaluate the status of TB-related stigma and its associated factors among TB patients in China. Cross-sectional survey. Thus, 1342 TB patients were recruited from TB dispensaries in three counties in Hubei Province using a multistage sampling method and surveyed using a structured anonymous questionnaire including validated scales to measure TB-related stigma. A generalised linear regression model was used to identify the factors associated with TB-related stigma. The average score on the TB-related Stigma Scale was 9.33 (SD = 4.25). Generalised linear regression analysis revealed that knowledge about TB (ß = -0.18, P = 0.0025), family function (ß = -0.29, P < 0.0001) and doctor-patient communication (ß = -0.32, P = 0.0005) were negatively associated with TB-related stigma. TB-related stigma was high among TB patients in China. Interventions concentrating on reducing TB patients' stigma in China should focus on improving patients' family function and patients' knowledge about TB. © 2017 John Wiley & Sons Ltd.

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

DTIC Science & Technology

2015-07-15

Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sexual orientation and quality of life of people living with HIV/Aids.

PubMed

Oliveira, Francisco Braz Milanez; Queiroz, Artur Acelino Francisco Luz Nunes; Sousa, Álvaro Francisco Lopes de; Moura, Maria Eliete Batista; Reis, Renata Karina

2017-01-01

To analyze whether sexual orientation affects the quality of life of people living with HIV/Aids (PLWHA). A cross-sectional analytical study was carried out with 146 PLWHA in Teresina, capital city of the state of Piauí, in 2013, by means of the WHOQOL-HIV-bref. Descriptive analysis and multiple linear regression were used for data analysis. There was a prevalence of men (63.7%), non-heterosexual (57.0%), aged between 19 and 39 years (89%). Of the total, 75.5% mentioned presence of negative feelings, such as fear and anxiety, and 38% reported have suffered stigma. With regard to the dimensions investigated, the most affected were "environment" and "level of independence". Non-heterosexual orientation was negatively associated with quality of life in almost all dimensions. Living with HIV/Aids and having a non-heterosexual orientation have a negative impact on quality of life. Analisar se a orientação sexual afeta a qualidade de vida de pessoas vivendo com HIV/aids (PVHAs). Estudo analítico, transversal, realizado com 146 PVHAs em Teresina, PI, no ano de 2013, por aplicação da escala WHOQOL HIV-bref. Para análise dos dados, utilizou-se análise descritiva e regressão linear múltipla. Houve predominância de homens (63,7%), não-heterossexuais (57,0%), com idade entre 19 e 39 anos (89%). Do total, 75,5% mencionaram presença de sentimentos negativos como medo e ansiedade e 38% informaram terem sofrido estigma. Com relação aos domínios investigados, os mais comprometidos foram "meio ambiente" e "nível de independência". A orientação não-heterossexual associou-se negativamente à qualidade de vida em, praticamente, todos os domínios. Viver com HIV/aids e ter uma orientação não-heterossexual tem impacto negativo na qualidade de vida.

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Serum total bilirubin levels are negatively correlated with metabolic syndrome in aged Chinese women: a community-based study.

PubMed

Zhong, P; Sun, D M; Wu, D H; Li, T M; Liu, X Y; Liu, H Y

2017-01-26

We evaluated serum total bilirubin levels as a predictor for metabolic syndrome (MetS) and investigated the relationship between serum total bilirubin levels and MetS prevalence. This cross-sectional study included 1728 participants over 65 years of age from Eastern China. Anthropometric data, lifestyle information, and previous medical history were collected. We then measured serum levels of fasting blood-glucose, total cholesterol, triglycerides, and total bilirubin, as well as alanine aminotransferase activity. The prevalence of MetS and each of its individual component were calculated per quartile of total bilirubin level. Logistic regression was used to assess the correlation between serum total bilirubin levels and MetS. Total bilirubin level in the women who did not have MetS was significantly higher than in those who had MetS (P<0.001). Serum total bilirubin quartiles were linearly and negatively correlated with MetS prevalence and hypertriglyceridemia (HTG) in females (P<0.005). Logistic regression showed that serum total bilirubin was an independent predictor of MetS for females (OR: 0.910, 95%CI: 0.863-0.960; P=0.001). The present study suggests that physiological levels of serum total bilirubin might be an independent risk factor for aged Chinese women, and the prevalence of MetS and HTG are negatively correlated to serum total bilirubin levels.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Optimism and positive and negative feelings in parents of young children with developmental delay.

PubMed

Kurtz-Nelson, E; McIntyre, L L

2017-07-01

Parents' positive and negative feelings about their young children influence both parenting behaviour and child problem behaviour. Research has not previously examined factors that contribute to positive and negative feelings in parents of young children with developmental delay (DD). The present study sought to examine whether optimism, a known protective factor for parents of children with DD, was predictive of positive and negative feelings for these parents. Data were collected from 119 parents of preschool-aged children with developmental delay. Two separate hierarchical linear regression analyses were conducted to determine if optimism significantly predicted positive feelings and negative feelings and whether optimism moderated relations between parenting stress and parent feelings. Increased optimism was found to predict increased positive feelings and decreased negative feelings after controlling for child problem behaviour and parenting stress. In addition, optimism was found to moderate the relation between parenting stress and positive feelings. Results suggest that optimism may impact how parents perceive their children with DD. Future research should examine how positive and negative feelings impact positive parenting behaviour and the trajectory of problem behaviour specifically for children with DD. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Examining the specific dimensions of distress tolerance that prospectively predict perceived stress.

PubMed

Bardeen, Joseph R; Fergus, Thomas A; Orcutt, Holly K

2017-04-01

We examined five dimensions of distress tolerance (i.e. uncertainty, ambiguity, frustration, negative emotion, physical discomfort) as prospective predictors of perceived stress. Undergraduate students (N = 135) completed self-report questionnaires over the course of two assessment sessions (T1 and T2). Results of a linear regression in which the five dimensions of distress tolerance and covariates (i.e. T1 perceived stress, duration between T1 and T2) served as predictor variables and T2 perceived stress served as the outcome variable showed that intolerance of uncertainty was the only dimension of distress tolerance to predict T2 perceived stress. To better understand this prospective association, we conducted a post hoc analysis simultaneously regressing two subdimensions of intolerance of uncertainty on T2 perceived stress. The subdimension representing beliefs that "uncertainty has negative behavioral and self-referent implications" significantly predicted T2 perceived stress, while the subdimension indicating that "uncertainty is unfair and spoils everything" did not. Results support a growing body of research suggesting intolerance of uncertainty as a risk factor for a wide variety of maladaptive psychological outcomes. Clinical implications will be discussed.

An ecological study on suicide and homicide in Brazil.

PubMed

Bando, Daniel Hideki; Lester, David

2014-04-01

The objective was to evaluate correlations between suicide, homicide and socio-demographic variables by an ecological study. Mortality and socio-demographic data were collected from official records of the Ministry of Health and IBGE (2010), aggregated by state (27). The data were analyzed using correlation techniques, factor analysis, principal component analysis with a varimax rotation and multiple linear regression. Suicide age-adjusted rates for the total population, men and women were 5.0, 8.0, and 2.2 per 100,000 inhabitants respectively. The suicide rates ranged from 2.7 in Pará to 9.1 in Rio Grande do Sul. Homicide for the total population, men and women were 27.2, 50.8, and 4.5 per 100,000, respectively. The homicide rates ranged from 13.0 in Santa Catarina to 68.9 in Alagoas. Suicide and homicide were negatively associated, the significance persisted among men. Unemployment was negatively correlated with suicide and positively with homicide. Different socio-demographic variables were found to correlate with suicide and homicide in the regressions. Suicide showed a pattern suggesting that, in Brazil, it is related to high socioeconomic status. Homicide seemed to follow the pattern found in other countries, associated with lower social and economic status.

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

A quantile count model of water depth constraints on Cape Sable seaside sparrows

USGS Publications Warehouse

Cade, B.S.; Dong, Q.

2008-01-01

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992-2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (-50% to -83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5-10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.

Age variations in anthropometric and body composition characteristics and undernutrition among female Bathudis: a tribal population of Keonjhar District, Orissa, India.

PubMed

Bose, Kaushik; Chakraborty, Falguni; Bisai, Samiran

2007-09-01

A cross-sectional study of 183 female Bathudis, a tribal population of the Keonjhar District, Orissa, India, was undertaken to investigate age variations in anthropometric and body composition characteristics and nutritional status. The subjects were categorized into three age groups: < or =30 years, 31-50 years, >50 years. Height, weight, circumferences and skinfolds data were collected. Body mass index (BMI) and several body composition variables and indices were derived using standard equations. The results revealed that there existed significant negative age variations for most of the anthropometric and body composition variables and indices. Correlation studies of age with these variables and indices revealed significant negative correlations. Linear regression analyses revealed that for all variables, age had a significant negative impact. Studies on the nutritional status of these women revealed that with increasing age, there was an increase in the frequency of undernutrition. In conclusion, this study demonstrated that among Bathudi women, age was significantly negatively related with anthropometric and body composition variables and indices. Moreover, with increasing age, the level of undernutrition increased.

Predictors of burnout among correctional mental health professionals.

PubMed

Gallavan, Deanna B; Newman, Jody L

2013-02-01

This study focused on the experience of burnout among a sample of correctional mental health professionals. We examined the relationship of a linear combination of optimism, work family conflict, and attitudes toward prisoners with two dimensions derived from the Maslach Burnout Inventory and the Professional Quality of Life Scale. Initially, three subscales from the Maslach Burnout Inventory and two subscales from the Professional Quality of Life Scale were subjected to principal components analysis with oblimin rotation in order to identify underlying dimensions among the subscales. This procedure resulted in two components accounting for approximately 75% of the variance (r = -.27). The first component was labeled Negative Experience of Work because it seemed to tap the experience of being emotionally spent, detached, and socially avoidant. The second component was labeled Positive Experience of Work and seemed to tap a sense of competence, success, and satisfaction in one's work. Two multiple regression analyses were subsequently conducted, in which Negative Experience of Work and Positive Experience of Work, respectively, were predicted from a linear combination of optimism, work family conflict, and attitudes toward prisoners. In the first analysis, 44% of the variance in Negative Experience of Work was accounted for, with work family conflict and optimism accounting for the most variance. In the second analysis, 24% of the variance in Positive Experience of Work was accounted for, with optimism and attitudes toward prisoners accounting for the most variance.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Thyroid Function and Premature Delivery in TPO Antibody-Negative Women: The Added Value of hCG.

PubMed

Korevaar, Tim I M; Steegers, Eric A P; Chaker, Layal; Medici, Marco; Jaddoe, Vincent W V; Visser, Theo J; de Rijke, Yolanda B; Peeters, Robin P

2017-09-01

Human chorionic gonadotropin (hCG) stimulates thyroid function during pregnancy. We recently showed that thyroid autoimmunity severely attenuated the thyroidal response to hCG stimulation and that this may underlie the higher risk of premature delivery in thyroperoxidase antibody (TPOAb)-positive women. We hypothesized that a lower thyroidal response to hCG stimulation in TPOAb-negative women is also associated with a higher risk of premature delivery and preterm premature rupture of membranes (pPROM). Thyrotropin (TSH), free thyroxine (FT4), and hCG concentrations were available in 5644 TPOAb-negative women from a prospective cohort. We tested for interaction between TSH or FT4 and hCG in linear regression models for duration of pregnancy and logistic regression models for premature delivery/pPROM. Accordingly, analyses were stratified per TSH percentile (TSH ≥ 85th percentile) and hCG per 10,000 IU/L. Women with high TSH and low hCG concentrations did not have a higher risk of premature delivery or pPROM, with protective effect estimates. In contrast, women with a high TSH concentration despite a high hCG concentration had twofold to 10-fold higher risk of premature delivery (Pdifference = 0.022) and an up to fourfold higher risk of pPROM (Pdifference = 0.079). hCG concentrations were not associated with premature delivery or pPROM. In TPOAb-negative women with high-normal TSH concentrations, only women with high hCG concentrations had a higher risk of premature delivery or pPROM. These results suggest a lower thyroidal response to hCG stimulation is also associated with premature delivery in TPOAb-negative women and that an additional measurement of hCG may improve thyroid-related risk assessments during pregnancy. Copyright © 2017 Endocrine Society

Attitudes Toward Medications and the Relationship to Outcomes in Patients with Schizophrenia.

PubMed

Campbell, Angela H; Scalo, Julieta F; Crismon, M Lynn; Barner, Jamie C; Argo, Tami R; Lawson, Kenneth A; Miller, Alexander

The determinants of attitudes toward medication (ATM) are not well elucidated. In particular, literature remains equivocal regarding the influence of cognition, adverse events, and psychiatric symptomatology. This study evaluated relationships between those outcomes in schizophrenia and ATM. This is a retrospective analysis of data collected during the Texas Medication Algorithm Project (TMAP, n=307 with schizophrenia-related diagnoses), in outpatient clinics at baseline and every 3 months for ≥1 year (for cognition: 3rd and 9th month only). The Drug Attitude Inventory (DAI-30) measured ATM, and independent variables were: cognition (Trail Making Test [TMT], Verbal Fluency Test, Hopkins Verbal Learning Test), adverse events (Systematic Assessment for Treatment-Emergent Adverse Events, Barnes Akathisia Rating Scale), psychiatric symptomatology (Brief Psychiatric Rating Scale, Scale for Assessment of Negative Symptoms [SANS]), and medication adherence (Medication Compliance Scale). Analyses included binary logistic regression (cognition, psychiatric symptoms) and chi-square (adverse events, adherence) for baseline comparisons, and linear regression (cognition) or ANOVA (adverse events, adherence) for changes over time. Mean DAI-30 scores did not change over 12 months. Odds of positive ATM increased with higher TMT Part B scores (p=0.03) and lower SANS scores (p=0.02). Worsening of general psychopathology (p<0.001), positive symptoms (p<0.001), and negative symptoms (p=0.007) correlated with negative changes in DAI-30 scores. Relationships between cognition, negative symptoms, and ATM warrant further investigation. Studies evaluating therapies for cognitive deficits and negative symptoms should consider including ATM measures as endpoints. Patterns and inconsistencies in findings across studies raise questions about whether some factors thought to influence ATM have nonlinear relationships.

Impact of facial defect reconstruction on attractiveness and negative facial perception.

PubMed

Dey, Jacob K; Ishii, Masaru; Boahene, Kofi D O; Byrne, Patrick; Ishii, Lisa E

2015-06-01

Measure the impact of facial defect reconstruction on observer-graded attractiveness and negative facial perception. Prospective, randomized, controlled experiment. One hundred twenty casual observers viewed images of faces with defects of varying sizes and locations before and after reconstruction as well as normal comparison faces. Observers rated attractiveness, defect severity, and how disfiguring, bothersome, and important to repair they considered each face. Facial defects decreased attractiveness -2.26 (95% confidence interval [CI]: -2.45, -2.08) on a 10-point scale. Mixed effects linear regression showed this attractiveness penalty varied with defect size and location, with large and central defects generating the greatest penalty. Reconstructive surgery increased attractiveness 1.33 (95% CI: 1.18, 1.47), an improvement dependent upon size and location, restoring some defect categories to near normal ranges of attractiveness. Iterated principal factor analysis indicated the disfiguring, important to repair, bothersome, and severity variables were highly correlated and measured a common domain; thus, they were combined to create the disfigured, important to repair, bothersome, severity (DIBS) factor score, representing negative facial perception. The DIBS regression showed defect faces have a 1.5 standard deviation increase in negative perception (DIBS: 1.69, 95% CI: 1.61, 1.77) compared to normal faces, which decreased by a similar magnitude after surgery (DIBS: -1.44, 95% CI: -1.49, -1.38). These findings varied with defect size and location. Surgical reconstruction of facial defects increased attractiveness and decreased negative social facial perception, an impact that varied with defect size and location. These new social perception data add to the evidence base demonstrating the value of high-quality reconstructive surgery. NA. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Relationship Between Intrinsic Motivation and Undergraduate Students' Depression and Stress: The Moderating Effect of Interpersonal Conflict.

PubMed

Huang, Yunhui; Lv, Wei; Wu, Jiang

2016-10-01

This study examined the effect of intrinsic academic motivation and interpersonal conflict on the perceived depression and stress. Participants were 537 Chinese undergraduate students (191 males and 346 females; M age = 20.4 years, SD age = 1.3). They completed four scales measuring intrinsic academic motivation, interpersonal conflict, stress, and depression. Linear regressions were conducted with intrinsic academic motivation, interpersonal conflict, and their interaction as independent variables to predict depression and stress. Results showed that intrinsic academic motivation was negatively, while interpersonal conflict was positively, associated with depression and stress. Moreover, the interaction was significant: negative association of "intrinsic academic motivation and depression" and that of "intrinsic academic motivation and stress" was weaker among participants who reported higher (vs. lower) levels of interpersonal conflict. © The Author(s) 2016.

An investigation to improve the Menhaden fishery prediction and detection model through the application of ERTS-A data

NASA Technical Reports Server (NTRS)

Maughan, P. M. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Linear regression of secchi disc visibility against number of sets yielded significant results in a number of instances. The variability seen in the slope of the regression lines is due to the nonuniformity of sample size. The longer the period sampled, the larger the total number of attempts. Further, there is no reason to expect either the influence of transparency or of other variables to remain constant throughout the season. However, the fact that the data for the entire season, variable as it is, was significant at the 5% level, suggests its potential utility for predictive modeling. Thus, this regression equation will be considered representative and will be utilized for the first numerical model. Secchi disc visibility was also regressed against number of sets for the three day period September 27-September 29, 1972 to determine if surface truth data supported the intense relationship between ERTS-1 identified turbidity and fishing effort previously discussed. A very negative correlation was found. These relationship lend additional credence to the hypothesis that ERTS imagery, when utilized as a source of visibility (turbidity) data, may be useful as a predictive tool.

Does higher education protect against obesity? Evidence using Mendelian randomization.

PubMed

Böckerman, Petri; Viinikainen, Jutta; Pulkki-Råback, Laura; Hakulinen, Christian; Pitkänen, Niina; Lehtimäki, Terho; Pehkonen, Jaakko; Raitakari, Olli T

2017-08-01

The aim of this explorative study was to examine the effect of education on obesity using Mendelian randomization. Participants (N=2011) were from the on-going nationally representative Young Finns Study (YFS) that began in 1980 when six cohorts (aged 30, 33, 36, 39, 42 and 45 in 2007) were recruited. The average value of BMI (kg/m 2 ) measurements in 2007 and 2011 and genetic information were linked to comprehensive register-based information on the years of education in 2007. We first used a linear regression (Ordinary Least Squares, OLS) to estimate the relationship between education and BMI. To identify a causal relationship, we exploited Mendelian randomization and used a genetic score as an instrument for education. The genetic score was based on 74 genetic variants that genome-wide association studies (GWASs) have found to be associated with the years of education. Because the genotypes are randomly assigned at conception, the instrument causes exogenous variation in the years of education and thus enables identification of causal effects. The years of education in 2007 were associated with lower BMI in 2007/2011 (regression coefficient (b)=-0.22; 95% Confidence Intervals [CI]=-0.29, -0.14) according to the linear regression results. The results based on Mendelian randomization suggests that there may be a negative causal effect of education on BMI (b=-0.84; 95% CI=-1.77, 0.09). The findings indicate that education could be a protective factor against obesity in advanced countries. Copyright © 2017 Elsevier Inc. All rights reserved.

Inflammation, negative nitrogen balance, and outcome after aneurysmal subarachnoid hemorrhage.

PubMed

Badjatia, Neeraj; Monahan, Aimee; Carpenter, Amanda; Zimmerman, Jacqueline; Schmidt, J Michael; Claassen, Jan; Connolly, E Sander; Mayer, Stephan A; Karmally, Wahida; Seres, David

2015-02-17

To analyze the impact of inflammation and negative nitrogen balance (NBAL) on nutritional status and outcomes after subarachnoid hemorrhage (SAH). This was a prospective observational study of SAH patients admitted between May 2008 and June 2012. Measurements of C-reactive protein (CRP), transthyretin (TTR), resting energy expenditure (REE), and NBAL (g/day) were performed over 4 preset time periods during the first 14 postbleed days (PBD) in addition to daily caloric intake. Factors associated with REE and NBAL were analyzed with multivariable linear regression. Hospital-acquired infections (HAI) were tracked daily for time-to-event analyses. Poor outcome at 3 months was defined as a modified Rankin Scale score ≥ 4 and assessed by multivariable logistic regression. There were 229 patients with an average age of 55 ± 15 years. Higher REE was associated with younger age (p = 0.02), male sex (p < 0.001), higher Hunt Hess grade (p = 0.001), and higher modified Fisher score (p = 0.01). Negative NBAL was associated with lower caloric intake (p < 0.001), higher body mass index (p < 0.001), aneurysm clipping (p = 0.03), and higher CRP:TTR ratio (p = 0.03). HAIs developed in 53 (23%) patients on mean PBD 8 ± 3. Older age (p = 0.002), higher Hunt Hess (p < 0.001), lower caloric intake (p = 0.001), and negative NBAL (p = 0.04) predicted time to first HAI. Poor outcome at 3 months was associated with higher Hunt Hess grade (p < 0.001), older age (p < 0.001), negative NBAL (p = 0.01), HAI (p = 0.03), higher CRP:TTR ratio (p = 0.04), higher body mass index (p = 0.03), and delayed cerebral ischemia (p = 0.04). Negative NBAL after SAH is influenced by inflammation and associated with an increased risk of HAI and poor outcome. Underfeeding and systemic inflammation are potential modifiable risk factors for negative NBAL and poor outcome after SAH. © 2015 American Academy of Neurology.

Inflammation, negative nitrogen balance, and outcome after aneurysmal subarachnoid hemorrhage

PubMed Central

Monahan, Aimee; Carpenter, Amanda; Zimmerman, Jacqueline; Schmidt, J. Michael; Claassen, Jan; Connolly, E. Sander; Mayer, Stephan A.; Karmally, Wahida; Seres, David

2015-01-01

Objective: To analyze the impact of inflammation and negative nitrogen balance (NBAL) on nutritional status and outcomes after subarachnoid hemorrhage (SAH). Methods: This was a prospective observational study of SAH patients admitted between May 2008 and June 2012. Measurements of C-reactive protein (CRP), transthyretin (TTR), resting energy expenditure (REE), and NBAL (g/day) were performed over 4 preset time periods during the first 14 postbleed days (PBD) in addition to daily caloric intake. Factors associated with REE and NBAL were analyzed with multivariable linear regression. Hospital-acquired infections (HAI) were tracked daily for time-to-event analyses. Poor outcome at 3 months was defined as a modified Rankin Scale score ≥4 and assessed by multivariable logistic regression. Results: There were 229 patients with an average age of 55 ± 15 years. Higher REE was associated with younger age (p = 0.02), male sex (p < 0.001), higher Hunt Hess grade (p = 0.001), and higher modified Fisher score (p = 0.01). Negative NBAL was associated with lower caloric intake (p < 0.001), higher body mass index (p < 0.001), aneurysm clipping (p = 0.03), and higher CRP:TTR ratio (p = 0.03). HAIs developed in 53 (23%) patients on mean PBD 8 ± 3. Older age (p = 0.002), higher Hunt Hess (p < 0.001), lower caloric intake (p = 0.001), and negative NBAL (p = 0.04) predicted time to first HAI. Poor outcome at 3 months was associated with higher Hunt Hess grade (p < 0.001), older age (p < 0.001), negative NBAL (p = 0.01), HAI (p = 0.03), higher CRP:TTR ratio (p = 0.04), higher body mass index (p = 0.03), and delayed cerebral ischemia (p = 0.04). Conclusions: Negative NBAL after SAH is influenced by inflammation and associated with an increased risk of HAI and poor outcome. Underfeeding and systemic inflammation are potential modifiable risk factors for negative NBAL and poor outcome after SAH. PMID:25596503

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

Serial measurement of type-specific human papillomavirus load enables classification of cervical intraepithelial neoplasia lesions according to occurring human papillomavirus-induced pathway.

PubMed

Verhelst, Stefanie; Poppe, Willy A J; Bogers, Johannes J; Depuydt, Christophe E

2017-03-01

This retrospective study examined whether human papillomavirus (HPV) type-specific viral load changes measured in two or three serial cervical smears are predictive for the natural evolution of HPV infections and correlate with histological grades of cervical intraepithelial neoplasia (CIN), allowing triage of HPV-positive women. A cervical histology database was used to select consecutive women with biopsy-proven CIN in 2012 who had at least two liquid-based cytology samples before the diagnosis of CIN. Before performing cytology, 18 different quantitative PCRs allowed HPV type-specific viral load measurement. Changes in HPV-specific load between measurements were assessed by linear regression, with calculation of coefficient of determination (R) and slope. All infections could be classified into one of five categories: (i) clonal progressing process (R≥0.85; positive slope), (ii) simultaneously occurring clonal progressive and transient infection, (iii) clonal regressing process (R≥0.85; negative slope), (iv) serial transient infection with latency [R<0.85; slopes (two points) between 0.0010 and -0.0010 HPV copies/cell/day], and (v) transient productive infection (R<0.85; slope: ±0.0099 HPV copies/cell/day). Three hundred and seven women with CIN were included; 124 had single-type infections and 183 had multiple HPV types. Only with three consecutive measurements could a clonal process be identified in all CIN3 cases. We could clearly demonstrate clonal regressing lesions with a persistent linear decrease in viral load (R≥0.85; -0.003 HPV copies/cell/day) in all CIN categories. Type-specific viral load increase/decrease in three consecutive measurements enabled classification of CIN lesions in clonal HPV-driven transformation (progression/regression) and nonclonal virion-productive (serial transient/transient) processes.

The Relationship between TOC and pH with Exchangeable Heavy Metal Levels in Lithuanian Podzols

NASA Astrophysics Data System (ADS)

Khaledian, Yones; Pereira, Paulo; Brevik, Eric C.; Pundyte, Neringa; Paliulis, Dainius

2017-04-01

Heavy metals can have a negative impact on public and environmental health. The objective of this study was to investigate the relationship between total organic carbon (TOC) and pH with exchangeable heavy metals (Pb, Cd, Cu and Zn) in order to predict exchangeable heavy metal content in soils sampled near Panevėžys and Kaunas, Lithuania. Principal component regression (PCR) and nonlinear regression methods were tested to find the statistical relationship between TOC and pH with heavy metals. The results of PCR [R2 = 0.68, RMSE = 0.07] and non-linear regression [R2 = 0.74, RMSE= 0.065] (pH with TOC and exchangeable parameters) were statistically significant. However, this was not observed in the relationships of pH and TOC separately with exchangeable heavy metals. The results indicated that pH had a higher correlation with exchangeable heavy metals (non-linear regression [R2 = 0.72, RMSE= 0.066]) than TOC with heavy metals [R2 = 0.30, RMSE= 0.004]. It can be concluded that even though there was a strong relationship between TOC and pH with exchangeable metals, the metal mobility (exchangeable metals) can be explained by pH better than TOC in this study. Finally, manipulating soil pH could likely be productive to assess and control heavy metals when financial and time limitations exist (Khaledian et al. 2016). Reference(s) Khaledian Y, Pereira P, Brevik E.C, Pundyte N, Paliulis D. 2016. The Influence of Organic Carbon and pH on Heavy Metals, Potassium, and Magnesium Levels in Lithuanian Podzols. Land Degradation and Development. DOI: 10.1002/ldr.2638

Predicting heavy metal concentrations in soils and plants using field spectrophotometry

NASA Astrophysics Data System (ADS)

Muradyan, V.; Tepanosyan, G.; Asmaryan, Sh.; Sahakyan, L.; Saghatelyan, A.; Warner, T. A.

2017-09-01

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R2 0.9, RPD 2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R2 0.7, RPD 1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R2, rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

An Analysis of COLA (Cost of Living Adjustment) Allocation within the United States Coast Guard.

DTIC Science & Technology

1983-09-01

books Applied Linear Regression [Ref. 39], and Statistical Methods in Research and Production [Ref. 40], or any other book on regression. In the event...Indexes, Master’s Thesis, Air Force Institute of Technology, Wright-Patterson AFB, 1976. 39. Weisberg, Stanford, Applied Linear Regression , Wiley, 1980. 40

Testing hypotheses for differences between linear regression lines

Treesearch

Stanley J. Zarnoch

2009-01-01

Five hypotheses are identified for testing differences between simple linear regression lines. The distinctions between these hypotheses are based on a priori assumptions and illustrated with full and reduced models. The contrast approach is presented as an easy and complete method for testing for overall differences between the regressions and for making pairwise...

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

Estimating monotonic rates from biological data using local linear regression.

PubMed

Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

2017-03-01

Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

Locally linear regression for pose-invariant face recognition.

PubMed

Chai, Xiujuan; Shan, Shiguang; Chen, Xilin; Gao, Wen

2007-07-01

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

The Impact of Work and Volunteer Hours on the Health of Undergraduate Students.

PubMed

Lederer, Alyssa M; Autry, Dana M; Day, Carol R T; Oswalt, Sara B

2015-01-01

To examine the impact of work and volunteer hours on 4 health issues among undergraduate college students. Full-time undergraduate students (N = 70,068) enrolled at 129 institutions who participated in the Spring 2011 American College Health Association-National College Health Assessment II survey. Multiple linear regression and binary logistic regression were used to examine work and volunteer hour impact on depression, feelings of being overwhelmed, sleep, and physical activity. The impact of work and volunteer hours was inconsistent among the health outcomes. Increased work hours tended to negatively affect sleep and increase feelings of being overwhelmed. Students who volunteered were more likely to meet physical activity guidelines, and those who volunteered 1 to 9 hours per week reported less depression. College health professionals should consider integrating discussion of students' employment and volunteering and their intersection with health outcomes into clinical visits, programming, and other services.

Enterprise systems in financial sector - an application in precious metal trading forecasting

NASA Astrophysics Data System (ADS)

Chen, Xiaozhu; Fang, Yiwei

2013-11-01

The use of enterprise systems has become increasingly popular in the financial service industry. This paper discusses the applications of enterprise systems in the financial sectors and presents an application in gold price forecasting. We carefully examine the impacts of a few most widely assumed factors that have significant impact on the long-term gold price using statistical regression techniques. The analysis on our proposed linear regression mode indicates that the United States ultra scale of M2 money supply has been the most important catalyst for the rising price of gold, and the CRB index upward trend has also been the weighty factor for pushing up the gold price. In addition, the gold price has a low negative correlation with the Dow Jones Industrial Average, and low positive correlations with the US dollar index and the gold ETFs holdings.

Gender conformity, self-objectification, and body image for sorority and nonsorority women: A closer look.

PubMed

Adams, David Francis; Behrens, Erica; Gann, Lianne; Schoen, Eva

2017-01-01

Sororities have been identified as placing young women at risk for body image concerns due to a focus on traditional gender role norms and objectification of women. This study assessed the relationship between conformity to feminine gender role norms, self-objectification, and body image surveillance among undergraduate women. In a random sample of undergraduates, the authors examined data from sorority and nonsorority women. In a random sample of undergraduate women, the authors assessed the impact of traditional feminine gender role norms on self-objectification, body image, and feedback regarding physical appearance for sorority and nonsorority undergraduate women. Three linear regressions were conducted, and only conformity to feminine gender role norms contributed significantly in each regression model. Regardless of sorority membership, conformity to feminine gender role norms was found to significantly contribute to increased body consciousness, negative body image, and feedback on physical appearance.

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Contributions of fat mass and fat distribution to hip bone strength in healthy postmenopausal Chinese women.

