Emission and distribution of phosphine in paddy fields and its relationship with greenhouse gases.

PubMed

Chen, Weiyi; Niu, Xiaojun; An, Shaorong; Sheng, Hong; Tang, Zhenghua; Yang, Zhiquan; Gu, Xiaohong

2017-12-01

Phosphine (PH 3 ), as a gaseous phosphide, plays an important role in the phosphorus cycle in ecosystems. In this study, the emission and distribution of phosphine, carbon dioxide (CO 2 ) and methane (CH 4 ) in paddy fields were investigated to speculate the future potential impacts of enhanced greenhouse effect on phosphorus cycle involved in phosphine by the method of Pearson correlation analysis and multiple linear regression analysis. During the whole period of rice growth, there was a significant positive correlation between CO 2 emission flux and PH 3 emission flux (r=0.592, p=0.026, n=14). Similarly, a significant positive correlation of emission flux was also observed between CH 4 and PH 3 (r=0.563, p=0.036, n=14). The linear regression relationship was determined as [PH 3 ] flux =0.007[CO 2 ] flux +0.063[CH 4 ] flux -4.638. No significant differences were observed for all values of matrix-bound phosphine (MBP), soil carbon dioxide (SCO 2 ), and soil methane (SCH 4 ) in paddy soils. However, there was a significant positive correlation between MBP and SCO 2 at heading, flowering and ripening stage. The correlation coefficients were 0.909, 0.890 and 0.827, respectively. In vertical distribution, MBP had the analogical variation trend with SCO 2 and SCH 4 . Through Pearson correlation analysis and multiple stepwise linear regression analysis, pH, redox potential (Eh), total phosphorus (TP) and acid phosphatase (ACP) were identified as the principal factors affecting MBP levels, with correlative rankings of Eh>pH>TP>ACP. The multiple stepwise regression model ([MBP]=0.456∗[ACP]+0.235∗[TP]-1.458∗[Eh]-36.547∗[pH]+352.298) was obtained. The findings in this study hold great reference values to the global biogeochemical cycling of phosphorus in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Time Series Analysis and Forecasting of Wastewater Inflow into Bandar Tun Razak Sewage Treatment Plant in Selangor, Malaysia

NASA Astrophysics Data System (ADS)

Abunama, Taher; Othman, Faridah

2017-06-01

Analysing the fluctuations of wastewater inflow rates in sewage treatment plants (STPs) is essential to guarantee a sufficient treatment of wastewater before discharging it to the environment. The main objectives of this study are to statistically analyze and forecast the wastewater inflow rates into the Bandar Tun Razak STP in Kuala Lumpur, Malaysia. A time series analysis of three years’ weekly influent data (156weeks) has been conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model. Various combinations of ARIMA orders (p, d, q) have been tried to select the most fitted model, which was utilized to forecast the wastewater inflow rates. The linear regression analysis was applied to testify the correlation between the observed and predicted influents. ARIMA (3, 1, 3) model was selected with the highest significance R-square and lowest normalized Bayesian Information Criterion (BIC) value, and accordingly the wastewater inflow rates were forecasted to additional 52weeks. The linear regression analysis between the observed and predicted values of the wastewater inflow rates showed a positive linear correlation with a coefficient of 0.831.

Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

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

Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh

2005-12-16

Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less

Analysis of reciprocal creatinine plots by two-phase linear regression.

PubMed

Rowe, P A; Richardson, R E; Burton, P R; Morgan, A G; Burden, R P

1989-01-01

The progression of renal diseases is often monitored by the serial measurement of plasma creatinine. The slope of the linear relation that is frequently found between the reciprocal of creatinine concentration and time delineates the rate of change in renal function. Minor changes in slope, perhaps indicating response to therapeutic intervention, can be difficult to identify and yet be of clinical importance. We describe the application of two-phase linear regression to identify and characterise changes in slope using a microcomputer. The method fits two intersecting lines to the data by computing a least-squares estimate of the position of the slope change and its 95% confidence limits. This avoids the potential bias of fixing the change at a preconceived time corresponding with an alteration in treatment. The program then evaluates the statistical and clinical significance of the slope change and produces a graphical output to aid interpretation.

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.

The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

USGS Publications Warehouse

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

2009-01-01

The Digital Shoreline Analysis System (DSAS) version 4.0 is a software extension to ESRI ArcGIS v.9.2 and above that enables a user to calculate shoreline rate-of-change statistics from multiple historic shoreline positions. A user-friendly interface of simple buttons and menus guides the user through the major steps of shoreline change analysis. Components of the extension and user guide include (1) instruction on the proper way to define a reference baseline for measurements, (2) automated and manual generation of measurement transects and metadata based on user-specified parameters, and (3) output of calculated rates of shoreline change and other statistical information. DSAS computes shoreline rates of change using four different methods: (1) endpoint rate, (2) simple linear regression, (3) weighted linear regression, and (4) least median of squares. The standard error, correlation coefficient, and confidence interval are also computed for the simple and weighted linear-regression methods. The results of all rate calculations are output to a table that can be linked to the transect file by a common attribute field. DSAS is intended to facilitate the shoreline change-calculation process and to provide rate-of-change information and the statistical data necessary to establish the reliability of the calculated results. The software is also suitable for any generic application that calculates positional change over time, such as assessing rates of change of glacier limits in sequential aerial photos, river edge boundaries, land-cover changes, and so on.

Representational change and strategy use in children's number line estimation during the first years of primary school.

PubMed

White, Sonia L J; Szűcs, Dénes

2012-01-04

The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice.

Representational change and strategy use in children's number line estimation during the first years of primary school

PubMed Central

2012-01-01

Background The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Methods Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Results Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. Conclusion In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice. PMID:22217191

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.

Catheter placement for lysis of spontaneous intracerebral hematomas: does a catheter position in the core of the hematoma allow more effective and faster hematoma lysis?

PubMed

Malinova, Vesna; Schlegel, Anna; Rohde, Veit; Mielke, Dorothee

2017-07-01

For the fibrinolytic therapy of intracerebral hematomas (ICH) using recombinant tissue plasminogen activator (rtPA), a catheter position in the core of the hematoma along the largest clot diameter was assumed to be optimal for an effective clot lysis. However, it never had been proven that core position indeed enhances clot lysis if compared with less optimal catheter positions. In this study, the impact of the catheter position on the effectiveness and on the time course of clot lysis was evaluated. We analyzed the catheter position using a relative error calculating the distance perpendicular to the catheter's center in relation to hematoma's diameter and evaluated the relative hematoma volume reduction (RVR). The correlation of the RVR with the catheter position was evaluated. Additionally, we tried to identify patterns of clot lysis with different catheter positions. The patient's outcome at discharge was evaluated using the Glasgow outcome score. A total of 105 patients were included in the study. The mean hematoma volume was 56 ml. The overall RVR was 62.7 %. In 69 patients, a catheter position in the core of the clot was achieved. We found no significant correlation between catheter position and hematoma RVR (linear regression, p = 0.14). Core catheter position leads to more symmetrical hematoma RVR. Faster clot lysis happens in the vicinity of the catheter openings. We found no significant difference in the patient's outcome dependent on the catheter position (linear regression, p = 0.90). The catheter position in the core of the hematoma along its largest diameter does not significantly influence the effectiveness of clot lysis after rtPA application.

Serum Iron Level Is Associated with Time to Antibiotics in Cystic Fibrosis.

PubMed

Gifford, Alex H; Dorman, Dana B; Moulton, Lisa A; Helm, Jennifer E; Griffin, Mary M; MacKenzie, Todd A

2015-12-01

Serum levels of hepcidin-25, a peptide hormone that reduces blood iron content, are elevated when patients with cystic fibrosis (CF) develop pulmonary exacerbation (PEx). Because hepcidin-25 is unavailable as a clinical laboratory test, we questioned whether a one-time serum iron level was associated with the subsequent number of days until PEx, as defined by the need to receive systemic antibiotics (ABX) for health deterioration. Clinical, biochemical, and microbiological parameters were simultaneously checked in 54 adults with CF. Charts were reviewed to determine when they first experienced a PEx after these parameters were assessed. Time to ABX was compared in subgroups with and without specific attributes. Multivariate linear regression was used to identify parameters that significantly explained variation in time to ABX. In univariate analyses, time to ABX was significantly shorter in subjects with Aspergillus-positive sputum cultures and CF-related diabetes. Multivariate linear regression models demonstrated that shorter time to ABX was associated with younger age, lower serum iron level, and Aspergillus sputum culture positivity. Serum iron, age, and Aspergillus sputum culture positivity are factors associated with shorter time to subsequent PEx in CF adults. © 2015 Wiley Periodicals, Inc.

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.

USE OF WEIBULL FUNCTION FOR NON-LINEAR ANALYSIS OF EFFECTS OF LOW LEVELS OF SIMULATED HERBICIDE DRIFT ON PLANTS

EPA Science Inventory

We compared two regression models, which are based on the Weibull and probit functions, for the analysis of pesticide toxicity data from laboratory studies on Illinois crop and native plant species. Both mathematical models are continuous, differentiable, strictly positive, and...

Extraction of object skeletons in multispectral imagery by the orthogonal regression fitting

NASA Astrophysics Data System (ADS)

Palenichka, Roman M.; Zaremba, Marek B.

2003-03-01

Accurate and automatic extraction of skeletal shape of objects of interest from satellite images provides an efficient solution to such image analysis tasks as object detection, object identification, and shape description. The problem of skeletal shape extraction can be effectively solved in three basic steps: intensity clustering (i.e. segmentation) of objects, extraction of a structural graph of the object shape, and refinement of structural graph by the orthogonal regression fitting. The objects of interest are segmented from the background by a clustering transformation of primary features (spectral components) with respect to each pixel. The structural graph is composed of connected skeleton vertices and represents the topology of the skeleton. In the general case, it is a quite rough piecewise-linear representation of object skeletons. The positions of skeleton vertices on the image plane are adjusted by means of the orthogonal regression fitting. It consists of changing positions of existing vertices according to the minimum of the mean orthogonal distances and, eventually, adding new vertices in-between if a given accuracy if not yet satisfied. Vertices of initial piecewise-linear skeletons are extracted by using a multi-scale image relevance function. The relevance function is an image local operator that has local maximums at the centers of the objects of interest.

Tolerance of ciliated protozoan Paramecium bursaria (Protozoa, Ciliophora) to ammonia and nitrites

NASA Astrophysics Data System (ADS)

Xu, Henglong; Song, Weibo; Lu, Lu; Alan, Warren

2005-09-01

The tolerance to ammonia and nitrites in freshwater ciliate Paramecium bursaria was measured in a conventional open system. The ciliate was exposed to different concentrations of ammonia and nitrites for 2h and 12h in order to determine the lethal concentrations. Linear regression analysis revealed that the 2h-LC50 value for ammonia was 95.94 mg/L and for nitrite 27.35 mg/L using probit scale method (with 95% confidence intervals). There was a linear correlation between the mortality probit scale and logarithmic concentration of ammonia which fit by a regression equation y=7.32 x 9.51 ( R 2=0.98; y, mortality probit scale; x, logarithmic concentration of ammonia), by which 2 h-LC50 value for ammonia was found to be 95.50 mg/L. A linear correlation between mortality probit scales and logarithmic concentration of nitrite is also followed the regression equation y=2.86 x+0.89 ( R 2=0.95; y, mortality probit scale; x, logarithmic concentration of nitrite). The regression analysis of toxicity curves showed that the linear correlation between exposed time of ammonia-N LC50 value and ammonia-N LC50 value followed the regression equation y=2 862.85 e -0.08 x ( R 2=0.95; y, duration of exposure to LC50 value; x, LC50 value), and that between exposed time of nitrite-N LC50 value and nitrite-N LC50 value followed the regression equation y=127.15 e -0.13 x ( R 2=0.91; y, exposed time of LC50 value; x, LC50 value). The results demonstrate that the tolerance to ammonia in P. bursaria is considerably higher than that of the larvae or juveniles of some metozoa, e.g. cultured prawns and oysters. In addition, ciliates, as bacterial predators, are likely to play a positive role in maintaining and improving water quality in aquatic environments with high-level ammonium, such as sewage treatment systems.

Predictive value of grade point average (GPA), Medical College Admission Test (MCAT), internal examinations (Block) and National Board of Medical Examiners (NBME) scores on Medical Council of Canada qualifying examination part I (MCCQE-1) scores.

PubMed

Roy, Banibrata; Ripstein, Ira; Perry, Kyle; Cohen, Barry

2016-01-01

To determine whether the pre-medical Grade Point Average (GPA), Medical College Admission Test (MCAT), Internal examinations (Block) and National Board of Medical Examiners (NBME) scores are correlated with and predict the Medical Council of Canada Qualifying Examination Part I (MCCQE-1) scores. Data from 392 admitted students in the graduating classes of 2010-2013 at University of Manitoba (UofM), College of Medicine was considered. Pearson's correlation to assess the strength of the relationship, multiple linear regression to estimate MCCQE-1 score and stepwise linear regression to investigate the amount of variance were employed. Complete data from 367 (94%) students were studied. The MCCQE-1 had a moderate-to-large positive correlation with NBME scores and Block scores but a low correlation with GPA and MCAT scores. The multiple linear regression model gives a good estimate of the MCCQE-1 (R2 =0.604). Stepwise regression analysis demonstrated that 59.2% of the variation in the MCCQE-1 was accounted for by the NBME, but only 1.9% by the Block exams, and negligible variation came from the GPA and the MCAT. Amongst all the examinations used at UofM, the NBME is most closely correlated with MCCQE-1.

Detecting trends in raptor counts: power and type I error rates of various statistical tests

USGS Publications Warehouse

Hatfield, J.S.; Gould, W.R.; Hoover, B.A.; Fuller, M.R.; Lindquist, E.L.

1996-01-01

We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

PubMed

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Validation of the Omni Scale of Perceived Exertion in a sample of Spanish-speaking youth from the USA.

PubMed

Suminski, Richard R; Robertson, Robert J; Goss, Fredric L; Olvera, Norma

2008-08-01

Whether the translation of verbal descriptors from English to Spanish affects the validity of the Children's OMNI Scale of Perceived Exertion is not known, so the validity of a Spanish version of the OMNI was examined with 32 boys and 36 girls (9 to 12 years old) for whom Spanish was the primary language. Oxygen consumption, ventilation, respiratory rate, respiratory exchange ratio, heart rate, and ratings of perceived exertion for the overall body (RPE-O) were measured during an incremental treadmill test. All response values displayed significant linear increases across test stages. The linear regression analyses indicated RPE-O values were distributed as positive linear functions of oxygen consumption, ventilation, respiratory rate, respiratory exchange ratio, heart rate, and percent of maximal oxygen consumption. All regression models were statistically significant. The Spanish OMNI Scale is valid for estimating exercise effort during walking and running amongst Hispanic youth whose primary language is Spanish.

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.

Identifying Aspects of Parental Involvement that Affect the Academic Achievement of High School Students

ERIC Educational Resources Information Center

Roulette-McIntyre, Ovella; Bagaka's, Joshua G.; Drake, Daniel D.

2005-01-01

This study identified parental practices that relate positively to high school students' academic performance. Parents of 643 high school students participated in the study. Data analysis, using a multiple linear regression model, shows parent-school connection, student gender, and race are significant predictors of student academic performance.…

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Circulating CD34-Positive Cells Are Associated with Handgrip Strength in Japanese Older Men: The Nagasaki Islands Study.

PubMed

Yamanashi, H; Shimizu, Y; Koyamatsu, J; Nagayoshi, M; Kadota, K; Tamai, M; Maeda, T

2017-01-01

Handgrip strength is a simple measurement of overall muscular strength and is used to detect sarcopenia. It also predicts adverse events in later life. Many mechanisms of sarcopenia development have been reported. A hypertensive status impairs endothelial dysfunction, which might deteriorate skeletal muscle if vascular angiogenesis is not maintained. This study investigated muscle strength and circulating CD34-positive cells as a marker of vascular angiogenesis. Cross-sectional study. 262 male Japanese community dwellers aged 60 to 69 years. The participants' handgrip strength, medical history, and blood samples were taken. We stratified the participants by hypertensive status to investigate the association between handgrip strength and circulating CD34-positive cells according to hypertensive status. Pearson correlation and linear regression analyses were used. In the Pearson correlation analysis, handgrip strength and the logarithm of circulating CD34-positive cells were significantly associated in hypertensive participants (r=0.22, p=0.021), but not in non-hypertensive participants (r=-0.01, p=0.943). This relationship was only significant in hypertensive participants (ß=1.94, p=0.021) in the simple linear regression analysis, and it remained significant after adjusting for classic cardiovascular risk factors (ß=1.92, p=0.020). The relationship was not significant in non-hypertensive participants (ß=-0.09, p=0.903). We found a positive association between handgrip strength and circulating CD34-positive cells in hypertensive men. Vascular maintenance attributed by circulating CD34-positive cells is thought to be a background mechanism of this association after hypertension-induced vascular injury in skeletal muscle.

A trend analysis of laboratory positive propoxyphene workplace urine drug screens before and after the product recall.

PubMed

Price, James

2015-01-01

Propoxyphene was withdrawn from the US market in November 2010. This drug is still tested for in the workplace as part of expanded panel nonregulated testing. A convenience sample of urine specimens (n = 7838) were provided by workers from various industries. The percentage of positive specimens with 95% confidence intervals was calculated for each year of the study. Logistic regression was used to assess the impact of the year upon the propoxyphene result. The prevalence of positive propoxyphene tests was much higher before the product's withdrawal from the market. Logistic regression provided evidence of a decreasing linear trend (P < 0.000; β = -0.71). The odds ratio signifies that for every additional year the urine specimens were 0.49 times less likely to be positive for propoxyphene. This favors the determination that the change in propoxyphene positive drug test over the years is not by chance. The conclusion supports no longer performing nonregulated workplace propoxyphene urine drug testing for this population.

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.

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.

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 ...

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…

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

PubMed

Fitzsimmons, Eric J; Kvam, Vanessa; Souleyrette, Reginald R; Nambisan, Shashi S; Bonett, Douglas G

2013-01-01

Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

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.

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…

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.

Does the high–tech industry consistently reduce CO{sub 2} emissions? Results from nonparametric additive regression model

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

Xu, Bin; Research Center of Applied Statistics, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013; Lin, Boqiang, E-mail: bqlin@xmu.edu.cn

China is currently the world's largest carbon dioxide (CO{sub 2}) emitter. Moreover, total energy consumption and CO{sub 2} emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO{sub 2} emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO{sub 2} emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Basedmore » on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO{sub 2} emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO{sub 2} emissions in the three regions should be given adequate attention in developing effective abatement policies. - Highlights: • The nonlinear effect of the high–tech industry on CO{sub 2} emissions was investigated. • The high–tech industry yields an inverted “U–shaped” effect in the eastern region. • The high–tech industry has a positive “U–shaped” nonlinear effect in other regions. • The linear impact of the high–tech industry in the eastern region is the strongest.« less

Influence of Curing Mode on the Surface Energy and Sorption/Solubility of Dental Self-Adhesive Resin Cements

PubMed Central

Kim, Hyun-Jin; Bagheri, Rafat; Kim, Young Kyung; Son, Jun Sik; Kwon, Tae-Yub

2017-01-01

This study investigated the influence of curing mode (dual- or self-cure) on the surface energy and sorption/solubility of four self-adhesive resin cements (SARCs) and one conventional resin cement. The degree of conversion (DC) and surface energy parameters including degree of hydrophilicity (DH) were determined using Fourier transform infrared spectroscopy and contact angle measurements, respectively (n = 5). Sorption and solubility were assessed by mass gain or loss after storage in distilled water or lactic acid for 60 days (n = 5). A linear regression model was used to correlate between the results (%DC vs. DH and %DC/DH vs. sorption/solubility). For all materials, the dual-curing consistently produced significantly higher %DC values than the self-curing (p < 0.05). Significant negative linear regressions were established between the %DC and DH in both curing modes (p < 0.05). Overall, the SARCs showed higher sorption/solubility values, in particular when immersed in lactic acid, than the conventional resin cement. Linear regression revealed that %DC and DH were negatively and positively correlated with the sorption/solubility values, respectively. Dual-curing of SARCs seems to lower the sorption and/or solubility in comparison with self-curing by increased %DC and occasionally decreased hydrophilicity. PMID:28772489

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.

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

Early Literacy Skills and English Language Learners: An Analysis of Students in a Title I School

ERIC Educational Resources Information Center

Ostayan, Jennifer R.

2016-01-01

This article examined student literacy assessments in light of students' levels of English language proficiency. The study supported the hypotheses that a student's level of language proficiency positively predicted their DIBELS Composite score at the beginning, middle, and end of kindergarten by utilizing a simple linear regression. An ANOVA…

Food insecurity, CD4 counts, and incomplete viral suppression among HIV+ patients from Texas Children's Hospital: A pilot study

USDA-ARS?s Scientific Manuscript database

Our goal was to determine the relationship between food insecurity and CD4 counts and viral suppression among pediatric HIV-positive patients. Food insecurity was assessed by validated survey. CD4 counts and viral load were abstracted from patients’ charts. We used linear regression for the dependen...

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

Measuring Astronomical Distances with Linear Programming

ERIC Educational Resources Information Center

Narain, Akshar

2015-01-01

A few years ago it was suggested that the distance to celestial bodies could be computed by tracking their position over about 24 hours and then solving a regression problem. One only needed to use inexpensive telescopes, cameras, and astrometry tools, and the experiment could be done from one's backyard. However, it is not obvious to an amateur…

Cognitive flexibility correlates with gambling severity in young adults.

PubMed

Leppink, Eric W; Redden, Sarah A; Chamberlain, Samuel R; Grant, Jon E

2016-10-01

Although gambling disorder (GD) is often characterized as a problem of impulsivity, compulsivity has recently been proposed as a potentially important feature of addictive disorders. The present analysis assessed the neurocognitive and clinical relationship between compulsivity on gambling behavior. A sample of 552 non-treatment seeking gamblers age 18-29 was recruited from the community for a study on gambling in young adults. Gambling severity levels included both casual and disordered gamblers. All participants completed the Intra/Extra-Dimensional Set Shift (IED) task, from which the total adjusted errors were correlated with gambling severity measures, and linear regression modeling was used to assess three error measures from the task. The present analysis found significant positive correlations between problems with cognitive flexibility and gambling severity (reflected by the number of DSM-5 criteria, gambling frequency, amount of money lost in the past year, and gambling urge/behavior severity). IED errors also showed a positive correlation with self-reported compulsive behavior scores. A significant correlation was also found between IED errors and non-planning impulsivity from the BIS. Linear regression models based on total IED errors, extra-dimensional (ED) shift errors, or pre-ED shift errors indicated that these factors accounted for a significant portion of the variance noted in several variables. These findings suggest that cognitive flexibility may be an important consideration in the assessment of gamblers. Results from correlational and linear regression analyses support this possibility, but the exact contributions of both impulsivity and cognitive flexibility remain entangled. Future studies will ideally be able to assess the longitudinal relationships between gambling, compulsivity, and impulsivity, helping to clarify the relative contributions of both impulsive and compulsive features. Copyright © 2016 Elsevier Ltd. All rights reserved.

How Financial Literacy Affects Household Wealth Accumulation.

PubMed

Behrman, Jere R; Mitchell, Olivia S; Soo, Cindy K; Bravo, David

2012-05-01

This study isolates the causal effects of financial literacy and schooling on wealth accumulation using a new household dataset and an instrumental variables (IV) approach. Financial literacy and schooling attainment are both strongly positively associated with wealth outcomes in linear regression models, whereas the IV estimates reveal even more potent effects of financial literacy. They also indicate that the schooling effect only becomes positive when interacted with financial literacy. Estimated impacts are substantial enough to imply that investments in financial literacy could have large wealth payoffs.

How Financial Literacy Affects Household Wealth Accumulation

PubMed Central

Behrman, Jere R.; Mitchell, Olivia S.; Soo, Cindy K.; Bravo, David

2012-01-01

This study isolates the causal effects of financial literacy and schooling on wealth accumulation using a new household dataset and an instrumental variables (IV) approach. Financial literacy and schooling attainment are both strongly positively associated with wealth outcomes in linear regression models, whereas the IV estimates reveal even more potent effects of financial literacy. They also indicate that the schooling effect only becomes positive when interacted with financial literacy. Estimated impacts are substantial enough to imply that investments in financial literacy could have large wealth payoffs. PMID:23355747

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.

Work stress, asthma control and asthma-specific quality of life: Initial evidence from a cross-sectional study.

PubMed

Hartmann, Bettina; Leucht, Verena; Loerbroks, Adrian

2017-03-01

Research has suggested that psychological stress is positively associated with asthma morbidity. One major source of stress in adulthood is one's occupation. However, to date, potential links of work stress with asthma control or asthma-specific quality of life have not been examined. We aimed to address this knowledge gap. In 2014/2015, we conducted a cross-sectional study among adults with asthma in Germany (n = 362). For the current analyses that sample was restricted to participants in employment and reporting to have never been diagnosed with chronic obstructive pulmonary disease (n = 94). Work stress was operationalized by the 16-item effort-reward-imbalance (ERI) questionnaire, which measures the subcomponents "effort", "reward" and "overcommitment." Participants further completed the Asthma Control Test and the Asthma Quality of Life Questionnaire-Sydney. Multivariable associations were quantified by linear regression and logistic regression. Effort, reward and their ratio (i.e. ERI ratio) did not show meaningful associations with asthma morbidity. By contrast, increasing levels of overcommitment were associated with poorer asthma control and worse quality of life in both linear regression (ß = -0.26, p = 0.01 and ß = 0.44, p < 0.01, respectively) and logistic regression (odds ratio [OR] = 1.87, 95% confidence interval [CI] = 1.14-3.07 and OR = 2.34, 95% CI = 1.32-4.15, respectively). The present study provides initial evidence of a positive relationship of work-related overcommitment with asthma control and asthma-specific quality of life. Longitudinal studies with larger samples are needed to confirm our findings and to disentangle the potential causality of associations.

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.

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.

[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.

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.…

Railway crossing risk area detection using linear regression and terrain drop compensation techniques.

PubMed

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-06-16

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.

Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

PubMed Central

Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing

2014-01-01

Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas. PMID:24936948

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...

Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

NASA Astrophysics Data System (ADS)

Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

2018-03-01

This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

Is adult gait less susceptible than paediatric gait to hip joint centre regression equation error?

PubMed

Kiernan, D; Hosking, J; O'Brien, T

2016-03-01

Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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.

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Use of Case History Data for the Development of Equations in Predicting High Risk, Reading Disabled Students.

ERIC Educational Resources Information Center

Stratton, Beverly D.; And Others

Demographic data on 92 subjects identified as having reading problems were used to develop equations useful in identifying high risk, reading disabled students. Multiple linear regression analysis of the data indicated that reading disability (1) had a significant positive relationship with birth order and number of siblings; (2) had a positive…

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.

Handheld Tissue Hardness Meters for Assessing the Mechanical Properties of Skeletal Muscle: A Feasibility Study.

PubMed

Chino, Kentaro; Takahashi, Hideyuki

2016-09-01

The purpose of this study was to examine the feasibility of using handheld tissue hardness meters to assess the mechanical properties of skeletal muscle. This observational study included 33 healthy men (age, 22.4 ± 4.4 years) and 33 healthy women (age, 23.7 ± 4.2 years). Participants were placed in a supine position, and tissue hardness overlying the rectus femoris and the shear modulus of the muscle were measured on the right side of the body at 50% thigh length. In the same position, subcutaneous adipose tissue thickness and muscle thickness were measured using B-mode ultrasonography. To examine the associations of subcutaneous adipose tissue thickness, muscle thickness, and muscle shear modulus with tissue hardness, linear regression using a stepwise bidirectional elimination approach was performed. Stepwise linear regression revealed that subcutaneous adipose tissue thickness (r = -0.38, P = .002) and muscle shear modulus (r = 0.27, P = .03) were significantly associated with tissue hardness. Significant associations among adipose tissue thickness, muscle shear modulus, and tissue hardness show the limitations and feasibility of handheld tissue hardness meters for assessing the mechanical properties of skeletal muscles. Copyright © 2016. Published by Elsevier Inc.

[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.

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).

Spatial and temporal variation of rainfall trends of Sri Lanka

NASA Astrophysics Data System (ADS)

Wickramagamage, P.

2016-08-01

This study was based on daily rainfall data of 48 stations distributed over the entire island covering a 30-year period from 1981 to 2010. Data analysis was done to identify the spatial pattern of rainfall trends. The methods employed in data analysis are linear regression and interpolation by Universal Kriging and Radial Basis function. The slope of linear regression curves of 48 stations was used in interpolation. The regression coefficients show spatially and seasonally variable positive and negative trends of annual and seasonal rainfall. About half of the mean annual pentad series show negative trends, while the rest shows positive trends. By contrast, the rainfall trends of the Southwest Monsoon (SWM) season are predominantly negative throughout the country. The first phase of the Northeast Monsoon (NEM1) displays downward trends everywhere, with the exception of the Southeastern coastal area. The strongest negative trends were found in the Northeast and in the Central Highlands. The second phase (NEM2) is mostly positive, except in the Northeast. The Inter-Monsoon (IM) periods have predominantly upward trends almost everywhere, but still the trends in some parts of the Highlands and Northeast are negative. The long-term data at Watawala Nuwara Eliya and Sandringham show a consistent decline in the rainfall over the last 100 years, particularly during the SWM. There seems to be a faster decline in the rainfall in the last 3 decades. These trends are consistent with the observations in India. It is generally accepted that there has been changes in the circulation pattern. Weakening of the SWM circulation parameters caused by global warming appears to be the main causes of recent changes. Effect of the Asian Brown Cloud may also play a role in these changes.

[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.

Self-rated and observer-rated measures of well-being and distress in adolescence: an exploratory study.

PubMed

Vescovelli, Francesca; Albieri, Elisa; Ruini, Chiara

2014-01-01

The evaluation of eudaimonic well-being in adolescence is hampered by the lack of specific assessment tools. Moreover, with younger populations, the assessment of positive functioning may be biased by self-report data only, and may be more accurate by adding significant adults' evaluations. The objective of this research was to measure adolescents' well-being and prosocial behaviours using self-rated and observer-rated instruments, and their pattern of associations. The sample included 150 Italian high school adolescents. Observed-evaluation was performed by their school teachers using the Strengths and Difficulties Questionnaire. Adolescents completed Ryff's Psychological Well-being Scales and Symptom Questionnaire. Pearson' r correlations and Linear regression were performed. Self-rated dimensions of psychological well-being significantly correlated with all observer-rated dimensions, but Strengths and Difficulties Emotional symptom scale. Multiple linear regression showed that the self-rated dimensions Environmental Mastery and Personal Growth, and surprisingly not Positive Relations, are related to the observer-rated dimension Prosocial Behaviour. Adolescents with higher levels of well-being in specific dimensions tend to be perceived as less problematic by their teachers. However, some dimensions of positive functioning present discrepancies between self and observer-rated instruments. Thus, the conjunct use of self-reports and observer-rated tools for a more comprehensive assessment of students' eudaimonic well-being is recommended.

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

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.

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…

Cone-Beam Computed Tomography as a Diagnostic Method for Determination of Gingival Thickness and Distance between Gingival Margin and Bone Crest

PubMed Central

Borges, Germana Jayme; Ruiz, Luis Fernando Naldi; de Alencar, Ana Helena Gonçalves; Porto, Olavo César Lyra; Estrela, Carlos

2015-01-01

The objective of the present study was to assess cone-beam computed tomography (CBCT) as a diagnostic method for determination of gingival thickness (GT) and distance between gingival margin and vestibular (GMBC-V) and interproximal bone crests (GMBC-I). GT and GMBC-V were measured in 348 teeth and GMBC-I was measured in 377 tooth regions of 29 patients with gummy smile. GT was assessed using transgingival probing (TP), ultrasound (US), and CBCT, whereas GMBC-V and GMBC-I were assessed by transsurgical clinical evaluation (TCE) and CBCT. Statistical analyses used independent t-test, Pearson's correlation coefficient, and simple linear regression. Difference was observed for GT: between TP, CBCT, and US considering all teeth; between TP and CBCT and between TP and US in incisors and canines; between TP and US in premolars and first molars. TP presented the highest means for GT. Positive correlation and linear regression were observed between TP and CBCT, TP and US, and CBCT and US. Difference was observed for GMBC-V and GMBC-I using TCE and CBCT, considering all teeth. Correlation and linear regression results were significant for GMBC-V and GMBC-I in incisors, canines, and premolars. CBCT is an effective diagnostic method to visualize and measure GT, GMBC-V, and GMBC-I. PMID:25918737

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.

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.

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.

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.

Weighted functional linear regression models for gene-based association analysis.

PubMed

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

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.

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.

Correlation between quantified breast densities from digital mammography and 18F-FDG PET uptake.