PubMed

Shao, Hong Da; Li, Guan Wu; Liu, Yong; Qiu, Yu You; Yao, Jian Hua; Tang, Guang Yu

2015-09-01

The fat and bone connection is complicated, and the effect of adipose tissue on hip bone strength remains unclear. The aim of this study was to clarify the relative contribution of body fat accumulation and fat distribution to the determination of proximal femur strength in healthy postmenopausal Chinese women. This cross-sectional study enrolled 528 healthy postmenopausal women without medication history or known diseases. Total lean mass (LM), appendicular LM (ALM), percentage of lean mass (PLM), total fat mass (FM), appendicular FM (AFM), percentage of body fat (PBF), android and gynoid fat amount, android-to-gynoid fat ratio (AOI), bone mineral density (BMD), and proximal femur geometry were measured by dual energy X-ray absorptiometry. Hip structure analysis was used to compute some variables as geometric strength-related parameters by analyzing the images of the hip generated from DXA scans. Correlation analyses among anthropometrics, variables of body composition and bone mass, and geometric indices of hip bone strength were performed with stepwise linear regression analyses as well as Pearson's correlation analysis. In univariate analysis, there were significantly inverse correlations between age, years since menopause (YSM), hip BMD, and hip geometric parameters. Bone data were positively related to height, body weight, LM, ALM, FM, AFM, and PBF but negatively related to AOI and amount of android fat (all P < 0.05). AFM and AOI were significantly related to most anthropometric parameters. AFM was positively associated with height, body weight, and BMI. AFM was negatively associated with age and YSM. AOI was negatively associated with height, body weight, and BMI. AOI positively associated with age and YSM. LM, ALM, and FM had a positive relationship with anthropometric parameters (P < 0.05 for all). PLM had a negative relationship with those parameters. The correlation between LM, ALM, FM, PLM, ALM, age, and YSM was not significant. In multivariate linear regression analysis, the hip bone strength was observed to have a consistent and unchanged positive association with AFM and a negative association with AOI, whereas its association with other variables of body composition was not significant after adjusting for age, years since menopause, height, body weight, and BMI. AFM may be a positively protective effect for hip bone strength while AOI, rather than android fat, shows a strong negative association with hip bone strength after making an adjustment for confounders (age, YSM, height, body weight, and BMI) in healthy postmenopausal Chinese women. Rational weight control and AOI reduction during menopause may have vital clinical significance in decreasing postmenopausal osteoporosis.

Effect of Malmquist bias on correlation studies with IRAS data base

NASA Technical Reports Server (NTRS)

Verter, Frances

1993-01-01

The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

Iron status as a covariate in methylmercury-associated neurotoxicity risk.

PubMed

Fonseca, Márlon de Freitas; De Souza Hacon, Sandra; Grandjean, Philippe; Choi, Anna Lai; Bastos, Wanderley Rodrigues

2014-04-01

Intrauterine methylmercury exposure and prenatal iron deficiency negatively affect offspring's brain development. Since fish is a major source of both methylmercury and iron, occurrence of negative confounding may affect the interpretation of studies concerning cognition. We assessed relationships between methylmercury exposure and iron-status in childbearing females from a population naturally exposed to methylmercury through fish intake (Amazon). We concluded a census (refuse <20%) collecting samples from 274 healthy females (12-49 years) for hair-mercury determination and assessed iron-status through red cell tests and determination of serum ferritin and iron. Reactive C protein and thyroid hormones was used for excluding inflammation and severe thyroid dysfunctions that could affect results. We assessed the association between iron-status and hair-mercury by bivariate correlation analysis and also by different multivariate models: linear regression (to check trends); hierarchical agglomerative clustering method (groups of variables correlated with each other); and factor analysis (to examine redundancy or duplication from a set of correlated variables). Hair-mercury correlated weakly with mean corpuscular volume (r=.141; P=.020) and corpuscular hemoglobin (r=.132; .029), but not with the best biomarker of iron-status, ferritin (r=.037; P=.545). In the linear regression analysis, methylmercury exposure showed weak association with age-adjusted ferritin; age had a significant coefficient (Beta=.015; 95% CI: .003-.027; P=.016) but ferritin did not (Beta=.034; 95% CI: -.147 to .216; P=.711). In the hierarchical agglomerative clustering method, hair-mercury and iron-status showed the smallest similarities. Regarding factor analysis, iron-status and hair-mercury loaded different uncorrelated components. We concluded that iron-status and methylmercury exposure probably occur in an independent way. Copyright © 2013 Elsevier Ltd. All rights reserved.

The relationship of environmental factors to the structure and distribution of subtidal seaweed vegetation of the western Basque coast (N Spain)

NASA Astrophysics Data System (ADS)

Díez, I.; Santolaria, A.; Gorostiaga, J. M.

2003-04-01

Subtidal vegetation distribution patterns in relation to environmental conditions (pollution, wave exposure, sedimentation, substratum slope and depth) were studied along the western Basque coast, northern Spain, by applying canonical correspondence analysis and log-linear regressions. A total of 90 species of macrophytes were recorded by systematic sampling along 21 transects. Mesophyllum lichenoides and Cystoseira baccata were the most abundant (accounting for 47% of the overall algal cover). Gelidium sesquipedale, Pterosiphonia complanata, Zanardinia prototypus, Codium decorticatum and Asparagopsis armata ( Falkenbergia phase) were other macrophytes with significant cover. Ordination analysis indicates that the five environmental variables explored account between them for 52% of the species data variance. Pollution, sedimentation and wave exposure were the principal factors explaining differences in flora composition and abundance (24, 14 and 12% of the explained variance, respectively). Log-linear regressions and canonical correspondence analyses reveal that C. baccata and G. sesquipedale exhibit a negative relationship with pollution, while sediment loading negatively affects G. sesquipedale, and C. baccata cannot stand high wave exposure levels. In contrast, P. complanata and C. decorticatum show a positive relationship with pollution and can bear high levels of sedimentation and wave exposure. M. lichenoides and Z. prototypus present a wide tolerance range for all these factors. Macroalgal cover, species richness and diversity remain practically constant from unpolluted to slightly polluted sites, but they decrease sharply under moderately polluted conditions. In the same way, algal cover decreases as sediment loading increases, but diversity and species richness show the highest values at intermediate levels of sedimentation. In relation to wave exposure, maximum algal cover was achieved at very exposed habitats whereas diversity and species richness were higher under semi-exposed conditions.

Quality of life in patients with depression, panic syndrome, other anxiety syndrome, alcoholism and chronic somatic diseases: a longitudinal study in Slovenian primary care patients.

PubMed

Cerne, Anja; Rifel, Janez; Rotar-Pavlic, Danica; Svab, Igor; Selic, Polona; Kersnik, Janko

2013-01-01

To analyse the correlates between the quality of life and chronic diseases and socio-demographic characteristics of patients in family medicine with a special emphasis on depression, panic syndrome, other anxiety syndrome and alcoholism. In a longitudinal study, the data set of 516 family practice attendees recruited from 60 family practices was analysed. Depression, panic syndrome, other anxiety syndrome and alcoholism were diagnosed using appropriate diagnostic interviews. Quality of life was assessed using the SF-12 questionnaire, measuring a mental health score and a physical health score. Data about the number of chronic somatic diseases were obtained from the patients' medical records. Physical health score was negatively associated with higher age (β = -0.25, p < 0.001), depression (β = -0.20, p < 0.001) and number of chronic somatic diseases (β = -0.10, p < 0.016) and positively associated with higher education level (β = 0.21, p < 0.001), single marital status (β = 0.09, p < 0.022) and better financial status (β = 0.14, p < 0.001). Linear regression explained 31.8 % of the variance (R(2) = 0.318; p < 0.001). Similarly, mental health score was negatively associated with depression (β = -0.45, p < 0.001) and panic syndrome (β = -0.07, p < 0.001) and positively associated with male gender (β = 0.10, p < 0.015) and better financial status (β = 0.13, p < 0.001). Linear regression explained 45.5 % of the variance (R (2) = 0.455; p < 0.001). In family medicine, special attention should be directed to major depression, panic syndrome and number of chronic somatic diseases as they are associated with poorer quality of life.

Effect of body mass index on hemiparetic gait.

PubMed

Sheffler, Lynne R; Bailey, Stephanie Nogan; Gunzler, Douglas; Chae, John

2014-10-01

To evaluate the relationship between body mass index (BMI) and spatiotemporal, kinematic, and kinetic gait parameters in chronic hemiparetic stroke survivors. Secondary analysis of data collected in a randomized controlled trial comparing two 12-week ambulation training treatments. Academic medical center. Chronic hemiparetic stroke survivors (N = 108, >3 months poststroke) Linear regression analyses were performed of BMI, and selected pretreatment gait parameters were recorded using quantitative gait analysis. Spatiotemporal, kinematic, and kinetic gait parameters. A series of linear regression models that controlled for age, gender, stroke type (ischemic versus hemorrhagic), interval poststroke, level of motor impairment (Fugl-Meyer score), and walking speed found BMI to be positively associated with step width (m) (β = 0.364, P < .001), positively associated with peak hip abduction angle of the nonparetic limb during stance (deg) (β = 0.177, P = .040), negatively associated with ankle dorsiflexion angle at initial contact of the paretic limb (deg) (β = -0.222, P = .023), and negatively associated with peak ankle power at push-off (W/kg) of the paretic limb (W/kg)(β = -0.142, P = .026). When walking at a similar speed, chronic hemiparetic stroke subjects with a higher BMI demonstrated greater step width, greater hip hiking of the paretic lower limb, less paretic limb dorsiflexion at initial contact, and less paretic ankle power at push-off as compared to stroke subjects with a lower BMI and similar level of motor impairment. Further studies are necessary to determine the clinical relevance of these findings with respect to rehabilitation strategies for gait dysfunction in hemiparetic patients with higher BMIs. Copyright © 2014 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Associations of learning style with cultural values and demographics in nursing students in Iran and Malaysia

PubMed Central

Abdollahimohammad, Abdolghani; Ja’afar, Rogayah

2015-01-01

Purpose: The goal of the current study was to identify associations between the learning style of nursing students and their cultural values and demographic characteristics. Methods: A non-probability purposive sampling method was used to gather data from two populations. All 156 participants were female, Muslim, and full-time degree students. Data were collected from April to June 2010 using two reliable and validated questionnaires: the Learning Style Scales and the Values Survey Module 2008 (VSM 08). A simple linear regression was run for each predictor before conducting multiple linear regression analysis. The forward selection method was used for variable selection. P-values ≤0.05 and ≤0.1 were considered to indicate significance and marginal significance, respectively. Moreover, multi-group confirmatory factor analysis was performed to determine the invariance of the Farsi and English versions of the VSM 08. Results: The perceptive learning style was found to have a significant negative relationship with the power distance and monumentalism indices of the VSM 08. Moreover, a significant negative association was observed between the solitary learning style and the power distance index. However, no significant association was found between the analytic, competitive, and imaginative learning styles and cultural values (P>0.05). Likewise, no significant associations were observed between learning style, including the perceptive, solitary, analytic, competitive, and imaginative learning styles, and year of study or age (P>0.05). Conclusion: Students who reported low values on the power distance and monumentalism indices are more likely to prefer perceptive and solitary learning styles. Within each group of students in our study sample from the same school the year of study and age did not show any significant associations with learning style. PMID:26268831

Associations of learning style with cultural values and demographics in nursing students in Iran and Malaysia.

PubMed

Abdollahimohammad, Abdolghani; Ja'afar, Rogayah

2015-01-01

The goal of the current study was to identify associations between the learning style of nursing students and their cultural values and demographic characteristics. A non-probability purposive sampling method was used to gather data from two populations. All 156 participants were female, Muslim, and full-time degree students. Data were collected from April to June 2010 using two reliable and validated questionnaires: the Learning Style Scales and the Values Survey Module 2008 (VSM 08). A simple linear regression was run for each predictor before conducting multiple linear regression analysis. The forward selection method was used for variable selection. P-values ≤0.05 and ≤0.1 were considered to indicate significance and marginal significance, respectively. Moreover, multi-group confirmatory factor analysis was performed to determine the invariance of the Farsi and English versions of the VSM 08. The perceptive learning style was found to have a significant negative relationship with the power distance and monumentalism indices of the VSM 08. Moreover, a significant negative association was observed between the solitary learning style and the power distance index. However, no significant association was found between the analytic, competitive, and imaginative learning styles and cultural values (P>0.05). Likewise, no significant associations were observed between learning style, including the perceptive, solitary, analytic, competitive, and imaginative learning styles, and year of study or age (P>0.05). Students who reported low values on the power distance and monumentalism indices are more likely to prefer perceptive and solitary learning styles. Within each group of students in our study sample from the same school the year of study and age did not show any significant associations with learning style.

Aptamer-Based Paper Strip Sensor for Detecting Vibrio fischeri.

PubMed

Shin, Woo-Ri; Sekhon, Simranjeet Singh; Rhee, Sung-Keun; Ko, Jung Ho; Ahn, Ji-Young; Min, Jiho; Kim, Yang-Hoon

2018-05-14

Aptamer-based paper strip sensor for detecting Vibrio fischeri was developed. Our method was based on the aptamer sandwich assay between whole live cells, V. fischeri and DNA aptamer probes. Following 9 rounds of Cell-SELEX and one of the negative-SELEX, V. fischeri Cell Aptamer (VFCA)-02 and -03 were isolated, with the former showing approximately 10-fold greater avidity (in the subnanomolar range) for the target cells when arrayed on a surface. The colorimetric response of a paper sensor based on VFCA-02 was linear in the range of 4 × 10 1 to 4 × 10 5 CFU/mL of target cell by using scanning reader. The linear regression correlation coefficient ( R 2 ) was 0.9809. This system shows promise for use in aptamer-conjugated gold nanoparticle probes in paper strip format for in-field detection of marine bioindicating bacteria.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea.

PubMed

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-10-01

The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD - negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. The results suggest that psychological and medical approaches should be combined in GERD assessment.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea

PubMed Central

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-01-01

Objectives The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Methods Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. Results GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD – negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. Conclusions The results suggest that psychological and medical approaches should be combined in GERD assessment. PMID:27691373

Analyzing Multilevel Data: Comparing Findings from Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2013-01-01

This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Classical Testing in Functional Linear Models.

PubMed

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.

Classical Testing in Functional Linear Models

PubMed Central

Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab

2016-01-01

We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155

Developing a dengue forecast model using machine learning: A case study in China

PubMed Central

Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-01-01

Background In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. Conclusion and significance The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics. PMID:29036169

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial

NASA Astrophysics Data System (ADS)

Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni

2017-12-01

Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.

A Linear Regression and Markov Chain Model for the Arabian Horse Registry

DTIC Science & Technology

1993-04-01

as a tax deduction? Yes No T-4367 68 26. Regardless of previous equine tax deductions, do you consider your current horse activities to be... (Mark one...E L T-4367 A Linear Regression and Markov Chain Model For the Arabian Horse Registry Accesion For NTIS CRA&I UT 7 4:iC=D 5 D-IC JA" LI J:13tjlC,3 lO...the Arabian Horse Registry, which needed to forecast its future registration of purebred Arabian horses . A linear regression model was utilized to

Cytologic regression in women with atypical squamous cells of unknown significance and negative human papillomavirus test.

PubMed

Wang, Shu; Lang, Jing He; Cheng, Xue Mei

2009-12-01

The aim of this study was to investigate the cytologic regression in women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus test. The 45 women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus at baseline were analyzed about the cytologic regression during 2 years of follow-up. The cumulative rate of cytologic regression was calculated by Kaplan-Meier curves. Of 45 women, the cumulative rates were as follows: 55.6% obtained cytologic regression before 6 months, 84.4% by 1 year, and 95.6% at 2 years. Cytologic regression was not influenced by age, menopausal status, and baseline human papillomavirus load. However, the 1-year cumulative regression rate in women with previous cervical lesions was significantly lower than those without (P=.02), even much lower in women with high-grade intraepithelial neoplasia or worse (P=.008). Most women with atypical squamous cells of unknown significance and negative high-risk human papillomavirus could obtain cytologic regression within 2 years. Women with antecedent cervical lesions need longer time to reach this regression.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

The association between meteorological factors and road traffic injuries: a case analysis from Shantou city, China

PubMed Central

Gao, Jinghong; Chen, Xiaojun; Woodward, Alistair; Liu, Xiaobo; Wu, Haixia; Lu, Yaogui; Li, Liping; Liu, Qiyong

2016-01-01

Few studies examined the associations of meteorological factors with road traffic injuries (RTIs). The purpose of the present study was to quantify the contributions of meteorological factors to RTI cases treated at a tertiary level hospital in Shantou city, China. A time-series diagram was employed to illustrate the time trends and seasonal variation of RTIs, and correlation analysis and multiple linear regression analysis were conducted to investigate the relationships between meteorological parameters and RTIs. RTIs followed a seasonal pattern as more cases occurred during summer and winter months. RTIs are positively correlated with temperature and sunshine duration, while negatively associated with wind speed. Temperature, sunshine hour and wind speed were included in the final linear model with regression coefficients of 0.65 (t = 2.36, P = 0.019), 2.23 (t = 2.72, P = 0.007) and −27.66 (t = −5.67, P < 0.001), respectively, accounting for 19.93% of the total variation of RTI cases. The findings can help us better understand the associations between meteorological factors and RTIs, and with potential contributions to the development and implementation of regional level evidence-based weather-responsive traffic management system in the future. PMID:27853316

Age-specific changes in the regulation of LH-dependent testosterone secretion: assessing responsiveness to varying endogenous gonadotropin output in normal men.

PubMed

Liu, Peter Y; Takahashi, Paul Y; Roebuck, Pamela D; Iranmanesh, Ali; Veldhuis, Johannes D

2005-09-01

Pulsatile and thus total testosterone (Te) secretion declines in older men, albeit for unknown reasons. Analytical models forecast that aging may reduce the capability of endogenous luteinizing hormone (LH) pulses to stimulate Leydig cell steroidogenesis. This notion has been difficult to test experimentally. The present study used graded doses of a selective gonadotropin releasing hormone (GnRH)-receptor antagonist to yield four distinct strata of pulsatile LH release in each of 18 healthy men ages 23-72 yr. Deconvolution analysis was applied to frequently sampled LH and Te concentration time series to quantitate pulsatile Te secretion over a 16-h interval. Log-linear regression was used to relate pulsatile LH secretion to attendant pulsatile Te secretion (LH-Te drive) across the four stepwise interventions in each subject. Linear regression of the 18 individual estimates of LH-Te feedforward dose-response slopes on age disclosed a strongly negative relationship (r = -0.721, P < 0.001). Accordingly, the present data support the thesis that aging in healthy men attenuates amplitude-dependent LH drive of burst-like Te secretion. The experimental strategy of graded suppression of neuroglandular outflow may have utility in estimating dose-response adaptations in other endocrine systems.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Personal Well-being and Stress Symptoms in Wives of Iranian Martyrs, Prisoners of wars and Disabled Veterans

PubMed Central

Sharif, Nasim

2010-01-01

Objective This study was conducted to compare the personal well-being among the wives of Iranian veterans living in the city of Qom. Method A sample of 300 was randomly selected from a database containing the addresses of veteran's families at Iran's Veterans Foundation in Qom (Bonyad-e-Shahid va Omoore Isargaran). The veterans' wives were divided into three groups: wives of martyrs (killed veterans), wives of prisoners of war, and wives of disabled veterans. The Persian translation of Personal Well-being Index and Stress Symptoms Checklist (SSC) were administered for data collection. Four women chose not to respond to Personal Well-being Index. Data were then analyzed using linear multivariate regression (stepwise method), analysis of variance, and by computing the correlation between variables. Results Results showed a negative correlation between well-being and stress symptoms. However, each group demonstrated different levels of stress symptoms. Furthermore, multivariate linear regression in the 3 groups showed that overall satisfaction of life and personal well-being (total score and its domains) could be predicted by different symptoms. Conclusion Each group experienced different challenges and thus different stress symptoms. Therefore, although they all need help, each group needs to be helped in a different way. PMID:22952487

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132

Association between Organizational Commitment and Personality Traits of Faculty Members of Ahvaz Jundishapur University of Medical Sciences.

PubMed

Khiavi, Farzad Faraji; Dashti, Rezvan; Mokhtari, Saeedeh

2016-03-01

Individual characteristics are important factors influencing organizational commitment. Also, committed human resources can lead organizations to performance improvement as well as personal and organizational achievements. This research aimed to determine the association between organizational commitment and personality traits among faculty members of Ahvaz Jundishapur University of Medical Sciences. the research population of this cross-sectional study was the faculty members of Ahvaz Jundishapur University of Medical Sciences (Ahvaz, Iran). The sample size was determined to be 83. Data collection instruments were the Allen and Meyer questionnaire for organizational commitment and Neo for characteristics' features. The data were analyzed through Pearson's product-moment correlation and the independent samples t-test, ANOVA, and simple linear regression analysis (SLR) by SPSS. Continuance commitment showed a significant positive association with neuroticism, extroversion, agreeableness, and conscientiousness. Normative commitment showed a significant positive association with conscientiousness and a negative association with extroversion (p = 0.001). Openness had a positive association with affective commitment. Openness and agreeableness, among the five characteristics' features, had the most effect on organizational commitment, as indicated by simple linear regression analysis. Faculty members' characteristics showed a significant association with their organizational commitment. Determining appropriate characteristic criteria for faculty members may lead to employing committed personnel to accomplish the University's objectives and tasks.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections.

PubMed

Chen, Chen; Xie, Yuanchang

2016-06-01

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamics of shearing force and its correlations with chemical compositions and in vitro dry matter digestibility of stylo (Stylosanthes guianensis) stem.

PubMed

Zi, Xuejuan; Li, Mao; Zhou, Hanlin; Tang, Jun; Cai, Yimin

2017-12-01

The study explored the dynamics of shearing force and its correlation with chemical compositions and in vitro dry matter digestibility (IVDMD) of stylo. The shearing force, diameter, linear density, chemical composition, and IVDMD of different height stylo stem were investigated. Linear regression analysis was done to determine the relationships between the shearing force and cut height, diameter, chemical composition, or IVDMD. The results showed that shearing force of stylo stem increased with plant height increasing and the crude protein (CP) content and IVDMD decreased but fiber content increased over time, resulting in decreased forage value. In addition, tall stem had greater shearing force than short stem. Moreover, shearing force is positively correlated with stem diameter, linear density and fiber fraction, but negatively correlated with CP content and IVDMD. Overall, shearing force is an indicator more direct, easier and faster to measure than chemical composition and digestibility for evaluation of forage nutritive value related to animal performance. Therefore, it can be used to evaluate the nutritive value of stylo.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-07-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach was justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatland sites in Finland and a tundra site in Siberia. The flux measurements were performed using transparent chambers on vegetated surfaces and opaque chambers on bare peat surfaces. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes and even lower for longer closure times. The degree of underestimation increased with increasing CO2 flux strength and is dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Evaluation of Hepatic Steatosis by Using Acoustic Structure Quantification US in a Rat Model: Comparison with Pathologic Examination and MR Spectroscopy.

PubMed

Lee, Dong Ho; Lee, Jae Young; Lee, Kyung Bun; Han, Joon Koo

2017-11-01

Purpose To determine factors that significantly affect the focal disturbance (FD) ratio calculated with an acoustic structure quantification (ASQ) technique in a dietary-induced fatty liver disease rat model and to assess the diagnostic performance of the FD ratio in the assessment of hepatic steatosis by using histopathologic examination as a standard of reference. Materials and Methods Twenty-eight male F344 rats were fed a methionine-choline-deficient diet with a variable duration (3.5 days [half week] or 1, 2, 3, 4, 5, or 6 weeks; four rats in each group). A control group of four rats was maintained on a standard diet. At the end of each diet period, ASQ ultrasonography (US) and magnetic resonance (MR) spectroscopy were performed. Then, the rat was sacrificed and histopathologic examination of the liver was performed. Receiver operating characteristic curve analysis was performed to assess the diagnostic performance of the FD ratio in the evaluation of the degree of hepatic steatosis. The Spearman correlation coefficient was calculated to assess the correlation between the ordinal values, and multivariate linear regression analysis was used to identify significant determinant factors for the FD ratio. Results The diagnostic performance of the FD ratio in the assessment of the degree of hepatic steatosis (area under the receiver operating characteristic curve: 1.000 for 5%-33% steatosis, 0.981 for >33% to 66% steatosis, and 0.965 for >66% steatosis) was excellent and was comparable to that of MR spectroscopy. There was a strong negative linear correlation between the FD ratio and the estimated fat fraction at MR spectroscopy (Spearman ρ, -0.903; P < .001). Multivariate linear regression analysis showed that the degree of hepatic steatosis (P < .001) and fibrosis stage (P = .022) were significant factors affecting the FD ratio. Conclusion The FD ratio may potentially provide good diagnostic performance in the assessment of the degree of hepatic steatosis, with a strong negative linear correlation with the estimated fat fraction at MR spectroscopy. The degree of steatosis and stage of fibrosis at histopathologic examination were significant factors that affected the FD ratio. © RSNA, 2017 Online supplemental material is available for this article.

The Role of Structural Barriers in Risky Sexual Behavior, Victimization and Readiness to Change HIV/STI-Related Risk Behavior Among Transgender Women.

PubMed

Raiford, Jerris L; Hall, Grace J; Taylor, Raekiela D; Bimbi, David S; Parsons, Jeffrey T

2016-10-01

This study examines the role of structural barriers experienced by a community-based sample of 63 HIV-positive and negative transgender women that may elevate HIV infection and transmission risks. Separate hierarchical linear multiple regression analyses tested the association between structural barriers (e.g., unemployment, lack of food, shelter) and condomless anal sex acts, abuse, and readiness to change risk behavior, while controlling for other related factors. Among this primarily Hispanic and African-American sample, HIV-positive and negative transgender women experienced a similar number of structural barriers and experiencing structural barriers was significantly associated with an increased number of condomless anal sex acts (p = .002), victimization (p = .000) and a decreased readiness to change HIV-related risk behavior (p = .014). Structural-level interventions are needed to address this elevated risk among this underserved and hard-to-reach population.

Physical activity measurement in older adults: relationships with mental health.

PubMed

Parker, Sarah J; Strath, Scott J; Swartz, Ann M

2008-10-01

This study examined the relationship between physical activity (PA) and mental health among older adults as measured by objective and subjective PA-assessment instruments. Pedometers (PED), accelerometers (ACC), and the Physical Activity Scale for the Elderly (PASE) were administered to measure 1 week of PA among 84 adults age 55-87 (mean = 71) years. General mental health was measured using the Positive and Negative Affect Scale (PANAS) and the Satisfaction With Life Scale (SWL). Linear regressions revealed that PA estimated by PED significantly predicted 18.1%, 8.3%, and 12.3% of variance in SWL and positive and negative affect, respectively, whereas PA estimated by the PASE did not predict any mental health variables. Results from ACC data were mixed. Hotelling-William tests between correlation coefficients revealed that the relationship between PED and SWL was significantly stronger than the relationship between PASE and SWL. Relationships between PA and mental health might depend on the PA measure used.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

Alcohol Policy Comprehension, Compliance and Consequences Among Young Adult Restaurant Workers

PubMed Central

Ames, Genevieve M.; Cunradi, Carol B.; Duke, Michael R.

2012-01-01

SUMMARY This study explores relationships between young adult restaurant employees' understanding and compliance with workplace alcohol control policies and consequences of alcohol policy violation. A mixed method analysis of 67 semi-structured interviews and 1,294 telephone surveys from restaurant chain employees found that alcohol policy details confused roughly a third of employees. Among current drinkers (n=1,093), multivariable linear regression analysis found that frequency of alcohol policy violation was positively associated with frequency of experiencing problems at work; perceived supervisor enforcement of alcohol policy was negatively associated with this outcome. Implications for preventing workplace alcohol-related problems include streamlining confusing alcohol policy guidelines. PMID:22984360

Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fotion, Katherine A.

2016-08-18

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

The utility of gravity and water-level monitoring at alluvial aquifer wells in southern Arizona

USGS Publications Warehouse

Pool, D.R.

2008-01-01

Coincident monitoring of gravity and water levels at 39 wells in southern Arizona indicate that water-level change might not be a reliable indicator of aquifer-storage change for alluvial aquifer systems. One reason is that water levels in wells that are screened across single or multiple aquifers might not represent the hydraulic head and storage change in a local unconfined aquifer. Gravity estimates of aquifer-storage change can be approximated as a one-dimensional feature except near some withdrawal wells and recharge sources. The aquifer storage coefficient is estimated by the linear regression slope of storage change (estimated using gravity methods) and water-level change. Nonaquifer storage change that does not percolate to the aquifer can be significant, greater than 3 ??Gal, when water is held in the root zone during brief periods following extreme rates of precipitation. Monitor-ing of storage change using gravity methods at wells also can improve understanding of local hydrogeologic conditions. In the study area, confined aquifer conditions are likely at three wells where large water-level variations were accompanied by little gravity change. Unconfined conditions were indicated at 15 wells where significant water-level and gravity change were positively linearly correlated. Good positive linear correlations resulted in extremely large specific-yield values, greater than 0.35, at seven wells where it is likely that significant ephemeral streamflow infiltration resulted in unsaturated storage change. Poor or negative linear correlations indicate the occurrence of confined, multiple, or perched aquifers. Monitoring of a multiple compressible aquifer system at one well resulted in negative correlation of rising water levels and subsidence-corrected gravity change, which suggests that water-level trends at the well are not a good indicatior of overall storage change. ?? 2008 Society of Exploration Geophysicists. All rights reserved.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

NASA Astrophysics Data System (ADS)

Kutzbach, L.; Schneider, J.; Sachs, T.; Giebels, M.; Nykänen, H.; Shurpali, N. J.; Martikainen, P. J.; Alm, J.; Wilmking, M.

2007-11-01

Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

Positive and Negative Affect More Concurrent among Blacks than Whites.

PubMed

Lankarani, Maryam Moghani; Assari, Shervin

2017-08-01

While positive and negative affect are inversely linked, people may experience and report both positive and negative emotions simultaneously. However, it is unknown if race alters the magnitude of the association between positive and negative affect. The current study compared Black and White Americans for the association between positive and negative affect. We used data from MIDUS (Midlife in the United States), a national study of Americans with an age range of 25 to 75. A total number of 7108 individuals were followed for 10 years from 1995 to 2004. Positive and negative affect was measured at baseline (1995) and follow-up (2004). Demographic (age and gender), socioeconomic (education and income) as well as health (self-rated health, chronic medical conditions, and body mass index) factors measured at baseline were covariates. A series of linear regressions were used to test the moderating effect of race on the reciprocal association between positive and negative affect at baseline and over time, net of covariates. In the pooled sample, positive and negative affect showed inverse correlation at baseline and over time, net of covariates. Blacks and Whites differed in the magnitude of the association between positive and negative affect, with weaker inverse associations among Blacks compared to Whites, beyond all covariates. Weaker reciprocal association between positive and negative affect in Blacks compared to Whites has implications for cross-racial measurement of affect and mood, including depression. Depression screening programs should be aware that race alters the concordance between positive and negative affect domains and that Blacks endorse higher levels of positive affect compared to Whites in the presence of high negative affect.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Caribou distribution during the post-calving period in relation to infrastructure in the Prudhoe Bay oil field, Alaska

USGS Publications Warehouse

Cronin, Matthew A.; Amstrup, Steven C.; Durner, George M.; Noel, Lynn E.; McDonald, Trent L.; Ballard, Warren B.