PubMed

Lakhani, Paras; Maidment, Andrew D A; Weinstein, Susan P; Kung, Justin W; Alavi, Abass

2009-01-01

To correlate breast density quantified from digital mammograms with mean and maximum standardized uptake values (SUVs) from positron emission tomography (PET). This was a prospective study that included 56 women with a history of suspicion of breast cancer (mean age 49.2 +/- 9.3 years), who underwent 18F-fluoro-2-deoxyglucose (FDG)-PET imaging of their breasts as well as digital mammography. A computer thresholding algorithm was applied to the contralateral nonmalignant breasts to quantitatively estimate the breast density on digital mammograms. The breasts were also classified into one of four Breast Imaging Reporting and Data System categories for density. Comparisons between SUV and breast density were made using linear regression and the Student's t-test. Linear regression of mean SUV versus average breast density showed a positive relationship with a Pearson's correlation coefficient of R(2) = 0.83. The quantified breast densities and mean SUVs were significantly greater for mammographically dense than nondense breasts (p < 0.0001 for both). The average quantified densities and mean SUVs of the breasts were significantly greater for premenopausal than postmenopausal patients (p < 0.05). 8/51 (16%) of the patients had maximum SUVs that equaled 1.6 or greater. There is a positive linear correlation between quantified breast density on digital mammography and FDG uptake on PET. Menopausal status affects the metabolic activity of normal breast tissue, resulting in higher SUVs in pre- versus postmenopausal patients.

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.

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.

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.

[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.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

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.

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

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

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.

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

Comparison of Positive Youth Development for Youth With Chronic Conditions With Healthy Peers.

PubMed

Maslow, Gary R; Hill, Sherika N; Pollock, McLean D

2016-12-01

Adolescents with childhood-onset chronic condition (COCC) are at increased risk for physical and psychological problems. Despite being at greater risk and having to deal with traumatic experiences and uncertainty, most adolescents with COCC do well across many domains. The Positive Youth Development (PYD) perspective provides a framework for examining thriving in youth and has been useful in understanding positive outcomes for general populations of youth as well as at-risk youth. This study aimed to compare levels of PYD assets between youth with COCC and youth without illness. Participants with COCC were recruited from specialty pediatric clinics while healthy participants were recruited from a large pediatric primary care practice. Inclusion criteria for participants included being (1) English speaking, (2) no documented intellectual disability in electronic medical record, and (3) aged between 13 and 18 years during the recruitment period. Univariate and bivariate analyses on key variables were conducted for adolescents with and without COCC. Finally, we performed multivariable linear regressions for PYD and its subdomains. There were no significant differences between overall PYD or any of the subdomains between the two groups. Multivariable linear regression models showed no statistically significant relationship between chronic condition status and PYD or the subdomains. The findings from this study support the application of the PYD perspective to this population of youth. The results of this study suggest that approaches shown to benefit healthy youth, could be used to promote positive outcomes for youth with COCC. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Factors that influence standard automated perimetry test results in glaucoma: test reliability, technician experience, time of day, and season.

PubMed

Junoy Montolio, Francisco G; Wesselink, Christiaan; Gordijn, Marijke; Jansonius, Nomdo M

2012-10-09

To determine the influence of several factors on standard automated perimetry test results in glaucoma. Longitudinal Humphrey field analyzer 30-2 Swedish interactive threshold algorithm data from 160 eyes of 160 glaucoma patients were used. The influence of technician experience, time of day, and season on the mean deviation (MD) was determined by performing linear regression analysis of MD against time on a series of visual fields and subsequently performing a multiple linear regression analysis with the MD residuals as dependent variable and the factors mentioned above as independent variables. Analyses were performed with and without adjustment for the test reliability (fixation losses and false-positive and false-negative answers) and with and without stratification according to disease stage (baseline MD). Mean follow-up was 9.4 years, with on average 10.8 tests per patient. Technician experience, time of day, and season were associated with the MD. Approximately 0.2 dB lower MD values were found for inexperienced technicians (P < 0.001), tests performed after lunch (P < 0.001), and tests performed in the summer or autumn (P < 0.001). The effects of time of day and season appeared to depend on disease stage. Independent of these effects, the percentage of false-positive answers strongly influenced the MD with a 1 dB increase in MD per 10% increase in false-positive answers. Technician experience, time of day, season, and the percentage of false-positive answers have a significant influence on the MD of standard automated perimetry.

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.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

NASA Astrophysics Data System (ADS)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

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

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

Safety of the lateral trauma position in cervical spine injuries: a cadaver model study.

PubMed

Hyldmo, P K; Horodyski, M B; Conrad, B P; Dubose, D N; Røislien, J; Prasarn, M; Rechtine, G R; Søreide, E

2016-08-01

Endotracheal intubation is not always an option for unconscious trauma patients. Prehospital personnel are then faced with the dilemma of maintaining an adequate airway without risking deleterious movement of a potentially unstable cervical spine. To address these two concerns various alternatives to the classical recovery position have been developed. This study aims to determine the amount of motion induced by the recovery position, two versions of the HAINES (High Arm IN Endangered Spine) position, and the novel lateral trauma position (LTP). We surgically created global cervical instability between the C5 and C6 vertebrae in five fresh cadavers. We measured the rotational and translational (linear) range of motion during the different maneuvers using an electromagnetic tracking device and compared the results using a general linear mixed model (GLMM) for regression. In the recovery position, the range of motion for lateral bending was 11.9°. While both HAINES positions caused a similar range of motion, the motion caused by the LTP was 2.6° less (P = 0.037). The linear axial range of motion in the recovery position was 13.0 mm. In comparison, the HAINES 1 and 2 positions showed significantly less motion (-5.8 and -4.6 mm, respectively), while the LTP did not (-4.0 mm, P = 0.067). Our results indicate that in unconscious trauma patients, the LTP or one of the two HAINES techniques is preferable to the standard recovery position in cases of an unstable cervical spine injury. © 2016 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.

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.

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

Society of Thoracic Surgeons Risk Score Predicts Hospital Charges and Resource Utilization After Aortic Valve Replacement

PubMed Central

Arnaoutakis, George J.; George, Timothy J.; Alejo, Diane E.; Merlo, Christian A.; Baumgartner, William A.; Cameron, Duke E.; Shah, Ashish S.

2011-01-01

Context The impact of Society of Thoracic Surgeons (STS) predicted mortality risk score on resource utilization after aortic valve replacement (AVR) has not been previously studied. Objective We hypothesize that increasing STS risk scores in patients having AVR are associated with greater hospital charges. Design, Setting, and Patients Clinical and financial data for patients undergoing AVR at a tertiary care, university hospital over a ten-year period (1/2000–12/2009) were retrospectively reviewed. The current STS formula (v2.61) for in-hospital mortality was used for all patients. After stratification into risk quartiles (Q), index admission hospital charges were compared across risk strata with Rank-Sum tests. Linear regression and Spearman’s coefficient assessed correlation and goodness of fit. Multivariable analysis assessed relative contributions of individual variables on overall charges. Main Outcome Measures Inflation-adjusted index hospitalization total charges Results 553 patients had AVR during the study period. Average predicted mortality was 2.9% (±3.4) and actual mortality was 3.4% for AVR. Median charges were greater in the upper Q of AVR patients [Q1–3,$39,949 (IQR32,708–51,323) vs Q4,$62,301 (IQR45,952–97,103), p=<0.01]. On univariate linear regression, there was a positive correlation between STS risk score and log-transformed charges (coefficient: 0.06, 95%CI 0.05–0.07, p<0.01). Spearman’s correlation R-value was 0.51. This positive correlation persisted in risk-adjusted multivariable linear regression. Each 1% increase in STS risk score was associated with an added $3,000 in hospital charges. Conclusions This study showed increasing STS risk score predicts greater charges after AVR. As competing therapies such as percutaneous valve replacement emerge to treat high risk patients, these results serve as a benchmark to compare resource utilization. PMID:21497834

Conceptual model of consumer’s willingness to eat functional foods

PubMed

Babicz-Zielinska, Ewa; Jezewska-Zychowicz, Maria

The functional foods constitute the important segment of the food market. Among factors that determine the intentions to eat functional foods, the psychological factors play very important roles. Motives, attitudes and personality are key factors. The relationships between socio-demographic characteristics, attitudes and willingness to purchase functional foods were not fully confirmed. Consumers’ beliefs about health benefits from eaten foods seem to be a strong determinant of a choice of functional foods. The objective of this study was to determine relations between familiarity, attitudes, and beliefs in benefits and risks about functional foods and develop some conceptual models of willingness to eat. The sample of Polish consumers counted 1002 subjects at age 15+. The foods enriched with vitamins or minerals, and cholesterol-lowering margarine or drinks were considered. The questionnaire focused on familiarity with foods, attitudes, beliefs about benefits and risks of their consumption was constructed. The Pearson’s correlations and linear regression equations were calculated. The strongest relations appeared between attitudes, high health value and high benefits, (r = 0.722 and 0.712 for enriched foods, and 0.664 and 0.693 for cholesterol-lowering foods), and between high health value and high benefits (0.814 for enriched foods and 0.758 for cholesterol-lowering foods). The conceptual models based on linear regression of relations between attitudes and all other variables, considering or not the familiarity with the foods, were developed. The positive attitudes and declared consumption are more important for enriched foods. The beliefs on high health value and high benefits play the most important role in the purchase. The interrelations between different variables may be described by new linear regression models, with the beliefs in high benefits, positive attitudes and familiarity being most significant predictors. Health expectations and trust to functional foods are the key factors in their choice.

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...

[Correlation of age, IGF-1 serum levels, muscular mass index and their influence as determinants of isokinetic variables in patients with osteoporosis].

PubMed

Coronado-Zarco, Roberto; Diez-García, María del Pilar; Chávez-Arias, Daniel; León-Hernández, Saúl Renán; Cruz-Medina, Eva; Arellano-Hernández, Aurelia

2005-01-01

Bone and skeletal muscle mass loss is related to age. Mechanisms by which they interact have not been well established. To establish a relationship of age with serum levels of IGF-1, skeletal muscle and appendicular muscle mass index, and their influence in isokinetic parameters in osteoporotic female patients. Pearson correlation coefficient and linear regression analyses were used. There were 38 patients with a mean age of 65.16 years (range: 50-84 years), mean appendicular skeletal mass index (ASMI) of 6.3 kg/m2 (range: 4.3-8.3) and mean skeletal mass index (SMI) of 12.4 kg/m2 (range: 9.6-15.7), mean serum IGF-1 levels of 82.97 ng/ml (range: 22-177). Linear regression predicted hip mineral bone density by SMI (p = 0.19) and age (p = 0.017, r = 0.50). Some isokinetic parameters had a positive correlation for work with age. Knee acceleration time had a positive correlation with age. Osteoporosis and sarcopenia may have related pathophysiologic mechanisms. Growth factor study must include the influence of sex hormones. Some isokinetic parameters are determined by the predominant muscle fiber, skeletal mass index and age.

Speech-language pathologist job satisfaction in school versus medical settings.

PubMed

Kalkhoff, Nicole L; Collins, Dana R

2012-04-01

The goal of this study was to determine if job satisfaction differs between speech-language pathologists (SLPs) working in school settings and SLPs working in medical settings. The Job Satisfaction Survey (JSS) by Spector (1997) was sent via electronic mail to 250 SLPs in each of the 2 settings. Job satisfaction scores were computed from subscale category ratings and were compared between the 2 settings. Subscale category ratings for pay, promotion, supervision, benefits, contingent rewards, operating conditions, coworkers, nature of work, and communication were analyzed for differences between and within settings. Age, caseload size, and years-at-position were analyzed by linear regression to determine whether these factors might predict SLPs' job satisfaction. The survey had a response rate of 19.6% (N = 98 participants). Although SLPs in both settings were generally satisfied with their jobs, SLPs in medical settings had significantly higher total job satisfaction scores. Respondents from both settings had similar satisfaction ratings for subscale categories, with nature of work receiving the highest rating and operating conditions and promotion the lowest. Results of the linear regression analysis for age, caseload size, and years-at-position were not significant. Further research should evaluate important aspects of job satisfaction in both settings, especially nature of work operating conditions, and promotion.

Influence of age on the correlations of hematological and biochemical variables with the stability of erythrocyte membrane in relation to sodium dodecyl sulfate.

PubMed

de Freitas, Mariana V; Marquez-Bernardes, Liandra F; de Arvelos, Letícia R; Paraíso, Lara F; Gonçalves E Oliveira, Ana Flávia M; Mascarenhas Netto, Rita de C; Neto, Morun Bernardino; Garrote-Filho, Mario S; de Souza, Paulo César A; Penha-Silva, Nilson

2014-10-01

To evaluate the influence of age on the relationships between biochemical and hematological variables and stability of erythrocyte membrane in relation to the sodium dodecyl sulfate (SDS) in population of 105 female volunteers between 20 and 90 years. The stability of RBC membrane was determined by non-linear regression of the dependency of the absorbance of hemoglobin released as a function of SDS concentration, represented by the half-transition point of the curve (D50) and the variation in the concentration of the detergent to promote lysis (dD). There was an age-dependent increase in the membrane stability in relation to SDS. Analyses by multiple linear regression showed that this stability increase is significantly related to the hematological variable red cell distribution width (RDW) and the biochemical variables blood albumin and cholesterol. The positive association between erythrocyte stability and RDW may reflect one possible mechanism involved in the clinical meaning of this hematological index.

[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.

The association of serum prolactin concentration with inflammatory biomarkers - cross-sectional findings from the population-based Study of Health in Pomerania.

PubMed

Friedrich, Nele; Schneider, Harald J; Spielhagen, Christin; Markus, Marcello Ricardo Paulista; Haring, Robin; Grabe, Hans J; Buchfelder, Michael; Wallaschofski, Henri; Nauck, Matthias

2011-10-01

Prolactin (PRL) is involved in immune regulation and may contribute to an atherogenic phenotype. Previous results on the association of PRL with inflammatory biomarkers have been conflicting and limited by small patient studies. Therefore, we used data from a large population-based sample to assess the cross-sectional associations between serum PRL concentration and high-sensitivity C-reactive protein (hsCRP), fibrinogen, interleukin-6 (IL-6), and white blood cell (WBC) count. From the population-based Study of Health in Pomerania (SHIP), a total of 3744 subjects were available for the present analyses. PRL and inflammatory biomarkers were measured. Linear and logistic regression models adjusted for age, sex, body-mass-index, total cholesterol and glucose were analysed. Multivariable linear regression models revealed a positive association of PRL with WBC. Multivariable logistic regression analyses showed a significant association of PRL with increased IL-6 in non-smokers [highest vs lowest quintile: odds ratio 1·69 (95% confidence interval 1·10-2·58), P = 0·02] and smokers [OR 2·06 (95%-CI 1·10-3·89), P = 0·02]. Similar results were found for WBC in non-smokers [highest vs lowest quintile: OR 2·09 (95%-CI 1·21-3·61), P = 0·01)] but not in smokers. Linear and logistic regression analyses revealed no significant associations of PRL with hsCRP or fibrinogen. Serum PRL concentrations are associated with inflammatory biomarkers including IL-6 and WBC, but not hsCRP or fibrinogen. The suggested role of PRL in inflammation needs further investigation in future prospective studies. © 2011 Blackwell Publishing 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.

Periodontal inflamed surface area as a novel numerical variable describing periodontal conditions

PubMed Central

2017-01-01

Purpose A novel index, the periodontal inflamed surface area (PISA), represents the sum of the periodontal pocket depth of bleeding on probing (BOP)-positive sites. In the present study, we evaluated correlations between PISA and periodontal classifications, and examined PISA as an index integrating the discrete conventional periodontal indexes. Methods This study was a cross-sectional subgroup analysis of data from a prospective cohort study investigating the association between chronic periodontitis and the clinical features of ankylosing spondylitis. Data from 84 patients without systemic diseases (the control group in the previous study) were analyzed in the present study. Results PISA values were positively correlated with conventional periodontal classifications (Spearman correlation coefficient=0.52; P<0.01) and with periodontal indexes, such as BOP and the plaque index (PI) (r=0.94; P<0.01 and r=0.60; P<0.01, respectively; Pearson correlation test). Porphyromonas gingivalis (P. gingivalis) expression and the presence of serum P. gingivalis antibodies were significant factors affecting PISA values in a simple linear regression analysis, together with periodontal classification, PI, bleeding index, and smoking, but not in the multivariate analysis. In the multivariate linear regression analysis, PISA values were positively correlated with the quantity of current smoking, PI, and severity of periodontal disease. Conclusions PISA integrates multiple periodontal indexes, such as probing pocket depth, BOP, and PI into a numerical variable. PISA is advantageous for quantifying periodontal inflammation and plaque accumulation. PMID:29093989

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR.

PubMed

Merchak, Noelle; Silvestre, Virginie; Loquet, Denis; Rizk, Toufic; Akoka, Serge; Bejjani, Joseph

2017-01-01

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13 C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13 C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13 C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13 C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13 C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information. Graphical abstract A strategy based on the multivariate linear regression of variables obtained by a rapid 13 C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

Female married illiteracy as the most important continual determinant of total fertility rate among districts of Empowered Action Group States of India: Evidence from Annual Health Survey 2011-12.

PubMed

Kumar, Rajesh; Dogra, Vishal; Rani, Khushbu; Sahu, Kanti

2017-01-01

District level determinants of total fertility rate in Empowered Action Group states of India can help in ongoing population stabilization programs in India. Present study intends to assess the role of district level determinants in predicting total fertility rate among districts of the Empowered Action Group states of India. Data from Annual Health Survey (2011-12) was analysed using STATA and R software packages. Multiple linear regression models were built and evaluated using Akaike Information Criterion. For further understanding, recursive partitioning was used to prepare a regression tree. Female married illiteracy positively associated with total fertility rate and explained more than half (53%) of variance. Under multiple linear regression model, married illiteracy, infant mortality rate, Ante natal care registration, household size, median age of live birth and sex ratio explained 70% of total variance in total fertility rate. In regression tree, female married illiteracy was the root node and splits at 42% determined TFR <= 2.7. The next left side branch was again married illiteracy with splits at 23% to determine TFR <= 2.1. We conclude that female married illiteracy is one of the most important determinants explaining total fertility rate among the districts of an Empowered Action Group states. Focus on female literacy is required to stabilize the population growth in long run.

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.

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…

Spiritual Coping: A Gateway to Enhancing Family Communication During Cancer Treatment.

PubMed

Prouty, Anne M; Fischer, Judith; Purdom, Ann; Cobos, Everardo; Helmeke, Karen B

2016-02-01

The researchers examined the spiritual coping, family communication, and family functioning of 95 participants in 34 families by an online survey. Multilevel linear regression was used to test whether individuals' and families' higher endorsement of more use of spiritual coping strategies to deal with a member's cancer would be associated with higher scores on family communication and family functioning, and whether better communication would also be associated with higher family functioning scores. Results revealed that spiritual coping was positively associated with family communication, and family communication was positively associated with healthier family functioning. The researchers provide suggestions for further research.

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.

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.

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.

High-flow oxygen therapy: pressure analysis in a pediatric airway model.

PubMed

Urbano, Javier; del Castillo, Jimena; López-Herce, Jesús; Gallardo, José A; Solana, María J; Carrillo, Ángel

2012-05-01

The mechanism of high-flow oxygen therapy and the pressures reached in the airway have not been defined. We hypothesized that the flow would generate a low continuous positive pressure, and that elevated flow rates in this model could produce moderate pressures. The objective of this study was to analyze the pressure generated by a high-flow oxygen therapy system in an experimental model of the pediatric airway. An experimental in vitro study was performed. A high-flow oxygen therapy system was connected to 3 types of interface (nasal cannulae, nasal mask, and oronasal mask) and applied to 2 types of pediatric manikin (infant and neonatal). The pressures generated in the circuit, in the airway, and in the pharynx were measured at different flow rates (5, 10, 15, and 20 L/min). The experiment was conducted with and without a leak (mouth sealed and unsealed). Linear regression analyses were performed for each set of measurements. The pressures generated with the different interfaces were very similar. The maximum pressure recorded was 4 cm H(2)O with a flow of 20 L/min via nasal cannulae or nasal mask. When the mouth of the manikin was held open, the pressures reached in the airway and pharynxes were undetectable. Linear regression analyses showed a similar linear relationship between flow and pressures measured in the pharynx (pressure = -0.375 + 0.138 × flow) and in the airway (pressure = -0.375 + 0.158 × flow) with the closed mouth condition. According to our hypothesis, high-flow oxygen therapy systems produced a low-level CPAP in an experimental pediatric model, even with the use of very high flow rates. Linear regression analyses showed similar linear relationships between flow and pressures measured in the pharynx and in the airway. This finding suggests that, at least in part, the effects may be due to other mechanisms.

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.

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

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…

Estimating standard errors in feature network models.

PubMed

Frank, Laurence E; Heiser, Willem J

2007-05-01

Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.

Sulfur Mustard Induces Apoptosis in Lung Epithelial Cells via a Caspase Amplification Loop

DTIC Science & Technology

2010-01-01

analysis using antibodies specific for exe- cutioner caspase-3. The positions of the immunoreactive proteins are indicated. Results shown are representative...respectively. The emission at 460nm from each sample was plotted against time, and linear regression analysis was used to determine the initial veloc- ity...follows, **pɘ.01, ***pɘ.001. .4. Immunoblot analysis SDS-PAGE and transfer of separated proteins to nitrocellulosemembranes were erformed according to

Analysis of carbon dioxide bands near 2.2 micrometers

NASA Technical Reports Server (NTRS)

Abubaker, M. S.; Shaw, J. H.

1984-01-01

Carbon dioxide is one of the more important atmospheric infrared-absorbing gases due to its relatively high, and increasing, concentration. The spectral parameters of its bands are required for understanding radiative heat transfer in the atmosphere. The line intensities, positions, line half-widths, rotational constants, and band centers of three overlapping bands of CO2 near 2.2 microns are presented. Non-linear least squares (NLLS) regression procedures were employed to determine these parameters.

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.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy

NASA Astrophysics Data System (ADS)

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-01

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R2 and pseudo R2 were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R2 ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R2 = 0.31), but there was still large variability between patients in R2. The R2 from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

PubMed

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-07

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.

PubMed

Zhang, J; Feng, J-Y; Ni, Y-L; Wen, Y-J; Niu, Y; Tamba, C L; Yue, C; Song, Q; Zhang, Y-M

2017-06-01

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

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.

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.

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Shengnan; Eggermont, Jeroen; Wolterbeek, Ron; Broersen, Alexander; Busk, Carol A. G. R.; Precht, Helle; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2016-12-01

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.

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.

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

Yang, Qichun; Zhang, Xuesong; Xu, Xingya

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Prediction of pulmonary hypertension in idiopathic pulmonary fibrosis☆

PubMed Central

Zisman, David A.; Ross, David J.; Belperio, John A.; Saggar, Rajan; Lynch, Joseph P.; Ardehali, Abbas; Karlamangla, Arun S.

2007-01-01

Summary Background Reliable, noninvasive approaches to the diagnosis of pulmonary hypertension in idiopathic pulmonary fibrosis are needed. We tested the hypothesis that the forced vital capacity to diffusing capacity ratio and room air resting pulse oximetry may be combined to predict mean pulmonary artery pressure (MPAP) in idiopathic pulmonary fibrosis. Methods Sixty-one idiopathic pulmonary fibrosis patients with available right-heart catheterization were studied. We regressed measured MPAP as a continuous variable on pulse oximetry (SpO2) and percent predicted forced vital capacity (FVC) to percent-predicted diffusing capacity ratio (% FVC/% DLco) in a multivariable linear regression model. Results Linear regression generated the following equation: MPAP = −11.9+0.272 × SpO2+0.0659 × (100−SpO2)2+3.06 × (% FVC/% DLco); adjusted R2 = 0.55, p<0.0001. The sensitivity, specificity, positive predictive and negative predictive value of model-predicted pulmonary hypertension were 71% (95% confidence interval (CI): 50–89%), 81% (95% CI: 68–92%), 71% (95% CI: 51–87%) and 81% (95% CI: 68–94%). Conclusions A pulmonary hypertension predictor based on room air resting pulse oximetry and FVC to diffusing capacity ratio has a relatively high negative predictive value. However, this model will require external validation before it can be used in clinical practice. PMID:17604151

Multiple regression equations modelling of groundwater of Ajmer-Pushkar railway line region, Rajasthan (India).

PubMed

Mathur, Praveen; Sharma, Sarita; Soni, Bhupendra

2010-01-01

In the present work, an attempt is made to formulate multiple regression equations using all possible regressions method for groundwater quality assessment of Ajmer-Pushkar railway line region in pre- and post-monsoon seasons. Correlation studies revealed the existence of linear relationships (r 0.7) for electrical conductivity (EC), total hardness (TH) and total dissolved solids (TDS) with other water quality parameters. The highest correlation was found between EC and TDS (r = 0.973). EC showed highly significant positive correlation with Na, K, Cl, TDS and total solids (TS). TH showed highest correlation with Ca and Mg. TDS showed significant correlation with Na, K, SO4, PO4 and Cl. The study indicated that most of the contamination present was water soluble or ionic in nature. Mg was present as MgCl2; K mainly as KCl and K2SO4, and Na was present as the salts of Cl, SO4 and PO4. On the other hand, F and NO3 showed no significant correlations. The r2 values and F values (at 95% confidence limit, alpha = 0.05) for the modelled equations indicated high degree of linearity among independent and dependent variables. Also the error % between calculated and experimental values was contained within +/- 15% limit.

Impact of organizational climate on organizational commitment and perceived organizational performance: empirical evidence from public hospitals.

PubMed

Berberoglu, Aysen

2018-06-01

Extant literature suggested that positive organizational climate leads to higher levels of organizational commitment, which is an important concept in terms of employee attitudes, likewise, the concept of perceived organizational performance, which can be assumed as a mirror of the actual performance. For healthcare settings, these are important matters to consider due to the fact that the service is delivered thoroughly by healthcare workers to the patients. Therefore, attitudes and perceptions of the employees can influence how they deliver the service. The aim of this study was to evaluate healthcare employees' perceptions of organizational climate and test the hypothesized impact of organizational climate on organizational commitment and perceived organizational performance. The study adopted a quantitative approach, by collecting data from the healthcare workers currently employed in public hospitals in North Cyprus, utilizing a self-administered questionnaire. Collected data was analyzed with the help of Statistical Package for Social Sciences, and ANOVA and Linear Regression analyses were used to test the hypothesis. Results revealed that organizational climate is highly correlated with organizational commitment and perceived organizational performance. Simple linear regression outcomes indicated that organizational climate is significant in predicting organizational commitment and perceived organizational performance. There was a positive and linear relationship between organizational climate with organizational commitment and perceived organizational performance. Results from the regression analysis suggested that organizational climate has an impact on predicting organizational commitment and perceived organizational performance of the employees in public hospitals of North Cyprus. Organizational climate was found to be statistically significant in determining the organizational commitment of the employees. The results of the study provided some critical issues regarding the relationship of three concepts in the study. According to the findings, if the organizational climate scores of the employees are high, organizational commitment scores of the employees are high at the same time. In other words, if the employees in public hospitals of North Cyprus perceive the organizational climate in a positive way, they will have higher levels of organizational commitment. Findings suggested that organizational climate is an important factor in healthcare settings in terms of employee commitment and how employees perceive organizational performance, which would lead to significant results about the provision of service in healthcare organizations.

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.

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.

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.

Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy

NASA Astrophysics Data System (ADS)

Lin, Tong; Cerviño, Laura I.; Tang, Xiaoli; Vasconcelos, Nuno; Jiang, Steve B.

2009-02-01

Accurate lung tumor tracking in real time is a keystone to image-guided radiotherapy of lung cancers. Existing lung tumor tracking approaches can be roughly grouped into three categories: (1) deriving tumor position from external surrogates; (2) tracking implanted fiducial markers fluoroscopically or electromagnetically; (3) fluoroscopically tracking lung tumor without implanted fiducial markers. The first approach suffers from insufficient accuracy, while the second may not be widely accepted due to the risk of pneumothorax. Previous studies in fluoroscopic markerless tracking are mainly based on template matching methods, which may fail when the tumor boundary is unclear in fluoroscopic images. In this paper we propose a novel markerless tumor tracking algorithm, which employs the correlation between the tumor position and surrogate anatomic features in the image. The positions of the surrogate features are not directly tracked; instead, we use principal component analysis of regions of interest containing them to obtain parametric representations of their motion patterns. Then, the tumor position can be predicted from the parametric representations of surrogates through regression. Four regression methods were tested in this study: linear and two-degree polynomial regression, artificial neural network (ANN) and support vector machine (SVM). The experimental results based on fluoroscopic sequences of ten lung cancer patients demonstrate a mean tracking error of 2.1 pixels and a maximum error at a 95% confidence level of 4.6 pixels (pixel size is about 0.5 mm) for the proposed tracking algorithm.

Impacts of a mass vaccination campaign against pandemic H1N1 2009 influenza in Taiwan: a time-series regression analysis.

PubMed

Wu, Un-In; Wang, Jann-Tay; Chang, Shan-Chwen; Chuang, Yu-Chung; Lin, Wei-Ru; Lu, Min-Chi; Lu, Po-Liang; Hu, Fu-Chang; Chuang, Jen-Hsiang; Chen, Yee-Chun

2014-06-01

A multicenter, hospital-wide, clinical and epidemiological study was conducted to assess the effectiveness of the mass influenza vaccination program during the 2009 H1N1 influenza pandemic, and the impact of the prioritization strategy among people at different levels of risk. Among the 34 359 medically attended patients who displayed an influenza-like illness and had a rapid influenza diagnostic test (RIDT) at one of the three participating hospitals, 21.0% tested positive for influenza A. The highest daily number of RIDT-positive cases in each hospital ranged from 33 to 56. A well-fitted multiple linear regression time-series model (R(2)=0.89) showed that the establishment of special community flu clinics averted an average of nine cases daily (p=0.005), and an increment of 10% in daily mean level of population immunity against pH1N1 through vaccination prevented five cases daily (p<0.001). Moreover, the regression model predicted five-fold or more RIDT-positive cases if the mass influenza vaccination program had not been implemented, and 39.1% more RIDT-positive cases if older adults had been prioritized for vaccination above school-aged children. Mass influenza vaccination was an effective control measure, and school-aged children should be assigned a higher priority for vaccination than older adults during an influenza pandemic. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

Positive predictors of quality of life for postpartum mothers with a history of childhood maltreatment.

PubMed

Irwin, Jessica L; Beeghly, Marjorie; Rosenblum, Katherine L; Muzik, Maria

2016-12-01

The postpartum period brings a host of biopsychosocial, familial, and economic changes, which may be challenging for new mothers, especially those with trauma histories. Trauma-exposed women are at heightened risk for psychiatric symptomatology and reduced quality of life. The current study sought to evaluate whether a set of hypothesized promotive factors assessed during the first 18 months postpartum (positive parenting, family cohesion, and maternal resilience) are associated with life satisfaction in this population, after controlling for income and postpartum psychiatric symptoms. Analyses were based on data collected for 266 mother-infant dyads from a longitudinal cohort study, Maternal Anxiety during the Childbearing Years (MACY), of women oversampled for childhood maltreatment history. Hierarchical linear regression was used to evaluate the study hypotheses. Consistent with prior work, greater postpartum psychiatric symptoms and less income predicted poor perceptions of life quality. In hierarchical regressions controlling for income and psychiatric symptoms, positive parenting and family cohesion predicted unique variance in mothers' positive perceptions of life quality, and resilience was predictive beyond all other factors. Factors from multiple levels of analysis (maternal, dyadic, and familial) may serve as promotive factors predicting positive perceptions of life quality among women with childhood trauma histories, even those struggling with high levels of psychiatric or economic distress.

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…

Ridge Regression Signal Processing

NASA Technical Reports Server (NTRS)

Kuhl, Mark R.

1990-01-01

The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

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.

Internal and external environmental factors affecting the performance of hospital-based home nursing care.

PubMed

Noh, J-W; Kwon, Y-D; Yoon, S-J; Hwang, J-I

2011-06-01

Numerous studies on HNC services have been carried out by signifying their needs, efficiency and effectiveness. However, no study has ever been performed to determine the critical factors associated with HNC's positive results despite the deluge of positive studies on the service. This study included all of the 89 training hospitals that were practising HNC service in Korea as of November 2006. The input factors affecting the performance were classified as either internal or external environmental factors. This analysis was conducted to understand the impact that the corresponding factors had on performance. Data were analysed by using multiple linear regressions. The internal and external environment variables affected the performance of HNC based on univariate analysis. The meaningful variables were internal environmental factors. Specifically, managerial resource (the number of operating beds and the outpatient/inpatient ratio) were meaningful when the multiple linear regression analysis was performed. Indeed, the importance of organizational culture (the passion of HNC nurses) was significant. This study, considering the limited market size of Korea, illustrates that the critical factor for the development of hospital-led HNC lies with internal environmental factors rather than external ones. Among the internal environmental factors, the hospitals' managerial resource-related factors (specifically, the passion of nurses) were the most important contributing element. © 2011 The Authors. International Nursing Review © 2011 International Council of Nurses.