1998-01-01

There is concern that caribou (Rangifer tarandus) may avoid roads and facilities (i.e., infrastructure) in the Prudhoe Bay oil field (PBOF) in northern Alaska, and that this avoidance can have negative effects on the animals. We quantified the relationship between caribou distribution and PBOF infrastructure during the post-calving period (mid-June to mid-August) with aerial surveys from 1990 to 1995. We conducted four to eight surveys per year with complete coverage of the PBOF. We identified active oil field infrastructure and used a geographic information system (GIS) to construct ten 1 km wide concentric intervals surrounding the infrastructure. We tested whether caribou distribution is related to distance from infrastructure with a chi-squared habitat utilization-availability analysis and log-linear regression. We considered bulls, calves, and total caribou of all sex/age classes separately. The habitat utilization-availability analysis indicated there was no consistent trend of attraction to or avoidance of infrastructure. Caribou frequently were more abundant than expected in the intervals close to infrastructure, and this trend was more pronounced for bulls and for total caribou of all sex/age classes than for calves. Log-linear regression (with Poisson error structure) of numbers of caribou and distance from infrastructure were also done, with and without combining data into the 1 km distance intervals. The analysis without intervals revealed no relationship between caribou distribution and distance from oil field infrastructure, or between caribou distribution and Julian date, year, or distance from the Beaufort Sea coast. The log-linear regression with caribou combined into distance intervals showed the density of bulls and total caribou of all sex/age classes declined with distance from infrastructure. Our results indicate that during the post-calving period: 1) caribou distribution is largely unrelated to distance from infrastructure; 2) caribou regularly use habitats in the PBOF; 3) caribou often occur close to infrastructure; and 4) caribou do not appear to avoid oil field infrastructure.

The Intricate Relationship between Psychotic-Like Experiences and Associated Subclinical Symptoms in Healthy Individuals.

PubMed

Unterrassner, Lui; Wyss, Thomas A; Wotruba, Diana; Haker, Helene; Rössler, Wulf

2017-01-01

The interplay between subclinical psychotic, negative, and affective symptoms has gained increased attention regarding the etiology of psychosis spectrum and other mental disorders. Importantly, research has tended to not differentiate between different subtypes of psychotic-like experiences (PLE) although they may not have the same significance for mental health. In order to gain information on the subclinical interplay between specific PLE and other symptoms as well as the significance of PLE for mental health, we investigated their specific associations in 206 healthy individuals (20-60 years, 73 females) using correlational and linear regression analyses. PLE were assessed with the Magical Ideation Questionnaire, the revised Exceptional Experiences Questionnaire, and subscales of the Schizotypal Personality Questionnaire (SPQ). The revised Symptom Checklist 90, the SPQ, and the Physical Anhedonia Scale were used to measure subclinical negative symptoms, affective symptoms, and other symptoms such as, emotional instability. As hypothesized, we found that (1) most affective symptoms and all other subclinical symptoms correlated positively with all PLE, whereas we found only partial associations between negative symptoms and PLE. Notably, (2) magical ideation and paranormal beliefs correlated negatively with physical anhedonia. In the regression analyses we found (3) similar patterns of specific positive associations between PLE and other subclinical symptoms: Suspiciousness was a specific predictor of negative-like symptoms, whereas ideas of reference, unusual perceptual experiences, and dissociative anomalous perceptions specifically predicted anxiety symptoms. Interestingly, (4) ideas of reference negatively predicted physical anhedonia. Similarly, paranormal beliefs were negatively associated with constricted affect. Moreover, odd beliefs were a negative predictor of depression, emotional instability, and unspecific symptoms. Our findings indicated that subtypes of PLE are differentially implicated in psychological functioning and should therefore not be categorized homogeneously. Moreover, paranormal beliefs, odd beliefs, and partly ideas of reference might also contribute to subjective well being in healthy individuals. Our results might serve as a starting point for longitudinal studies investigating the interplay of subtypes of subclinical symptoms along a psychopathological trajectory leading to mental disorders. Importantly, this research might help to improve therapeutic strategies for psychosis prevention.

The Intricate Relationship between Psychotic-Like Experiences and Associated Subclinical Symptoms in Healthy Individuals

PubMed Central

Unterrassner, Lui; Wyss, Thomas A.; Wotruba, Diana; Haker, Helene; Rössler, Wulf

2017-01-01

The interplay between subclinical psychotic, negative, and affective symptoms has gained increased attention regarding the etiology of psychosis spectrum and other mental disorders. Importantly, research has tended to not differentiate between different subtypes of psychotic-like experiences (PLE) although they may not have the same significance for mental health. In order to gain information on the subclinical interplay between specific PLE and other symptoms as well as the significance of PLE for mental health, we investigated their specific associations in 206 healthy individuals (20–60 years, 73 females) using correlational and linear regression analyses. PLE were assessed with the Magical Ideation Questionnaire, the revised Exceptional Experiences Questionnaire, and subscales of the Schizotypal Personality Questionnaire (SPQ). The revised Symptom Checklist 90, the SPQ, and the Physical Anhedonia Scale were used to measure subclinical negative symptoms, affective symptoms, and other symptoms such as, emotional instability. As hypothesized, we found that (1) most affective symptoms and all other subclinical symptoms correlated positively with all PLE, whereas we found only partial associations between negative symptoms and PLE. Notably, (2) magical ideation and paranormal beliefs correlated negatively with physical anhedonia. In the regression analyses we found (3) similar patterns of specific positive associations between PLE and other subclinical symptoms: Suspiciousness was a specific predictor of negative-like symptoms, whereas ideas of reference, unusual perceptual experiences, and dissociative anomalous perceptions specifically predicted anxiety symptoms. Interestingly, (4) ideas of reference negatively predicted physical anhedonia. Similarly, paranormal beliefs were negatively associated with constricted affect. Moreover, odd beliefs were a negative predictor of depression, emotional instability, and unspecific symptoms. Our findings indicated that subtypes of PLE are differentially implicated in psychological functioning and should therefore not be categorized homogeneously. Moreover, paranormal beliefs, odd beliefs, and partly ideas of reference might also contribute to subjective well being in healthy individuals. Our results might serve as a starting point for longitudinal studies investigating the interplay of subtypes of subclinical symptoms along a psychopathological trajectory leading to mental disorders. Importantly, this research might help to improve therapeutic strategies for psychosis prevention. PMID:28936192

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

GIS Tools to Estimate Average Annual Daily Traffic

DOT National Transportation Integrated Search

2012-06-01

This project presents five tools that were created for a geographical information system to estimate Annual Average Daily : Traffic using linear regression. Three of the tools can be used to prepare spatial data for linear regression. One tool can be...

Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Paraoxonase 1: a better atherosclerotic risk predictor than HDL in type 2 diabetes mellitus.

PubMed

Patra, Surajeet Kumar; Singh, Kamna; Singh, Ritu

2013-01-01

Type 2 diabetes mellitus is a state of glycative stress and oxidative stress. Lower level of serum PON 1 has been correlated to higher morbidity and mortality related to cardiovascular complications in type 2 diabetes mellitus. To estimate and compare the serum PON 1 levels in type 2 diabetes mellitus and controls and to predict which one is the better atherosclerotic risk predictor among HDL and PON 1 in T2DM patients. An observational analytical case-control study was conducted with a sample size of 30 in two groups like group I (30 cases of type 2 diabetes mellitus diagnosed by ADA 2010 criteria) and group II (30 age and sex matched controls). Human serum paroxonase 1 levels were measured by ELISA. Both HDL and PON 1 were negatively correlated with the various atherogenic indices (AIP, AC, CRI I, CRI II) but the strength of negative correlation is always greater for PON 1. In multiple linear regression analysis, we found that the regression coefficient (β) is always higher for PON 1 than for HDL while taking the atherogenic indices as outcome variable. PON 1 can be a better predictor than HDL for atherosclerotic risk in type 2 diabetes mellitus. Copyright © 2013 Diabetes India. Published by Elsevier Ltd. All rights reserved.

The relationship between lifestyle factors and clinical symptoms of bipolar disorder patients in a Chinese population.

PubMed

Huang, Jia; Yuan, Cheng Mei; Xu, Xian Rong; Wang, Yong; Hong, Wu; Wang, Zuo Wei; Su, You Song; Hu, Ying Yan; Cao, Lan; Wang, Yu; Chen, Jun; Fang, Yi Ru

2018-05-06

There is evidence that bipolar disorder (BD) patients with an unhealthy lifestyle have a worse course of illness. This study was designed to examine the extent to which lifestyle could influence the severity of clinical symptoms associated with BD. A total of 113 BD patients were recruited in this study. The lifestyle information including data on dietary patterns, physical activity, and sleep quality were collected using a self-rated questionnaire. The results showed that the consumption of whole grain, seafood, and dairy products were significantly negatively correlated with the 17-item Hamilton Rating Scale for Depression (HAMD-17) total score. The consumption of sugar, soft drinks, and alcohol as well as being a current smoker were positively correlated with the severity of clinical symptoms. Multiple linear regression and binary logistic regression analyses demonstrated an independent negative correlation between both whole grain and dairy product consumption with the HAMD-17 score. The results from the current study suggested that lifestyle factors, especially dietary patterns, might be associated with clinical symptoms of BD. The association between the consumption of specific foods and severity of depressive symptoms may offer some useful information and further understanding of the role of lifestyle factors in the development of BD. Copyright © 2018. Published by Elsevier B.V.

Advancing paternal age at birth is associated with poorer social functioning earlier and later in life of schizophrenia patients in a founder population.

PubMed

Liebenberg, Rudolf; van Heerden, Brigitte; Ehlers, René; Du Plessis, Anna M E; Roos, J Louw

2016-09-30

Consistent associations have been found between advanced paternal age and an increased risk of psychiatric disorders, such as schizophrenia, in their offspring. This increase appears to be linear as paternal age increases. The present study investigates the relationship between early deviant behaviour in the first 10 years of life of patients as well as longer term functional outcome and paternal age in sporadic Afrikaner founder population cases of schizophrenia. This might improve our understanding of Paternal Age-Related Schizophrenia (PARS). Follow-up psychiatric diagnoses were confirmed by the Diagnostic Interview for Genetic Studies (DIGS). An early deviant childhood behaviour semi-structured questionnaire and the Specific Level of Functioning Assessment (SLOF) were completed. From the logistic regression models fitted, a significant negative relationship was found between paternal age at birth and social dysfunction as early deviant behaviour. Additionally, regression analysis revealed a significant negative relationship between paternal age at birth and the SLOF for interpersonal relationships later in life. Early social dysfunction may represent a phenotypic trait for PARS. Further research is required to understand the relationship between early social dysfunction and deficits in interpersonal relationships later in life. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

NASA Astrophysics Data System (ADS)

Yuli Rusdiana, Riza; Zain, Ismaini; Wulan Purnami, Santi

2017-06-01

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

[Factors influencing glucose metabolism in young obese subjects with obstructive sleep apnea hypopnea syndrome].

PubMed

Gu, C J; Li, Q Y; Li, M; Zhou, J; Du, J; Yi, H H; Feng, J; Zhou, L N; Wang, Q

2016-05-17

To explore the factors influencing glucose metabolism in young obese subjects with obstructive sleep apnea hypopnea syndrome (OSAHS). A total of 106 young obese subjects[18-44 years old, body mass index (BMI) ≥30 kg/m(2)]were enrolled and divided into two groups based on full-night polysomnography (PSG), OSAHS group[apnea hypopnea index (AHI) ≥5 events/h]and non-OSAHS group (AHI<5 events/h). Oral glucose tolerance-insulin releasing test (OGTT-IRT) was performed and serum glycosylated hemoglobin A1 (HbA1c) levels were measured after an overnight fast. Homeostasis model assessment-IR (HOMA-IR), Matsuda insulin sensitivity index (MI), homeostasis model assessment-β (HOMA-β), the early phase insulinogenic index (ΔI(30)/ΔG(30)), total area under the curve of insulin in 180 minutes (AUC-I180) and oral disposition index (DIo) were calculated to evaluate insulin resistance and pancreatic β cell function. Stepwise multiple linear regressions were conducted to determine the independent linear correlation of glucose measurements with PSG parameters. Prevalence of diabetes was higher in OSAHS than in non-OSAHS group (22.0% vs 4.3%, P=0.009). OGTT 0, 30, 60 min glucose and HbA1c levels were higher in OSAHS group than those in non-OASHS group (all P<0.05). DIo were lower in OSAHS group than those in non-OASHS group (P=0.024), HOMA-IR, MI, HOMA-β, ΔI(30)/ΔG(30), and AUC-I(180) were similar between two groups (all P>0.05). In stepwise multiple linear regressions, OGTT 0, 30 and 60 min glucose were positively correlated with oxygen desaturation index (ODI) (β=0.243, 0.273 and 0.371 respectively, all P<0.05). HOMA-β was negatively correlated with AHI (β=-0.243, P=0.011). DIo was negatively correlated with ODI (β=-0.234, P=0.031). OSAHS worsens glucose metabolism and compensatory pancreatic β-cell function in young obese subjects, which could probably be attributed to sleep apnea related oxygen desaturation during sleep.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Addressing the unemployment-mortality conundrum: non-linearity is the answer.

PubMed

Bonamore, Giorgio; Carmignani, Fabrizio; Colombo, Emilio

2015-02-01

The effect of unemployment on mortality is the object of a lively literature. However, this literature is characterized by sharply conflicting results. We revisit this issue and suggest that the relationship might be non-linear. We use data for 265 territorial units (regions) within 23 European countries over the period 2000-2012 to estimate a multivariate regression of mortality. The estimating equation allows for a quadratic relationship between unemployment and mortality. We control for various other determinants of mortality at regional and national level and we include region-specific and time-specific fixed effects. The model is also extended to account for the dynamic adjustment of mortality and possible lagged effects of unemployment. We find that the relationship between mortality and unemployment is U shaped. In the benchmark regression, when the unemployment rate is low, at 3%, an increase by one percentage point decreases average mortality by 0.7%. As unemployment increases, the effect decays: when the unemployment rate is 8% (sample average) a further increase by one percentage point decreases average mortality by 0.4%. The effect changes sign, turning from negative to positive, when unemployment is around 17%. When the unemployment rate is 25%, a further increase by one percentage point raises average mortality by 0.4%. Results hold for different causes of death and across different specifications of the estimating equation. We argue that the non-linearity arises because the level of unemployment affects the psychological and behavioural response of individuals to worsening economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Associations of parenting styles, parental feeding practices and child characteristics with young children's fruit and vegetable consumption.

PubMed

Vereecken, Carine; Rovner, Alisha; Maes, Lea

2010-12-01

The purpose of this study was to investigate the role of parent and child characteristics in explaining children's fruit and vegetable intakes. In 2008, parents of preschoolers (mean age 3.5 years) from 56 schools in Belgium-Flanders completed questionnaires including a parent and child fruit and vegetable food frequency questionnaire, general parenting styles (laxness, overreactivity and positive interactions), specific food parenting practices (child-centered and parent-centered feeding practices) and children's characteristics (children's shyness, emotionality, stubbornness, activity, sociability, and negative reactions to food). Multiple linear regression analyses (n = 755) indicated a significant positive association between children's fruit and vegetable intake and parent's intake and a negative association with children's negative reactions to food. No general parenting style dimension or child personality characteristic explained differences in children's fruit and vegetable intakes. Child-centered feeding practices were positively related to children's fruit and vegetable intakes, while parent-centered feeding practices were negatively related to children's vegetable intakes. In order to try to increase children's fruit and vegetable consumption, parents should be guided to improve their own diet and to use child-centered parenting practices and strategies known to decrease negative reactions to food. Copyright © 2010 Elsevier Ltd. All rights reserved.

Observations of, and sources of the spatial and temporal variability of ozone in the middle atmosphere on climatological time scales (OZMAP) and equatorial dynamics: Seasonal variations of ozone trends

NASA Technical Reports Server (NTRS)

Entzian, G.; Grasnick, K. H.; Taubenheim, J.

1989-01-01

The long term trends (least square linear regression with time) of ozone content at seven European, seven North American, three Japanese and two tropical stations during 21 years (1964 to 1984) are analyzed. In all regions negative trends are observed during the 1970s, but are partly compensated by limited periods of positive trends during the late 1960s and late 1970s. Solely the North American ozone data show negative trends in all 10 year periods. When the long term ozone trends are evaluated for each month of the year separately, a seasonal variation is revealed, which in Europe and North America has largest negative trends in late winter and spring. While in Europe the negative trends in winter/spring are partly compensated by positive trends in summer, in North America the summer values reach only zero, retaining the significant negative trend in annual mean values. In contrast to the antarctic ozone hole, the spring reduction of ozone in Europe and in North America is associated with stratospheric temperatures increasing in the analyzed period and therefore is consistent with the major natural ozone production and loss processes.

Associations among Screen Time and Unhealthy Behaviors, Academic Performance, and Well-Being in Chinese Adolescents.

PubMed

Yan, Hanyi; Zhang, Rui; Oniffrey, Theresa M; Chen, Guoxun; Wang, Yueqiao; Wu, Yingru; Zhang, Xinge; Wang, Quan; Ma, Lu; Li, Rui; Moore, Justin B

2017-06-04

Screen time is negatively associated with markers of health in western youth, but very little is known about these relationships in Chinese youth. Middle-school and high-school students ( n = 2625) in Wuhan, China, completed questionnaires assessing demographics, health behaviors, and self-perceptions in spring/summer 2016. Linear and logistic regression analyses were conducted to determine whether, after adjustment for covariates, screen time was associated with body mass index (BMI), eating behaviors, average nightly hours of sleep, physical activity (PA), academic performance, and psychological states. Watching television on school days was negatively associated with academic performance, PA, anxiety, and life satisfaction. Television viewing on non-school days was positively associated with sleep duration. Playing electronic games was positively associated with snacking at night and less frequently eating breakfast, and negatively associated with sleep duration and self-esteem. Receiving electronic news and study materials on non-school days was negatively associated with PA, but on school days, was positively associated with anxiety. Using social networking sites was negatively associated with academic performance, but positively associated with BMI z-score, PA and anxiety. Screen time in adolescents is associated with unhealthy behaviors and undesirable psychological states that can contribute to poor quality of life.

Associations among Screen Time and Unhealthy Behaviors, Academic Performance, and Well-Being in Chinese Adolescents

PubMed Central

Yan, Hanyi; Zhang, Rui; Oniffrey, Theresa M.; Chen, Guoxun; Wang, Yueqiao; Wu, Yingru; Zhang, Xinge; Wang, Quan; Ma, Lu; Li, Rui; Moore, Justin B.

2017-01-01

Screen time is negatively associated with markers of health in western youth, but very little is known about these relationships in Chinese youth. Middle-school and high-school students (n = 2625) in Wuhan, China, completed questionnaires assessing demographics, health behaviors, and self-perceptions in spring/summer 2016. Linear and logistic regression analyses were conducted to determine whether, after adjustment for covariates, screen time was associated with body mass index (BMI), eating behaviors, average nightly hours of sleep, physical activity (PA), academic performance, and psychological states. Watching television on school days was negatively associated with academic performance, PA, anxiety, and life satisfaction. Television viewing on non-school days was positively associated with sleep duration. Playing electronic games was positively associated with snacking at night and less frequently eating breakfast, and negatively associated with sleep duration and self-esteem. Receiving electronic news and study materials on non-school days was negatively associated with PA, but on school days, was positively associated with anxiety. Using social networking sites was negatively associated with academic performance, but positively associated with BMI z-score, PA and anxiety. Screen time in adolescents is associated with unhealthy behaviors and undesirable psychological states that can contribute to poor quality of life. PMID:28587225

A Technique of Treating Negative Weights in WENO Schemes

NASA Technical Reports Server (NTRS)

Shi, Jing; Hu, Changqing; Shu, Chi-Wang

2000-01-01

High order accurate weighted essentially non-oscillatory (WENO) schemes have recently been developed for finite difference and finite volume methods both in structural and in unstructured meshes. A key idea in WENO scheme is a linear combination of lower order fluxes or reconstructions to obtain a high order approximation. The combination coefficients, also called linear weights, are determined by local geometry of the mesh and order of accuracy and may become negative. WENO procedures cannot be applied directly to obtain a stable scheme if negative linear weights are present. Previous strategy for handling this difficulty is by either regrouping of stencils or reducing the order of accuracy to get rid of the negative linear weights. In this paper we present a simple and effective technique for handling negative linear weights without a need to get rid of them.

Alexithymia and Mood: Recognition of Emotion in Self and Others.

PubMed

Lyvers, Michael; Kohlsdorf, Susan M; Edwards, Mark S; Thorberg, Fred Arne

2017-01-01

The present study explored relationships between alexithymia-a trait characterized by difficulties identifying and describing feelings and an external thinking style-and negative moods, negative mood regulation expectancies, facial recognition of emotions, emotional empathy, and alcohol consumption. The sample consisted of 102 university (primarily psychology) students (13 men, 89 women) aged 18 to 50 years (M = 22.18 years). Participants completed the Toronto Alexithymia Scale (TAS-20), Negative Mood Regulation Scale (NMRS), Depression Anxiety Stress Scales (DASS-21), Reading the Mind in the Eyes Test (RMET), Interpersonal Reactivity Index (IRI), and Alcohol Use Disorders Identification Test (AUDIT). Results were consistent with previous findings of positive relationships of TAS-20 alexithymia scores with both alcohol use (AUDIT) and negative moods (DASS-21) and a negative relationship with emotional self-regulation as indexed by NMRS. Predicted negative associations of both overall TAS-20 alexithymia scores and the externally oriented thinking (EOT) subscale of the TAS-20 with both RMET facial recognition of emotions and the empathic concern (EC) subscale of the IRI were supported. The mood self-regulation index NMRS fully mediated the relationship between alexithymia and negative moods. Hierarchical linear regressions revealed that, after other relevant variables were controlled for, the EOT subscale of the TAS-20 predicted RMET and EC. The concrete thinking or EDT facet of alexithymia thus appears to be associated with diminished facial recognition of emotions and reduced emotional empathy. The negative moods associated with alexithymia appear to be linked to subjective difficulties in self-regulation of emotions.

Positive and negative mood following imaging-guided core needle breast biopsy and receipt of biopsy results.

PubMed

Perlman, Katherine L; Shelby, Rebecca A; Wren, Anava A; Kelleher, Sarah A; Dorfman, Caroline S; O'Connor, Erin; Kim, Connie; Johnson, Karen S; Soo, Mary Scott

2017-12-01

Positive and negative mood are independent psychological responses to stressful events. Negative mood negatively impacts well-being and co-occurring positive mood leads to improved adjustment. Women undergoing core needle breast biopsies (CNB) experience distress during CNB and awaiting results; however, influences of mood are not well known. This longitudinal study examines psychosocial and biopsy- and spirituality-related factors associated with mood in patients day of CNB and one week after receiving results. Ninety women undergoing CNB completed questionnaires on psychosocial factors (chronic stress, social support), biopsy experiences (pain, radiologist communication), and spirituality (peace, meaning, faith) day of CNB. Measures of positive and negative mood were completed day of CNB and one week after receiving results (benign n = 50; abnormal n = 25). Multiple linear regression analyses were conducted. Greater positive mood correlated with greater peace (β = .25, p = .02) day of CNB. Lower negative mood correlated with greater peace (β = -.29, p = .004) and there was a trend for a relationship with less pain during CNB (β = .19, p = .07). For patients with benign results, day of CNB positive mood predicted positive mood post-results (β = .31, p = .03) and only chronic stress predicted negative mood (β = .33, p = .03). For women with abnormal results, greater meaning day of CNB predicted lower negative mood post-results (β = -.45, p = .03). Meaning and peace may be important for women undergoing CNB and receiving abnormal results.

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Social anxiety and the Big Five personality traits: the interactive relationship of trust and openness.

PubMed

Kaplan, Simona C; Levinson, Cheri A; Rodebaugh, Thomas L; Menatti, Andrew; Weeks, Justin W

2015-01-01

It is well established that social anxiety (SA) has a positive relationship with neuroticism and a negative relationship with extraversion. However, findings on the relationships between SA and agreeableness, conscientiousness, and openness to experience are mixed. In regard to facet-level personality traits, SA is negatively correlated with trust (a facet of agreeableness) and self-efficacy (a facet of conscientiousness). No research has examined interactions among the Big Five personality traits (e.g., extraversion) and facet levels of personality in relation to SA. In two studies using undergraduate samples (N = 502; N = 698), we examined the relationships between trust, self-efficacy, the Big Five, and SA. SA correlated positively with neuroticism, negatively with extraversion, and had weaker relationships with agreeableness, openness, and trust. In linear regression predicting SA, there was a significant interaction between trust and openness over and above gender. In addition to supporting previous research on SA and the Big Five, we found that openness is related to SA for individuals low in trust. Our results suggest that high openness may protect against the higher SA levels associated with low trust.

Faecal nitrogen excretion as an approach to estimate forage intake of wethers.

PubMed

Kozloski, G V; Oliveira, L; Poli, C H E C; Azevedo, E B; David, D B; Ribeiro Filho, H M N; Collet, S G

2014-08-01

Data from twenty-two digestibility trials were compiled to examine the relationship between faecal N concentration and organic matter (OM) digestibility (OMD), and between faecal N excretion and OM intake (OMI) by wethers fed tropical or temperate forages alone or with supplements. Data set was grouped by diet type as follows: only tropical grass (n = 204), only temperate grass (n = 160), tropical grass plus supplement (n = 216), temperate grass plus supplement (n = 48), tropical grass plus tropical legume (n = 60) and temperate grass with ruminal infusion of tannins (n = 16). Positive correlation between OMD and either total faecal N concentration (Nfc, % of OM) or metabolic faecal N concentration (Nmetfc, % of OM) was significant for most diet types. Exceptions were the diet that included a tropical legume, where both relationships were negative, and the diet that included tannin extract, where the correlation between OMD and Nfc was not significant. Pearson correlation and linear regressions between OM intake (OMI, g/day) and faecal N excretion (Nf, g/day) were significant for all diet types. When OMI was estimated from the OM faecal excretion and Nfc-based OMD values, the linear comparison between observed and estimated OMI values showed intercept different from 0 and slope different from 1. When OMI was estimated using the Nf-based linear regressions, the linear comparison between observed and estimated OMI values showed neither intercept different from 0 nor slope different from 1. Both linear comparisons showed similar R(2) values (i.e. 0.78 vs. 0.79). In conclusion, linear equations are suitable for directly estimating OM intake by wethers, fed only forage or forage plus supplements, from the amount of N excreted in faeces. The use of this approach in experiments with grazing wethers has the advantage of accounting for individual variations in diet selection and digestion processes and precludes the use of techniques to estimate forage digestibility. Journal of Animal Physiology and Animal Nutrition © 2013 Blackwell Verlag GmbH.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

A simplified competition data analysis for radioligand specific activity determination.

PubMed

Venturino, A; Rivera, E S; Bergoc, R M; Caro, R A

1990-01-01

Non-linear regression and two-step linear fit methods were developed to determine the actual specific activity of 125I-ovine prolactin by radioreceptor self-displacement analysis. The experimental results obtained by the different methods are superposable. The non-linear regression method is considered to be the most adequate procedure to calculate the specific activity, but if its software is not available, the other described methods are also suitable.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009.

PubMed

Moran, John L; Solomon, Patricia J

2012-05-16

For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

The relationship of social function to depressive and negative symptoms in individuals at clinical high risk for psychosis.

PubMed

Corcoran, C M; Kimhy, D; Parrilla-Escobar, M A; Cressman, V L; Stanford, A D; Thompson, J; David, S Ben; Crumbley, A; Schobel, S; Moore, H; Malaspina, D

2011-02-01

Social dysfunction is a hallmark symptom of schizophrenia which commonly precedes the onset of psychosis. It is unclear if social symptoms in clinical high-risk patients reflect depressive symptoms or are a manifestation of negative symptoms. We compared social function scores on the Social Adjustment Scale-Self Report between 56 young people (aged 13-27 years) at clinical high risk for psychosis and 22 healthy controls. The cases were also assessed for depressive and 'prodromal' symptoms (subthreshold positive, negative, disorganized and general symptoms). Poor social function was related to both depressive and negative symptoms, as well as to disorganized and general symptoms. The symptoms were highly intercorrelated but linear regression analysis demonstrated that poor social function was primarily explained by negative symptoms within this cohort, particularly in ethnic minority patients. Although this study demonstrated a relationship between social dysfunction and depressive symptoms in clinical high-risk cases, this association was primarily explained by the relationship of each of these to negative symptoms. In individuals at heightened risk for psychosis, affective changes may be related to a progressive decrease in social interaction and loss of reinforcement of social behaviors. These findings have relevance for potential treatment strategies for social dysfunction in schizophrenia and its risk states and predict that antidepressant drugs, cognitive behavioral therapy and/or social skills training may be effective.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Just showing up is not enough: Homework adherence and outcome in cognitive-behavioral therapy for cocaine dependence

PubMed Central

Decker, Suzanne E.; Kiluk, Brian. D.; Frankforter, Tami; Babuscio, Theresa; Nich, Charla; Carroll, Kathleen M.

2017-01-01

Objective Homework in cognitive behavioral therapy (CBT) provides opportunities to practice skills. In prior studies, homework adherence was associated with improved outcome across a variety of disorders. Few studies have examined whether the relationship between homework adherence and outcome is maintained after treatment end or is independent of treatment attendance. Method This study combined data from four randomized clinical trials of CBT for cocaine dependence to examine relationships among homework adherence, participant variables, and cocaine use outcomes during treatment and at follow-up. The dataset included only participants who attended at least two CBT sessions to allow for assignment and return of homework (N = 158). Results Participants returned slightly less than half (41.1%) of assigned homework. Longitudinal random effects regression suggested a greater reduction in cocaine use during treatment and through 12 month follow-up for participants who completed half or more of assigned homework (3 way interaction F(2, 910.69) = 4.28, p = .01). In multiple linear regression, the percentage of homework adherence was associated with greater number of cocaine-negative urine toxicology screens during treatment, even when accounting for baseline cocaine use frequency and treatment attendance; at three-months follow-up, multiple logistic regression indicated homework adherence was associated with cocaine-negative urine toxicology screen, controlling for baseline cocaine use and treatment attendance. Conclusions These results extend findings from prior studies regarding the importance of homework adherence by demonstrating associations among homework and cocaine use outcomes during treatment and up to 12 months after, independent of treatment attendance and baseline cocaine use severity. PMID:27454780

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation

PubMed Central

Patterson, Brent R.; Anderson, Morgan L.; Rodgers, Arthur R.; Vander Vennen, Lucas M.; Fryxell, John M.

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism–a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors–has negative consequences for the viability of woodland caribou. PMID:29117234

Compensatory selection for roads over natural linear features by wolves in northern Ontario: Implications for caribou conservation.

PubMed

Newton, Erica J; Patterson, Brent R; Anderson, Morgan L; Rodgers, Arthur R; Vander Vennen, Lucas M; Fryxell, John M

2017-01-01

Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism-a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors-has negative consequences for the viability of woodland caribou.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

The impact of subjective memory complaints on quality of life in community-dwelling older adults.

PubMed

Maki, Yohko; Yamaguchi, Tomoharu; Yamagami, Tetsuya; Murai, Tatsuhiko; Hachisuka, Kenji; Miyamae, Fumiko; Ito, Kae; Awata, Shuichi; Ura, Chiaki; Takahashi, Ryutaro; Yamaguchi, Haruyasu

2014-09-01

The aim of this study was to evaluate the impact of memory complaints on quality of life (QOL) in elderly community dwellers with or without mild cognitive impairment (MCI). Participants included 120 normal controls (NC) and 37 with MCI aged 65 and over. QOL was measured using the Japanese version of Satisfaction in Daily Life, and memory complaints were measured using a questionnaire consisting of four items. The relevance of QOL was evaluated with psychological factors of personality traits, sense of self-efficacy, depressive mood, self-evaluation of daily functioning, range of social activities (Life-Space Assessment), social network size, and cognitive functions including memory. The predictors of QOL were analyzed by multiple linear regression analysis. QOL was not significantly different between the NC and MCI groups. In both groups, QOL was positively correlated with self-efficacy, daily functioning, social network size, Life-Space Assessment, and the personality traits of extraversion and agreeableness; QOL was negatively correlated with memory complaints, depressive mood, and the personality trait of neuroticism. In regression analysis, memory complaints were a negative predictor of QOL in the MCI group, but not in the NC group. The partial correlation coefficient between QOL and memory complaints was -0.623 (P < 0.05), after scores of depressive mood and self-efficacy were controlled. Depressive mood was a common negative predictor in both groups. Positive predictors were Life-Space Assessment in the NC group and sense of self-efficacy in the MCI group. Memory complaints exerted a negative impact on self-rated QOL in the MCI group, whereas a negative correlation was weak in the NC group. Memory training has been widely practised in individuals with MCI to prevent the development of dementia. However, such approaches inevitably identify their memory deficits and could aggravate their awareness of memory decline. Thus, it is critical to give sufficient consideration not to reduce QOL in the intervention for those with MCI. © 2014 The Authors. Psychogeriatrics © 2014 Japanese Psychogeriatric Society.

The relationship between apical root resorption and orthodontic tooth movement in growing subjects.

PubMed

Xu, Tianmin; Baumrind, S

2002-07-01

To investigate the relationship between apical root resorption and orthodontic tooth movement in growing subjects. 58 growing subjects were collected randomly into the study sample and another 40 non-treated cases were used as control. The apical resoption of the upper central incisors was measured on periapical film and the incisor displacement was measured on lateral cephalogram. Using multiple linear regression analysis to examine the relationship between root resoption and the displacement of the upper incisor apex in each of four direction (retraction, advancement, intrusion and extrusion). The statistically significant negative association were found between resorption and both intrusion (P < 0.001) and extrusion (P < 0.05), but no significant association was found between resorption and both retraction and advancement. The regression analysis implied an average of 2.29 mm resorption in the absence of apical displacement. The likelihood that the magnitude of displacement of the incisor root is positively associated with root resoption in the population of treated growing subjects is very small.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Internet sexuality research with rural men who have sex with men: can we recruit and retain them?

PubMed

Bowen, Anne

2005-11-01

This study examines the utility of internet banner ads for recruiting rural MSM and identifies correlates of internet HIV risk survey initiation and completion. Banner ads were shown on a popular internet dating site for one month and resulted in 1,045 rural MSM, from 49 States, Canada, Australia/New Zealand, and 5 from other countries initiating the questionnaire. Logistic regression indicated that progression beyond screening questions was negatively related to "expecting pay, but not being paid" and positively related to "using chat rooms to find friends" and identifying as gay. Linear regression indicated that the absolute number of responses by consenting participants was positively correlated with reimbursement, number of sexual partners, motivated by money, and having been HIV tested. Overall, this sample represents one of the largest rural MSM samples; survey completion was high and strengthened by reimbursement and possibly by awareness of HIV risk. Generalizability was limited by low participation of minority and non-gay identified MSM.

Ataque de nervios: relationship to anxiety sensitivity and dissociation predisposition.

PubMed

Hinton, Devon E; Chong, Roberto; Pollack, Mark H; Barlow, David H; McNally, Richard J

2008-01-01

We investigated the relative importance of "fear of arousal symptoms" (i.e., anxiety sensitivity) and "dissociation tendency" in generating ataque de nervios. Puerto Rican patients attending an outpatient psychiatric clinic were assessed for ataque de nervios frequency in the previous month, and they completed the Anxiety Sensitivity Index (ASI) and the Dissociation Experiences Scale (DES). ASI scores were especially high in the ataque-positive group (M=41.6, SD=12.8) as compared with the ataque-negative group (M=27.2, SD=11.7), t(2, 68)=4.6, P<.001. Among the whole sample (N=70), in a logistic regression analysis, the ASI significantly predicted (odds ratio=2.6) the presence of ataque de nervios, but the DES did not. In a linear regression analysis, ataque severity was significantly predicted by both the ASI (beta=.46) and the DES (beta=.29). The theoretical and clinical implications of the strong relationship of the ASI to ataque severity are discussed.

Cognitive load, emotion, and performance in high-fidelity simulation among beginning nursing students: a pilot study.

PubMed

Schlairet, Maura C; Schlairet, Timothy James; Sauls, Denise H; Bellflowers, Lois

2015-03-01

Establishing the impact of the high-fidelity simulation environment on student performance, as well as identifying factors that could predict learning, would refine simulation outcome expectations among educators. The purpose of this quasi-experimental pilot study was to explore the impact of simulation on emotion and cognitive load among beginning nursing students. Forty baccalaureate nursing students participated in teaching simulations, rated their emotional state and cognitive load, and completed evaluation simulations. Two principal components of emotion were identified representing the pleasant activation and pleasant deactivation components of affect. Mean rating of cognitive load following simulation was high. Linear regression identiffed slight but statistically nonsignificant positive associations between principal components of emotion and cognitive load. Logistic regression identified a negative but statistically nonsignificant effect of cognitive load on assessment performance. Among lower ability students, a more pronounced effect of cognitive load on assessment performance was observed; this also was statistically non-significant. Copyright 2015, SLACK Incorporated.

Effect of a fall prevention program on balance maintenance using a quasi-experimental design in real-world settings.

PubMed

Robitaille, Yvonne; Fournier, Michel; Laforest, Sophie; Gauvin, Lise; Filiatrault, Johanne; Corriveau, Hélène

2012-08-01

To examine the effect of a fall prevention program offered under real-world conditions on balance maintenance several months after the program. To explore the program's impact on falls. A quasi-experimental study was conducted among community-dwelling seniors, with pre- and postintervention measures of balance performance and self-reported falls. Ten community-based organizations offered the intervention (98 participants) and 7 recruited participants to the study's control arm (102 participants). An earlier study examined balance immediately after the 12-week program. The present study focuses on the 12-month effect. Linear regression (balance) and negative binomial regression (falls) procedures were performed.falls. During the 12-month study period, experimental participants improved and maintained their balance as reflected by their scores on three performance tests. There was no evidence of an effect on falls.falls. Structured group exercise programs offered in community-based settings can maintain selected components of balance for several months after the program's end.

Negative life events and school adjustment among Chinese nursing students: The mediating role of psychological capital.

PubMed

Liu, Chunqin; Zhao, Yuanyuan; Tian, Xiaohong; Zou, Guiyuan; Li, Ping

2015-06-01

Adjustment difficulties of college students are common and their school adjustment has gained wide concern in recent years. Negative life events and psychological capital (PsyCap) have been associated with school adjustment. However, the potential impact of negative life events on PsyCap, and whether PsyCap mediates the relationship between negative life events and school adjustment among nursing students have not been studied. To investigate the relationship among negative life events, PsyCap, and school adjustment among five-year vocational high school nursing students in China and the mediating role of PsyCap between negative life events and school adjustment. A cross-sectional survey design was conducted. 643 five-year vocational high school nursing students were recruited from three public high vocational colleges in Shandong of China. Adolescent Self-Rating Life Event Checklist (ASLEC), the Psychological Capital Questionnaire for Adolescent Students scale (PCQAS), and the Chinese College Student Adjustment Scale (CCSAS) were used in this study. Hierarchical linear regression analyses were performed to explore the mediating role of PsyCap. Negative life events were negatively associated with the dimensions of school adjustment (interpersonal relationship adaptation, learning adaptation, campus life adaptation, career adaptation, emotional adaptation, self-adaptation, and degree of satisfaction). PsyCap was positively associated with the dimensions of school adjustment and negatively associated with negative life events. PsyCap partially mediated the relationship between negative life events and school adjustment. Negative life events may increase the risk of school maladjustment in individuals with low PsyCap. Interventions designed to increase nursing students' PsyCap might buffer the stress of adverse life events, and thereby, enhance students' positive adjustment to school. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictors of linear growth in the first year of life of a prospective cohort of full term children with normal birth weight.

PubMed

Queiroz, Valterlinda A O; Assis, Ana Marlúcia O; Pinheiro, Sandra Maria C; Ribeiro, Hugo C Ribeiro

2012-01-01

To investigate covariates that could affect the variation in mean length/age z scores in the first year of life of children born full term with normal birth weight. This was a prospective study of a cohort of mother-infant pairs recruited at public maternity units in two municipalities in the Brazilian state of Bahia, from March 2005 to October 2006. This paper reports the results for linear growth of 489 children who were followed-up for the first 12 months of their lives. A mixed-effect regression model was used to investigate the influence of covariates of mean length/age z score during the first year of life. The multivariate mixed effect analysis indicated that mothers not cohabiting with a partner (beta = 0.2347; p = 0.004) and increased duration of exclusive breastfeeding (beta = 0.0031; p < 0.001) had a positive impact, whereas mother's height less than 150 cm (beta = -0.4393; p < 0.001), birth weight of 2,500-2,999 g (beta = -0.8084; p < 0.001) and anemia in the child (beta = -0.0875; p < 0.001) all had a negative impact on the variation in estimated length/age z score. Therefore, the results of this study indicate that short maternal stature, birth weight < 3,000 g and anemia in the infant had a negative effect on linear growth during the first year of life, whereas longer duration of exclusive breastfeeding and mothers who did not cohabit with a partner had a positive effect.

Specialization Agreements in the Council for Mutual Economic Assistance

DTIC Science & Technology

1988-02-01

proportions to stabilize variance (S. Weisberg, Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134). If the dependent...27, 1986, p. 3. Weisberg, S., Applied Linear Regression , 2nd ed., John Wiley & Sons, New York, 1985, p. 134. Wiles, P. J., Communist International

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Data Transformations for Inference with Linear Regression: Clarifications and Recommendations

ERIC Educational Resources Information Center

Pek, Jolynn; Wong, Octavia; Wong, C. M.

2017-01-01

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES

EPA Science Inventory

The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Negative inferential style, emotional clarity, and life stress: integrating vulnerabilities to depression in adolescence.

PubMed

Stange, Jonathan P; Alloy, Lauren B; Flynn, Megan; Abramson, Lyn Y

2013-01-01

Negative inferential style and deficits in emotional clarity have been identified as vulnerability factors for depression in adolescence, particularly when individuals experience high levels of life stress. However, previous research has not integrated these characteristics when evaluating vulnerability to depression. In the present study, a racially diverse community sample of 256 early adolescents (ages 12 and 13) completed a baseline visit and a follow-up visit 9 months later. Inferential style, emotional clarity, and depressive symptoms were assessed at baseline, and intervening life events and depressive symptoms were assessed at follow-up. Hierarchical linear regressions indicated that there was a significant three-way interaction between adolescents' weakest-link negative inferential style, emotional clarity, and intervening life stress predicting depressive symptoms at follow-up, controlling for initial depressive symptoms. Adolescents with low emotional clarity and high negative inferential styles experienced the greatest increases in depressive symptoms following life stress. Emotional clarity buffered against the impact of life stress on depressive symptoms among adolescents with negative inferential styles. Similarly, negative inferential styles exacerbated the impact of life stress on depressive symptoms among adolescents with low emotional clarity. These results provide evidence of the utility of integrating inferential style and emotional clarity as constructs of vulnerability in combination with life stress in the identification of adolescents at risk for depression. They also suggest the enhancement of emotional clarity as a potential intervention technique to protect against the effects of negative inferential styles and life stress on depression in early adolescence.

Contribution of neurocognition to 18-month employment outcomes in first-episode psychosis.

PubMed

Karambelas, George J; Cotton, Sue M; Farhall, John; Killackey, Eóin; Allott, Kelly A

2017-10-27

To examine whether baseline neurocognition predicts vocational outcomes over 18 months in patients with first-episode psychosis enrolled in a randomized controlled trial of Individual Placement and Support or treatment as usual. One-hundred and thirty-four first-episode psychosis participants completed an extensive neurocognitive battery. Principal axis factor analysis using PROMAX rotation was used to determine the underlying structure of the battery. Setwise (hierarchical) multiple linear and logistic regressions were used to examine predictors of (1) total hours employed over 18 months and (2) employment status, respectively. Neurocognition factors were entered in the models after accounting for age, gender, premorbid IQ, negative symptoms, treatment group allocation and employment status at baseline. Five neurocognitive factors were extracted: (1) processing speed, (2) verbal learning and memory, (3) knowledge and reasoning, (4) attention and working memory and (5) visual organization and memory. Employment status over 18 months was not significantly predicted by any of the predictors in the final model. Total hours employed over 18 months were significantly predicted by gender (P = .027), negative symptoms (P = .032) and verbal learning and memory (P = .040). Every step of the regression model was a significant predictor of total hours worked overall (final model: P = .013). Verbal learning and memory, negative symptoms and gender were implicated in duration of employment in first-episode psychosis. The other neurocognitive domains did not significantly contribute to the prediction of vocational outcomes over 18 months. Interventions targeting verbal memory may improve vocational outcomes in early psychosis. © 2017 John Wiley & Sons Australia, Ltd.

Predictability of depression severity based on posterior alpha oscillations.

PubMed

Jiang, H; Popov, T; Jylänki, P; Bi, K; Yao, Z; Lu, Q; Jensen, O; van Gerven, M A J

2016-04-01

We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively. Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

An Application of Robust Method in Multiple Linear Regression Model toward Credit Card Debt

NASA Astrophysics Data System (ADS)

Amira Azmi, Nur; Saifullah Rusiman, Mohd; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Salleh, Rohayu Mohd; Hamzah, Nur Shamsidah Amir

2018-04-01

Credit card is a convenient alternative replaced cash or cheque, and it is essential component for electronic and internet commerce. In this study, the researchers attempt to determine the relationship and significance variables between credit card debt and demographic variables such as age, household income, education level, years with current employer, years at current address, debt to income ratio and other debt. The provided data covers 850 customers information. There are three methods that applied to the credit card debt data which are multiple linear regression (MLR) models, MLR models with least quartile difference (LQD) method and MLR models with mean absolute deviation method. After comparing among three methods, it is found that MLR model with LQD method became the best model with the lowest value of mean square error (MSE). According to the final model, it shows that the years with current employer, years at current address, household income in thousands and debt to income ratio are positively associated with the amount of credit debt. Meanwhile variables for age, level of education and other debt are negatively associated with amount of credit debt. This study may serve as a reference for the bank company by using robust methods, so that they could better understand their options and choice that is best aligned with their goals for inference regarding to the credit card debt.

Fatigue in Type 2 Diabetes: Impact on Quality of Life and Predictors.

PubMed

Singh, Rupali; Teel, Cynthia; Sabus, Carla; McGinnis, Patricia; Kluding, Patricia

2016-01-01

Fatigue is a persistent symptom, impacting quality of life (QoL) and functional status in people with type 2 diabetes, yet the symptom of fatigue has not been fully explored. The purpose of this study was to explore the relationship between fatigue, QoL functional status and to investigate the predictors of fatigue. These possible predictors included body mass index (BMI), Hemoglobin A1C (HbA1C), sleep quality, pain, number of complications from diabetes, years since diagnosis and depression. Forty-eight individuals with type 2 diabetes (22 females, 26 males; 59.66±7.24 years of age; 10.45 ±7.38 years since diagnosis) participated in the study. Fatigue was assessed by using Multidimensional Fatigue Inventory (MFI-20). Other outcomes included: QoL (Audit of Diabetes Dependent QoL), and functional status (6 minute walk test), BMI, HbA1c, sleep (Pittsburg sleep quality index, PSQI), pain (Visual Analog Scale), number of complications, years since diagnosis, and depression (Beck's depression Inventory-2). The Pearson correlation analysis followed by multivariable linear regression model was used. Fatigue was negatively related to quality of life and functional status. Multivariable linear regression analysis revealed sleep, pain and BMI as the independent predictors of fatigue signaling the presence of physiological (sleep, pain, BMI) phenomenon that could undermine health outcomes.

Fatigue in Type 2 Diabetes: Impact on Quality of Life and Predictors

PubMed Central

Teel, Cynthia; Sabus, Carla; McGinnis, Patricia; Kluding, Patricia

2016-01-01

Fatigue is a persistent symptom, impacting quality of life (QoL) and functional status in people with type 2 diabetes, yet the symptom of fatigue has not been fully explored. The purpose of this study was to explore the relationship between fatigue, QoL functional status and to investigate the predictors of fatigue. These possible predictors included body mass index (BMI), Hemoglobin A1C (HbA1C), sleep quality, pain, number of complications from diabetes, years since diagnosis and depression. Forty-eight individuals with type 2 diabetes (22 females, 26 males; 59.66±7.24 years of age; 10.45 ±7.38 years since diagnosis) participated in the study. Fatigue was assessed by using Multidimensional Fatigue Inventory (MFI-20). Other outcomes included: QoL (Audit of Diabetes Dependent QoL), and functional status (6 minute walk test), BMI, HbA1c, sleep (Pittsburg sleep quality index, PSQI), pain (Visual Analog Scale), number of complications, years since diagnosis, and depression (Beck’s depression Inventory-2). The Pearson correlation analysis followed by multivariable linear regression model was used. Fatigue was negatively related to quality of life and functional status. Multivariable linear regression analysis revealed sleep, pain and BMI as the independent predictors of fatigue signaling the presence of physiological (sleep, pain, BMI) phenomenon that could undermine health outcomes. PMID:27824886

Models for forecasting the flowering of Cornicabra olive groves.

PubMed

Rojo, Jesús; Pérez-Badia, Rosa

2015-11-01

This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.

Association between Organizational Commitment and Personality Traits of Faculty Members of Ahvaz Jundishapur University of Medical Sciences

PubMed Central

Khiavi, Farzad Faraji; Dashti, Rezvan; Mokhtari, Saeedeh

2016-01-01

Introduction Individual characteristics are important factors influencing organizational commitment. Also, committed human resources can lead organizations to performance improvement as well as personal and organizational achievements. This research aimed to determine the association between organizational commitment and personality traits among faculty members of Ahvaz Jundishapur University of Medical Sciences. Methods the research population of this cross-sectional study was the faculty members of Ahvaz Jundishapur University of Medical Sciences (Ahvaz, Iran). The sample size was determined to be 83. Data collection instruments were the Allen and Meyer questionnaire for organizational commitment and Neo for characteristics’ features. The data were analyzed through Pearson’s product-moment correlation and the independent samples t-test, ANOVA, and simple linear regression analysis (SLR) by SPSS. Results Continuance commitment showed a significant positive association with neuroticism, extroversion, agreeableness, and conscientiousness. Normative commitment showed a significant positive association with conscientiousness and a negative association with extroversion (p = 0.001). Openness had a positive association with affective commitment. Openness and agreeableness, among the five characteristics’ features, had the most effect on organizational commitment, as indicated by simple linear regression analysis. Conclusion Faculty members’ characteristics showed a significant association with their organizational commitment. Determining appropriate characteristic criteria for faculty members may lead to employing committed personnel to accomplish the University’s objectives and tasks. PMID:27123222

Boredom proneness and emotion regulation predict emotional eating.

PubMed

Crockett, Amanda C; Myhre, Samantha K; Rokke, Paul D

2015-05-01

Emotional eating is considered a risk factor for eating disorders and an important contributor to obesity and its associated health problems. It has been suggested that boredom may be an important contributor to overeating, but has received relatively little attention. A sample of 552 college students was surveyed. Linear regression analyses found that proneness to boredom and difficulties in emotion regulation simultaneously predicted inappropriate eating behavior, including eating in response to boredom, other negative emotions, and external cues. The unique contributions of these variables to emotional eating were discussed. These findings help to further identify which individuals could be at risk for emotional eating and potentially for unhealthy weight gain. © The Author(s) 2015.

Deployment cycle stressors and post-traumatic stress symptoms in Army National Guard women: the mediating effect of resilience.

PubMed

Wooten, Nikki R

2012-01-01

This study examined the associations between deployment cycle stressors, post-traumatic stress symptoms (PTSS), and resilience in Army National Guard (ARNG) women deployed to Operations Enduring Freedom and Iraqi Freedom. Resilience was also tested as a mediator. Hierarchical linear regression indicated that deployment and post-deployment stressors were positively associated, and resilience was negatively associated with PTSS. Resilience fully mediated the association between post-deployment stressors and PTSS. Findings suggest assessing deployment and post-deployment stressors in ARNG women may be helpful in identifying those at risk for severe PTSS; and highlight the potential of individual-level resilient characteristics in mitigating the adverse impact of post-deployment stressors.

Marginalized zero-inflated negative binomial regression with application to dental caries

PubMed Central

Preisser, John S.; Das, Kalyan; Long, D. Leann; Divaris, Kimon

2015-01-01

The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared to marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children. PMID:26568034

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Validation of a Clinical Global Impression Scale for Aggression (CGI-A) in a sample of 558 psychiatric patients.

PubMed

Huber, Christian G; Lambert, Martin; Naber, Dieter; Schacht, Alexander; Hundemer, Hans-Peter; Wagner, Thomas T; Schimmelmann, Benno G

2008-03-01

Clinical management of aggression depends on the availability of easily administrable measurements allowing reliable evaluation. The present study's aim is to validate a Clinical Global Impression-Severity of Aggression scale (CGI-A). 558 inpatients with psychiatric disorders and an agitated-aggressive syndrome at baseline were continuously assessed over 5 days using CGI-A and the Positive and Negative Syndrome Scale-Excited Component (PANSS-EC). Equipercentile linking, correlation analyses and linear regression were applied. Relationship between CGI-A and PANSS-EC total score was found to be linear. On a 5-level CGI-A scale, values of 1 to 5 points were found to correspond to PANSS-EC scores of 12.2, 16.7, 21.3, 25.8, and 30.4, respectively (average increase: 4.6). All findings remained stable when only data from patients with schizophrenia spectrum disorders were analyzed. The CGI-A is proposed as a quickly administrable scale for the assessment of patients' aggressiveness.

Limits of detection and decision. Part 3

NASA Astrophysics Data System (ADS)

Voigtman, E.

2008-02-01

It has been shown that the MARLAP (Multi-Agency Radiological Laboratory Analytical Protocols) for estimating the Currie detection limit, which is based on 'critical values of the non-centrality parameter of the non-central t distribution', is intrinsically biased, even if no calibration curve or regression is used. This completed the refutation of the method, begun in Part 2. With the field cleared of obstructions, the true theory underlying Currie's limits of decision, detection and quantification, as they apply in a simple linear chemical measurement system (CMS) having heteroscedastic, Gaussian measurement noise and using weighted least squares (WLS) processing, was then derived. Extensive Monte Carlo simulations were performed, on 900 million independent calibration curves, for linear, "hockey stick" and quadratic noise precision models (NPMs). With errorless NPM parameters, all the simulation results were found to be in excellent agreement with the derived theoretical expressions. Even with as much as 30% noise on all of the relevant NPM parameters, the worst absolute errors in rates of false positives and false negatives, was only 0.3%.

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming

USGS Publications Warehouse

Clow, David W.

2010-01-01

Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978-2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2-3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November-May air temperatures increased by a median of 0.9 degrees C decade-1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade-1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

Association between metabolic syndrome and intravesical prostatic protrusion in patients with benign prostatic enlargement and lower urinary tract symptoms (MIPS Study).

PubMed

Russo, Giorgio I; Regis, Federica; Spatafora, Pietro; Frizzi, Jacopo; Urzì, Daniele; Cimino, Sebastiano; Serni, Sergio; Carini, Marco; Gacci, Mauro; Morgia, Giuseppe

2018-05-01

To investigate the association between metabolic syndrome (MetS) and morphological features of benign prostatic enlargement (BPE), including total prostate volume (TPV), transitional zone volume (TZV) and intravesical prostatic protrusion (IPP). Between January 2015 and January 2017, 224 consecutive men aged >50 years presenting with lower urinary tract symptoms (LUTS) suggestive of BPE were recruited to this multicentre cross-sectional study. MetS was defined according to International Diabetes Federation criteria. Multivariate linear and logistic regression models were performed to verify factors associated with IPP, TZV and TPV. Patients with MetS were observed to have a significant increase in IPP (P < 0.01), TPV (P < 0.01) and TZV (P = 0.02). On linear regression analysis, adjusted for age and metabolic factors of MetS, we found that high-density lipoprotein (HDL) cholesterol was negatively associated with IPP (r = -0.17), TPV (r = -0.19) and TZV (r = -0.17), while hypertension was positively associated with IPP (r = 0.16), TPV (r = 0.19) and TZV (r = 0.16). On multivariate logistic regression analysis adjusted for age and factors of MetS, hypertension (categorical; odds ratio [OR] 2.95), HDL cholesterol (OR 0.94) and triglycerides (OR 1.01) were independent predictors of TPV ≥ 40 mL. We also found that HDL cholesterol (OR 0.86), hypertension (OR 2.0) and waist circumference (OR 1.09) were significantly associated with TZV ≥ 20 mL. On age-adjusted logistic regression analysis, MetS was significantly associated with IPP ≥ 10 mm (OR 34.0; P < 0.01), TZV ≥ 20 mL (OR 4.40; P < 0.01) and TPV ≥ 40 mL (OR 5.89; P = 0.03). We found an association between MetS and BPE, demonstrating a relationship with IPP. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

Organizational and environmental effects on voluntary and involuntary turnover.

PubMed

Donoghue, Christopher; Castle, Nicholas G

2007-01-01

There are few studies of voluntary and involuntary turnover in the nursing home literature. Previous research in this area has focused mainly on the linear effects of individual and organizational characteristics on total turnover. The purpose of this study was to examine both linear and nonlinear effects of organizational and environmental conditions on voluntary and involuntary nursing home staff turnover. We analyzed both primary and secondary data on 854 nursing homes in six states. A negative binomial regression model was used to study both linear and curvilinear effects of organizational and environmental factors on voluntary and involuntary turnover among registered nurses, licensed practical nurses, and nurse aides. Staffing levels and deficiency citations were the organizational characteristics most consistently linked with turnover among all nurse types. Links were also found between unemployment and type of location (urban or rural) and turnover, indicating that the economic environment is influential for retention. The results of this study support the notion that policy makers need to consider both the organization and the environment when evaluating the nature of nursing home staff turnover. The findings also offer further evidence that the antecedents of voluntary and involuntary turnover are not necessarily the same.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Developmental changes in neural correlates of cognitive reappraisal: An ERP study using the late positive potential.

PubMed

Van Cauwenberge, Valerie; Van Leeuwen, Karla; Hoppenbrouwers, Karel; Wiersema, Jan R

2017-01-27

The reduction of the amplitude of the late positive potential (LPP) following cognitive reappraisal has been used as a neural marker of emotion regulation. However, studies employing this neural marker in children are scarce and findings are not conclusive, with most studies showing a lack of LPP modulation after reappraisal in children in the age range of 5-12 years. The aim of the current study was therefore to investigate developmental changes in sensitivity of LPP modulation to cognitive reappraisal. To do so, LPP modulation due to cognitive reappraisal of negative pictures was compared between two age groups (8- to 11- versus 12- to 15-year-olds) and regression analyses were applied within the total sample to test whether sensitivity of LPP modulation shows a linear increase with age. In 63 children the LPP was measured after negative pictures that were either combined with a negative story or with a neutral, reappraising story. Although groups did not differ for self-reports on reappraisal, a significant reduction of LPP following cognitive reappraisal was only found in the older children, whereas such an effect was absent in the younger children. Findings were similar for boys and girls. Additional analyses showed a linear increase in sensitivity of LPP modulation with age. The results indicate that LPP modulation as measured in the current paradigm can be used as a valid index of emotion regulation in boys and girls but that caution is recommended using it in younger children. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Genetic variation throughout the folate metabolic pathway influences negative symptom severity in schizophrenia.

PubMed

Roffman, Joshua L; Brohawn, David G; Nitenson, Adam Z; Macklin, Eric A; Smoller, Jordan W; Goff, Donald C

2013-03-01

Low serum folate levels previously have been associated with negative symptom risk in schizophrenia, as has the hypofunctional 677C>T variant of the MTHFR gene. This study examined whether other missense polymorphisms in folate-regulating enzymes, in concert with MTHFR, influence negative symptoms in schizophrenia, and whether total risk allele load interacts with serum folate status to further stratify negative symptom risk. Medicated outpatients with schizophrenia (n = 219), all of European origin and some included in a previous report, were rated with the Positive and Negative Syndrome Scale. A subset of 82 patients also underwent nonfasting serum folate testing. Patients were genotyped for the MTHFR 677C>T (rs1801133), MTHFR 1298A>C (rs1801131), MTR 2756A>G (rs1805087), MTRR 203A>G (rs1801394), FOLH1 484T>C (rs202676), RFC 80A>G (rs1051266), and COMT 675G>A (rs4680) polymorphisms. All genotypes were entered into a linear regression model to determine significant predictors of negative symptoms, and risk scores were calculated based on total risk allele dose. Four variants, MTHFR 677T, MTR 2756A, FOLH1 484C, and COMT 675A, emerged as significant independent predictors of negative symptom severity, accounting for significantly greater variance in negative symptoms than MTHFR 677C>T alone. Total allele dose across the 4 variants predicted negative symptom severity only among patients with low folate levels. These findings indicate that multiple genetic variants within the folate metabolic pathway contribute to negative symptoms of schizophrenia. A relationship between folate level and negative symptom severity among patients with greater genetic vulnerability is biologically plausible and suggests the utility of folate supplementation in these patients.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Symmetry Breaking and Optical Negative Index of Closed Nanorings

NASA Astrophysics Data System (ADS)

Kante, Boubacar; Park, Yong-Shik; O'Brien, Kevin; Shuldman, Daniel; Lanzillotti-Kimura, Norberto; Wong, Zi; Yin, Xiaobo; Zhang, Xiang; UC Berkeley Team

2013-03-01

We report the first experimental demonstration of broadband negative-index metamaterial made solely of closed metallic nanorings. Using symmetry breaking that negatively couples the discrete nanorings, we measured negative phase delay in our composite chess metamaterial. Our approach open avenues towards topological nanophotonics with on demand linear and non-linear responses.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Evaluation of treatment outcomes for patients on first-line regimens in US President's Emergency Plan for AIDS Relief (PEPFAR) clinics in Uganda: predictors of virological and immunological response from RV288 analyses.