Female married illiteracy as the most important continual determinant of total fertility rate among districts of Empowered Action Group States of India: Evidence from Annual Health Survey 2011–12

PubMed Central

Kumar, Rajesh; Dogra, Vishal; Rani, Khushbu; Sahu, Kanti

2017-01-01

Background: District level determinants of total fertility rate in Empowered Action Group states of India can help in ongoing population stabilization programs in India. Objective: Present study intends to assess the role of district level determinants in predicting total fertility rate among districts of the Empowered Action Group states of India. Material and Methods: Data from Annual Health Survey (2011-12) was analysed using STATA and R software packages. Multiple linear regression models were built and evaluated using Akaike Information Criterion. For further understanding, recursive partitioning was used to prepare a regression tree. Results: Female married illiteracy positively associated with total fertility rate and explained more than half (53%) of variance. Under multiple linear regression model, married illiteracy, infant mortality rate, Ante natal care registration, household size, median age of live birth and sex ratio explained 70% of total variance in total fertility rate. In regression tree, female married illiteracy was the root node and splits at 42% determined TFR <= 2.7. The next left side branch was again married illiteracy with splits at 23% to determine TFR <= 2.1. Conclusion: We conclude that female married illiteracy is one of the most important determinants explaining total fertility rate among the districts of an Empowered Action Group states. Focus on female literacy is required to stabilize the population growth in long run. PMID:29416999

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

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Resilience and positive affect contribute to lower cancer-related fatigue among Chinese patients with gastric cancer.

PubMed

Zou, Guiyuan; Li, Ye; Xu, Ruicai; Li, Ping

2018-04-01

To investigate the prevalence of cancer-related fatigue and explore the relationship between resilience, positive affect, and fatigue among Chinese patients with gastric cancer. Cancer-related fatigue is the most distressing symptom reported frequently by cancer patients during both treatment and survival phases. Resilience and positive affect as vital protective factors against cancer-related fatigue have been examined, but the underlying psychological mechanisms are not well understood. A cross-sectional study. Two hundred and three gastric cancer patients were enrolled from three hospitals in China. The Cancer Fatigue Scale, the positive affect subscale of the Positive and Negative Affect Schedule and the Connor-Davidson Resilience Scale (CD-RISC10) were administered. Hierarchical linear regression modelling was conducted to examine the association between resilience and cancer-related fatigue, and the mediating effect of positive affect. The incidence of clinically relevant fatigue among patients with gastric cancer was 91.6%. Regression analysis showed that resilience was negatively associated with cancer-related fatigue, explaining 15.4% of variance in cancer-related fatigue. Mediation analysis showed that high resilience was associated with increased positive affect, which was associated with decreased cancer-related fatigue. Cancer-related fatigue is prevalent among patients with gastric cancer. Positive affect may mediate the relationship between resilience and cancer-related fatigue. Interventions that attend to resilience training and promotion of positive affect may be the focus for future clinical and research endeavours. © 2017 John Wiley & Sons Ltd.

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.

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

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes.

PubMed

Saha, Sanjib; Gerdtham, Ulf-G; Siddiqui, Faiza; Bennet, Louise

2018-02-27

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes

PubMed Central

Siddiqui, Faiza

2018-01-01

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations. PMID:29495529

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.

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study.

PubMed

Komaroff, Marina

2016-01-01

The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06-2.66). The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation.

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study

PubMed Central

Komaroff, Marina

2016-01-01

Objective. The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. Methods. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. Results. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06–2.66). Conclusions. The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation. PMID:26953120

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.

Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

PubMed Central

Wang, Fengfeng; Wong, S. C. Cesar; Chan, Lawrence W. C.; Cho, William C. S.; Yip, S. P.; Yung, Benjamin Y. M.

2014-01-01

Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC. PMID:24895601

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.

Depression and coping in subthreshold eating disorders.

PubMed

Dennard, E Eliot; Richards, C Steven

2013-08-01

The eating disorder literature has sought to understand the role of comorbid psychiatric diagnoses and coping in relation to eating disorders. The present research extends these findings by studying the relationships among depression, coping, and the entire continuum of disordered eating behaviors, with an emphasis on subthreshold eating disorders. 109 undergraduate females completed questionnaires to assess disordered eating symptoms, depressive symptoms, and the use of active and avoidant coping mechanisms. Hypotheses were tested using bivariate linear regression and multivariate linear regression. Results indicated that depression was a significant predictor of disordered eating symptoms after controlling for relationships between depression and coping. Although avoidant coping was positively associated with disordered eating, it was not a significant predictor after controlling for depression and coping. Previous research has found associations between depression and diagnosable eating disorders, and this research extends those findings to the entire continuum of disordered eating. Future research should continue to investigate the predictors and correlates of the disordered eating continuum using more diverse samples. Testing for mediation and moderation among these variables may also be a fruitful area of investigation. Published by Elsevier Ltd.

Palus Somni - Anomalies in the correlation of Al/Si X-ray fluorescence intensity ratios and broad-spectrum visible albedos. [lunar surface mineralogy

NASA Technical Reports Server (NTRS)

Clark, P. E.; Andre, C. G.; Adler, I.; Weidner, J.; Podwysocki, M.

1976-01-01

The positive correlation between Al/Si X-ray fluorescence intensity ratios determined during the Apollo 15 lunar mission and a broad-spectrum visible albedo of the moon is quantitatively established. Linear regression analysis performed on 246 1 degree geographic cells of X-ray fluorescence intensity and visible albedo data points produced a statistically significant correlation coefficient of .78. Three distinct distributions of data were identified as (1) within one standard deviation of the regression line, (2) greater than one standard deviation below the line, and (3) greater than one standard deviation above the line. The latter two distributions of data were found to occupy distinct geographic areas in the Palus Somni region.

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.

Overhead longwave infrared hyperspectral material identification using radiometric models

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

Zelinski, M. E.

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimalmore » atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.« less

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.

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

[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.

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.)

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.

Total-skin electron irradiation for cutaneous T-cell lymphoma: the Northern Israel Oncology Center experience.

PubMed

Kuten, A; Stein, M; Mandelzweig, Y; Tatcher, M; Yaacov, G; Epelbaum, R; Rosenblatt, E

1991-07-01

Total-skin electron irradiation (TSEI) is effective and frequently used in the treatment of cutaneous T-cell lymphoma. A treatment technique has been developed at our center, using the Philips SL 75/10 linear accelerator. In our method, the patient is irradiated in a recumbent position by five pairs of uncollimated electron beams at a source to skin distance of 150 cm. This method provides a practical solution to clinical requirements with respect to uniformity of electron dose and low X-ray contamination. Its implementation does not require special equipment or modification of the linear accelerator, 19 of 23 patients (83%) with mycosis fungoides, treated by this method, achieved complete regression of their cutaneous lesions.

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.

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

Society of Thoracic Surgeons Risk Score predicts hospital charges and resource use after aortic valve replacement.

PubMed

Arnaoutakis, George J; George, Timothy J; Alejo, Diane E; Merlo, Christian A; Baumgartner, William A; Cameron, Duke E; Shah, Ashish S

2011-09-01

The impact of Society of Thoracic Surgeons predicted mortality risk score on resource use has not been previously studied. We hypothesize that increasing Society of Thoracic Surgeons risk scores in patients undergoing aortic valve replacement are associated with greater hospital charges. Clinical and financial data for patients undergoing aortic valve replacement at The Johns Hopkins Hospital over a 10-year period (January 2000 to December 2009) were reviewed. The current Society of Thoracic Surgeons formula (v2.61) for in-hospital mortality was used for all patients. After stratification into risk quartiles, index admission hospital charges were compared across risk strata with rank-sum and Kruskal-Wallis tests. Linear regression and Spearman's coefficient assessed correlation and goodness of fit. Multivariable analysis assessed relative contributions of individual variables on overall charges. A total of 553 patients underwent aortic valve replacement during the study period. Average predicted mortality was 2.9% (±3.4) and actual mortality was 3.4% for aortic valve replacement. Median charges were greater in the upper quartile of patients undergoing aortic valve replacement (quartiles 1-3, $39,949 [interquartile range, 32,708-51,323] vs quartile 4, $62,301 [interquartile range, 45,952-97,103], P < .01]. On univariate linear regression, there was a positive correlation between Society of Thoracic Surgeons risk score and log-transformed charges (coefficient, 0.06; 95% confidence interval, 0.05-0.07; P < .01). Spearman's correlation R-value was 0.51. This positive correlation persisted in risk-adjusted multivariable linear regression. Each 1% increase in Society of Thoracic Surgeons risk score was associated with an added $3000 in hospital charges. This is the first study to show that increasing Society of Thoracic Surgeons risk score predicts greater charges after aortic valve replacement. As competing therapies, such as percutaneous valve replacement, emerge to treat high-risk patients, these results serve as a benchmark to compare resource use. Copyright © 2011 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Economic dependency and divorce: implications for the private sphere.

PubMed

Clark, R

1990-01-01

"This paper asserts a connection between economic dependency and divorce. It argues that, because dependency deprives women of equal access to the public sphere and because it confines them, through normative definition, to the private sphere, it reduces their likelihood of seeking divorce. The paper also argues, contrary to recent findings, that socioeconomic development should be linearly and positively associated with divorce. Data from 51 nations are examined and multiple regression analysis [suggests] considerable support for these arguments." excerpt

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.

Incorporating TPC observed parameters and QuikSCAT surface wind observations into hurricane initialization using 4D-VAR approaches

NASA Astrophysics Data System (ADS)

Park, Kyungjeen

This study aims to develop an objective hurricane initialization scheme which incorporates not only forecast model constraints but also observed features such as the initial intensity and size. It is based on the four-dimensional variational (4D-Var) bogus data assimilation (BDA) scheme originally proposed by Zou and Xiao (1999). The 4D-Var BDA consists of two steps: (i) specifying a bogus sea level pressure (SLP) field based on parameters observed by the Tropical Prediction Center (TPC) and (ii) assimilating the bogus SLP field under a forecast model constraint to adjust all model variables. This research focuses on improving the specification of the bogus SLP indicated in the first step. Numerical experiments are carried out for Hurricane Bonnie (1998) and Hurricane Gordon (2000) to test the sensitivity of hurricane track and intensity forecasts to specification of initial vortex. Major results are listed below: (1) A linear regression model is developed for determining the size of initial vortex based on the TPC observed radius of 34kt. (2) A method is proposed to derive a radial profile of SLP from QuikSCAT surface winds. This profile is shown to be more realistic than ideal profiles derived from Fujita's and Holland's formulae. (3) It is found that it takes about 1 h for hurricane prediction model to develop a conceptually correct hurricane structure, featuring a dominant role of hydrostatic balance at the initial time and a dynamic adjustment in less than 30 minutes. (4) Numerical experiments suggest that track prediction is less sensitive to the specification of initial vortex structure than intensity forecast. (5) Hurricane initialization using QuikSCAT-derived initial vortex produced a reasonably good forecast for hurricane landfall, with a position error of 25 km and a 4-h delay at landfalling. (6) Numerical experiments using the linear regression model for the size specification considerably outperforms all the other formulations tested in terms of the intensity prediction for both Hurricanes. For examples, the maximum track error is less than 110 km during the entire three-day forecasts for both hurricanes. The simulated Hurricane Gordon using the linear regression model made a nearly perfect landfall, with no position error and only 1-h error in landfalling time. (7) Diagnosis of model output indicates that the initial vortex specified by the linear regression model produces larger surface fluxes of sensible heat, latent heat and moisture, as well as stronger downward angular momentum transport than all the other schemes do. These enhanced energy supplies offset the energy lost caused by friction and gravity wave propagation, allowing for the model to maintain a strong and realistic hurricane during the entire forward model integration.

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.

Modeling Laterality of the Globus Pallidus Internus in Patients With Parkinson's Disease.

PubMed

Sharim, Justin; Yazdi, Daniel; Baohan, Amy; Behnke, Eric; Pouratian, Nader

2017-04-01

Neurosurgical interventions such as deep brain stimulation surgery of the globus pallidus internus (GPi) play an important role in the treatment of medically refractory Parkinson's disease (PD), and require high targeting accuracy. Variability in the laterality of the GPi across patients with PD has not been well characterized. The aim of this report is to identify factors that may contribute to differences in position of the motor region of GPi. The charts and operative reports of 101 PD patients following deep brain stimulation surgery (70 males, aged 11-78 years) representing 201 GPi were retrospectively reviewed. Data extracted for each subject include age, gender, anterior and posterior commissures (AC-PC) distance, and third ventricular width. Multiple linear regression, stepwise regression, and relative importance of regressors analysis were performed to assess the predictive ability of these variables on GPi laterality. Multiple linear regression for target vs. third ventricular width, gender, AC-PC distance, and age were significant for normalized linear regression coefficients of 0.333 (p < 0.0001), 0.206 (p = 0.00219), 0.168 (p = 0.0119), and 0.159 (p = 0.0136), respectively. Third ventricular width, gender, AC-PC distance, and age each account for 44.06% (21.38-65.69%, 95% CI), 20.82% (10.51-35.88%), 21.46% (8.28-37.05%), and 13.66% (2.62-28.64%) of the R 2 value, respectively. Effect size calculation was significant for a change in the GPi laterality of 0.19 mm per mm of ventricular width, 0.11 mm per mm of AC-PC distance, 0.017 mm per year in age, and 0.54 mm increase for male gender. This variability highlights the limitations of indirect targeting alone, and argues for the continued use of MRI as well as intraoperative physiological testing to account for such factors that contribute to patient-specific variability in GPi localization. © 2016 International Neuromodulation Society.

Integrated Central-Autonomic Multifractal Complexity in the Heart Rate Variability of Healthy Humans

PubMed Central

Lin, D. C.; Sharif, A.

2012-01-01

Purpose of Study: The aim of this study was to characterize the central-autonomic interaction underlying the multifractality in heart rate variability (HRV) of healthy humans. Materials and Methods: Eleven young healthy subjects participated in two separate ~40 min experimental sessions, one in supine (SUP) and one in, head-up-tilt (HUT), upright (UPR) body positions. Surface scalp electroencephalography (EEG) and electrocardiogram (ECG) were collected and fractal correlation of brain and heart rate data was analyzed based on the idea of relative multifractality. The fractal correlation was further examined with the EEG, HRV spectral measures using linear regression of two variables and principal component analysis (PCA) to find clues for the physiological processing underlying the central influence in fractal HRV. Results: We report evidence of a central-autonomic fractal correlation (CAFC) where the HRV multifractal complexity varies significantly with the fractal correlation between the heart rate and brain data (P = 0.003). The linear regression shows significant correlation between CAFC measure and EEG Beta band spectral component (P = 0.01 for SUP and P = 0.002 for UPR positions). There is significant correlation between CAFC measure and HRV LF component in the SUP position (P = 0.04), whereas the correlation with the HRV HF component approaches significance (P = 0.07). The correlation between CAFC measure and HRV spectral measures in the UPR position is weak. The PCA results confirm these findings and further imply multiple physiological processes underlying CAFC, highlighting the importance of the EEG Alpha, Beta band, and the HRV LF, HF spectral measures in the supine position. Discussion and Conclusion: The findings of this work can be summarized into three points: (i) Similar fractal characteristics exist in the brain and heart rate fluctuation and the change toward stronger fractal correlation implies the change toward more complex HRV multifractality. (ii) CAFC is likely contributed by multiple physiological mechanisms, with its central elements mainly derived from the EEG Alpha, Beta band dynamics. (iii) The CAFC in SUP and UPR positions is qualitatively different, with a more predominant central influence in the fractal HRV of the UPR position. PMID:22403548

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.

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.

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.

Impact of a New Law to Reduce the Legal Blood Alcohol Concentration Limit - A Poisson Regression Analysis and Descriptive Approach.

PubMed

Nistal-Nuño, Beatriz

2017-03-31

In Chile, a new law introduced in March 2012 lowered the blood alcohol concentration (BAC) limit for impaired drivers from 0.1% to 0.08% and the BAC limit for driving under the influence of alcohol from 0.05% to 0.03%, but its effectiveness remains uncertain. The goal of this investigation was to evaluate the effects of this enactment on road traffic injuries and fatalities in Chile. A retrospective cohort study. Data were analyzed using a descriptive and a Generalized Linear Models approach, type of Poisson regression, to analyze deaths and injuries in a series of additive Log-Linear Models accounting for the effects of law implementation, month influence, a linear time trend and population exposure. A review of national databases in Chile was conducted from 2003 to 2014 to evaluate the monthly rates of traffic fatalities and injuries associated to alcohol and in total. It was observed a decrease by 28.1 percent in the monthly rate of traffic fatalities related to alcohol as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 20.9 percent (P<0.001).There was a reduction in the monthly rate of traffic injuries related to alcohol by 10.5 percent as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 24.8 percent (P<0.001). Positive results followed from this new 'zero-tolerance' law implemented in 2012 in Chile. Chile experienced a significant reduction in alcohol-related traffic fatalities and injuries, being a successful public health intervention.