PubMed

Crawford, K W; Wakabi, S; Magala, F; Kibuuka, H; Liu, M; Hamm, T E

2015-02-01

Viral load (VL) monitoring is recommended, but seldom performed, in resource-constrained countries. RV288 is a US President's Emergency Plan for AIDS Relief (PEPFAR) basic programme evaluation to determine the proportion of patients on treatment who are virologically suppressed and to identify predictors of virological suppression and recovery of CD4 cell count. Analyses from Uganda are presented here. In this cross-sectional, observational study, patients on first-line antiretroviral therapy (ART) (efavirenz or nevirapine+zidovudine/lamivudine) from Kayunga District Hospital and Kagulamira Health Center were randomly selected for a study visit that included determination of viral load (HIV-1 RNA), CD4 cell count and clinical chemistry tests. Subjects were recruited by time on treatment: 6-12, 13-24 or >24 months. Logistic regression modelling identified predictors of virological suppression. Linear regression modelling identified predictors of CD4 cell count recovery on ART. We found that 85.2% of 325 subjects were virologically suppressed (viral load<47 HIV-1 RNA copies/ml). There was no difference in the proportion of virologically suppressed subjects by time on treatment, yet CD4 counts were higher in each successive stratum. Women had higher median CD4 counts than men overall (406 vs. 294 cells/μL, respectively; P<0.0001) and in each time-on-treatment stratum. In a multivariate logistic regression model, predictors of virological suppression included efavirenz use [odds ratio (OR) 0.47; 95% confidence interval (CI) 0.22-1.02; P=0.057], lower cost of clinic visits (OR 0.815; 95% CI 0.66-1.00; P=0.05), improvement in CD4 percentage (OR 1.06; 95% CI 1.014-1.107; P=0.009), and care at Kayunga vs. Kangulamira (OR 0.47; 95% CI 0.23-0.92; P=0.035). In a multivariate linear regression model of covariates associated with CD4 count recovery, time on highly active antiretroviral therapy (ART) (P<0.0001), patient satisfaction with care (P=0.038), improvements in total lymphocyte count (P<0.0001) and haemoglobin concentration (P=0.05) were positively associated, whereas age at start of ART (P=0.0045) was negatively associated with this outcome. High virological suppression rates are achievable on first-line ART in Uganda. The odds of virological suppression were positively associated with efavirenz use and improvements in CD4 cell percentage and total lymphocyte count and negatively associated with the cost of travel to the clinic. CD4 cell reconstitution was positively associated with CD4 count at study visit, time on ART, satisfaction with care at clinic, haemoglobin concentration and total lymphocyte count and negatively associated with age. © 2014 British HIV Association.

Continuous infusion of low-dose unfractionated heparin after aneurysmal subarachnoid hemorrhage: a preliminary study of cognitive outcomes.

PubMed

James, Robert F; Khattar, Nicolas K; Aljuboori, Zaid S; Page, Paul S; Shao, Elaine Y; Carter, Lacey M; Meyer, Kimberly S; Daniels, Michael W; Craycroft, John; Gaughen, John R; Chaudry, M Imran; Rai, Shesh N; Everhart, D Erik; Simard, J Marc

2018-05-11

OBJECTIVE Cognitive dysfunction occurs in up to 70% of aneurysmal subarachnoid hemorrhage (aSAH) survivors. Low-dose intravenous heparin (LDIVH) infusion using the Maryland protocol was recently shown to reduce clinical vasospasm and vasospasm-related infarction. In this study, the Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive changes in aSAH patients treated with the Maryland LDIVH protocol compared with controls. METHODS A retrospective analysis of all patients treated for aSAH between July 2009 and April 2014 was conducted. Beginning in 2012, aSAH patients were treated with LDIVH in the postprocedural period. The MoCA was administered to all aSAH survivors prospectively during routine follow-up visits, at least 3 months after aSAH, by trained staff blinded to treatment status. Mean MoCA scores were compared between groups, and regression analyses were performed for relevant factors. RESULTS No significant differences in baseline characteristics were observed between groups. The mean MoCA score for the LDIVH group (n = 25) was 26.4 compared with 22.7 in controls (n = 22) (p = 0.013). Serious cognitive impairment (MoCA ≤ 20) was observed in 32% of controls compared with 0% in the LDIVH group (p = 0.008). Linear regression analysis demonstrated that only LDIVH was associated with a positive influence on MoCA scores (β = 3.68, p =0.019), whereas anterior communicating artery aneurysms and fevers were negatively associated with MoCA scores. Multivariable linear regression analysis resulted in all 3 factors maintaining significance. There were no treatment complications. CONCLUSIONS This preliminary study suggests that the Maryland LDIVH protocol may improve cognitive outcomes in aSAH patients. A randomized controlled trial is needed to determine the safety and potential benefit of unfractionated heparin in aSAH patients.

An event-based approach to understanding decadal fluctuations in the Atlantic meridional overturning circulation

NASA Astrophysics Data System (ADS)

Allison, Lesley; Hawkins, Ed; Woollings, Tim

2015-01-01

Many previous studies have shown that unforced climate model simulations exhibit decadal-scale fluctuations in the Atlantic meridional overturning circulation (AMOC), and that this variability can have impacts on surface climate fields. However, the robustness of these surface fingerprints across different models is less clear. Furthermore, with the potential for coupled feedbacks that may amplify or damp the response, it is not known whether the associated climate signals are linearly related to the strength of the AMOC changes, or if the fluctuation events exhibit nonlinear behaviour with respect to their strength or polarity. To explore these questions, we introduce an objective and flexible method for identifying the largest natural AMOC fluctuation events in multicentennial/multimillennial simulations of a variety of coupled climate models. The characteristics of the events are explored, including their magnitude, meridional coherence and spatial structure, as well as links with ocean heat transport and the horizontal circulation. The surface fingerprints in ocean temperature and salinity are examined, and compared with the results of linear regression analysis. It is found that the regressions generally provide a good indication of the surface changes associated with the largest AMOC events. However, there are some exceptions, including a nonlinear change in the atmospheric pressure signal, particularly at high latitudes, in HadCM3. Some asymmetries are also found between the changes associated with positive and negative AMOC events in the same model. Composite analysis suggests that there are signals that are robust across the largest AMOC events in each model, which provides reassurance that the surface changes associated with one particular event will be similar to those expected from regression analysis. However, large differences are found between the AMOC fingerprints in different models, which may hinder the prediction and attribution of such events in reality.

Serum Vitamin D Levels and Markers of Severity of Childhood Asthma in Costa Rica

PubMed Central

Brehm, John M.; Celedón, Juan C.; Soto-Quiros, Manuel E.; Avila, Lydiana; Hunninghake, Gary M.; Forno, Erick; Laskey, Daniel; Sylvia, Jody S.; Hollis, Bruce W.; Weiss, Scott T.; Litonjua, Augusto A.

2009-01-01

Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood. Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood. Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses. Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]). Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity. PMID:19179486

Seminal Plasma HIV-1 RNA Concentration Is Strongly Associated with Altered Levels of Seminal Plasma Interferon-γ, Interleukin-17, and Interleukin-5

PubMed Central

Hoffman, Jennifer C.; Anton, Peter A.; Baldwin, Gayle Cocita; Elliott, Julie; Anisman-Posner, Deborah; Tanner, Karen; Grogan, Tristan; Elashoff, David; Sugar, Catherine; Yang, Otto O.

2014-01-01

Abstract Seminal plasma HIV-1 RNA level is an important determinant of the risk of HIV-1 sexual transmission. We investigated potential associations between seminal plasma cytokine levels and viral concentration in the seminal plasma of HIV-1-infected men. This was a prospective, observational study of paired blood and semen samples from 18 HIV-1 chronically infected men off antiretroviral therapy. HIV-1 RNA levels and cytokine levels in seminal plasma and blood plasma were measured and analyzed using simple linear regressions to screen for associations between cytokines and seminal plasma HIV-1 levels. Forward stepwise regression was performed to construct the final multivariate model. The median HIV-1 RNA concentrations were 4.42 log10 copies/ml (IQR 2.98, 4.70) and 2.96 log10 copies/ml (IQR 2, 4.18) in blood and seminal plasma, respectively. In stepwise multivariate linear regression analysis, blood HIV-1 RNA level (p<0.0001) was most strongly associated with seminal plasma HIV-1 RNA level. After controlling for blood HIV-1 RNA level, seminal plasma HIV-1 RNA level was positively associated with interferon (IFN)-γ (p=0.03) and interleukin (IL)-17 (p=0.03) and negatively associated with IL-5 (p=0.0007) in seminal plasma. In addition to blood HIV-1 RNA level, cytokine profiles in the male genital tract are associated with HIV-1 RNA levels in semen. The Th1 and Th17 cytokines IFN-γ and IL-17 are associated with increased seminal plasma HIV-1 RNA, while the Th2 cytokine IL-5 is associated with decreased seminal plasma HIV-1 RNA. These results support the importance of genital tract immunomodulation in HIV-1 transmission. PMID:25209674

Using Parametric Cost Models to Estimate Engineering and Installation Costs of Selected Electronic Communications Systems

DTIC Science & Technology

1994-09-01

Institute of Technology, Wright- Patterson AFB OH, January 1994. 4. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 5...Technology, Wright-Patterson AFB OH 5 April 1994. 29. Neter, John and others. Applied Linear Regression Models. Boston: Irwin, 1989. 30. Office of

An Evaluation of the Automated Cost Estimating Integrated Tools (ACEIT) System

DTIC Science & Technology

1989-09-01

residual and it is described as the residual divided by its standard deviation (13:App A,17). Neter, Wasserman, and Kutner, in Applied Linear Regression Models...others. Applied Linear Regression Models. Homewood IL: Irwin, 1983. 19. Raduchel, William J. "A Professional’s Perspective on User-Friendliness," Byte

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

Fitting program for linear regressions according to Mahon (1996)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trappitsch, Reto G.

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

ERIC Educational Resources Information Center

Thompson, Russel L.

Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

On the null distribution of Bayes factors in linear regression

USDA-ARS?s Scientific Manuscript database

We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Immunohistochemically detectable metallothionein expression in malignant pleural mesotheliomas is strongly associated with early failure to platin-based chemotherapy.

PubMed

Mairinger, Fabian D; Schmeller, Jan; Borchert, Sabrina; Wessolly, Michael; Mairinger, Elena; Kollmeier, Jens; Hager, Thomas; Mairinger, Thomas; Christoph, Daniel C; Walter, Robert F H; Eberhardt, Wilfried E E; Plönes, Till; Wohlschlaeger, Jeremias; Jasani, Bharat; Schmid, Kurt Werner; Bankfalvi, Agnes

2018-04-27

Malignant pleural mesothelioma (MPM) is a biologically highly aggressive tumor arising from the pleura with a dismal prognosis. Cisplatin is the drug of choice for the treatment of MPM, and carboplatin seems to have comparable efficacy. Nevertheless, cisplatin treatment results in a response rate of merely 14% and a median survival of less than seven months. Due to their role in many cellular processes, methallothioneins (MTs) have been widely studied in various cancers. The known heavy metal detoxifying effect of MT-I and MT-II may be the reason for heavy metal drug resistance of various cancers including MPM. 105 patients were retrospectively analyzed immunohistochemically for their MT expression levels. Survival analysis was done by Cox-regression, and statistical significance determined using likelihood ratio, Wald test and Score (logrank) tests. Cox-regression analyses were done in a linear and logarithmic scale revealing a significant association between expression of MT and shortened overall survival (OS) in a linear (p=0.0009) and logarithmic scale (p=0.0003). Reduced progression free survival (PFS) was also observed for MT expressing tumors (linear: p=0.0134, log: p=0.0152). Since both, overall survival and progression-free survival are negatively correlated with detectable MT expression in MPM, our results indicate a possible resistance to platin-based chemotherapy associated with MT expression upregulation, found exclusively in progressive MPM samples. Initial cell culture studies suggest promoter DNA hypomethylation and expression of miRNA-566 a direct regulator of copper transporter SLC31A1 and a putative regulator of MT1A and MT2A gene expression, to be responsible for the drug resistance.

Immunohistochemically detectable metallothionein expression in malignant pleural mesotheliomas is strongly associated with early failure to platin-based chemotherapy

PubMed Central

Borchert, Sabrina; Wessolly, Michael; Mairinger, Elena; Kollmeier, Jens; Hager, Thomas; Mairinger, Thomas; Christoph, Daniel C.; Walter, Robert F.H.; Eberhardt, Wilfried E.E.; Plönes, Till; Wohlschlaeger, Jeremias; Jasani, Bharat; Schmid, Kurt Werner; Bankfalvi, Agnes

2018-01-01

Background Malignant pleural mesothelioma (MPM) is a biologically highly aggressive tumor arising from the pleura with a dismal prognosis. Cisplatin is the drug of choice for the treatment of MPM, and carboplatin seems to have comparable efficacy. Nevertheless, cisplatin treatment results in a response rate of merely 14% and a median survival of less than seven months. Due to their role in many cellular processes, methallothioneins (MTs) have been widely studied in various cancers. The known heavy metal detoxifying effect of MT-I and MT-II may be the reason for heavy metal drug resistance of various cancers including MPM. Methods 105 patients were retrospectively analyzed immunohistochemically for their MT expression levels. Survival analysis was done by Cox-regression, and statistical significance determined using likelihood ratio, Wald test and Score (logrank) tests. Results Cox-regression analyses were done in a linear and logarithmic scale revealing a significant association between expression of MT and shortened overall survival (OS) in a linear (p=0.0009) and logarithmic scale (p=0.0003). Reduced progression free survival (PFS) was also observed for MT expressing tumors (linear: p=0.0134, log: p=0.0152). Conclusion Since both, overall survival and progression-free survival are negatively correlated with detectable MT expression in MPM, our results indicate a possible resistance to platin-based chemotherapy associated with MT expression upregulation, found exclusively in progressive MPM samples. Initial cell culture studies suggest promoter DNA hypomethylation and expression of miRNA-566 a direct regulator of copper transporter SLC31A1 and a putative regulator of MT1A and MT2A gene expression, to be responsible for the drug resistance. PMID:29854276

Flow-covariate prediction of stream pesticide concentrations.

PubMed

Mosquin, Paul L; Aldworth, Jeremy; Chen, Wenlin

2018-01-01

Potential peak functions (e.g., maximum rolling averages over a given duration) of annual pesticide concentrations in the aquatic environment are important exposure parameters (or target quantities) for ecological risk assessments. These target quantities require accurate concentration estimates on nonsampled days in a monitoring program. We examined stream flow as a covariate via universal kriging to improve predictions of maximum m-day (m = 1, 7, 14, 30, 60) rolling averages and the 95th percentiles of atrazine concentration in streams where data were collected every 7 or 14 d. The universal kriging predictions were evaluated against the target quantities calculated directly from the daily (or near daily) measured atrazine concentration at 32 sites (89 site-yr) as part of the Atrazine Ecological Monitoring Program in the US corn belt region (2008-2013) and 4 sites (62 site-yr) in Ohio by the National Center for Water Quality Research (1993-2008). Because stream flow data are strongly skewed to the right, 3 transformations of the flow covariate were considered: log transformation, short-term flow anomaly, and normalized Box-Cox transformation. The normalized Box-Cox transformation resulted in predictions of the target quantities that were comparable to those obtained from log-linear interpolation (i.e., linear interpolation on the log scale) for 7-d sampling. However, the predictions appeared to be negatively affected by variability in regression coefficient estimates across different sample realizations of the concentration time series. Therefore, revised models incorporating seasonal covariates and partially or fully constrained regression parameters were investigated, and they were found to provide much improved predictions in comparison with those from log-linear interpolation for all rolling average measures. Environ Toxicol Chem 2018;37:260-273. © 2017 SETAC. © 2017 SETAC.

Effect of water-based recovery on blood lactate removal after high-intensity exercise.

PubMed

Lucertini, Francesco; Gervasi, Marco; D'Amen, Giancarlo; Sisti, Davide; Rocchi, Marco Bruno Luigi; Stocchi, Vilberto; Benelli, Piero

2017-01-01

This study assessed the effectiveness of water immersion to the shoulders in enhancing blood lactate removal during active and passive recovery after short-duration high-intensity exercise. Seventeen cyclists underwent active water- and land-based recoveries and passive water and land-based recoveries. The recovery conditions lasted 31 minutes each and started after the identification of each cyclist's blood lactate accumulation peak, induced by a 30-second all-out sprint on a cycle ergometer. Active recoveries were performed on a cycle ergometer at 70% of the oxygen consumption corresponding to the lactate threshold (the control for the intensity was oxygen consumption), while passive recoveries were performed with subjects at rest and seated on the cycle ergometer. Blood lactate concentration was measured 8 times during each recovery condition and lactate clearance was modeled over a negative exponential function using non-linear regression. Actual active recovery intensity was compared to the target intensity (one sample t-test) and passive recovery intensities were compared between environments (paired sample t-tests). Non-linear regression parameters (coefficients of the exponential decay of lactate; predicted resting lactates; predicted delta decreases in lactate) were compared between environments (linear mixed model analyses for repeated measures) separately for the active and passive recovery modes. Active recovery intensities did not differ significantly from the target oxygen consumption, whereas passive recovery resulted in a slightly lower oxygen consumption when performed while immersed in water rather than on land. The exponential decay of blood lactate was not significantly different in water- or land-based recoveries in either active or passive recovery conditions. In conclusion, water immersion at 29°C would not appear to be an effective practice for improving post-exercise lactate removal in either the active or passive recovery modes.

[Ecological Trendofthe Incidence of Tuberculosis in Mianyang City During 2004-2013].

PubMed

Zhang, Wen-Hao; Xiao, Chuan; Ren, Tao; Wang, Li-Ping; Wang, Lan; Yuan, Ping

2016-09-01

To determine the trend of the incidence of tuberculosis (TB) in Mianyang City during 2004-2013 and its ecological determinants. Linear correlations between TB incidence and ecological factors were analyzed using the data collected in Mianyang City from 2004 to 2013. A multivariate linear regression model was established to determine the ecological predictors of TB incidence. The incidence of TB in Mianyang City decreased over the period of 2004-2013. Economic development and increased health resources were negatively correlated with TB incidence. Population density was positively correlated with TB incidence. A multivariate linear regression equationon TB incidence ( y ) was established with the independent variables ( x₁ to x ₁₀) forming a component (using principal component analysis) to eliminate multicollinearity: y =117.692-1.467 x ₁-1.145 x ₂-1.961 x ₃-4.777 x ₄-2.690 x ₅-6.181 x ₆+82.234 x ₇-2.721 x ₈-0.351 x ₉-0.382 x ₁₀. The incidence of TB decreased with the increases of real GDP per capita ( x ₁), average wage of workers( x ₂), per capita disposable income of urban residents ( x ₃), rural per capita net income ( x ₄), per capita consumption expenditure of urban residents ( x ₅), per capita living consumption expenditure of rural residents ( x ₆), number of licensed (assistant) physicians per thousand population ( x ₈), urbanization rate ( x ₉),and per capita housing construction area of urban ( x ₁₀),while it increased with the increase of density of population ( x ₇). Socio-economic development, health resources and population density are predictors of TB incidence.

[Association between distribution of bacillary dysentery and meteorological factors in Beijing, 2004-2015].

PubMed

Du, Z; Zhang, J; Lu, J X; Lu, L P

2018-05-10

Objective: To analyze the distribution characteristics of bacillary dysentery in Beijing during 2004-2015 and evaluate the influence of meteorological factors on the temporal and spatial distribution of bacillary dysentery. Methods: The incidence data of bacterial dysentery and meteorological data in Beijing from 2004 to 2015 were collected. Descriptive epidemiological analysis was conducted to study the distribution characteristics of bacterial dysentery. Linear correlation analysis and multiple linear regression analysis were carried out to investigate the relationship between the incidence of bacillary dysentery and average precipitation, average air temperature, sunshine hours, average wind speed, average air pressure, gale and rain days. Results: A total of 280 704 cases of bacterial dysentery, including 36 deaths, were reported from 2004 to 2015 in Beijing, the average annual incidence was 130.15/100 000. The annual incidence peak was mainly between May and October, the cases occurred during this period accounted for 80.75 % of the total, and the incidence was highest in age group 0 year. The population distribution showed that most cases were children outside child care settings and students, and the sex ratio of the cases was 1.22∶1. The reported incidence of bacillary dysentery was positively associated with average precipitation, average air temperature and rain days with the correlation coefficients of 0.931, 0.878 and 0.888, but it was negatively associated with the average pressure, the correlation coefficient was -0.820. Multiple linear regression equation for fitting analysis of bacillary dysentery and meteorological factors was Y =3.792+0.162 X (1). Conclusion: The reported incidence of bacillary dysentery in Beijing was much higher than national level. The annual incidence peak was during July to August, and the average precipitation was an important meteorological factor influencing the incidence of bacillary dysentery.

Relationships between tuna catch and variable frequency oceanographic conditions

NASA Astrophysics Data System (ADS)

Ormaza-González, Franklin Isaac; Mora-Cervetto, Alejandra; María Bermúdez-Martínez, Raquel

2016-08-01

Skipjack (Katsuwunus pelamis), yellow fin (Thunnus albacares) and albacore (Thunnus alulunga) tunas landed in the Eastern Pacific Ocean (EPO) countries and Ecuador were correlated to the Indexes Oceanic El Niño (ONI) and Multivariate Enso Index (MEI). The temporal series 1983-2012, and 1977-1999 (warm Pacific Decadal Oscillation, PDO), and 2000-2012 (cold PDO) were analyzed. Linear correlation showed that at least 11 % of the total landings were associated with the MEI, with a slightly negative gradient from cold to warm conditions. When non-linear regression (n = 6), the R2 was higher up to 0.304 (MEI, r = 0.551). The correlation shows high spread from -0.5 to +0.5 for both MEI/ONI; the highest landings occurred at 0.34-0.45; both indexes suggested that at extreme values < -1.0 and > 1.1 total landings tend to decrease. Landings were associated up to 21.9 % (MEI) in 2000-2012, 1983-1999 rendered lower R2 (< 0.09); i.e., during cold PDO periods there was a higher association between landings and oceanographic conditions. For the non-linear regression (n = 6) a R2 of 0.374 (MEI) and 0.408 (ONI) were registered, for the 2000-2012, a higher R2 was observed in 1983-1999, 0.443 and 0.711 for MEI and ONI respectively, suggesting that is better to analyze split series (1983-1999, 2000-2012) than as a whole (1983-2012), due to noise produced by the transition from hot to cold PDOs. The highest landings were in the range -0.2 to 0.5 for MEI/ONI. The linear regression of skipjack landings in Ecuador gave an R2 of 0.140 (MEI) and 0.066 (ONI) and the non-linear were 0.440 and 0.183 respectively. Total landings in the EPO associated to oceanographic events of high and low frequencies could be used somehow as predictors of the high El Niño o La Niña. There is a clear evidence that tuna fish biomass are at higher levels when the PDO is on cold phase (2000-2030) and vice versa on warm phase (1980-1999). The analysis of the skipjack catch per unit effort (CPUE) on floating aggregating devices (FADs) suggests higher CPUE on FADs (around 20 mt set-1) when oceanographic indexes ONI/MEI are below -0.5. Findings of this work suggest that fishing and management of commercial fish must be analyzed under the light of oceanographic conditions.

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

NASA Astrophysics Data System (ADS)

Wu, Cheng; Zhen Yu, Jian

2018-03-01

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

Coping-motivated Marijuana Use Correlates with DSM-5 Cannabis Use Disorder and Psychological Distress among Emerging Adults

PubMed Central

Moitra, Ethan; Christopher, Paul P.; Anderson, Bradley J.; Stein, Michael D.

2015-01-01

Compared to other age cohorts, emerging adults, ages 18–25 years old, have the highest rates of marijuana (MJ) use. We examined the relationship of using MJ to cope with negative emotions, relative to using MJ for enhancement or social purposes, to MJ-associated problems and psychological distress among emerging adults. Participants were 288 community-dwelling emerging adults who reported current MJ use as part of a ‘Health Behaviors’ study. Linear and logistic regressions were used to evaluate the adjusted association of coping-motivated MJ use with DSM-5 Cannabis Use Disorder, MJ-related problem severity, depressive symptoms, and perceived stress. After adjusting for other variables in the regression model, using MJ to cope was positively associated with having DSM-5 cannabis use disorder (OR = 1.85, 95%CI 1.31; 2.62, p < .01), MJ problem severity (b = .41, 95% CI .24; .57, p < .01), depression (b = .36, 95% CI .23; .49, p < .01), and perceived stress (b = .37, 95% CI .22; .51, p < .01). Using MJ for enhancement purposes or for social reasons was not associated significantly with any of the dependent variables. Using MJ to cope with negative emotions in emerging adults is associated with MJ-related problems and psychological distress. Assessment of MJ use motivation may be clinically important among emerging adults. PMID:25915689

Musculotendinous Stiffness of Triceps Surae, Maximal Rate of Force Development, and Vertical Jump Performance

PubMed Central

Driss, Tarak; Rouis, Majdi; Jaafar, Hamdi; Vandewalle, Henry

2015-01-01

The relationships between ankle plantar flexor musculotendinous stiffness (MTS) and performance in a countermovement vertical jump (CMJ) and maximal rate of torque development (MRTD) were studied in 27 active men. MTS was studied by means of quick releases at 20 (S 0.2), 40 (S 0.4), 60 (S 0.6), and 80% (S 0.8) of maximal voluntary torque (T MVC). CMJ was not correlated with strength indices but was positively correlated with MRTD/BM, S 0.4/BM. The slope α 2 and intercept β 2 of the torque-stiffness relationships from 40 to 80% T MVC were correlated negatively (α 2) and positively (β 2) with CMJ. The different stiffness indices were not correlated with MRTD. The prediction of CMJ was improved by the introduction of MRTD in multiple regressions between CMJ and stiffness. CMJ was also negatively correlated with indices of curvature of the torque-stiffness relationship. The subjects were subdivided in 3 groups in function of CMJ (groups H, M, and L for high, medium, and low performers, resp.). There was a downward curvature of the torque-stiffness relationship at high torques in group H or M and the torque-stiffness regression was linear in group L only. These results suggested that torque-stiffness relationships with a plateau at high torques are more frequent in the best jumpers. PMID:25710026

Impaired Social and Role Function in Ultra-High Risk for Psychosis and First-Episode Schizophrenia: Its Relations with Negative Symptoms.

PubMed

Lee, So Jung; Kim, Kyung Ran; Lee, Su Young; An, Suk Kyoon

2017-03-01

Psychosocial dysfunction was a nettlesome of schizophrenia even in their prodromal phase as well as first episode and its relations with psychopathology were not determined. The aim of the present study was to examine whether the social and role function impairment was found in ultra-high risk for psychosis (UHR) individuals as well as first-episode schizophrenia patients and to explore its relations with psychopathology. Thirty-seven normal controls, 63 UHR participants and 28 young, first-episode schizophrenia patients were recruited. Psychosocial functioning was examined by using Global function: Social and Role scale. Psychopathologies of positive, negative and depressive symptom were also measured. Social and role functioning in UHR were compromised at the equivalent level of those of first-episode schizophrenia patients. Multiple linear regression analysis revealed that social and role dysfunction was associated with negative symptoms in each UHR and first-episode schizophrenia group. These findings suggest that the significant impairment of social and role function may be appeared before the active psychosis onset at the level of extent to those of first-episode schizophrenia patients. The psychosocial intervention strategy especially targeting the negative symptoms should be developed and provided to individuals from their prepsychotic stage of schizophrenia.

Impaired Social and Role Function in Ultra-High Risk for Psychosis and First-Episode Schizophrenia: Its Relations with Negative Symptoms.

PubMed

Lee, So Jung; Kim, Kyung Ran; Lee, Su Young; An, Suk Kyoon

2017-09-01

Psychosocial dysfunction was a nettlesome problem of schizophrenia even in their prodromal phase as well as in their first-episode. In addition, its relations with psychopathology were not determined. The aim of the present study was to examine whether the social and role function impairment was found in ultra-high risk for psychosis (UHR) individuals as well as first-episode schizophrenia patients and to explore its relations with psychopathology. Thirty-seven normal controls, 63 UHR participants and 28 young, first-episode schizophrenia patients were recruited. Psychosocial functioning was examined by using Global function: Social and Role scale. Psychopathologies of positive, negative and depressive symptom were also measured. Social and role functioning in UHR were compromised at the equivalent level of those of first-episode schizophrenia patients. Multiple linear regression analysis revealed that social and role dysfunction was associated with negative symptoms in each UHR and first-episode schizophrenia group. These findings suggest that the significant impairment of social and role function may be appeared before the active psychosis onset at the level of extent to those of first-episode schizophrenia patients. The psychosocial intervention strategy especially targeting the negative symptoms should be developed and provided to individuals from their prepsychotic stage of schizophrenia.

Positive media portrayals of obese persons: impact on attitudes and image preferences.

PubMed

Pearl, Rebecca L; Puhl, Rebecca M; Brownell, Kelly D

2012-11-01

The purpose of this research was to assess the impact of nonstereotypical, positive media portrayals of obese persons on biased attitudes, as well as propose a change in media practices that could reduce public weight bias and consequent negative health outcomes for those who experience weight stigma. Two online experiments were conducted in which participants viewed either a stigmatizing or a positive photograph of an obese model. In Experiment 1 (N = 146), participants viewed a photograph of either a Caucasian or African American obese woman; in Experiment 2 (N = 145), participants viewed either a Caucasian male or female obese model. Multiple linear regression models were used to analyze outcomes for social distance attitudes toward the obese models depicted in the images, in addition to other negative attitudes and image preferences. Participants who viewed the stigmatizing images endorsed stronger social distance attitudes and more negative attitudes toward obese persons than participants who viewed the positive images, and there was a stronger preference for the positive images than the stigmatizing images. These results were consistent regardless of the race or gender of the obese model pictured. The findings indicate that more positive media portrayals of obese individuals may help reduce weight stigma and its associated negative health outcomes.

Association of spiritual/religious coping with depressive symptoms in high- and low-risk pregnant women.

PubMed

Vitorino, Luciano M; Chiaradia, Raíssa; Low, Gail; Cruz, Jonas Preposi; Pargament, Kenneth I; Lucchetti, Alessandra L G; Lucchetti, Giancarlo

2018-02-01

To investigate the role of spiritual/religious coping (SRC) on depressive symptoms in high- and low-risk pregnant women. Spiritual/religious coping is associated with physical and mental health outcomes. However, only few studies investigated the role of these strategies during pregnancy and whether low- and high-risk pregnant women have different coping mechanisms. This study is a cross-sectional comparative study. This study included a total of 160 pregnant women, 80 with low-risk pregnancy and 80 with high-risk pregnancy. The Beck Depression Inventory, the brief SRC scale and a structured questionnaire on sociodemographic and obstetric aspects were used. General linear model regression analysis was used to identify the factors associated with positive and negative SRC strategies in both groups of pregnant women. Positive SRC use was high, whereas negative SRC use was low in both groups. Although we found no difference in SRC strategies between the two groups, negative SRC was associated with depression in women with high-risk pregnancy, but not in those with low-risk pregnancy. Furthermore, positive SRC was not associated with depressive symptoms in both groups. Results showed that only the negative SRC strategies of Brazilian women with high-risk pregnancies were associated with worsened mental health outcomes. Healthcare professionals, obstetricians and nurse midwives should focus on the use of negative SRC strategies in their pregnant patients. © 2017 John Wiley & Sons Ltd.

Understanding the effect of compositions on electronegativity, atomic radius and thermal stability of Mg-Ni-Y amorphous alloy

NASA Astrophysics Data System (ADS)

Deshmukh, A. A.; Kuthe, S. A.; Palikundwar, U. A.