Ambient temperature and FIT performance in the Emilia-Romagna colorectal cancer screening programme.

PubMed

De Girolamo, Gianfranco; Goldoni, Carlo A; Corradini, Rossella; Giuliani, Orietta; Falcini, Fabio; Sassoli De'Bianchi, Priscilla; Naldoni, Carlo; Zauli Sajani, Stefano

2016-12-01

To assess the impact of ambient temperature on faecal immunochemical test (FIT) performance in the colorectal cancer screening programme of Emilia-Romagna (Italy). A population-based retrospective cohort study on data from 2005 to 2011. Positive rate, detection rate, and positive predictive value rate for cancers and adenomas, and incidence rate of interval cancers after negative tests were analysed using Poisson regression models. In addition to ambient temperature, gender, age, screening history, and Local Health Unit were also considered. In 1,521,819 tests analysed, the probability of a positive result decreased linearly with increasing temperature. Point estimates and 95% Confidence Intervals were estimated for six temperature classes (<5, 5 |-10, 10 |-15, 15 |-20, 20|-25 and ≥25℃), and referred to the 5|-10℃ class. The positive rate ratio was significantly related to temperature increase: 0.99 (0.97-1.02), 1, 0.98 (0.96-1.00), 0.96 (0.94-0.99), 0.93 (0.91-0.96), 0.92 (0.89-0.95). A linear trend was also evident for advanced adenoma detection rate ratio: 1.00 (0.96-1.04), 1, 0.98 (0.93-1.02), 0.96 (0.92-1.00), 0.92 (0.88-0.96), 0.94 (0.88-1.01). The effect was less linear, but still important, for cancer detection rates: 0.95 (0.85-1.06), 1, 1.00 (0.90-1.10), 0.94 (0.85-1.05), 0.81 (0.72-0.92), 0.93 (0.80-1.09). No association or linear trend was found for positive predictive values or risk of interval cancer, despite an excess of +16% in the highest temperature class for interval cancer. Ambient temperatures can affect screening performance. Continued monitoring is needed to verify the effect of introducing FIT tubes with a new buffer, which should guarantee a higher stability of haemoglobin. © The Author(s) 2016.

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.

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.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

General anesthesia in orthognathic surgeries: does it affect horizontal jaw relations?

PubMed

Yaghmaei, Masoud; Ejlali, Masoud; Nikzad, Sekieneh; Sayyedi, Ashraf; Shafaeifard, Shahrouz; Pourdanesh, Fereydoun

2013-10-01

The aim of this study was to evaluate the influence of general anesthesia on centric jaw relation (CR) records of orthognathic surgical patients in different postural positions. Fifty patients undergoing orthognathic surgery at Taleghani Hospital (Tehran, Iran) in 2008 were prospectively studied. CR records were obtained in conscious patients in 2 different positions (upright and supine) 1 day before surgery and in the supine position under general anesthesia. The impressions were made and the corresponding casts were mounted on a semiadjustable articulator. Differences were measured to the nearest 0.10 mm using a caliper. Paired t test and a general linear regression model were used for statistical analysis. Fifty patients (27 women and 23 men; mean age, 22.5 ± 3.5 yr) were enrolled. Angle Class I (group I), Class II (group II), and Class III (group III) malocclusions were detected in 16% (n = 8), 54% (n = 27), and 30% (n = 15) of patients, respectively. Although mean changes were smaller than 2 mm, statistically significant differences were found by paired t test in all Angle classification groups. No significant differences were found between the supine and conscious and the supine and unconscious patient positions in groups I and III (P > .05). However, in group II, this difference was statistically significant (P = .001). Regarding the impact of anesthesia on CR records of patients with different Angle classes, this study showed a significant effect, particularly in group II. Assessment of the outcome of interest (difference between the supine and conscious and the upright and conscious positions) versus position after adjustment for Angle class using a general linear regression model showed that the difference was significant only for Angle class (β = +0.29; t = 3.05; P = .003). General anesthesia may not adversely affect the mandibular condylar position in orthognathic patients in a supine position compared with a supine and conscious position. However, among all study groups, group II showed more significant changes in CR records under general anesthesia. Oral and maxillofacial surgeons should be well aware of such changes in these particular positions and avoid possible mismanagement and potential complications. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Dimensional analysis of detrimental ozone generation by positive wire-to-plate corona discharge in air

NASA Astrophysics Data System (ADS)

Bo, Z.; Chen, J. H.

2010-02-01

The dimensional analysis technique is used to formulate a correlation between ozone generation rate and various parameters that are important in the design and operation of positive wire-to-plate corona discharges in indoor air. The dimensionless relation is determined by linear regression analysis based on the results from 36 laboratory-scale experiments. The derived equation is validated by experimental data and a numerical model published in the literature. Applications of such derived equation are illustrated through an example selection of the appropriate set of operating conditions in the design/operation of a photocopier to follow the federal regulations of ozone emission. Finally, a new current-voltage characteristic equation is proposed for positive wire-to-plate corona discharges based on the derived dimensionless equation.

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.

Effect of feeding heat-treated colostrum on risk for infection with Mycobacterium avium ssp. paratuberculosis, milk production, and longevity in Holstein dairy cows.

PubMed

Godden, S M; Wells, S; Donahue, M; Stabel, J; Oakes, J M; Sreevatsan, S; Fetrow, J

2015-08-01

In summer 2007, a randomized controlled field trial was initiated on 6 large Midwest commercial dairy farms to investigate the effect of feeding heat-treated (HT) colostrum on transmission of Mycobacterium avium ssp. paratuberculosis (MAP) and on future milk production and longevity within the herd. On each farm, colostrum was collected daily from fresh cows, pooled, divided into 2 aliquots, and then 1 aliquot was heat-treated in a commercial batch pasteurizer at 60°C for 60min. A sample from each batch of colostrum was collected for PCR testing (MAP-positive vs. MAP-negative). Newborn heifer calves were removed from the dam within 30 to 60min of birth and systematically assigned to be fed 3.8 L of either fresh (FR; n=434) or heat-treated (HT; n=490) colostrum within 2h of birth. After reaching adulthood (>2 yr old), study animals were tested once annually for 3 yr (2010, 2011, 2012) for infection with MAP using serum ELISA and fecal culture. Lactation records describing milk production data and death or culling events were collected during the 3-yr testing period. Multivariable model logistic and linear regression was used to investigate the effect of feeding HT colostrum on risk for testing positive to MAP during the 3-yr testing period (positive/negative; logistic regression) and on first and second lactation milk yield (kg/cow; linear regression), respectively. Cox proportional hazards regression was used to investigate the effect of feeding HT colostrum on risk and time to removal from the herd. Fifteen percent of all study animals were fed PCR-positive colostrum. By the end of the 3-yr testing period, no difference was noted in the proportion of animals testing positive for MAP, with either serum ELISA or fecal culture, when comparing the HT group (10.5%) versus the FR group (8.1%). There was no effect of treatment on first- (HT=11.797kg; FR=11,671kg) or second-lactation (HT=11,013kg; FR=11,235kg) milk production. The proportion of cows leaving the herd by study conclusion was not different for animals originally fed HT (68.0%) versus FR (71.7%) colostrum. Although a previous study showed that feeding HT colostrum (60°C for 60min) produces short-term benefits, including improved passive transfer of IgG and reduced morbidity in the preweaning period, the current study found no benefit of feeding HT colostrum on long-term outcomes including risk for transmission of Mycobacterium avium ssp. paratuberculosis, milk production in the first and second lactation, and longevity within the herd. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

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.

Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

EMG-Torque Relation in Chronic Stroke: A Novel EMG Complexity Representation With a Linear Electrode Array.

PubMed

Zhang, Xu; Wang, Dongqing; Yu, Zaiyang; Chen, Xiang; Li, Sheng; Zhou, Ping

2017-11-01

This study examines the electromyogram (EMG)-torque relation for chronic stroke survivors using a novel EMG complexity representation. Ten stroke subjects performed a series of submaximal isometric elbow flexion tasks using their affected and contralateral arms, respectively, while a 20-channel linear electrode array was used to record surface EMG from the biceps brachii muscles. The sample entropy (SampEn) of surface EMG signals was calculated with both global and local tolerance schemes. A regression analysis was performed between SampEn of each channel's surface EMG and elbow flexion torque. It was found that a linear regression can be used to well describe the relation between surface EMG SampEn and the torque. Each channel's root mean square (RMS) amplitude of surface EMG signal in the different torque level was computed to determine the channel with the highest EMG amplitude. The slope of the regression (observed from the channel with the highest EMG amplitude) was smaller on the impaired side than on the nonimpaired side in 8 of the 10 subjects, regardless of the tolerance scheme (global or local) and the range of torques (full or matched range) used for comparison. The surface EMG signals from the channels above the estimated muscle innervation zones demonstrated significantly lower levels of complexity compared with other channels between innervation zones and muscle tendons. The study provides a novel point of view of the EMG-torque relation in the complexity domain, and reveals its alterations post stroke, which are associated with complex neural and muscular changes post stroke. The slope difference between channels with regard to innervation zones also confirms the relevance of electrode position in surface EMG analysis.

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

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.

Intimate Partner Violence Associated with Postpartum Depression, Regardless of Socioeconomic Status.

PubMed

Kothari, Catherine L; Liepman, Michael R; Shama Tareen, R; Florian, Phyllis; Charoth, Remitha M; Haas, Suzanne S; McKean, Joseph W; Moe, Angela; Wiley, James; Curtis, Amy

2016-06-01

Objective This study examined whether socioeconomic status moderated the association between intimate partner violence (IPV) and postpartum depression among a community-based sample of women. Defining the role of poverty in the risk of postpartum depression for IPV victims enables prioritization of health promotion efforts to maximize the effectiveness of existing maternal-infant resources. Methods This cross-sectional telephone-survey study interviewed 301 postpartum women 2 months after delivery, screening them for IPV and depression [using Edinburgh Postnatal Depression Scale (EPDS)]. Socioeconomic status was defined by insurance (Medicaid-paid-delivery or not). This analysis controlled for the following covariates, collected through interview and medical-record review: demographics, obstetric history, prenatal health and additional psychosocial risk factors. After adjusting for significant covariates, multiple linear regression was conducted to test whether socioeconomic status confounded or moderated IPV's relationship with EPDS-score. Results Ten percent of participants screened positive for postpartum depression, 21.3 % screened positive for current or previous adult emotional or physical abuse by a partner, and 32.2 % met poverty criteria. IPV and poverty were positively associated with each other (χ(2) (1) = 11.76, p < .001) and with EPDS score (IPV: beta 3.2 (CI 2.0, 4.5) p < .001, poverty: beta 1.3 (CI 0.2, 2.4) p = .017). In the multiple linear regression, IPV remained significantly associated, but poverty did not (IPV: adjusted beta 3.1 (CI 1.8, 4.3) p < .001, poverty: adjusted beta 0.8 (CI -0.3, 1.9) p = .141), and no statistically significant interaction between IPV and poverty was found. Conclusions Study findings illustrated that IPV was strongly associated with postpartum depression, outweighing the influence of socioeconomic status upon depression for postpartum women.

Relationship between neighbourhood socioeconomic position and neighbourhood public green space availability: An environmental inequality analysis in a large German city applying generalized linear models.

PubMed

Schüle, Steffen Andreas; Gabriel, Katharina M A; Bolte, Gabriele

2017-06-01

The environmental justice framework states that besides environmental burdens also resources may be social unequally distributed both on the individual and on the neighbourhood level. This ecological study investigated whether neighbourhood socioeconomic position (SEP) was associated with neighbourhood public green space availability in a large German city with more than 1 million inhabitants. Two different measures were defined for green space availability. Firstly, percentage of green space within neighbourhoods was calculated with the additional consideration of various buffers around the boundaries. Secondly, percentage of green space was calculated based on various radii around the neighbourhood centroid. An index of neighbourhood SEP was calculated with principal component analysis. Log-gamma regression from the group of generalized linear models was applied in order to consider the non-normal distribution of the response variable. All models were adjusted for population density. Low neighbourhood SEP was associated with decreasing neighbourhood green space availability including 200m up to 1000m buffers around the neighbourhood boundaries. Low neighbourhood SEP was also associated with decreasing green space availability based on catchment areas measured from neighbourhood centroids with different radii (1000m up to 3000 m). With an increasing radius the strength of the associations decreased. Social unequally distributed green space may amplify environmental health inequalities in an urban context. Thus, the identification of vulnerable neighbourhoods and population groups plays an important role for epidemiological research and healthy city planning. As a methodical aspect, log-gamma regression offers an adequate parametric modelling strategy for positively distributed environmental variables. Copyright © 2017 Elsevier GmbH. All rights reserved.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China.

PubMed

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui; Su, Jianrong

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China

PubMed Central

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management. PMID:29324901

Possible association between obesity and periodontitis in patients with Down syndrome.

PubMed

Culebras-Atienza, E; Silvestre, F-J; Silvestre-Rangil, J

2018-05-01

The present study was carried out to evaluate the possible association between obesity and periodontitis in patients with DS, and to explore which measure of obesity is most closely correlated to periodontitis. A prospective observational study was made to determine whether obesity is related to periodontal disease in patients with DS. The anthropometric variables were body height and weight, which were used to calculate BMI and stratify the patients into three categories: < 25(normal weight), 25-29.9 (overweight) and ≥ 30.0 kg/m2 (obese). Waist circumference and hip circumference in turn was recorded as the greatest circumference at the level of the buttocks, while the waist/hip ratio (WHR) was calculated. Periodontal evaluation was made of all teeth recording the plaque index (PI), pocket depth (PD), clinical attachment level (CAL) and the gingival index. We generated a multivariate linear regression model to examine the relationship between PD and the frequency of tooth brushing, gender, BMI, WHI, WHR, age and PI. Significant positive correlations were observed among the anthropometric parameters BMI, WHR, WHI and among the periodontal parameters PI, PD, CAL and GI. The only positive correlation between the anthropometric and periodontal parameters corresponded to WHR. Upon closer examination, the distribution of WHR was seen to differ according to gender. Among the women, the correlation between WHR and the periodontal variables decreased to nonsignificant levels. In contrast, among the males the correlation remained significant and even increased. In a multivariate linear regression model, the coefficients relating PD to PI, WHR and age were positive and significant in all cases. Our results suggest that there may indeed be an association between obesity and periodontitis in male patients with DS. Also, we found a clear correlation with WHR, which was considered to be the ideal adiposity indicator in this context.

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 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.

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.

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.

Systemic Factors Associated With Prosocial Skills and Maladaptive Functioning in Youth Exposed to Intimate Partner Violence.

PubMed

Howell, Kathryn H; Thurston, Idia B; Hasselle, Amanda J; Decker, Kristina; Jamison, Lacy E

2018-04-01

Children are frequently present in homes in which intimate partner violence (IPV) occurs. Following exposure to IPV, children may develop behavioral health difficulties, struggle with regulating emotions, or exhibit aggression. Despite the negative outcomes associated with witnessing IPV, many children also display resilience. Guided by Bronfenbrenner's bioecological model, this study examined person-level, process-level (microsystem), and context-level (mesosystem) factors associated with positive and negative functioning among youth exposed to IPV. Participants were 118 mothers who reported on their 6- to 14-year-old children. All mothers experienced severe physical, psychological, and/or sexual IPV in the past 6 months. Linear regression modeling was conducted separately for youth maladaptive functioning and prosocial skills. The linear regression model for maladaptive functioning was significant, F(6, 110) = 9.32, p < .001, adj R 2 = 27%, with more severe IPV (β = .18, p < .05) and more negative parenting practices (β = .34, p < .001) associated with worse child outcomes. The model for prosocial skills was also significant, F(6, 110) = 3.34, p < .01, adj. R 2 = 14%, with less negative parenting practices (β = -.26, p < .001) and greater community connectedness (β = .17, p < .05) linked to more prosocial skills. These findings provide critical knowledge on specific mutable factors associated with positive and negative functioning among children in the context of IPV exposure. Such factors could be incorporated into strength-based interventions following family violence.

White Blood Cells, Neutrophils, and Reactive Oxygen Metabolites among Asymptomatic Subjects.

PubMed

Kotani, Kazuhiko; Sakane, Naoki

2012-06-01

Chronic inflammation and oxidative stress are associated with health and the disease status. The objective of the present study was to investigate the association among white blood cell (WBC) counts, neutrophil counts as a WBC subpopulation, and diacron reactive oxygen metabolites (d-ROMs) levels in an asymptomatic population. The clinical data, including general cardiovascular risk variables and high-sensitivity C-reactive protein (hs-CRP), were collected from 100 female subjects (mean age, 62 years) in outpatient clinics. The correlation of the d-ROMs with hs-CRP, WBC, and neutrophil counts was examined. The mean/median levels were WBC counts 5.9 × 10(9)/L, neutrophil counts 3.6 × 10(9)/L, hs-CRP 0.06 mg/dL, and d-ROMs 359 CURR U. A simple correlation analysis showed a significant positive correlation of the d-ROMs with the WBC counts, neutrophil counts, or hs-CRP levels. The correlation between d-ROMs and neutrophil counts (β = 0.22, P < 0.05), as well as that between d-ROMs and hs-CRP (β = 0.28, P < 0.01), remained significant and independent in a multiple linear regression analysis adjusted for other variables. A multiple linear regression analysis showed that WBC counts had only a positive correlation tendency to the d-ROMs. Neutrophils may be slightly but more involved in the oxidative stress status, as assessed by d-ROMs, in comparison to the overall WBC. Further studies are needed to clarify the biologic mechanism(s) of the observed relationship.

Psychological Characteristics and Traits for Finding Benefit From Prostate Cancer: Correlates and Predictors.

PubMed

Pascoe, Elizabeth C; Edvardsson, David

Although beginning evidence suggests that the capacity to derive benefit from cancer-associated experiences may be influenced by some individual psychological characteristics and traits, little is known about predictors for finding benefit from prostate cancer. The aim of this study was to explore the correlates and predictors for finding benefit from prostate cancer among a sample of men undergoing androgen deprivation. Pearson correlation and multiple linear regression modeling were performed on data collected in an acute tertiary hospital outpatient setting (N = 209) between July 2011 and December 2013 to determine correlates and predictors for finding benefit from prostate cancer. Multiple linear regression modeling showed that while the 6 predictors of self-reported coping, depression, anxiety, distress, resilience, and hope explained 38% of the variance in finding benefit, coping provided the strongest and statistically significant predictive contribution. Self-reported coping was strongly predictive of finding benefit from prostate cancer, but questions remain about if subtypes of coping strategies can be more or less predictive of finding benefit. Self-reported levels of depression, anxiety, distress, resilience, and hope had a less predictive and nonsignificant role in finding benefit from prostate cancer and raise questions about their function in this subpopulation. The findings suggest that coping strategies can maximize finding benefit from prostate cancer. Knowledge of influential coping strategies for finding benefit from prostate cancer can be immensely valuable to support men in rebuilding positive meaning amid a changed illness reality. Developing practice initiatives that foster positive meaning-making coping strategies seems valuable.

Contribution of Sand-Associated Enterococci to Dry Weather Water Quality

PubMed Central

2015-01-01

Culturable enterococci and a suite of environmental variables were collected during a predominantly dry summer at a beach impacted by nonpoint source pollution. These data were used to evaluate sands as a source of enterococci to nearshore waters, and to assess the relationship between environmental factors and dry-weather enterococci abundance. Best-fit multiple linear regressions used environmental variables to explain more than half of the observed variation in enterococci in water and dry sands. Notably, during dry weather the abundance of enterococci in dry sands at the mean high-tide line was significantly positively related to sand moisture content (ranging from <1–4%), and the daily mean ENT in water could be predicted by a linear regression with turbidity alone. Temperature was also positively correlated with ENT abundance in this study, which may indicate an important role of seasonal warming in temperate regions. Inundation by spring tides was the primary rewetting mechanism that sustained culturable enterococci populations in high-tide sands. Tidal forcing modulated the abundance of enterococci in the water, as both turbidity and enterococci were elevated during ebb and flood tides. The probability of samples violating the single-sample maximum was significantly greater when collected during periods with increased tidal range: spring ebb and flood tides. Tidal forcing also affected groundwater mixing zones, mobilizing enterococci from sand to water. These data show that routine monitoring programs using discrete enterococci measurements may be biased by tides and other environmental factors, providing a flawed basis for beach closure decisions. PMID:25479559

Contribution of sand-associated enterococci to dry weather water quality.

PubMed

Halliday, Elizabeth; Ralston, David K; Gast, Rebecca J

2015-01-06

Culturable enterococci and a suite of environmental variables were collected during a predominantly dry summer at a beach impacted by nonpoint source pollution. These data were used to evaluate sands as a source of enterococci to nearshore waters, and to assess the relationship between environmental factors and dry-weather enterococci abundance. Best-fit multiple linear regressions used environmental variables to explain more than half of the observed variation in enterococci in water and dry sands. Notably, during dry weather the abundance of enterococci in dry sands at the mean high-tide line was significantly positively related to sand moisture content (ranging from <1-4%), and the daily mean ENT in water could be predicted by a linear regression with turbidity alone. Temperature was also positively correlated with ENT abundance in this study, which may indicate an important role of seasonal warming in temperate regions. Inundation by spring tides was the primary rewetting mechanism that sustained culturable enterococci populations in high-tide sands. Tidal forcing modulated the abundance of enterococci in the water, as both turbidity and enterococci were elevated during ebb and flood tides. The probability of samples violating the single-sample maximum was significantly greater when collected during periods with increased tidal range: spring ebb and flood tides. Tidal forcing also affected groundwater mixing zones, mobilizing enterococci from sand to water. These data show that routine monitoring programs using discrete enterococci measurements may be biased by tides and other environmental factors, providing a flawed basis for beach closure decisions.

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.

Topography of eye-position sensitivity of saccades evoked electrically from the cat's superior colliculus.

PubMed

McIlwain, J T

1990-03-01

Saccades evoked electrically from the deep layers of the superior colliculus have been examined in the alert cat with its head fixed. Amplitudes of the vertical and horizontal components varied linearly with the starting position of the eye. The slopes of the linear-regression lines provided an estimate of the sensitivity of these components to initial eye position. In observations on 29 sites in nine cats, the vertical and horizontal components of saccades evoked from a given site were rarely influenced to the same degree by initial eye position. For most sites, the horizontal component was more sensitive than the vertical component. Sensitivities of vertical and horizontal components were lowest near the representations of the horizontal and vertical meridians, respectively, of the collicular retinotopic map, but otherwise exhibited no systematic retinotopic dependence. Estimates of component amplitudes for saccades evoked from the center of the oculomotor range also diverged significantly from those predicted from the retinotopic map. The results of this and previous studies indicate that electrical stimulation of the cat's superior colliculus cannot yield a unique oculomotor map or one that is in register everywhere with the sensory retinotopic map. Several features of these observations suggest that electrical stimulation of the colliculus produces faulty activation of a saccadic control system that computes target position with respect to the head and that small and large saccades are controlled differently.

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

Effects of Body Mass Index on Lung Function Index of Chinese Population

NASA Astrophysics Data System (ADS)

Guo, Qiao; Ye, Jun; Yang, Jian; Zhu, Changan; Sheng, Lei; Zhang, Yongliang

2018-01-01

To study the effect of body mass index (BMI) on lung function indexes in Chinese population. A cross-sectional study was performed on 10, 592 participants. The linear relationship between lung function and BMI was evaluated by multivariate linear regression analysis, and the correlation between BMI and lung function was assessed by Pearson correlation analysis. Correlation analysis showed that BMI was positively related with the decreasing of forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC (P <0.05), the increasing of FVC% predicted value (FVC%pre) and FEV1% predicted value (FEV1%pre). These suggested that Chinese people can restrain the decline of lung function to prevent the occurrence and development of COPD by the control of BMI.

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.

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.

Relationship Between Ktrans and K1 with Simultaneous Versus Separate MR/PET in Rabbits with VX2 Tumors.

PubMed

Lee, Kyung Hee; Kang, Seung Kwan; Goo, Jin Mo; Lee, Jae Sung; Cheon, Gi Jeong; Seo, Seongho; Hwang, Eui Jin

2017-03-01

To compare the relationship between K trans from DCE-MRI and K 1 from dynamic 13 N-NH 3 -PET, with simultaneous and separate MR/PET in the VX-2 rabbit carcinoma model. MR/PET was performed simultaneously and separately, 14 and 15 days after VX-2 tumor implantation at the paravertebral muscle. The K trans and K 1 values were estimated using an in-house software program. The relationships between K trans and K 1 were analyzed using Pearson's correlation coefficients and linear/non-linear regression function. Assuming a linear relationship, K trans and K 1 exhibited a moderate positive correlations with both simultaneous (r=0.54-0.57) and separate (r=0.53-0.69) imaging. However, while the K trans and K 1 from separate imaging were linearly correlated, those from simultaneous imaging exhibited a non-linear relationship. The amount of change in K 1 associated with a unit increase in K trans varied depending on K trans values. The relationship between K trans and K 1 may be mis-interpreted with separate MR and PET acquisition. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

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…

BRCA1/2 Test Results Impact Risk Management Attitudes, Intentions and Uptake

PubMed Central

O’Neill, Suzanne C.; Valdimarsdottir, Heiddis B.; DeMarco, Tiffani A.; Peshkin, Beth N.; Graves, Kristi D.; Brown, Karen; Hurley, Karen E.; Isaacs, Claudine; Hecker, Sharon; Schwartz, Marc D.

2011-01-01

BACKGROUND Women who receive positive or uninformative BRCA1/2 test results face a number of decisions about how to manage their cancer risk. The purpose of this study was to prospectively examine the effect of receiving a positive vs. uninformative BRCA1/2 genetic test result on the perceived pros and cons of risk-reducing mastectomy (RRM) and risk-reducing oophorectomy (RRO) and breast cancer screening. We further examined how perceived pros and cons of surgery predict intention for and uptake of surgery. METHODS 308 women (146 positive, 162 uninformative) were included in RRM and breast cancer screening analyses. 276 women were included in RRO analyses. Participants completed questionnaires at pre-disclosure baseline and 1-, 6-and 12-months post-disclosure. We used linear multiple regression to assess whether test result contributed to change in pros and cons and logistic regression to predict intentions and surgery uptake. RESULTS Receipt of a positive BRCA1/2 test result predicted stronger pros for RRM and RRO (Ps < .001), but not perceived cons of RRM and RRO. Pros of surgery predicted RRM and RRO intentions in carriers and RRO intentions in uninformatives. Cons predicted RRM intentions in carriers. Pros and cons predicted carriers’ RRO uptake in the year after testing (Ps < .001). CONCLUSIONS Receipt of BRCA1/2 mutation test results impacts how carriers see the positive aspects of RRO and RRM and their surgical intentions. Both the positive and negative aspects predict uptake of surgery. PMID:20383578

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.

Year-round measurements of CH4 exchange in a forested drained peatland using automated chambers

NASA Astrophysics Data System (ADS)

Korkiakoski, Mika; Koskinen, Markku; Penttilä, Timo; Arffman, Pentti; Ojanen, Paavo; Minkkinen, Kari; Laurila, Tuomas; Lohila, Annalea

2016-04-01

Pristine peatlands are usually carbon accumulating ecosystems and sources of methane (CH4). Draining peatlands for forestry increases the thickness of the oxic layer, thus enhancing CH4 oxidation which leads to decreased CH4 emissions. Closed chambers are commonly used in estimating the greenhouse gas exchange between the soil and the atmosphere. However, the closed chamber technique alters the gas concentration gradient making the concentration development against time non-linear. Selecting the correct fitting method is important as it can be the largest source of uncertainty in flux calculation. We measured CH4 exchange rates and their diurnal and seasonal variations in a nutrient-rich drained peatland located in southern Finland. The original fen was drained for forestry in 1970s and now the tree stand is a mixture of Scots pine, Norway spruce and Downy birch. Our system consisted of six transparent polycarbonate chambers and stainless steel frames, positioned on different types of field and moss layer. During winter, the frame was raised above the snowpack with extension collars and the height of the snowpack inside the chamber was measured regularly. The chambers were closed hourly and the sample gas was sucked into a cavity ring-down spectrometer and analysed for CH4, CO2 and H2O concentration with 5 second time resolution. The concentration change in time in the beginning of a closure was determined with linear and exponential fits. The results show that linear regression systematically underestimated the CH4 flux when compared to exponential regression by 20-50 %. On the other hand, the exponential regression seemed not to work reliably with small fluxes (< 3.5 μg CH4 m-2 h-1): using exponential regression in such cases typically resulted in anomalously large fluxes and high deviation. Due to these facts, we recommend first calculating the flux with the linear regression and, if the flux is high enough, calculate the flux again using the exponential regression and use this value in later analysis. The forest floor at the site (including the ground vegetation) acted as a CH4 sink most of the time. CH4 emission peaks were occasionally observed, particularly in spring during the snow melt, and during rainfall events in summer. Diurnal variation was observed mainly in summer. The net CH4 exchange for the two year measurement period in the six chambers varied from -31 to -155 mg CH4 m-2 yr-1, the average being -67 mg CH4 m-2 yr-1. However, this does not include the ditches which typically act as a significant source for CH4.

Response to antiretroviral therapy (ART): comparing women with previous use of zidovudine monotherapy (ZDVm) in pregnancy with ART naïve women.

PubMed

Huntington, Susie; Thorne, Claire; Anderson, Jane; Newell, Marie-Louise; Taylor, Graham P; Pillay, Deenan; Hill, Teresa; Tookey, Pat; Sabin, Caroline

2014-03-04

Short-term zidovudine monotherapy (ZDVm) remains an option for some pregnant HIV-positive women not requiring treatment for their own health but may affect treatment responses once antiretroviral therapy (ART) is subsequently started. Data were obtained by linking two UK studies: the UK Collaborative HIV Cohort (UK CHIC) study and the National Study of HIV in Pregnancy and Childhood (NSHPC). Treatment responses were assessed for 2028 women initiating ART at least one year after HIV-diagnosis. Outcomes were compared using logistic regression, proportional hazards regression or linear regression. In adjusted analyses, ART-naïve (n = 1937) and ZDVm-experienced (n = 91) women had similar increases in CD4 count and a similar proportion achieving virological suppression; both groups had a low risk of AIDS. In this setting, antenatal ZDVm exposure did not adversely impact on outcomes once ART was initiated for the woman's health.

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

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.

An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States

DOE PAGES

Yang, Qichun; Zhang, Xuesong; Xu, Xingya; ...

2017-05-29

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

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.

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

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.

The Correlation Between Metacognition Level with Self-Efficacy of Biology Education College Students

NASA Astrophysics Data System (ADS)

Ridlo, S.; Lutfiya, F.

2017-04-01

Self-efficacy is a strong predictor of academic achievement. Self-efficacy refers to the ability of college students to achieve the desired results. The metacognition level can influence college student’s self-efficacy. This study aims to identify college student’s metacognition level and self-efficacy, as well as determine the relationship between self-efficacy and metacognition level for college students of Biology Education 2013, Semarang State University. The ex-post facto quantitative research was conducted on 99 students Academic Year 2015/2016. Saturation sampling technique determined samples. E-D scale collected data for self-efficacy identification. Data for assess the metacognition level collected by Metacognitive Awareness Inventory. Data were analysed quantitatively by Pearson correlation and linear regression. Most college students have the high level of metacognition and average self-efficacy. Pearson correlation coefficient result was 0.367. This result showed that metacognition level and self-efficacy has a weak relationship. Based on linear regression test, self-efficacy influenced by metacognition level up to 13.5%. The results of the study showed that positive and significant relationships exist between metacognition level and self-efficacy. Therefore, if the metacognition level is high, then self-efficacy will also be high (appropriate).

Use of a tracing task to assess visuomotor performance for evidence of concussion and recuperation.

PubMed

Kelty-Stephen, Damian G; Qureshi Ahmad, Mona; Stirling, Leia

2015-12-01

The likelihood of suffering a concussion while playing a contact sport ranges from 15-45% per year of play. These rates are highly variable as athletes seldom report concussive symptoms, or do not recognize their symptoms. We performed a prospective cohort study (n = 206, aged 10-17) to examine visuomotor tracing to determine the sensitivity for detecting neuromotor components of concussion. Tracing variability measures were investigated for a mean shift with presentation of concussion-related symptoms and a linear return toward baseline over subsequent return visits. Furthermore, previous research relating brain injury to the dissociation of smooth movements into "submovements" led to the expectation that cumulative micropause duration, a measure of motion continuity, might detect likelihood of injury. Separate linear mixed effects regressions of tracing measures indicated that 4 of the 5 tracing measures captured both short-term effects of injury and longer-term effects of recovery with subsequent visits. Cumulative micropause duration has a positive relationship with likelihood of participants having had a concussion. The present results suggest that future research should evaluate how well the coefficients for the tracing parameter in the logistic regression help to detect concussion in novel cases. (c) 2015 APA, all rights reserved).

Language and hope in schizophrenia-spectrum disorders.