2018-05-01

In the present paper, the consequences of variation in compositions on the electronegativity (ΔX), atomic radius difference (δ) and the thermal stability (ΔTx) of Mg-Ni-Y bulk metallic glasses (BMGs) are evaluated. In order to understand the effect of variation in compositions on ΔX, δ and ΔTx, regression analysis is performed on the experimentally available data. A linear correlation between both δ and ΔX with regression coefficient 0.93 is observed. Further, compositional variation is performed with δ and then it is correlated to the ΔTx by deriving subsequent equations. It is observed that concentration of Mg, Ni and Y are directly proportional to the δ with regression coefficients 0.93, 0.93 and 0.50 respectively. The positive slope of Ni and Y stated that ΔTx will increase if it has more contribution from both Ni and Y. On the other hand negative slope stated that composition of Mg should be selected in such a way that it will have more stability with Ni and Y. The results obtained from mathematical calculations are also tested by regression analysis of ΔTx with the compositions of individual elements in the alloy. These results conclude that there is a strong dependence of ΔTx of the alloy on the compositions of the constituting elements in the alloy.

[Associations between dormitory environment/other factors and sleep quality of medical students].

PubMed

Zheng, Bang; Wang, Kailu; Pan, Ziqi; Li, Man; Pan, Yuting; Liu, Ting; Xu, Dan; Lyu, Jun

2016-03-01

To investigate the sleep quality and related factors among medical students in China, understand the association between dormitory environment and sleep quality, and provide evidence and recommendations for sleep hygiene intervention. A total of 555 undergraduate students were selected from a medical school of an university in Beijing through stratified-cluster random-sampling to conduct a questionnaire survey by using Chinese version of Pittsburgh Sleep Quality Index (PSQI) and self-designed questionnaire. Analyses were performed by using multiple logistic regression model as well as multilevel linear regression model. The prevalence of sleep disorder was 29.1%(149/512), and 39.1%(200/512) of the students reported that the sleep quality was influenced by dormitory environment. PSQI score was negatively correlated with self-reported rating of dormitory environment (γs=-0.310, P<0.001). Logistic regression analysis showed the related factors of sleep disorder included grade, sleep regularity, self-rated health status, pressures of school work and employment, as well as dormitory environment. RESULTS of multilevel regression analysis also indicated that perception on dormitory environment (individual level) was associated with sleep quality with the dormitory level random effects under control (b=-0.619, P<0.001). The prevalence of sleep disorder was high in medical students, which was associated with multiple factors. Dormitory environment should be taken into consideration when the interventions are taken to improve the sleep quality of students.

A structural equation model of the relationship between insomnia, negative affect, and paranoid thinking

PubMed Central

Rowse, Georgina; Webb, Thomas L.

2017-01-01

Background A growing body of evidence points to relationships between insomnia, negative affect, and paranoid thinking. However, studies are needed to examine (i) whether negative affect mediates the relation between insomnia and paranoid thinking, (ii) whether different types of insomnia exert different effects on paranoia, and (iii) to compare the impact of objective and self-reported sleeping difficulties. Method Structural equation modelling was therefore used to test competing models of the relationships between self-reported insomnia, negative affect, and paranoia. n = 348 participants completed measures of insomnia, negative affect and paranoia. A subset of these participants (n = 91) went on to monitor their sleep objectively (using a portable sleep monitor made by Zeo) for seven consecutive nights. Associations between objectively recorded sleep, negative affect, and paranoia were explored using linear regression. Results The findings supported a fully mediated model where self-reported delayed sleep onset, but not self-reported problems with sleep maintenance or objective measures of sleep, was directly associated with negative affect that, in turn, was associated with paranoia. There was no evidence of a direct association between delayed sleep onset or sleep maintenance problems and paranoia. Conclusions Taken together, the findings point to an association between perceived (but not objective) difficulties initially falling asleep (but not maintaining sleep) and paranoid thinking; a relationship that is fully mediated by negative affect. Future research should seek to disentangle the causal relationships between sleep, negative affect, and paranoia (e.g., by examining the effect of an intervention using prospective designs that incorporate experience sampling). Indeed, interventions might profitably target (i) perceived sleep quality, (ii) sleep onset, and / or (iii) emotion regulation as a route to reducing negative affect and, thus, paranoid thinking. PMID:29049381

Hospital support services and the impacts of outsourcing on occupational health and safety.

PubMed

Siganporia, Pearl; Astrakianakis, George; Alamgir, Hasanat; Ostry, Aleck; Nicol, Anne-Marie; Koehoorn, Mieke

2016-10-01

Outsourcing labor is linked to negative impacts on occupational health and safety (OHS). In British Columbia, Canada, provincial health care service providers outsource support services such as cleaners and food service workers (CFSWs) to external contractors. This study investigates the impact of outsourcing on the occupational health safety of hospital CFSWs through a mixed methods approach. Worker's compensation data for hospital CFSWs were analyzed by negative binomial and multiple linear regressions supplemented by iterative thematic analysis of telephone interviews of the same job groups. Non-significant decreases in injury rates and days lost per injury were observed in outsourced CFSWs post outsourcing. Significant decreases (P < 0.05) were observed in average costs per injury for cleaners post outsourcing. Outsourced workers interviewed implied instances of underreporting workplace injuries. This mixed methods study describes the impact of outsourcing on OHS of healthcare workers in British Columbia. Results will be helpful for policy-makers and workplace regulators to assess program effectiveness for outsourced workers.

[Negative and positive predictive relationships between coping strategies and the three dimensions of burnout among Hungarian medical students].

PubMed

Ádám, Szilvia; Nistor, Anikó; Nistor, Katalin; Hazag, Anikó

2014-08-10

Effective management and prevention of widespread burnout among medical students in Hungary require thorough understanding of its relations to coping strategies, which lacks sufficient data. To explore the prevalence of burnout and its relations to coping strategies among medical students. Cross-sectional study with 292 participants. Burnout was assessed by the Maslach Burnout Inventory-Student Survey. Coping strategies were evaluated by the Folkman-Lazarus Ways of Coping Questionnaire and questions about health-maintenance behaviours. Associations between burnout and coping strategies were explored with linear regression analyses. The prevalence of high-level burnout was 25-56%. Both problem-focused coping and support-seeking were protective factors of exhaustion and cynicism, however, they predicted reduced personal accomplishment. Emotion-focused coping predicted exhaustion and cynicism and correlated negatively with reduced personal accomplishment. Health-maintenance behaviours were protective factors for exhaustion and predicted reduced personal accomplishment. Deployment of coping strategies that target the most prevalent burnout dimension may improve effective management of burnout.

Positive Aspects of Caregiving and Caregiver Burden: A Study of Caregivers of Patients With Dementia.

PubMed

Abdollahpour, Ibrahim; Nedjat, Saharnaz; Salimi, Yahya

2018-01-01

Now positive aspect of caregiving (PAC) is well-defined as caregiver gains, satisfaction, meaningful life, and enhanced family relationship. The adjusted association of PAC and caregiver burden is not well acknowledged. This study investigated the association of caregiver burden and PAC adjusting for potential confounders. This was a cross-sectional study that recruited 132 caregivers. A linear regression model with PAC was used to estimate the adjusted associations. The caregiver burden was negatively associated with PAC (mean difference in PAC per a 1-unit increase in caregiver burden = -0.12, 95% confidence interval: -0.18 to -0.056; P < .001). This association remained after adjustment for caregivers' age and marital status as well as patients' dependency level. The negative significant association of caregiver burden with PAC reinforces the need for interventional and/or educational programs aiming at decreasing the overall imposed burden. This can play an important role in improving caregivers' general health and quality of life.

The correlation of serum bilirubin levels with disease activity in patients with rheumatoid arthritis.

PubMed

Peng, You-Fan; Wang, Jun-Li; Pan, Guo-Gang

2017-06-01

We investigated the relationship between serum bilirubin and disease activity in patients with rheumatoid arthritis (RA). We included a total of 173 consecutive RA patients without steroid treatment and 346 healthy subjects; the disease activity score in 28 joints (DAS28) was used to assess disease activity in patients with RA. Serum bilirubin concentrations were significantly lower in RA patients than in controls. Serum bilirubin was found to be negatively correlated with C-reactive protein (CRP) concentration and erythrocyte sedimentation rate (ESR) (r=-0.165, P=0.030; r=-192, P=0.012) in patients with RA. There was a negative correlation between the serum bilirubin and DAS28 score (r=-0.331, P<0.001). Serum bilirubin was independently associated with the DAS28 score (b=-0.225, P=0.001) in the multiple linear regression analysis. Serum bilirubin concentrations are lower in patients with RA compared to controls and correlate with disease activity in patients with RA. Copyright © 2017. Published by Elsevier B.V.

Attitudes Toward Nursing Students With Disabilities: Promoting Social Inclusion.

PubMed

Shpigelman, Carmit-Noa; Zlotnick, Cheryl; Brand, Rachel

2016-08-01

Nursing education programs rarely refer to individuals with disabilities as potential nursing students; more often, the assumption is that they are patients. Thus, this study aimed to capture nursing students' perspectives of social inclusion through examination of their attitudes toward nursing student colleagues with disabilities. Paper-and-pencil structured surveys containing two validated scales were collected from Israeli nursing students (N = 270). Analyses included measuring associations using Pearson's correlation coefficient and general linear regression models. Nursing students held relatively negative attitudes toward colleagues with disabilities, and these negative attitudes were correlated to attitudes toward people with disabilities in general, even after adjusting for noted confounders. Nurse educators and nursing students should be aware of prejudicial attitudes with their respective communities toward nursing student colleagues with disabilities, and they should work toward a better understanding that cultural competence and awareness extends not only to patients but also to one's colleagues. [J Nurs Educ. 2016;55(8):441-449.]. Copyright 2016, SLACK Incorporated.

Report on Provider-Client Interaction From 68 Methadone Maintenance Clinics in China.

PubMed

Li, Li; Comulada, W Scott; Lin, Chunqing; Lan, Chiao-Wen; Cao, Xiaobin; Wu, Zunyou

2017-11-01

Provider-client interaction is an integral of clinical practice and central to the delivery of high-quality medical care. This article examines factors related to the provider-client interaction in the context of methadone maintenance treatment (MMT). Data were collected from 68 MMT clinics in China. In total, 418 service providers participated in the survey. Linear mixed effects regression models were performed to identify factors associated with provider-client interaction. It was observed that negative attitude toward drug users was associated with lower level of provider-client interaction and less time spent with each client. Other factors associated with lower level of interaction included being female, being younger, being a nurse, and fewer years in medical field. Higher provider-client interaction was associated with provider reported job satisfaction. The findings of this study call for a need to address provider negative attitudes that can impact provider-client interaction and the effectiveness of MMT. Future intervention efforts targeting MMT providers should be tailored by gender, provider type, and medical experiences.

Resident Reactions to Person-Centered Communication by Long-Term Care Staff.

PubMed

Savundranayagam, Marie Y; Sibalija, Jovana; Scotchmer, Emma

2016-09-01

Long-term care staff caregivers who are person centered incorporate the life history, preferences, and feelings of residents with dementia during care interactions. Communication is essential for person-centered care. However, little is known about residents' verbal reactions when staff use person-centered communication. Accordingly, this study investigated the impact of person-centered communication and missed opportunities for such communication by staff on resident reactions. Conversations (N = 46) between staff-resident dyads were audio-recorded during routine care tasks over 12 weeks. Staff utterances were coded for person-centered communication and missed opportunities. Resident utterances were coded for positive reactions, such as cooperation, and negative reactions, such as distress. Linear regression analyses revealed that the more staff used person-centered communication, the more likely that residents reacted positively. Additionally, the more missed opportunities in a conversation, the more likely that the residents reacted negatively. Conversation illustrations elaborate on the quantitative findings and implications for staff training are discussed. © The Author(s) 2016.

Hospital support services and the impacts of outsourcing on occupational health and safety

PubMed Central

Alamgir, Hasanat; Ostry, Aleck; Nicol, Anne-Marie; Koehoorn, Mieke

2016-01-01

Background Outsourcing labor is linked to negative impacts on occupational health and safety (OHS). In British Columbia, Canada, provincial health care service providers outsource support services such as cleaners and food service workers (CFSWs) to external contractors. Objectives This study investigates the impact of outsourcing on the occupational health safety of hospital CFSWs through a mixed methods approach. Methods Worker’s compensation data for hospital CFSWs were analyzed by negative binomial and multiple linear regressions supplemented by iterative thematic analysis of telephone interviews of the same job groups. Results Non-significant decreases in injury rates and days lost per injury were observed in outsourced CFSWs post outsourcing. Significant decreases (P < 0.05) were observed in average costs per injury for cleaners post outsourcing. Outsourced workers interviewed implied instances of underreporting workplace injuries. Conclusions This mixed methods study describes the impact of outsourcing on OHS of healthcare workers in British Columbia. Results will be helpful for policy-makers and workplace regulators to assess program effectiveness for outsourced workers. PMID:27696988

STIGMA, SOCIAL SUPPORT, AND TREATMENT ADHERENCE AMONG HIV-POSITIVE PATIENTS IN CHIANG MAI, THAILAND

PubMed Central

Li, Michael Jonathan; Murray, Jordan Keith; Suwanteerangkul, Jiraporn; Wiwatanadate, Phongtape

2016-01-01

Our study assessed the influence of HIV-related stigma on treatment adherence among people living with HIV in Chiang Mai, Thailand, and whether social support had a moderating effect on this relationship. We recruited 128 patients living with HIV from Sansai Hospital, a community hospital in Chiang Mai, Thailand, and collected data through structured interviews. All forms of HIV-related stigma considered in this study (personalized experience, disclosure, negative self-image, and public attitudes) were negatively correlated with adherence to anti-retroviral regimens. Multiple linear regression indicated that total HIV-related stigma was more predictive of treatment adherence than any individual stigma type, after adjusting for socio-demographic and health characteristics. Tests of interaction showed that social support did not appear to moderate the association between HIV stigma and treatment adherence. Our findings suggest that community and government efforts to improve public perceptions about people living with HIV might promote treatment adherence behaviors among HIV-positive patients. PMID:25299810

Stigma, social support, and treatment adherence among HIV-positive patients in Chiang Mai, Thailand.

PubMed

Li, Michael Jonathan; Murray, Jordan Keith; Suwanteerangkul, Jiraporn; Wiwatanadate, Phongtape

2014-10-01

Our study assessed the influence of HIV-related stigma on treatment adherence among people living with HIV in Chiang Mai, Thailand, and whether social support had a moderating effect on this relationship. We recruited 128 patients living with HIV from Sansai Hospital, a community hospital in Chiang Mai, Thailand, and collected data through structured interviews. All forms of HIV-related stigma considered in this study (personalized experience, disclosure, negative self-image, and public attitudes) were negatively correlated with adherence to anti-retroviral regimens. Multiple linear regression indicated that total HIV-related stigma was more predictive of treatment adherence than any individual stigma type, after adjusting for socio-demographic and health characteristics. Tests of interaction showed that social support did not appear to moderate the association between HIV stigma and treatment adherence. Our findings suggest that community and government efforts to improve public perceptions about people living with HIV might promote treatment adherence behaviors among HIV-positive patients.

Relationship between self-focused attention, mindfulness and distress in individuals with auditory verbal hallucinations.

PubMed

Úbeda-Gómez, J; León-Palacios, M G; Escudero-Pérez, S; Barros-Albarrán, M D; López-Jiménez, A M; Perona-Garcelán, S

2015-01-01

The purpose of this study was to investigate the relationships among self-focused attention, mindfulness and distress caused by the voices in psychiatric patients. Fifty-one individuals with a psychiatric diagnosis participated in this study. The Psychotic Symptom Rating Scale (PSYRATS) emotional factor was applied to measure the distress caused by the voices, the Self-Absorption Scale (SAS) was given for measuring the levels of self-focused attention, and the Mindful Attention Awareness Scale (MAAS) was used to measure mindfulness. The results showed that distress caused by the voices correlated positively with self-focused attention (private and public) and negatively with mindfulness. A negative correlation was also found between mindfulness and self-focused attention (private and public). Finally, multiple linear regression analysis showed that public self-focus was the only factor predicting distress caused by the voices. Intervention directed at diminishing public self-focused attention and increasing mindfulness could improve distress caused by the voices.

An Exploration of the Associations Among Multiple Aspects of Religiousness, Body Image, Eating Pathology, and Appearance Investment.

PubMed

Goulet, Carol; Henrie, James; Szymanski, Lynda

2017-04-01

The purpose of this study was to investigate the influence of positive and negative aspects of religiousness on eating pathology, body satisfaction, and appearance investment beyond previously established variables (age, BMI, exercise frequency, weight stability, and self-esteem). Data collected from 168 adult females at a Catholic-affiliated university were analyzed using hierarchical linear regressions. As expected, some religiousness variables (spirituality and seeing one's body as having sacred qualities) were associated with eating pathology, body satisfaction, and appearance investment in potentially beneficial ways, and others (negative interaction with one's religious community) were associated in potentially harmful ways. Interestingly, greater religious meaning, or the importance of religion in one's life, was associated with greater eating pathology, and some variables (religious coping, participation in and support from one's religious community) expected to be associated with greater body satisfaction were unrelated. Results are discussed in terms of mechanisms through which the aspects of religiousness may influence body satisfaction, appearance investment, and eating pathology.

Spatial Disparities in the Distribution of Parks and Green Spaces in the USA

PubMed Central

Wen, Ming; Zhang, Xingyou; Harris, Carmen D.; Holt, James B.; Croft, Janet B.

2013-01-01

Background Little national evidence is available on spatial disparities in distributions of parks and green spaces in the USA. Purpose This study examines ecological associations of spatial access to parks and green spaces with percentages of black, Hispanic, and low-income residents across the urban–rural continuum in the conterminous USA. Methods Census tract-level park and green space data were linked with data from the 2010 U.S. Census and 2006–2010 American Community Surveys. Linear mixed regression models were performed to examine these associations. Results Poverty levels were negatively associated with distances to parks and percentages of green spaces in urban/suburban areas while positively associated in rural areas. Percentages of blacks and Hispanics were in general negatively linked to distances to parks and green space coverage along the urban–rural spectrum. Conclusions Place-based race–ethnicity and poverty are important correlates of spatial access to parks and green spaces, but the associations vary across the urbanization levels. PMID:23334758

Spatial disparities in the distribution of parks and green spaces in the USA.

PubMed

Wen, Ming; Zhang, Xingyou; Harris, Carmen D; Holt, James B; Croft, Janet B

2013-02-01

Little national evidence is available on spatial disparities in distributions of parks and green spaces in the USA. This study examines ecological associations of spatial access to parks and green spaces with percentages of black, Hispanic, and low-income residents across the urban-rural continuum in the conterminous USA. Census tract-level park and green space data were linked with data from the 2010 U.S. Census and 2006-2010 American Community Surveys. Linear mixed regression models were performed to examine these associations. Poverty levels were negatively associated with distances to parks and percentages of green spaces in urban/suburban areas while positively associated in rural areas. Percentages of blacks and Hispanics were in general negatively linked to distances to parks and green space coverage along the urban-rural spectrum. Place-based race-ethnicity and poverty are important correlates of spatial access to parks and green spaces, but the associations vary across the urbanization levels.

Health service staff's attitudes towards patients with mental illness.

PubMed

Arvaniti, Aikaterini; Samakouri, Maria; Kalamara, Eleni; Bochtsou, Valentini; Bikos, Constantinos; Livaditis, Miltos

2009-08-01

Stereotypes and prejudices against patients with mental illness are widespread in many societies. The aim of the present study is to investigate such attitudes among the staff and medical students, including employees and trainees, in a general university hospital. Six hundred individuals (361 employees, 231 students) completed the following questionnaires: Level of Contact Report (LCR), Authoritarianism Scale (AS), and Opinion about Mental Illness (OMI), a scale yielding five factors (social discrimination, social restriction, social care, social integration, and aetiology). Multivariate linear regression models were applied in order to search for the simultaneous effect of many variables on the scores of OMI factors. An important part of the sample held negative attitudes especially concerning social discrimination and restriction of the patients. Women, older and less educated staff, nursing staff, and people scoring higher on authoritarianism were more prejudiced. Higher scores on familiarity were associated with less negative attitudes. The results indicate the need to develop sensitisation and training programs considering mental health topics among health service employees.

Relational conflict and outcomes from an online divorce education program.

PubMed

Cronin, Sarah; Becher, Emily H; McCann, Ellie; McGuire, Jenifer; Powell, Sharon

2017-06-01

The impact of conflict on co-parenting outcomes of divorce education programs is not widely explored in the literature despite the prevalence of conflict in divorce. This study used outcome data from a sample of participants (N=272) who took the online Parents Forever™ course between 2012 and 2014. Participants were asked questions about positive and negative co-parenting behaviors as well their levels of conflict before and after the divorce or separation. There was on average a slight increase in conflict from post to follow-up (M=-0.397, SD=1.54). Simple linear regression analyses indicated that change in conflict explained a significant proportion of the variance in positive co-parenting scores, R 2 =0.07, F(1, 270)=19.98, p<0.001 and negative co-parenting scores, R 2 =0.08, F(1, 270)=23.78, p<0.001. Results suggest that conflict significantly impacts co-parenting behaviors targeted in the Parents Forever ™ course. Copyright © 2017 Elsevier Ltd. All rights reserved.

Skin problems of the stump in lower-limb amputees: 2. Influence on functioning in daily life.

PubMed

Meulenbelt, Henk E J; Geertzen, Jan H B; Jonkman, Marcel F; Dijkstra, Pieter U

2011-03-01

The aim of this study was to analyse the influence of stump skin problems on functioning in daily life in lower-limb amputees. A cross-sectional study was performed by means of a questionnaire containing 9 questions assessing functioning in daily life. Question scores were added to give a total score (range 0 (no influence) to 27 (maximum negative influence)). Two thousand and thirty-nine people were invited to participate, with 805 participants completing a questionnaire. Of these, 507 reported one or more skin problems. Skin problems had a negative influence on ability to perform household tasks, prosthesis use, social functioning, and participation in sports. The mean total score was 5.5 ± 4.1. This correlated significantly with the number of skin complaints (r = 0.483; p = 0.01). In linear regression analyses, gender (β = -0.15) and number of skin problems (β = 0.25) accounted for 23% of the total score. This study confirms the influence of skin problems on functioning in daily life.

Stigma towards mental illness among medical and nursing students in Singapore: a cross-sectional study

PubMed Central

Chang, Sherilyn; Ong, Hui Lin; Seow, Esmond; Chua, Boon Yiang; Abdin, Edimansyah; Samari, Ellaisha; Chong, Siow Ann; Subramaniam, Mythily

2017-01-01

Objectives To assess stigma towards people with mental illness among Singapore medical and nursing students using the Opening Minds Stigma Scale for Health Care Providers (OMS-HC), and to examine the relationship of students’ stigmatising attitudes with sociodemographic and education factors. Design and setting Cross-sectional study conducted in Singapore Participants The study was conducted among 1002 healthcare (502 medical and 500 nursing) students during April to September 2016. Students had to be Singapore citizens or permanent residents and enrolled in public educational institutions to be included in the study. The mean (SD) age of the participants was 21.3 (3.3) years, with the majority being females (71.1%). 75.2% of the participants were Chinese, 14.1% were Malays, and 10.7% were either Indians or of other ethnicity. Methods Factor analysis was conducted to validate the OMS-HC scale in the study sample and to examine its factor structure. Descriptive statistics and multivariate linear regression were used to examine sociodemographic and education correlates. Results Factor analysis revealed a three-factor structure with 14 items. The factors were labelled as attitudes towards help-seeking and people with mental illness, social distance and disclosure. Multivariable linear regression analysis showed that medical students were found to be associated with lower total OMS-HC scores (P<0.05), less negative attitudes (P<0.001) and greater disclosure (P<0.05) than nursing students. Students who had a monthly household income of below S$4000 had more unfavourable attitudes than those with an income of SGD$10 000 and above (P<0.05). Having attended clinical placement was associated with more negative attitudes (P<0.05) among the students. Conclusion Healthcare students generally possessed positive attitudes towards help-seeking and persons with mental illness, though they preferred not to disclose their own mental health condition. Academic curriculum may need to enhance the component of mental health training, particularly on reducing stigma in certain groups of students. PMID:29208617

[Application of transcription mediated amplification and real-time reverse transcription polymerase chain reaction in detection of human immunodeficiency virus RNA].

PubMed

Wu, Daxian; Tao, Shuhui; Liu, Shuiping; Zhou, Jiebin; Tan, Deming; Hou, Zhouhua

2017-07-28

To observe the sensitivity of transcription mediated amplification (TMA), and to compare its performance with real-time reverse transcription polymerase chain reaction (real-time RT-PCR) in detecting human immunodeficiency virus RNA (HIV RNA).
 Methods: TMA system was established with TaqMan probes, specific primers, moloney murine leukemia virus (MMLV) reverse transcriptase, T7 RNA polymerase, and reaction substrates. The sensitivity of TMA was evaluated by amplifying a group of 10-fold diluted HIV RNA standards which were transcribed in vitro. A total of 60 plasma of HIV infected patients were measured by TMA and Cobas Amplicor HIV-1 Monitor test to observe the positive rate. The correlation and concordance of the above two technologies were investigated by linear regression and Bland-Altman analysis.
 Results: TMA system was established successfully and HIV RNA transcribed standards at concentration of equal or more than 10 copies/mL could be detected by TMA technology. Among 60 samples of plasma from HIV infected patients, 46 were positively detected and 12 were negatively amplified by both TMA and Cobas reagents; 2 samples were positively tested by Cobas reagent but negatively tested by TMA system. The concordance rate of the two methods was 97.1% and the difference of positive detection rate between the two methods was not statistically significant (P>0.05). Linear regression was used for 46 samples which were positively detected by both TMA and Cobas reagents and showed an excellent correlation between the two reagents (r=0.997, P<0.001). Bland-Altma analysis revealed that the mean different value of HIV RNA levels for denary logarithm was 0.02. Forty-four samples were included in 95% of credibility interval of concordance.
 Conclusion: TMA system has the potential of high sensitivity. TMA and real-time RT-PCR keep an excellent correlation and consistency in detecting HIV RNA.

Environmental risks for nontuberculous mycobacteria. Individual exposures and climatic factors in the cystic fibrosis population.

PubMed

Prevots, D Rebecca; Adjemian, Jennifer; Fernandez, Aisling G; Knowles, Michael R; Olivier, Kenneth N

2014-09-01

Persons with cystic fibrosis are at high risk of pulmonary nontuberculous mycobacterial infection, with a national prevalence estimated at 13%. The risk of nontuberculous mycobacteria associated with specific environmental exposures, and the correlation with climatic conditions in this population has not been described. To describe the association of pulmonary nontuberculous mycobacteria with individual exposures to water and soil aerosols, and the population associations of these infections with climatic factors. We conducted a nested case-control study within a cohort study of pulmonary nontuberculous mycobacteria prevalence at 21 geographically diverse national cystic fibrosis centers. Incident nontuberculous mycobacterial infection cases (at least one prior negative culture followed by one positive culture) were age- and sex-matched to culture-negative controls. Exposures to water and soil were assessed by administering a standardized questionnaire. Cohort prevalence at each of the 21 centers was correlated with climatic conditions in the same area through linear regression modeling. Overall, 48 cases and 85 control subjects were enrolled. Indoor swimming was associated with incident infection (adjusted odds ratio, 5.9, 95% confidence interval, 1.3-26.1), although only nine cases (19%) and five control subjects (6%) reported indoor swimming in the 4 months prior to infection. Exposure to showering and municipal water supply was common among both cases and control subjects: 77% of cases and 76% of control subjects reported showering at least daily. In linear regression, average annual atmospheric water vapor content was significantly predictive of center prevalence (P = 0.0019), with R(2) = 0.40. Atmospheric conditions explain more of the variation in disease prevalence than individual behaviors. The risk of specific exposures may vary by geographic region due to differences in conditions favoring mycobacterial growth and survival. However, because exposure to these organisms is ubiquitous and behaviors are similar among persons with and without pulmonary nontuberculous mycobacteria, genetic susceptibility beyond cystic fibrosis is likely to be important for disease development. Common individual risk factors in high-risk populations remain to be identified.

Impact of sleep quality on the quality of life of patients with Parkinson's disease: a questionnaire based study.

PubMed

Pandey, Shweta; Bajaj, Bhupender Kumar; Wadhwa, Ankur; Anand, Kuljeet Singh

2016-09-01

Poor sleep quality contributes to the inferior quality of life of patients with Parkinson's disease (PD) despite appropriate treatment of motor symptoms. The literature about the impact of sleep quality on quality of life of patients with PD is as yet sparse. One hundred patients of PD diagnosed as per UK Brain Bank criteria were assessed for severity and stage of PD using UPDRS and modified Hoehn &Yahr scales. The quality of sleep was assessed by Pittsburgh Sleep Quality Index and excessive daytime somnolence (EDS) was evaluated using Epworth Sleepiness Scale. Parkinson's Disease Questionnaire -39 (PDQ-39) was used to determine quality of life of the patients. Comorbid depression and anxiety were assessed using Inventory of Depressive Symptoms-Self Rated and Hamilton Anxiety Rating Scale. Pearson's correlation and multiple linear regressions were used to analyze relation of sleep quality with quality of life of patients. Fifty patients had poor sleep quality. EDS was present in only 9 patients. Co-morbid depression and anxiety were present in 52 and 34 patients respectively. While the motor severity assessed by UPDRS-III was observed to adversely affect quality of life, it did not negatively impact quality of sleep. Higher score on UPDRS-total and UPDRS IV suggesting advanced disease correlated with poor sleep quality. Depression and anxiety were significantly more frequent in patients with poor sleep quality (p<0.01). Patients with poor sleep quality had worse quality of life (r=0.338, p<0.05). Depression and anxiety were also observed to have significant negative impact on quality of life of PD patients (p<0.01). Poor sleep quality was not found to be an independent predictor of quality of life using multiple linear regression analysis. Poor sleep quality along with comorbid depression, anxiety and advanced stage of disease is associated with poor quality of life. Copyright © 2016 Elsevier B.V. All rights reserved.

What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data.

PubMed

Lasserson, Daniel S; Scherpbier de Haan, Nynke; de Grauw, Wim; van der Wel, Mark; Wetzels, Jack F; O'Callaghan, Christopher A

2016-06-10

To determine the relationship between renal function and visit-to-visit blood pressure (BP) variability in a cohort of primary care patients. Retrospective cohort study from routinely collected healthcare data. Primary care in Nijmegen, the Netherlands, from 2007 to 2012. 19 175 patients who had a measure of renal function, and 7 separate visits with BP readings in the primary care record. Visit-to-visit variability in systolic BP, calculated from the first 7 office measurements, including SD, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyse the influence of estimated glomerular filtration rate (eGFR) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. In the patient cohort, 57% were women, mean (SD) age was 65.5 (12.3) years, mean (SD) eGFR was 75.6 (18.0) mL/min/1.73m(2) and SD systolic BP 148.3 (21.4) mm Hg. All BP variability measures were negatively correlated with eGFR and positively correlated with age. However, multiple linear regressions demonstrated consistent, small magnitude negative relationships between eGFR and all measures of BP variability adjusting for confounding variables. Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population, and provides a mechanism whereby chronic kidney disease may raise the risk of cardiovascular events. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Empirical prediction and validation of antibacterial inhibitory effects of various plant essential oils on common pathogenic bacteria.

PubMed

Akdemir Evrendilek, Gulsun

2015-06-02

In this study, fractional compound composition, antioxidant capacity, and phenolic substance content of 14 plant essential oils-anise (Pimpinella anisum), bay leaves (Laurus nobilis), cinnamon bark (Cinnamomum verum), clove (Eugenia caryophyllata), fennel (Foeniculum vulgare), hop (Humulus lupulus), Istanbul oregano (Origanum vulgare subsp. hirtum), Izmir oregano (Origanum onites), mint (Mentha piperita), myrtus (Myrtus communis), orange peel (Citrus sinensis), sage (Salvia officinalis), thyme (Thymbra spicata), and Turkish oregano (Origanum minutiflorum)--were related to inhibition of 10 bacteria through multiple linear or non-linear (M(N)LR) models-four Gram-positive bacteria of Listeria innocua, coagulase-negative staphylococci, Staphylococcus aureus, and Bacillus subtilis, and six Gram-negative bacteria of Yersinia enterocolitica, Salmonella Enteritidis, Salmonella Typhimurium, Proteus mirabilis, Escherichia coli O157:H7, and Klebsiella oxytoca. A total of 65 compounds with different antioxidant capacity, phenolic substance content and antibacterial properties were detected with 14 plant essential oils. The best-fit M(N)LR models indicated that relative to anise essential oil, the essential oils of oreganos, cinnamon, and thyme had consistently high inhibitory effects, while orange peel essential oil had consistently a low inhibitory effect. Regression analysis indicated that beta-bisabolene (Turkish and Istanbul oreganos), and terpinolene (thyme) were found to be the most inhibitory compounds regardless of the bacteria type tested. Copyright © 2015 Elsevier B.V. All rights reserved.

Psychosocial issues on-orbit: results from two space station programs

NASA Astrophysics Data System (ADS)

Kanas, N. A.; Salnitskiy, V. P.; Ritsher, J. B.; Gushin, V. I.; Weiss, D. S.; Saylor, S. A.; Marmar, C. R.

PURPOSE Psychosocial issues affecting people working in isolated and confined environments such as spacecraft can jeopardize mental health and mission safety Our team has completed two large NASA-funded studies involving missions to the Mir and International Space Stations where crewmembers were on-orbit for four to seven months Combining these two datasets allows us to generalize across these two settings and maximize statistical power in testing our hypotheses This paper presents results from three sets of hypotheses concerning possible changes in mood and social climate over time displacement of negative emotions to outside monitoring personnel and the task and support roles of the leader METHODS The combined sample of 216 participants included 13 American astronauts 17 Russian cosmonauts and 150 U S and 36 Russian mission control personnel Subjects completed a weekly questionnaire that included items from the Profile of Mood States the Group Environment Scale and the Work Environment Scale producing 20 subscale scores The analytic strategy included piecewise linear regression and general linear modeling and it accounted for the effects of multiple observations per person and multiple analyses RESULTS There was little evidence to suggest that universal changes in levels of mood and group climate occurred among astronauts and cosmonauts over time Although a few individuals experienced decrements in the second half of the mission the majority did not However there was evidence that subjects displaced negative emotions to outside

Structure-function relationships using spectral-domain optical coherence tomography: comparison with scanning laser polarimetry.

PubMed

Aptel, Florent; Sayous, Romain; Fortoul, Vincent; Beccat, Sylvain; Denis, Philippe

2010-12-01

To evaluate and compare the regional relationships between visual field sensitivity and retinal nerve fiber layer (RNFL) thickness as measured by spectral-domain optical coherence tomography (OCT) and scanning laser polarimetry. Prospective cross-sectional study. One hundred and twenty eyes of 120 patients (40 with healthy eyes, 40 with suspected glaucoma, and 40 with glaucoma) were tested on Cirrus-OCT, GDx VCC, and standard automated perimetry. Raw data on RNFL thickness were extracted for 256 peripapillary sectors of 1.40625 degrees each for the OCT measurement ellipse and 64 peripapillary sectors of 5.625 degrees each for the GDx VCC measurement ellipse. Correlations between peripapillary RNFL thickness in 6 sectors and visual field sensitivity in the 6 corresponding areas were evaluated using linear and logarithmic regression analysis. Receiver operating curve areas were calculated for each instrument. With spectral-domain OCT, the correlations (r(2)) between RNFL thickness and visual field sensitivity ranged from 0.082 (nasal RNFL and corresponding visual field area, linear regression) to 0.726 (supratemporal RNFL and corresponding visual field area, logarithmic regression). By comparison, with GDx-VCC, the correlations ranged from 0.062 (temporal RNFL and corresponding visual field area, linear regression) to 0.362 (supratemporal RNFL and corresponding visual field area, logarithmic regression). In pairwise comparisons, these structure-function correlations were generally stronger with spectral-domain OCT than with GDx VCC and with logarithmic regression than with linear regression. The largest areas under the receiver operating curve were seen for OCT superior thickness (0.963 ± 0.022; P < .001) in eyes with glaucoma and for OCT average thickness (0.888 ± 0.072; P < .001) in eyes with suspected glaucoma. The structure-function relationship was significantly stronger with spectral-domain OCT than with scanning laser polarimetry, and was better expressed logarithmically than linearly. Measurements with these 2 instruments should not be considered to be interchangeable. Copyright © 2010 Elsevier Inc. All rights reserved.

A Simulation-Based Comparison of Several Stochastic Linear Regression Methods in the Presence of Outliers.

ERIC Educational Resources Information Center

Rule, David L.

Several regression methods were examined within the framework of weighted structural regression (WSR), comparing their regression weight stability and score estimation accuracy in the presence of outlier contamination. The methods compared are: (1) ordinary least squares; (2) WSR ridge regression; (3) minimum risk regression; (4) minimum risk 2;…

Unit Cohesion and the Surface Navy: Does Cohesion Affect Performance

DTIC Science & Technology

1989-12-01

v. 68, 1968. Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. Rand Corporation R-2607...Neter, J., Wasserman, W., and Kutner, M. H., Applied Linear Regression Models, 2d ed., Boston, MA: Irwin, 1989. SAS User’s Guide: Basics, Version 5 ed

Comparison of Selection Procedures and Validation of Criterion Used in Selection of Significant Control Variates of a Simulation Model

DTIC Science & Technology

1990-03-01

and M.H. Knuter. Applied Linear Regression Models. Homewood IL: Richard D. Erwin Inc., 1983. Pritsker, A. Alan B. Introduction to Simulation and SLAM...Control Variates in Simulation," European Journal of Operational Research, 42: (1989). Neter, J., W. Wasserman, and M.H. Xnuter. Applied Linear Regression Models

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

ERIC Educational Resources Information Center

Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course

ERIC Educational Resources Information Center

Enders, Felicity B.; Jenkins, Sarah; Hoverman, Verna

2010-01-01

Biostatistics is traditionally a difficult subject for students to learn. While the mathematical aspects are challenging, it can also be demanding for students to learn the exact language to use to correctly interpret statistical results. In particular, correctly interpreting the parameters from linear regression is both a vital tool and a…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

Using Simple Linear Regression to Assess the Success of the Montreal Protocol in Reducing Atmospheric Chlorofluorocarbons

ERIC Educational Resources Information Center

Nelson, Dean

2009-01-01

Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can…

Latent profile analysis of regression-based norms demonstrates relationship of compounding MS symptom burden and negative work events.

PubMed

Frndak, Seth E; Smerbeck, Audrey M; Irwin, Lauren N; Drake, Allison S; Kordovski, Victoria M; Kunker, Katrina A; Khan, Anjum L; Benedict, Ralph H B

2016-10-01

We endeavored to clarify how distinct co-occurring symptoms relate to the presence of negative work events in employed multiple sclerosis (MS) patients. Latent profile analysis (LPA) was utilized to elucidate common disability patterns by isolating patient subpopulations. Samples of 272 employed MS patients and 209 healthy controls (HC) were administered neuroperformance tests of ambulation, hand dexterity, processing speed, and memory. Regression-based norms were created from the HC sample. LPA identified latent profiles using the regression-based z-scores. Finally, multinomial logistic regression tested for negative work event differences among the latent profiles. Four profiles were identified via LPA: a common profile (55%) characterized by slightly below average performance in all domains, a broadly low-performing profile (18%), a poor motor abilities profile with average cognition (17%), and a generally high-functioning profile (9%). Multinomial regression analysis revealed that the uniformly low-performing profile demonstrated a higher likelihood of reported negative work events. Employed MS patients with co-occurring motor, memory and processing speed impairments were most likely to report a negative work event, classifying them as uniquely at risk for job loss.

Quantum State Tomography via Linear Regression Estimation

PubMed Central

Qi, Bo; Hou, Zhibo; Li, Li; Dong, Daoyi; Xiang, Guoyong; Guo, Guangcan

2013-01-01

A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the unknown parameters. An asymptotic mean squared error (MSE) upper bound for all possible states to be estimated is given analytically, which depends explicitly upon the involved measurement bases. This analytical MSE upper bound can guide one to choose optimal measurement sets. The computational complexity of LRE is O(d4) where d is the dimension of the quantum state. Numerical examples show that LRE is much faster than maximum-likelihood estimation for quantum state tomography. PMID:24336519

Social environmental factors and preteen health-related behaviors.

PubMed

Adelmann, Pamela K

2005-01-01

To examine associations among risk and protective factors with negative (substance use, delinquent behavior, sedentary recreation, empty calorie consumption, suicidal behavior) and positive behaviors (prosocial recreation, productive behavior, exercise, nutrition behavior, academic achievement, seatbelt use). Data were from sixth-grade public school students (n = 133,629) in 2001. Factor analysis produced five risks, five protectors, seven negative and six positive behaviors. Associations were tested among single and cumulative risks and protectors with behaviors (linear, logit regression) and combinations of high and low risks and protectors with behaviors (analysis of variance, Chi-square). Individual and cumulative risks were related to higher and protectors were related to lower negative behaviors. Protectors were associated with higher positive behaviors, with some exceptions. Risks and their sum were associated with lower academic achievement and seatbelt use, but were linked to higher, rather than lower productive behavior. Being bullied or victimized was correlated with higher levels of exercise, good nutrition, and prosocial recreation. The high risk/low protection combination had the highest negative behaviors and low risk/high protection the lowest, but for positive behaviors, high protectors with either high or low risks showed higher positive behaviors. These findings provide new information about how (a) risks and protectors are associated with negative behaviors besides substance use and delinquency, (b) cumulative protectors, as well as risks, are related to negative and positive behaviors, and (c) combinations of high and low risks and protectors are related to behaviors. The unusual findings for positive behaviors merit further exploration.

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Coagulation changes during lower body negative pressure and blood loss in humans.

PubMed

van Helmond, Noud; Johnson, Blair D; Curry, Timothy B; Cap, Andrew P; Convertino, Victor A; Joyner, Michael J

2015-11-01

We tested the hypothesis that markers of coagulation activation are greater during lower body negative pressure (LBNP) than those obtained during blood loss (BL). We assessed coagulation using both standard clinical tests and thrombelastography (TEG) in 12 men who performed a LBNP and BL protocol in a randomized order. LBNP consisted of 5-min stages at 0, -15, -30, and -45 mmHg of suction. BL included 5 min at baseline and following three stages of 333 ml of blood removal (up to 1,000 ml total). Arterial blood draws were performed at baseline and after the last stage of each protocol. We found that LBNP to -45 mmHg is a greater central hypovolemic stimulus versus BL; therefore, the coagulation markers were plotted against central venous pressure (CVP) to obtain stimulus-response relationships using the linear regression line slopes for both protocols. Paired t-tests were used to determine whether the slopes of these regression lines fell on similar trajectories for each protocol. Mean regression line slopes for coagulation markers versus CVP fell on similar trajectories during both protocols, except for TEG α° angle (-0.42 ± 0.96 during LBNP vs. -2.41 ± 1.13°/mmHg during BL; P < 0.05). During both LBNP and BL, coagulation was accelerated as evidenced by shortened R-times (LBNP, 9.9 ± 2.4 to 6.2 ± 1.1; BL, 8.7 ± 1.3 to 6.4 ± 0.4 min; both P < 0.05). Our results indicate that LBNP models the general changes in coagulation markers observed during BL. Copyright © 2015 the American Physiological Society.

Just showing up is not enough: Homework adherence and outcome in cognitive-behavioral therapy for cocaine dependence.

PubMed

Decker, Suzanne E; Kiluk, Brian D; Frankforter, Tami; Babuscio, Theresa; Nich, Charla; Carroll, Kathleen M

2016-10-01

Homework in cognitive-behavioral therapy (CBT) provides opportunities to practice skills. In prior studies, homework adherence was associated with improved outcome across a variety of disorders. Few studies have examined whether the relationship between homework adherence and outcome is maintained after treatment end or is independent of treatment attendance. This study combined data from 4 randomized clinical trials of CBT for cocaine dependence to examine relationships among homework adherence, participant variables, and cocaine use outcomes during treatment and at follow-up. The data set included only participants who attended at least 2 CBT sessions to allow for assignment and return of homework (N = 158). Participants returned slightly less than half (41.1%) of assigned homework. Longitudinal random effects regression suggested a greater reduction in cocaine use during treatment and through 12-month follow-up for participants who completed half or more of assigned homework (3-way interaction), F(2, 910.69) = 4.28, p = .01. In multiple linear regression, the percentage of homework adherence was associated with greater number of cocaine-negative urine toxicology screens during treatment, even when accounting for baseline cocaine use frequency and treatment attendance; at 3 months follow-up, multiple logistic regression indicated homework adherence was associated with cocaine-negative urine toxicology screen, controlling for baseline cocaine use and treatment attendance. These results extend findings from prior studies regarding the importance of homework adherence by demonstrating associations among homework and cocaine use outcomes during treatment and up to 12 months after, independent of treatment attendance and baseline cocaine use severity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The relationship between 25-hydroxyvitamin D levels and ambulatory arterial stiffness index in newly diagnosed and never-treated hypertensive patients.

PubMed

Malçok Gürel, Özgül; Bilgiç, Ayşe; Demirçelik, Bora; Özaydin, Meltem; Bozduman, Fadime; Aytürk, Zübeyde; Yilmaz, Hakki; Atar, Asli; Selçoki, Yusuf; Eryonucu, Beyhan

2016-02-01

Vitamin D insufficiency has been shown to be associated with cardiac dysfunctions, such as cardiac hypertrophy and hypertension, in animal studies. Arterial stiffness is a prognostic marker for cardiovascular disease. Previous studies have demonstrated that 25-hydroxyvitamin D [25(OH)D] levels were negatively correlated with arterial stiffness index. The aim of this study was to investigate the relationship between 25(OH)D levels and arterial stiffness, which is evaluated using an ambulatory arterial stiffness index (AASI), in patients who have untreated and newly diagnosed essential hypertension. A total of 123 consecutive patients with newly diagnosed and untreated essential hypertension were included. Patients were divided into two groups according to their 25(OH)D levels. Vitamin D insufficiency was defined by 25(OH)D levels less than 20 ng/ml. All patients were referred for ambulatory blood pressure monitoring. The regression slope of diastolic and systolic blood pressure was computed for each individual on the basis of ambulatory blood pressure readings. AASI was described as one minus the respective regression slope. The mean AASI was significantly higher in patients with 25(OH)D levels less than 20 as compared with patients with 25(OH)D levels greater than or equal to 20 (0.50±0.20 vs. 0.34±0.17, P<0.001). In Pearson's correlation analysis, AASI had a significantly strong negative correlation with vitamin D levels (r=-0.385, P<0.001). In multivariate linear regression analysis, vitamin D levels were found to be significantly and independently associated with AASI (β=-0.317, P=0.035). Arterial stiffness measured by AASI in newly diagnosed and untreated patients with essential hypertension were significantly related to vitamin D levels.

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Regression of non-linear coupling of noise in LIGO detectors

NASA Astrophysics Data System (ADS)

Da Silva Costa, C. F.; Billman, C.; Effler, A.; Klimenko, S.; Cheng, H.-P.

2018-03-01

In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener–Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Maternal adiposity negatively influences infant brain white matter development.

PubMed

Ou, Xiawei; Thakali, Keshari M; Shankar, Kartik; Andres, Aline; Badger, Thomas M

2015-05-01

To study potential effects of maternal body composition on central nervous system (CNS) development of newborn infants. Diffusion tensor imaging (DTI) was used to evaluate brain white matter development in 2-week-old, full-term, appropriate for gestational age (AGA) infants from uncomplicated pregnancies of normal-weight (BMI < 25 at conception) or obese ( BMI = 30 at conception) and otherwise healthy mothers. Tract-based spatial statistics (TBSS) analyses were used for voxel-wise group comparison of fractional anisotropy (FA), a sensitive measure of white matter integrity. DNA methylation analyses of umbilical cord tissue focused on genes known to be important in CNS development were also performed. Newborns from obese women had significantly lower FA values in multiple white matter regions than those born of normal-weight mothers. Global and regional FA values negatively correlated (P < 0.05) with maternal fat mass percentage. Linear regression analysis followed by gene ontology enrichment showed that methylation status of 68 CpG sites representing 57 genes with GO terms related to CNS development was significantly associated with maternal adiposity status. These results suggest a negative association between maternal adiposity and white matter development in offspring. © 2015 The Obesity Society.

Dysfunctional error-related processing in incarcerated youth with elevated psychopathic traits

PubMed Central

Maurer, J. Michael; Steele, Vaughn R.; Cope, Lora M.; Vincent, Gina M.; Stephen, Julia M.; Calhoun, Vince D.; Kiehl, Kent A.

2016-01-01

Adult psychopathic offenders show an increased propensity towards violence, impulsivity, and recidivism. A subsample of youth with elevated psychopathic traits represent a particularly severe subgroup characterized by extreme behavioral problems and comparable neurocognitive deficits as their adult counterparts, including perseveration deficits. Here, we investigate response-locked event-related potential (ERP) components (the error-related negativity [ERN/Ne] related to early error-monitoring processing and the error-related positivity [Pe] involved in later error-related processing) in a sample of incarcerated juvenile male offenders (n = 100) who performed a response inhibition Go/NoGo task. Psychopathic traits were assessed using the Hare Psychopathy Checklist: Youth Version (PCL:YV). The ERN/Ne and Pe were analyzed with classic windowed ERP components and principal component analysis (PCA). Using linear regression analyses, PCL:YV scores were unrelated to the ERN/Ne, but were negatively related to Pe mean amplitude. Specifically, the PCL:YV Facet 4 subscale reflecting antisocial traits emerged as a significant predictor of reduced amplitude of a subcomponent underlying the Pe identified with PCA. This is the first evidence to suggest a negative relationship between adolescent psychopathy scores and Pe mean amplitude. PMID:26930170

A Classroom Game on a Negative Externality Correcting Tax: Revenue Return, Regressivity, and the Double Dividend

ERIC Educational Resources Information Center

Duke, Joshua M.; Sassoon, David M.

2017-01-01

The concept of negative externality is central to the teaching of environmental economics, but corrective taxes are almost always regressive. How exactly might governments return externality-correcting tax revenue to overcome regressivity and not alter marginal incentives? In addition, there is a desire to achieve a double dividend in the use of…

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Influenza vaccine effectiveness estimates in the Dutch population from 2003 to 2014: The test-negative design case-control study with different control groups.

PubMed

van Doorn, Eva; Darvishian, Maryam; Dijkstra, Frederika; Donker, Gé A; Overduin, Pieter; Meijer, Adam; Hak, Eelko

2017-05-15

Information about influenza vaccine effectiveness (IVE) is important for vaccine strain selection and immunization policy decisions. The test-negative design (TND) case-control study is commonly used to obtain IVE estimates. However, the definition of the control patients may influence IVE estimates. We have conducted a TND study using the Dutch Sentinel Practices of NIVEL Primary Care Database which includes data from patients who consulted the General Practitioner (GP) for an episode of acute influenza-like illness (ILI) or acute respiratory infection (ARI) with known influenza vaccination status. Cases were patients tested positive for influenza virus. Controls were grouped into those who tested (1) negative for influenza virus (all influenza negative), (2) negative for influenza virus, but positive for respiratory syncytial virus, rhinovirus or enterovirus (non-influenza virus positive), and (3) negative for these four viruses (pan-negative). We estimated the IVE over all epidemic seasons from 2003/2004 through 2013/2014, pooled IVE for influenza vaccine partial/full matched and mismatched seasons and the individual seasons using generalized linear mixed-effect and multiple logistic regression models. The overall IVE adjusted for age, GP ILI/ARI diagnosis, chronic disease and respiratory allergy was 35% (95% CI: 15-48), 64% (95% CI: 49-75) and 21% (95% CI: -1 to 39) for all influenza negative, non-influenza virus positive and pan-negative controls, respectively. In both the main and subgroup analyses IVE estimates were the highest using non-influenza virus positive controls, likely due to limiting inclusion of controls without laboratory-confirmation of a virus causing the respiratory disease. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations. PMID:22444074

Ecosystem services response to urbanization in metropolitan areas: Thresholds identification.

PubMed

Peng, Jian; Tian, Lu; Liu, Yanxu; Zhao, Mingyue; Hu, Yi'na; Wu, Jiansheng

2017-12-31

Ecosystem service is the key comprehensive indicator for measuring the ecological effects of urbanization. Although various studies have found a causal relationship between urbanization and ecosystem services degradation, the linear or non-linear characteristics are still unclear, especially identifying the impact thresholds in this relationship. This study quantified four ecosystem services (i.e. soil conservation, carbon sequestration and oxygen production, water yield, and food production) and total ecosystem services (TES), and then identified multiple advantageous area of ecosystem services in the peri-urban area of Beijing City. Using piecewise linear regression, the response of TES to urbanization (i.e., population density, GDP density, and construction land proportion) and its thresholds were detected. The results showed that, the TES was high in the north and west and low in the southeast, and there were seven multiple advantageous areas (distributed in the new urban development zone and ecological conservation zone), one single advantageous area (distributed in the ecological conservation zone), and six disadvantageous areas (mainly distributed in the urban function extended zone). TES response to population and economic urbanization each had a threshold (229personkm -2 and 107.15millionyuankm -2 , respectively), above which TES decreased rapidly with intensifying urbanization. However, there was a negative linear relationship between land urbanization and TES, which indicated that the impact of land urbanization on ecosystem services was more direct and effective than that of population and economic urbanization. It was also found that the negative impact of urbanization on TES was highest in the urban function extended zone, followed in descending order by that in the new urban development zone and ecological conservation zone. According to the detected relationships between urbanization and TES, the economic and population urbanization should be strengthened accompanied by slowing or even reducing land urbanization, so as to achieve urban ecological sustainability with less ecosystem services degradation. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Negative Inferential Style, Emotional Clarity, and Life Stress: Integrating Vulnerabilities to Depression in Adolescence

PubMed Central

Stange, Jonathan P.; Alloy, Lauren B.; Flynn, Megan; Abramson, Lyn Y.

2012-01-01

Objective Negative inferential style and deficits in emotional clarity have been identified as vulnerability factors for depression in adolescence, particularly when individuals experience high levels of life stress. However, previous research has not integrated these characteristics when evaluating vulnerability to depression. Method In the present study, a racially-diverse community sample of 256 early adolescents (ages 12 and 13) completed a baseline visit and a follow-up visit nine months later. Inferential style, emotional clarity, and depressive symptoms were assessed at baseline, and intervening life events and depressive symptoms were assessed at follow-up. Results Hierarchical linear regressions indicated that there was a significant three-way interaction between adolescents’ weakest-link negative inferential style, emotional clarity, and intervening life stress predicting depressive symptoms at follow-up, controlling for initial depressive symptoms. Adolescents with low emotional clarity and high negative inferential styles experienced the greatest increases in depressive symptoms following life stress. Emotional clarity buffered against the impact of life stress on depressive symptoms among adolescents with negative inferential styles. Similarly, negative inferential styles exacerbated the impact of life stress on depressive symptoms among adolescents with low emotional clarity. Conclusions These results provide evidence of the utility of integrating inferential style and emotional clarity as constructs of vulnerability in combination with life stress in the identification of adolescents at risk for depression. They also suggest the enhancement of emotional clarity as a potential intervention technique to protect against the effects of negative inferential styles and life stress on depression in early adolescence. PMID:23215673

The Increase of Energy Consumption and Carbon Dioxide (CO2) Emission in Indonesia

NASA Astrophysics Data System (ADS)

Sasana, Hadi; Putri, Annisa Eka

2018-02-01

In the last decade, the increase of energy consumption that has multiplied carbondioxide emissions becomes world problems, especially in the developing countries undergoing industrialization to be developed ones like Indonesia. This aim of this study was to analyze the effect of fossil energy consumption, population growth, and consumption of renewable energy on carbon dioxide emission. The method used was multiple linear regression analysis with Ordinary Least Square approach using time series in the period of 1990 - 2014. The result showed that fossil energy consumption and population growth have a positive influence on carbon dioxide emission in Indonesia. Meanwhile, the consumption variable of renewable energy has a negative effect on the level of carbon dioxide emissions produced.

Sentiment analysis in twitter data using data analytic techniques for predictive modelling

NASA Astrophysics Data System (ADS)

Razia Sulthana, A.; Jaithunbi, A. K.; Sai Ramesh, L.

2018-04-01

Sentiment analysis refers to the task of natural language processing to determine whether a piece of text contains subjective information and the kind of subjective information it expresses. The subjective information represents the attitude behind the text: positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great concern. We have made data analysis with huge amount of tweets taken as big data and thereby classifying the polarity of words, sentences or entire documents. We use linear regression for modelling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. We conduct a series of experiments to test the performance of the system.

Measurement of Women’s Empowerment in Rural Bangladesh

PubMed Central

Mahmud, Simeen; Shah, Nirali M.; Becker, Stan

2013-01-01

SUMMARY Women’s empowerment is a dynamic process that has been quantified, measured and described in a variety of ways. We measure empowerment in a sample of 3500 rural women in 128 villages of Bangladesh with five indicators. A conceptual framework is presented, together with descriptive data on the indicators. Linear regressions to examine effects of covariates show that a woman’s exposure to television is a significant predictor of three of the five indicators. A woman’s years of schooling is significantly associated with one of two self-esteem indicators and with freedom of mobility. Household wealth has a significant and positive association with a woman’s resource control but a significant negative association with her total decision-making score. PMID:23637468

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Adding a Parameter Increases the Variance of an Estimated Regression Function

ERIC Educational Resources Information Center

Withers, Christopher S.; Nadarajah, Saralees

2011-01-01

The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression…

Using nonlinear quantile regression to estimate the self-thinning boundary curve

Treesearch

Quang V. Cao; Thomas J. Dean

2015-01-01

The relationship between tree size (quadratic mean diameter) and tree density (number of trees per unit area) has been a topic of research and discussion for many decades. Starting with Reineke in 1933, the maximum size-density relationship, on a log-log scale, has been assumed to be linear. Several techniques, including linear quantile regression, have been employed...

Simultaneous spectrophotometric determination of salbutamol and bromhexine in tablets.

PubMed

Habib, I H I; Hassouna, M E M; Zaki, G A

2005-03-01

Typical anti-mucolytic drugs called salbutamol hydrochloride and bromhexine sulfate encountered in tablets were determined simultaneously either by using linear regression at zero-crossing wavelengths of the first derivation of UV-spectra or by application of multiple linear partial least squares regression method. The results obtained by the two proposed mathematical methods were compared with those obtained by the HPLC technique.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Source Credibility and E-Cigarette Attitudes: Implications for Tobacco Communication.

PubMed

Case, Kathleen R; Lazard, Allison J; Mackert, Michael S; Perry, Cheryl L

2018-09-01

As there are many conflicting sources of e-cigarette information, research is needed to determine the impact of these sources on e-cigarette attitudes to inform future communication campaigns. Source credibility is important in shaping attitudes toward other health topics; however, no study has examined its role in influencing e-cigarette attitudes. Data from the 2015 Health Information National Trends Survey-FDA (HINTS-FDA) were utilized to assess differences in trust in different sources by e-cigarette user status and to investigate the associations between trust in sources and e-cigarette attitudes (n = 3,738). Differences in trust in sources were examined using weighted linear regression. Associations between trust in sources of e-cigarette health effects and attitudes toward e-cigarettes were assessed using weighted logistic regression. Overall, e-cigarette ever users reported significantly lower trust in governmental agencies as compared to never users. Trust in e-cigarette companies was negatively associated with perceived addictiveness of e-cigarettes (AOR = 0.76, 95% CI = 0.58, 1.00), while trust in doctors/pharmacists/healthcare providers was negatively associated with harm perceptions of e-cigarettes relative to conventional cigarettes (AOR = 0.72, 95% CI = 0.55, 0.95). Trust in tobacco companies and trust in e-cigarette companies were negatively associated with absolute perceived harm of e-cigarettes (AOR = 0.70, 95% CI = 0.51, 0.95; AOR = 0.60, 95% CI = 0.46, 0.79, respectively). Results from this study indicate that the associations between trust in sources of e-cigarette health effects and e-cigarette attitudes differ both by source and specific attitude assessed. Ultimately, future campaigns should incorporate messaging to discredit industry sources of information and utilize non-governmental sources to effectively influence e-cigarette attitudes.

Self-rated health, generalized trust, and the Affordable Care Act: A US panel study, 2006-2014.

PubMed

Mewes, Jan; Giordano, Giuseppe Nicola

2017-10-01

Previous research shows that generalized trust, the belief that most people can be trusted, is conducive to people's health. However, only recently have longitudinal studies suggested an additional reciprocal pathway from health back to trust. Drawing on a diverse body of literature that shows how egalitarian social policy contributes to the promotion of generalized trust, we hypothesize that this other 'reverse' pathway could be sensitive to health insurance context. Drawing on nationally representative US panel data from the General Social Survey, we examine whether the Affordable Care Act of 2010 could have had influence on the deteriorating impact of worsening self-rated health (SRH) on generalized trust. Firstly, using two-wave panel data (2008-2010, N = 1403) and employing random effects regression models, we show that a lack of health insurance coverage negatively determines generalized trust in the United States. However, this association is attenuated when additionally controlling for (perceived) income inequality. Secondly, utilizing data from two separate three-wave panel studies from the US General Social Survey (2006-10; N = 1652; 2010-2014; N = 1187), we employ fixed-effects linear regression analyses to control for unobserved heterogeneity from time-invariant factors. We demonstrate that worsening SRH was a stronger predictor for a decrease in generalized trust prior (2006-2010) to the implementation of the Affordable Care Act. Further, the negative effect of fair/poor SRH seen in the 2006-2010 data becomes attenuated in the 2010-2014 panel data. We thus find evidence for a substantial weakening of the previously established negative impact of decreasing SRH on generalized trust, coinciding with the most significant US healthcare reforms in decades. Social policy and healthcare policy implications are discussed. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Urinary excretion of uric acid is negatively associated with albuminuria in patients with chronic kidney disease: a cross-sectional study.

PubMed

Li, Fengqin; Guo, Hui; Zou, Jianan; Chen, Weijun; Lu, Yijun; Zhang, Xiaoli; Fu, Chensheng; Xiao, Jing; Ye, Zhibin

2018-04-24

Increasing evidence has shown that albuminuria is related to serum uric acid. Little is known about whether this association may be interrelated via renal handling of uric acid. Therefore, we aim to study urinary uric acid excretion and its association with albuminuria in patients with chronic kidney disease (CKD). A cross-sectional study of 200 Chinese CKD patients recruited from department of nephrology of Huadong hospital was conducted. Levels of 24 h urinary excretion of uric acid (24-h Uur), fractional excretion of uric acid (FEur) and uric acid clearance rate (Cur) according to gender, CKD stages, hypertension and albuminuria status were compared by a multivariate analysis. Pearson and Spearman correlation and multiple regression analyses were used to study the correlation of 24-h Uur, FEur and Cur with urinary albumin to creatinine ratio (UACR). The multivariate analysis showed that 24-h Uur and Cur were lower and FEur was higher in the hypertension group, stage 3-5 CKD and macro-albuminuria group (UACR> 30 mg/mmol) than those in the normotensive group, stage 1 CKD group and the normo-albuminuria group (UACR< 3 mg/mmol) (all P < 0.05). Moreover, males had higher 24-h Uur and lower FEur than females (both P < 0.05). Multiple linear regression analysis showed that UACR was negatively associated with 24-h Uur and Cur (P = 0.021, P = 0.007, respectively), but not with FEur (P = 0.759), after adjusting for multiple confounding factors. Our findings suggested that urinary excretion of uric acid is negatively associated with albuminuria in patients with CKD. This phenomenon may help to explain the association between albuminuria and serum uric acid.

Image interpolation via regularized local linear regression.

PubMed

Liu, Xianming; Zhao, Debin; Xiong, Ruiqin; Ma, Siwei; Gao, Wen; Sun, Huifang

2011-12-01

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l(2)-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation. © 2011 IEEE

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

PubMed Central

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

PubMed

Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Comparison of various error functions in predicting the optimum isotherm by linear and non-linear regression analysis for the sorption of basic red 9 by activated carbon.

PubMed

Kumar, K Vasanth; Porkodi, K; Rocha, F

2008-01-15

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of basic red 9 sorption by activated carbon. The r(2) was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions namely coefficient of determination (r(2)), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), the average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. Non-linear regression was found to be a better way to obtain the parameters involved in the isotherms and also the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r(2) was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K(2) was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm.

The role of social relationships among elderly community-dwelling and nursing-home residents: findings from a quality of life study.

PubMed

Scocco, Paolo; Nassuato, Mario

2017-07-01

In Western countries, older adults' needs are often managed through institutionalization. Based on the assumption that quality of life, particularly social relationships, may be perceived differently according to residential setting, the aims of this study were to compare World Health Organization Quality of Life brief version (WHOQOL-BREF) scores of elderly community-dwelling residents and nursing home residents. A sample of 207 older adults (135 community-dwelling residents, 72 nursing home residents) was evaluated with Mini-Mental State Examination (MMSE), WHOQOL-BREF, and Geriatric Depression Scale (GDS). Nursing home residents achieved lower WHOQOL-BREF scores on the physical health scale only (P = 0.002). In a linear regression model, physical score correlated negatively with GDS score (P = 0.0001) and Mini-Mental State Examination score (P = 0.04), but positively with male gender (P = 0.02) and community-dwelling residence (P = 0.001); psychological score correlated negatively with GDS score (P = 0.0001) and being married (P = 0.03), but positively with male gender (P = 0.009) and being unmarried (P = 0.03). The social relationships score correlated negatively with the GDS score (P = 0.0001) and male gender (P = 0.02), but positively with high education level (P = 0.04). The environment score negatively correlated with GDS score (P = 0.0001). In a logistic regression model, living in a nursing home correlated with female gender (P = 0.001), age (P = 0.0001), a lower physical score (P = 0.0001), and a higher social relationships score (P = 0.02). Depressive symptoms correlated with low scores in all WHOQOL-BREF domains. The variables that correlated with living conditions in a nursing home were older age, male gender, lower physical domain scores, and higher social relationship scores. Opportunities for socialization in nursing homes may thus improve perception of quality of life in this domain. © 2017 Japanese Psychogeriatric Society.

Socio-demographic, behavioural and cognitive correlates of work-related sitting time in German men and women.

PubMed

Wallmann-Sperlich, Birgit; Bucksch, Jens; Schneider, Sven; Froboese, Ingo

2014-12-11

Sitting time is ubiquitous for most adults in developed countries and is most prevalent in three domains: in the workplace, during transport and during leisure time. The correlates of prolonged sitting time in workplace settings are not well understood. Therefore, the aim of this study was to examine the gender-specific associations between the socio-demographic, behavioural and cognitive correlates of work-related sitting time. A cross-sectional sample of working German adults (n = 1515; 747 men; 43.5 ± 11.0 years) completed questionnaires regarding domain-specific sitting times and physical activity (PA) and answered statements concerning beliefs about sitting. To identify gender-specific correlates of work-related sitting time, we used a series of linear regressions. The overall median was 2 hours of work-related sitting time/day. Regression analyses showed for men (β = -.43) and for women (β = -.32) that work-related PA was negatively associated with work-related sitting time, but leisure-related PA was not a significant correlate. For women only, transport-related PA (β = -.07) was a negative correlate of work-related sitting time, suggesting increased sitting times during work with decreased PA in transport. Education and income levels were positively associated, and in women only, age (β = -.14) had a negative correlation with work-related sitting time. For both genders, TV-related sitting time was negatively associated with work-related sitting time. The only association with cognitive correlates was found in men for the belief 'Sitting for long periods does not matter to me' (β = .10) expressing a more positive attitude towards sitting with increasing sitting durations. The present findings show that in particular, higher educated men and women as well as young women are high-risk groups to target for reducing prolonged work-related sitting time. In addition, our findings propose considering increasing transport-related PA, especially in women, as well as promoting recreation-related PA in conjunction with efforts to reduce long work-related sitting times.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Natural bond orbital approach to the transmission of substituent effect through the fulvene and benzene ring systems.

PubMed

Oziminski, Wojciech P; Krygowski, Tadeusz M

2011-03-01

Electronic structure of 22 monosubstituted derivatives of benzene and exocyclically substituted fulvene with substituents: B(OH)(2), BH(2), CCH, CF(3), CH(3), CHCH(2), CHO, Cl, CMe(3), CN, COCH(3), CONH(2), COOH, F, NH(2), NMe(2), NO, NO(2), OCH(3), OH, SiH(3), SiMe(3) were studied theoretically by means of Natural Bond Orbital analysis. It is shown, that sum of π-electron population of carbon atoms of the fulvene and benzene rings, pEDA(F) and pEDA(B), respectively correlate well with Hammett substituent constants [Formula in text] and aromaticity index NICS. The substituent effect acting on pi-electron occupation at carbon atoms of the fulvene ring is significantly stronger than in the case of benzene. Electron occupations of ring carbon atoms (except C1) in fulvene plotted against each other give linear regressions with high correlation coefficients. The same is true for ortho- and para-carbon atoms in benzene. Positive slopes of the regressions indicate similar for fulvene and benzene kind of substituent effect - mostly resonance in nature. Only the regressions of occupation at the carbon atom in meta- position of benzene against ortho- and para-positions gives negative slopes and low correlation coefficients.

Validity and validation of expert (Q)SAR systems.

PubMed

Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

2005-08-01

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

[Phonological characteristics and rehabilitation training of abnormal velar in children with functional articulation disorders].

PubMed

Lina, Xu; Feng, Li; Yanyun, Zhang; Nan, Gao; Mingfang, Hu

2016-12-01

To explore the phonological characteristics and rehabilitation training of abnormal velar in patients with functional articulation disorders (FAD). Eighty-seven patients with FAD were observed of the phonological characteristics of velar. Seventy-two patients with abnormal velar accepted speech training. The correlation and simple linear regression analysis were carried out on abnormal velar articulation and age. The articulation disorder of /g/ mainly showed replacement by /d/, /b/ or omission. /k/ mainly showed replacement by /d/, /t/, /g/, /p/, /b/. /h/ mainly showed replacement by /g/, /f/, /p/, /b/ or omission. The common erroneous articulation forms of /g/, /k/, /h/ were fronting of tongue and replacement by bilabial consonants. When velar combined with vowels contained /a/ and /e/, the main error was fronting of tongue. When velar combined with vowels contained /u/, the errors trended to be replacement by bilabial consonants. After 3 to 10 times of speech training, the number of erroneous words decreased to (6.24±2.61) from (40.28±6.08) before the speech training was established, the difference was statistically significant (Z=-7.379, P=0.000). The number of erroneous words was negatively correlated with age (r=-0.691, P=0.000). The result of simple linear regression analysis showed that the determination coefficient was 0.472. The articulation disorder of velar mainly shows replacement, varies with the vowels. The targeted rehabilitation training hereby established is significantly effective. Age plays an important role in the outcome of velar.

Factors that determine self-reported immunosuppressant adherence in kidney transplant recipients: a correlational study.

PubMed

Weng, Li-Chueh; Yang, Ya-Chen; Huang, Hsiu-Li; Chiang, Yang-Jen; Tsai, Yu-Hsia

2017-01-01

To determine the factors related to immunosuppressant therapy adherence in kidney transplant recipients in Taiwan. Adherence to immunosuppressant treatment is critical after kidney transplantation. Thus, the factors associated with self-reported medication adherence in kidney transplant recipients warrant investigation. The study used a cross-sectional and correlation design. A convenience sample of 145 kidney transplant recipients was included. Structured questionnaires were used to collect data during 2012-2013. Multivariate linear regression was used to examine the factors related to immunosuppressant therapy adherence. Over half of the participants were female (54·5%), mean age was 45·5 years, and mean year after transplant was 7·4. The mean score for medication adherence was 29·73 (possible score range 7-35). The results of the multivariate linear regression analysis showed that gender (male), low income with a high school or college education, years after transplantation and concerns about medication taking were negatively associated with adherence. Medication self-efficacy was positively associated with adherence. Therapy-related factors, partnerships with healthcare professionals and having private healthcare insurance did not significantly relate to immunosuppressant therapy adherence. Kidney transplant recipients demonstrated a high level of adherence. Strategies to enhance patients' self-efficacy and alleviate concerns about medication may promote medication adherence. Male patients, those with a lower income and those with a higher education level, should be a focus of efforts to maintain adherence to the medication regimen. © 2016 John Wiley & Sons Ltd.

Peculiarities of stochastic regime of Arctic ice cover time evolution over 1987-2014 from microwave satellite sounding on the basis of NASA team 2 algorithm

NASA Astrophysics Data System (ADS)

Raev, M. D.; Sharkov, E. A.; Tikhonov, V. V.; Repina, I. A.; Komarova, N. Yu.

2015-12-01

The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08-F17 satellites; it is permanently supplemented with new data on the Earth's exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987-2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95-98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect.

Relationships between seminal plasma metals/metalloids and semen quality, sperm apoptosis and DNA integrity.

PubMed

Wang, Yi-Xin; Wang, Peng; Feng, Wei; Liu, Chong; Yang, Pan; Chen, Ying-Jun; Sun, Li; Sun, Yang; Yue, Jing; Gu, Long-Jie; Zeng, Qiang; Lu, Wen-Qing

2017-05-01

This study aimed to investigate the relationships between environmental exposure to metals/metalloids and semen quality, sperm apoptosis and DNA integrity using the metal/metalloids levels in seminal plasma as biomarkers. We determined 18 metals/metalloids in seminal plasma using an inductively coupled plasma-mass spectrometry among 746 men recruited from a reproductive medicine center. Associations of these metals/metalloids with semen quality (n = 746), sperm apoptosis (n = 331) and DNA integrity (n = 404) were evaluated using multivariate linear and logistic regression models. After accounting for multiple comparisons and confounders, seminal plasma arsenic (As) quartiles were negatively associated with progressive and total sperm motility using multivariable linear regression analysis, which were in accordance with the trends for increased odds ratios (ORs) for below-reference semen quality parameters in the logistic models. We also found inverse correlations between cadmium (Cd) quartiles and progressive and total sperm motility, whereas positive correlations between zinc (Zn) quartiles and sperm concentration, between copper (Cu) and As quartiles and the percentage of tail DNA, between As and selenium (Se) quartiles and tail extent and tail distributed moment, and between tin (Sn) categories and the percentage of necrotic spermatozoa (all P trend <0.05). These relationships remained after the simultaneous consideration of various elements. Our results indicate that environmental exposure to As, Cd, Cu, Se and Sn may impair male reproductive health, whereas Zn may be beneficial to sperm concentration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Attitudes toward abortion among students at the University of Cape Coast, Ghana.

PubMed

Rominski, Sarah D; Darteh, Eugene; Dickson, Kwamena Sekyi; Munro-Kramer, Michelle

2017-03-01

This study aimed to describe the attitudes toward abortion of Ghanaian University students and to determine factors which are associated with supporting a woman's right to an abortion. This cross-sectional survey was administered to residential students at the University of Cape Coast. Participants were posed a series of 26 statements to determine to what extent they were supportive of abortion as a woman's right. An exploratory factor analysis was used to create a scale with the pertinent factors that relate to abortion attitudes and a multivariable linear regression model explored the relationships among significant variables noted during exploratory factor analysis. 1038 students completed the survey and these students had a generally negative view of abortion. Two factors emerged: (1) the Abortion as a Right scale consisted of five questions (α = .755) and (2) the Moral Objection to Abortion scale consisted of three questions (α = .740). In linear regression, being older (β = 1.9), sexually experienced (β = 1.2), having a boyfriend/girlfriend (β = 1.4), and knowing someone who has terminated a pregnancy (β = 1.1) were significantly associated with a more liberal view of a right to an abortion. This work supports the idea that students who have personal exposure to an abortion experience hold more liberal views on abortion than those who have not had a similar exposure. Copyright © 2016 Elsevier B.V. All rights reserved.

Effects of Frequency Drift on the Quantification of Gamma-Aminobutyric Acid Using MEGA-PRESS

NASA Astrophysics Data System (ADS)

Tsai, Shang-Yueh; Fang, Chun-Hao; Wu, Thai-Yu; Lin, Yi-Ru

2016-04-01

The MEGA-PRESS method is the most common method used to measure γ-aminobutyric acid (GABA) in the brain at 3T. It has been shown that the underestimation of the GABA signal due to B0 drift up to 1.22 Hz/min can be reduced by post-frequency alignment. In this study, we show that the underestimation of GABA can still occur even with post frequency alignment when the B0 drift is up to 3.93 Hz/min. The underestimation can be reduced by applying a frequency shift threshold. A total of 23 subjects were scanned twice to assess the short-term reproducibility, and 14 of them were scanned again after 2-8 weeks to evaluate the long-term reproducibility. A linear regression analysis of the quantified GABA versus the frequency shift showed a negative correlation (P < 0.01). Underestimation of the GABA signal was found. When a frequency shift threshold of 0.125 ppm (15.5 Hz or 1.79 Hz/min) was applied, the linear regression showed no statistically significant difference (P > 0.05). Therefore, a frequency shift threshold at 0.125 ppm (15.5 Hz) can be used to reduce underestimation during GABA quantification. For data with a B0 drift up to 3.93 Hz/min, the coefficients of variance of short-term and long-term reproducibility for the GABA quantification were less than 10% when the frequency threshold was applied.

Determination of grain-size characteristics from electromagnetic seabed mapping data: A NW Iberian shelf study

NASA Astrophysics Data System (ADS)

Baasch, Benjamin; Müller, Hendrik; von Dobeneck, Tilo; Oberle, Ferdinand K. J.

2017-05-01

The electric conductivity and magnetic susceptibility of sediments are fundamental parameters in environmental geophysics. Both can be derived from marine electromagnetic profiling, a novel, fast and non-invasive seafloor mapping technique. Here we present statistical evidence that electric conductivity and magnetic susceptibility can help to determine physical grain-size characteristics (size, sorting and mud content) of marine surficial sediments. Electromagnetic data acquired with the bottom-towed electromagnetic profiler MARUM NERIDIS III were analysed and compared with grain size data from 33 samples across the NW Iberian continental shelf. A negative correlation between mean grain size and conductivity (R=-0.79) as well as mean grain size and susceptibility (R=-0.78) was found. Simple and multiple linear regression analyses were carried out to predict mean grain size, mud content and the standard deviation of the grain-size distribution from conductivity and susceptibility. The comparison of both methods showed that multiple linear regression models predict the grain-size distribution characteristics better than the simple models. This exemplary study demonstrates that electromagnetic benthic profiling is capable to estimate mean grain size, sorting and mud content of marine surficial sediments at a very high significance level. Transfer functions can be calibrated using grains-size data from a few reference samples and extrapolated along shelf-wide survey lines. This study suggests that electromagnetic benthic profiling should play a larger role for coastal zone management, seafloor contamination and sediment provenance studies in worldwide continental shelf systems.

Randomized noninferiority field trial evaluating cephapirin sodium for treatment of nonsevere clinical mastitis.

PubMed

Tomazi, T; Lopes, T A F; Masson, V; Swinkels, J M; Santos, M V

2018-05-16

The general objective of this study was to evaluate whether cephapirin sodium is noninferior compared with a positive control broad-spectrum product formulated with a combination of antimicrobials for intramammary treatment of nonsevere clinical mastitis. In addition, we compared the efficacy of treatments on the cure risks of pathogen groups (gram-positive, gram-negative, and cultures with no growth) based on culture results. A total of 346 cows distributed in 31 commercial dairy herds were selected to participate in the study, although only 236 met the criteria for evaluation of microbiological cure. Coagulase-negative staphylococci were the most isolated gram-positive pathogens in pretreatment milk samples, whereas the most common gram-negative bacterium was Escherichia coli. Cows attending the postadmission criteria were treated with 4 intramammary infusions (12 h apart) of one of the following antimicrobials: 300 mg of cephapirin sodium + 20 mg of prednisolone (CS), or the positive control treatment formulated with a combination of antimicrobials (200 mg of tetracycline + 250 mg of neomycin + 28 mg of bacitracin + 10 mg of prednisolone; TNB). Noninferiority analysis and mixed regression models (overall and considering the pathogen groups) were performed for the following outcomes: bacteriological cure (absence of the causative pathogens in cultures performed in milk samples collected at 14 and 21 ± 3 d after enrollment), pathogen cure (absence of any pathogen on both follow-up samples), clinical cure (absence of clinical sign in the milk and mammary gland at 48 h after the last antimicrobial infusion), extended clinical cure (normal milk and normal gland on the second posttreatment sample collection (d 21), and linear score of somatic cell count cure [linear score of somatic cell count recovery (≤4.0) on d 21 ± 3 after enrollment]. No significant differences were observed between treatments regarding any of the evaluated outcomes in both regression models (overall and considering the pathogen groups). Noninferiority of CS relative to TNB was inconclusive for bacteriological cure (CS = 0.68; TNB = 0.73) and clinical cure (CS = 0.88; TNB = 0.94), as the confidence intervals crossed the pre-stated margin of noninferiority (Δ = -0.15). Cephapirin sodium was noninferior compared with TNB for pathogen cure (CS = 0.36; TNB = 0.35), extended clinical cure (CS = 0.93; TNB = 0.92), and linear score of somatic cell count cure (CS = 0.29; TNB = 0.28). In conclusion, the use of intramammary CS for treatment of nonsevere clinical mastitis has similar efficacy as a treatment regimen with a combination of antimicrobial agents (tetracycline + neomycin + bacitracin), although noninferiority analysis showed inconclusive results for bacteriological and clinical cures. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Responses of the tropical gorgonian coral Eunicea fusca to ocean acidification conditions

NASA Astrophysics Data System (ADS)

Gómez, C. E.; Paul, V. J.; Ritson-Williams, R.; Muehllehner, N.; Langdon, C.; Sánchez, J. A.

2015-06-01

Ocean acidification can have negative repercussions from the organism to ecosystem levels. Octocorals deposit high-magnesium calcite in their skeletons, and according to different models, they could be more susceptible to the depletion of carbonate ions than either calcite or aragonite-depositing organisms. This study investigated the response of the gorgonian coral Eunicea fusca to a range of CO2 concentrations from 285 to 4,568 ppm (pH range 8.1-7.1) over a 4-week period. Gorgonian growth and calcification were measured at each level of CO2 as linear extension rate and percent change in buoyant weight and calcein incorporation in individual sclerites, respectively. There was a significant negative relationship for calcification and CO2 concentration that was well explained by a linear model regression analysis for both buoyant weight and calcein staining. In general, growth and calcification did not stop in any of the concentrations of pCO2; however, some of the octocoral fragments experienced negative calcification at undersaturated levels of calcium carbonate (>4,500 ppm) suggesting possible dissolution effects. These results highlight the susceptibility of the gorgonian coral E. fusca to elevated levels of carbon dioxide but suggest that E. fusca could still survive well in mid-term ocean acidification conditions expected by the end of this century, which provides important information on the effects of ocean acidification on the dynamics of coral reef communities. Gorgonian corals can be expected to diversify and thrive in the Atlantic-Eastern Pacific; as scleractinian corals decline, it is likely to expect a shift in these reef communities from scleractinian coral dominated to octocoral/soft coral dominated under a "business as usual" scenario of CO2 emissions.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

An Application to the Prediction of LOD Change Based on General Regression Neural Network

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.

2011-07-01

Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.

Solving a mixture of many random linear equations by tensor decomposition and alternating minimization.

DOT National Transportation Integrated Search

2016-09-01

We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample...

[Relationship of insight with depression and suicidal ideation in psychotic disorders].

PubMed

Patelaros, E; Zournatzis, E; Kontstantakopoulos, G

2015-01-01

The associations of insight into psychosis (i.e., awareness of illness) with clinical variables have been examined by a great number of studies. Most of these studies revealed that the level of insight is negatively correlated with psychotic symptoms but positively correlated with depression and suicidal ideation. The aim of this study was to test these findings in a Greek sample of patients. Forty-three outpatients (30 men and 13 women) with schizophrenia or delusional disorder being followed up at the Mental Health Centre of Kavala took part in the study. Patients with bipolar or schizoaffective disorder were excluded. Patients' mean age was 40.7 years and the mean duration of illness was 18.67 years. All participants were under treatment and clinically stable at the time of the study. We used the Positive and Negative Syndrome Scale (PANSS) for the assessment of positive and negative symptoms, the Schedule for the Assessment of Insight-Expanded (SAI-E) to assess the insight into psychosis, and the Montgomery-Asberg Depression Rating Scale (MADRS) for the evaluation of depression recording separately the score for item 10 as an estimate of suicidal ideation. All the scales used have been adapted to Greek population. We used Spearman rho coefficient to assess the strength of correlations between the scales because the distributions of some scores were not normal. In order to assess the predictive value of insight for depression and suicidal ideation, we used hierarchical linear regression analysis. Correlation coefficients between SAI-E and the clinical scales of psychopathology, depression and suicide ideation was statistically significant at the p<0.01 level. The correlations between the clinical scales and the three subscales of SAI-E were also significant at the aforementioned p level. The regression analysis showed that our model of positive and negative psychopathology and insight explained 47.4% of the variance of depression and 32.2% of the variance of suicidal ideation. The predictive value of insight was critically important, because only after the introduction of the SAI-E score in the analysis our regression models reached statistical significance. Taking into account its limitations regarding the sample size and the chronicity of the illness, our study confirms the positive correlation of insight with depression and suicidal ideation, offering support to the psychological model of insight.

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Statistical guides to estimating the number of undiscovered mineral deposits: an example with porphyry copper deposits

USGS Publications Warehouse

Singer, Donald A.; Menzie, W.D.; Cheng, Qiuming; Bonham-Carter, G. F.

2005-01-01

Estimating numbers of undiscovered mineral deposits is a fundamental part of assessing mineral resources. Some statistical tools can act as guides to low variance, unbiased estimates of the number of deposits. The primary guide is that the estimates must be consistent with the grade and tonnage models. Another statistical guide is the deposit density (i.e., the number of deposits per unit area of permissive rock in well-explored control areas). Preliminary estimates and confidence limits of the number of undiscovered deposits in a tract of given area may be calculated using linear regression and refined using frequency distributions with appropriate parameters. A Poisson distribution leads to estimates having lower relative variances than the regression estimates and implies a random distribution of deposits. Coefficients of variation are used to compare uncertainties of negative binomial, Poisson, or MARK3 empirical distributions that have the same expected number of deposits as the deposit density. Statistical guides presented here allow simple yet robust estimation of the number of undiscovered deposits in permissive terranes. 

Salutogenic resources in relation to teachers' work-life balance.

PubMed

Nilsson, Marie; Blomqvist, Kerstin; Andersson, Ingemar

2017-01-01

Experiencing work-life balance is considered a health promoting resource. To counter-balance the negative development of teachers' work situation, salutogenic resources need to be examined among teachers. To examine resources related to teachers' experience of their work-life balance. Using a cross-sectional design, a questionnaire was distributed to 455 teachers in compulsory schools in a Swedish community. A total of 338 teachers participated (74%). A multiple linear regression method was used for the analysis. Four variables in the regression model significantly explained work-life balance and were thereby possible resources: time experience at work; satisfaction with everyday life; self-rated health; and recovery. The strongest association with work-life balance was time experience at work. Except time experience at work, all were individual-related. This study highlights the importance of school management's support in reducing teachers' time pressure. It also emphasizes the need to address teachers' individual resources in relation to work-life balance. In order to support teachers' work-life balance, promote their well-being, and preventing teachers' attrition, we suggest that the school management would benefit from creating a work environment with strengthened resources.

Discrimination and Depressive Symptoms Among Latina/o Adolescents of Immigrant Parents.

PubMed

Lopez, William D; LeBrón, Alana M W; Graham, Louis F; Grogan-Kaylor, Andrew

2016-01-01

Discrimination is associated with negative mental health outcomes for Latina/o adolescents. While Latino/a adolescents experience discrimination from a number of sources and across contexts, little research considers how the source of discrimination and the context in which it occurs affect mental health outcomes among Latina/o children of immigrants. We examined the association between source-specific discrimination, racial or ethnic background of the source, and school ethnic context with depressive symptoms for Latina/o adolescents of immigrant parents. Using multilevel linear regression with time-varying covariates, we regressed depressive symptoms on source-specific discrimination, racial or ethnic background of the source of discrimination, and school percent Latina/o. Discrimination from teachers (β = 0.06, p < .05), students (β = 0.05, p < .05), Cubans (β = 0.19, p < .001), and Latinas/os (β = 0.19, p < .001) were positively associated with depressive symptoms. These associations were not moderated by school percent Latina/o. The findings indicate a need to reduce discrimination to improve Latina/o adolescents' mental health. © The Author(s) 2016.

Peritraumatic tonic immobility is associated with posttraumatic stress symptoms in undergraduate Brazilian students.

PubMed

Portugal, Liana Catarina L; Pereira, Mirtes Garcia; Alves, Rita de Cássia S; Tavares, Gisella; Lobo, Isabela; Rocha-Rego, Vanessa; Marques-Portella, Carla; Mendlowicz, Mauro V; Coutinho, Evandro S; Fiszman, Adriana; Volchan, Eliane; Figueira, Ivan; Oliveira, Letícia de

2012-03-01

Tonic immobility is a defensive reaction occurring under extreme life threats. Patients with posttraumatic stress disorder (PTSD) reporting peritraumatic tonic immobility show the most severe symptoms and a poorer response to treatment. This study investigated the predictive value of tonic immobility for posttraumatic stress symptoms in a non-clinical sample. One hundred and ninety-eight college students exposed to various life threatening events were selected to participate. The Posttraumatic Stress Disorder Checklist - Civilian Version (PCL-C) and tonic immobility questions were used. Linear regression models were fitted to investigate the association between peritraumatic tonic immobility and PCL-C scores. Peritraumatic dissociation, peritraumatic panic reactions, negative affect, gender, type of trauma, and time since trauma were considered as confounding variables. We found significant association between peritraumatic tonic immobility and PTSD symptoms in a non-clinical sample exposed to various traumas, even after regression controlled for confounding variables (β = 1.99, p = 0.017). This automatic reaction under extreme life threatening stress, although adaptive for defense, may have pathological consequences as implied by its association with PTSD symptoms.

Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure.

PubMed

Senn, Stephen; Graf, Erika; Caputo, Angelika

2007-12-30

Stratifying and matching by the propensity score are increasingly popular approaches to deal with confounding in medical studies investigating effects of a treatment or exposure. A more traditional alternative technique is the direct adjustment for confounding in regression models. This paper discusses fundamental differences between the two approaches, with a focus on linear regression and propensity score stratification, and identifies points to be considered for an adequate comparison. The treatment estimators are examined for unbiasedness and efficiency. This is illustrated in an application to real data and supplemented by an investigation on properties of the estimators for a range of underlying linear models. We demonstrate that in specific circumstances the propensity score estimator is identical to the effect estimated from a full linear model, even if it is built on coarser covariate strata than the linear model. As a consequence the coarsening property of the propensity score-adjustment for a one-dimensional confounder instead of a high-dimensional covariate-may be viewed as a way to implement a pre-specified, richly parametrized linear model. We conclude that the propensity score estimator inherits the potential for overfitting and that care should be taken to restrict covariates to those relevant for outcome. Copyright (c) 2007 John Wiley & Sons, Ltd.

Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.

PubMed

Trninić, Marko; Jeličić, Mario; Papić, Vladan

2015-07-01

In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

PubMed

O'Leary, Neil; Chauhan, Balwantray C; Artes, Paul H

2012-10-01

To establish a method for estimating the overall statistical significance of visual field deterioration from an individual patient's data, and to compare its performance to pointwise linear regression. The Truncated Product Method was used to calculate a statistic S that combines evidence of deterioration from individual test locations in the visual field. The overall statistical significance (P value) of visual field deterioration was inferred by comparing S with its permutation distribution, derived from repeated reordering of the visual field series. Permutation of pointwise linear regression (PoPLR) and pointwise linear regression were evaluated in data from patients with glaucoma (944 eyes, median mean deviation -2.9 dB, interquartile range: -6.3, -1.2 dB) followed for more than 4 years (median 10 examinations over 8 years). False-positive rates were estimated from randomly reordered series of this dataset, and hit rates (proportion of eyes with significant deterioration) were estimated from the original series. The false-positive rates of PoPLR were indistinguishable from the corresponding nominal significance levels and were independent of baseline visual field damage and length of follow-up. At P < 0.05, the hit rates of PoPLR were 12, 29, and 42%, at the fifth, eighth, and final examinations, respectively, and at matching specificities they were consistently higher than those of pointwise linear regression. In contrast to population-based progression analyses, PoPLR provides a continuous estimate of statistical significance for visual field deterioration individualized to a particular patient's data. This allows close control over specificity, essential for monitoring patients in clinical practice and in clinical trials.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

NASA Astrophysics Data System (ADS)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Orthogonal Projection in Teaching Regression and Financial Mathematics

ERIC Educational Resources Information Center

Kachapova, Farida; Kachapov, Ilias

2010-01-01

Two improvements in teaching linear regression are suggested. The first is to include the population regression model at the beginning of the topic. The second is to use a geometric approach: to interpret the regression estimate as an orthogonal projection and the estimation error as the distance (which is minimized by the projection). Linear…

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Perceived reciprocity and well-being at work in non-professional employees: fairness or self-interest?

PubMed

Moliner, Carolina; Martínez-Tur, Vicente; Peiró, José M; Ramos, José; Cropanzano, Russell

2013-02-01

This article assesses the links between non-professional employees' perceptions of reciprocity in their relationships with their supervisors and the positive and negative sides of employees' well-being at work: burnout and engagement. Two hypotheses were explored. First, the fairness hypothesis assumes a curvilinear relationship where balanced reciprocity (when the person perceives that there is equilibrium between his/her efforts and the benefits he/she receives) presents the highest level of well-being. Second, the self-interest hypothesis proposes a linear pattern where over-benefitted situations for employees (when the person perceives that he/she is receiving more than he/she deserves) increase well-being. One study with two independent samples was conducted. The participants were 349 employees in 59 hotels (sample 1) and 690 employees in 89 centres providing attention to people with mental disabilities (sample 2). Linear and curvilinear regression models supported the self-interest hypothesis for the links from reciprocity to burnout and engagement. We conclude with theoretical implications and opportunities for future research. Copyright © 2012 John Wiley & Sons, Ltd.