PubMed

Bonfils, Kelsey A; Luther, Lauren; Firmin, Ruth L; Lysaker, Paul H; Minor, Kyle S; Salyers, Michelle P

2016-11-30

Hope is integral to recovery for those with schizophrenia. Considering recent advancements in the examination of clients' lexical qualities, we were interested in how clients' words reflect hope. Using computerized lexical analysis, we examined social, emotion, and future words' relations to hope and its pathways and agency components. Forty-five clients provided detailed narratives about their life and mental illness. Transcripts were analyzed using the Linguistic Inquiry and Word Count program (LIWC), which assigns words to categories (e.g., "anxiety") based on a pre-existing dictionary. Correlations and linear multiple regression were used to examine relationships between lexical qualities and hope. Hope and its subcomponents had significant or trending bivariate correlations in expected directions with several emotion-related word categories (anger and sadness) but were not associated with expected categories such as social words, positive emotions, optimism, achievement, and future words. In linear multiple regressions, no LIWC variable significantly predicted hope agency, but anger words significantly predicted both total hope and hope pathways. Our findings indicate lexical analysis tools can be used to investigate recovery-oriented concepts such as hope, and results may inform clinical practice. Future research should aim to replicate our findings in larger samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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.

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

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.

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.

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.

Cognitive ability correlates positively with son birth and predicts cross-cultural variation of the offspring sex ratio

NASA Astrophysics Data System (ADS)

Dama, Madhukar Shivajirao

2013-06-01

Human populations show remarkable variation in the sex ratio at birth which is believed to be related to the parental condition. In the present study, the global variation of sex ratio at birth (SRB, proportion of male offspring born) was analyzed with respect to indirect measure of condition, the intelligence quotient (IQ). IQ correlates strongly with lifespan across nations, which makes it a good indicator of health of the large populations. Relation between three standard measures of average national IQ and SRB was studied using multiple linear regression models. Average national IQ was positively correlated with SRB ( r = 0.54 to 0.57, p < 0.001). Further, IQ emerged as a powerful predictor of SRB after controlling for the effects of all the known covariates like fertility, maternal age, polygyny prevalence, wealth, son preference, latitude, low birth weight, and neonatal mortality in the regression models. These results suggest that the striking variation of offspring sex ratio across nations could be caused in part by the difference in general condition of populations.

Effects of Human Development Index and Its Components on Colorectal Cancer Incidence and Mortality: a Global Ecological Study.

PubMed

Khazaei, Salman; Rezaeian, Shahab; Khazaei, Somayeh; Mansori, Kamyar; Sanjari Moghaddam, Ali; Ayubi, Erfan

2016-01-01

Geographic disparity for colorectal cancer (CRC) incidence and mortality according to the human development index (HDI) might be expected. This study aimed at quantifying the effect measure of association HDI and its components on the CRC incidence and mortality. In this ecological study, CRC incidence and mortality was obtained from GLOBOCAN, the global cancer project for 172 countries. Data were extracted about HDI 2013 for 169 countries from the World Bank report. Linear regression was constructed to measure effects of HDI and its components on CRC incidence and mortality. A positive trend between increasing HDI of countries and age-standardized rates per 100,000 of CRC incidence and mortality was observed. Among HDI components education was the strongest effect measure of association on CRC incidence and mortality, regression coefficients (95% confidence intervals) being 2.8 (2.4, 3.2) and 0.9 (0.8, 1), respectively. HDI and its components were positively related with CRC incidence and mortality and can be considered as targets for prevention and treatment intervention or tracking geographic disparities.

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.

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).

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

PubMed

Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

2012-01-01

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

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.

Life stressors as mediators of the relation between socioeconomic position and mental health problems in early adolescence: the TRAILS study.

PubMed

Amone-P'Olak, Kennedy; Ormel, Johan; Huisman, Martijn; Verhulst, Frank C; Oldehinkel, Albertine J; Burger, Huibert

2009-10-01

Life stressors and family socioeconomic position have often been associated with mental health status. The aim of the present study is to contribute to the understanding of the pathways from low socioeconomic position and life stressors to mental problems. In a cross-sectional analysis using data from a longitudinal study of early adolescents (N = 2,149, 51% girls; mean age 13.6 years, SD 0.53, range 12-15), we assessed the extent of mediation of the association between family socioeconomic position and mental health problems by different types of life stressors in multiple regression models. Stressors were rated as environment related or person related. Information on socioeconomic position was obtained directly from parents, and internalizing and externalizing problem behaviors were assessed by reports from multiple informants (parents, self, and teachers). Low socioeconomic position was associated with more mental health problems and more life stressors. Both environment-related and person-related stressors predicted mental health problems independently of socioeconomic position. The associations between socioeconomic position and all mental health outcomes were partly mediated by environment-related life stressors. Mediation by environment-related and person-related stressors as assessed by linear regression amounted to 56% (95% confidence interval [CI] 35%-78%) and 7% (95% CI -25% to 38%) for internalizing problems and 13% (95% CI 7%-19%) and 5% (95% CI -2% to 13%) for externalizing problems, respectively. Environment-related, but not person-related, stressors partly mediated the association between socio economic position and adolescent mental problems. The extent of mediation was larger for internalizing than for externalizing problems. Because the effect sizes of the associations were relatively small, targeted interventions to prevent impaired mental health may have only modest benefits to adolescents from low socioeconomic background.

Medical mistrust as a key mediator in the association between perceived discrimination and adherence to antiretroviral therapy among HIV-positive Latino men.

PubMed

Galvan, Frank H; Bogart, Laura M; Klein, David J; Wagner, Glenn J; Chen, Ying-Tung

2017-10-01

Discrimination has been found to have deleterious effects on physical health. The goal of the present study was to examine the association between perceived discrimination and adherence to antiretroviral therapy (ART) among HIV-positive Latino men and the extent to which medical mistrust serves as a mediator of that association. A series of linear and logistic regression models was used to test for mediation for three types of perceived discrimination (related to being Latino, being perceived as gay and being HIV-positive). Medical mistrust was found to be significantly associated with perceived discrimination based on Latino ethnicity and HIV serostatus. Medical mistrust was found to mediate the associations between two types of perceived discrimination (related to being Latino and being HIV-positive) and ART adherence. Given these findings, interventions should be developed that increase the skills of HIV-positive Latino men to address both perceived discrimination and medical mistrust.

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.

How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

PubMed

Saqr, Mohammed; Fors, Uno; Tedre, Matti

2018-02-06

Collaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students' performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance. Interaction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students' performance was calculated, and automatic linear regression was used to predict students' performance. By using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user's position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student's position and role in information relay in online case discussions, combined with the strength of that student's network (social capital), can be used as predictors of performance in relevant settings. By using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students' and teachers' interactions that can be valuable in guiding teachers, improve students' engagement, and contribute to learning analytics insights.

Applicability of both dentist and patient perceptions of dentists' explanations to the evaluation of dentist-patient communication.

PubMed

Hamasaki, T; Soh, I; Takehara, T; Hagihara, A

2011-12-01

Very little is known about dentist-patient communicative behaviours in actual practice. This study evaluated dentist and patient perceptions of dentist-patient communication and patient outcome. The subjects were 171 dentist-patient pairs in Kitakyushu, Japan. Dentists and patients answered the same questionnaire items using the same response categories to evaluate dentist-patient communication. Based on the scores of patient and dentist perceptions with respect to dentist-patient communication, patient-dentist pairs were categorised into one of 3 groups. Data analyses used one-way ANOVA, multiple linear regression analysis, and multiple logistic regression analysis. We found that, with respect to dentist-patient communication, patients in the 'patient better' group (i.e., the patient's evaluation was more positive than the dentist's evaluation) were more likely to have a positive outcome (e.g., 'improvement of health and fear,' 'satisfaction with care') than those in the other two groups. Patients in the 'doctor better' group (i.e., the dentist's evaluation was the more positive) were more likely to have a negative outcome than those in the other two groups. A positive patient outcome is more likely when the patient's evaluation is better than a dentist's evaluation with respect to dentist-patient communicative behaviours. The method based on patient and dentist perceptions with respect to dentist-patient communication might be effective in evaluating dentist-patient communication.

Paternal mental health and socioemotional and behavioral development in their children.

PubMed

Kvalevaag, Anne Lise; Ramchandani, Paul G; Hove, Oddbjørn; Assmus, Jörg; Eberhard-Gran, Malin; Biringer, Eva

2013-02-01

To examine the association between symptoms of psychological distress in expectant fathers and socioemotional and behavioral outcomes in their children at age 36 months. The current study is based on data from the Norwegian Mother and Child Cohort Study on 31 663 children. Information about fathers' mental health was obtained by self-report (Hopkins Symptom Checklist) in week 17 or 18 of gestation. Information about mothers' pre- and postnatal mental health and children's socioemotional and behavioral development at 36 months of age was obtained from parent-report questionnaires. Linear multiple regression and logistic regression models were performed while controlling for demographics, lifestyle variables, and mothers' mental health. Three percent of the fathers had high levels of psychological distress. Using linear regression models, we found a small positive association between fathers' psychological distress and children's behavioral difficulties, B = 0.19 (95% confidence interval [CI] = 0.15-0.23); emotional difficulties, B = 0.22 (95% CI = 0.18-0.26); and social functioning, B = 0.12 (95% CI = 0.07-0.16). The associations did not change when adjusted for relevant confounders. Children whose fathers had high levels of psychological distress had higher levels of emotional and behavioral problems. This study suggests that some risk of future child emotional, behavioral, and social problems can be identified during pregnancy. The findings are of importance for clinicians and policy makers in their planning of health care in the perinatal period because this represents a significant opportunity for preventive intervention.

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.

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.

Design and baseline data from the Gratitude Research in Acute Coronary Events (GRACE) study

PubMed Central

Huffman, Jeff C.; Beale, Eleanor E.; Beach, Scott R.; Celano, Christopher M.; Belcher, Arianna M.; Moore, Shannon V.; Suarez, Laura; Gandhi, Parul U.; Motiwala, Shweta R.; Gaggin, Hanna; Januzzi, James L.

2015-01-01

Background Positive psychological constructs, especially optimism, have been linked with superior cardiovascular health. However, there has been minimal study of positive constructs in patients with acute coronary syndrome (ACS), despite the prevalence and importance of this condition. Furthermore, few studies have examined multiple positive psychological constructs and multiple cardiac-related outcomes within the same cohort to determine specifically which positive construct may affect a particular cardiac outcome. Materials and methods The Gratitude Research in Acute Coronary Events (GRACE) study examines the association between optimism/gratitude 2 weeks post-ACS and subsequent clinical outcomes. The primary outcome measure is physical activity at 6 months, measured via accelerometer, and key secondary outcome measures include levels of prognostic biomarkers and rates of nonelective cardiac rehospitalization at 6 months. These relationships will be analyzed using multivariate linear regression, controlling for sociodemographic, medical, and negative psychological factors; associations between baseline positive constructs and subsequent rehospitalizations will be assessed via Cox regression. Results Overall, 164 participants enrolled and completed the baseline 2-week assessment; the cohort had a mean age of 61.5 +/− 10.5 years and was 84% men; this was the first ACS for 58% of participants. Conclusion The GRACE study will determine whether optimism and gratitude are prospectively and independently associated with physical activity and other critical outcomes in the 6 months following an ACS. If these constructs are associated with superior outcomes, this may highlight the importance of these constructs as independent prognostic factors post-ACS. PMID:26166171

Design and baseline data from the Gratitude Research in Acute Coronary Events (GRACE) study.

PubMed

Huffman, Jeff C; Beale, Eleanor E; Beach, Scott R; Celano, Christopher M; Belcher, Arianna M; Moore, Shannon V; Suarez, Laura; Gandhi, Parul U; Motiwala, Shweta R; Gaggin, Hanna; Januzzi, James L

2015-09-01

Positive psychological constructs, especially optimism, have been linked with superior cardiovascular health. However, there has been minimal study of positive constructs in patients with acute coronary syndrome (ACS), despite the prevalence and importance of this condition. Furthermore, few studies have examined multiple positive psychological constructs and multiple cardiac-related outcomes within the same cohort to determine specifically which positive construct may affect a particular cardiac outcome. The Gratitude Research in Acute Coronary Events (GRACE) study examines the association between optimism/gratitude 2weeks post-ACS and subsequent clinical outcomes. The primary outcome measure is physical activity at 6months, measured via accelerometer, and key secondary outcome measures include levels of prognostic biomarkers and rates of nonelective cardiac rehospitalization at 6months. These relationships will be analyzed using multivariable linear regression, controlling for sociodemographic, medical, and negative psychological factors; associations between baseline positive constructs and subsequent rehospitalizations will be assessed via Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessment; the cohort had a mean age of 61.5+/?10.5years and was 84% men; this was the first ACS for 58% of participants. The GRACE study will determine whether optimism and gratitude are prospectively and independently associated with physical activity and other critical outcomes in the 6months following an ACS. If these constructs are associated with superior outcomes, this may highlight the importance of these constructs as independent prognostic factors post-ACS. Copyright © 2015 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…

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

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.

Social networks of HIV-positive women and their association with social support and depression symptoms.

PubMed

Cederbaum, Julie A; Rice, Eric; Craddock, Jaih; Pimentel, Veronica; Beaver, Patty

2017-02-01

Social support is important to the mental health and well-being of HIV-positive women. Limited information exists about the specific structure and composition of HIV-positive women's support networks or associations of these network properties with mental health outcomes. In this pilot study, the authors examine whether support network characteristics were associated with depressive symptoms. Survey and network data were collected from HIV-positive women (N = 46) via a web-based survey and an iPad application in August 2012. Data were analyzed using multivariate linear regression models in SAS. Depressive symptoms were positively associated with a greater number of doctors in a woman's network; having more HIV-positive network members was associated with less symptom reporting. Women who reported more individuals who could care for them had more family support. Those who reported feeling loved were less likely to report disclosure stigma. This work highlighted that detailed social network data can increase our understanding of social support so as to identify interventions to support the mental health of HIV-positive women. Most significant is the ongoing need for support from peers.

Road Traffic and Railway Noise Exposures and Adiposity in Adults: A Cross-Sectional Analysis of the Danish Diet, Cancer, and Health Cohort.

PubMed

Christensen, Jeppe Schultz; Raaschou-Nielsen, Ole; Tjønneland, Anne; Overvad, Kim; Nordsborg, Rikke B; Ketzel, Matthias; Sørensen, Thorkild Ia; Sørensen, Mette

2016-03-01

Traffic noise has been associated with cardiovascular and metabolic disorders. Potential modes of action are through stress and sleep disturbance, which may lead to endocrine dysregulation and overweight. We aimed to investigate the relationship between residential traffic and railway noise and adiposity. In this cross-sectional study of 57,053 middle-aged people, height, weight, waist circumference, and bioelectrical impedance were measured at enrollment (1993-1997). Body mass index (BMI), body fat mass index (BFMI), and lean body mass index (LBMI) were calculated. Residential exposure to road and railway traffic noise exposure was calculated using the Nordic prediction method. Associations between traffic noise and anthropometric measures at enrollment were analyzed using general linear models and logistic regression adjusted for demographic and lifestyle factors. Linear regression models adjusted for age, sex, and socioeconomic factors showed that 5-year mean road traffic noise exposure preceding enrollment was associated with a 0.35-cm wider waist circumference (95% CI: 0.21, 0.50) and a 0.18-point higher BMI (95% CI: 0.12, 0.23) per 10 dB. Small, significant increases were also found for BFMI and LBMI. All associations followed linear exposure-response relationships. Exposure to railway noise was not linearly associated with adiposity measures. However, exposure > 60 dB was associated with a 0.71-cm wider waist circumference (95% CI: 0.23, 1.19) and a 0.19-point higher BMI (95% CI: 0.0072, 0.37) compared with unexposed participants (0-20 dB). The present study finds positive associations between residential exposure to road traffic and railway noise and adiposity.

Neonatal MRI is associated with future cognition and academic achievement in preterm children

PubMed Central

Spencer-Smith, Megan; Thompson, Deanne K.; Doyle, Lex W.; Inder, Terrie E.; Anderson, Peter J.; Klingberg, Torkel

2015-01-01

School-age children born preterm are particularly at risk for low mathematical achievement, associated with reduced working memory and number skills. Early identification of preterm children at risk for future impairments using brain markers might assist in referral for early intervention. This study aimed to examine the use of neonatal magnetic resonance imaging measures derived from automated methods (Jacobian maps from deformation-based morphometry; fractional anisotropy maps from diffusion tensor images) to predict skills important for mathematical achievement (working memory, early mathematical skills) at 5 and 7 years in a cohort of preterm children using both univariable (general linear model) and multivariable models (support vector regression). Participants were preterm children born <30 weeks’ gestational age and healthy control children born ≥37 weeks’ gestational age at the Royal Women’s Hospital in Melbourne, Australia between July 2001 and December 2003 and recruited into a prospective longitudinal cohort study. At term-equivalent age ( ±2 weeks) 224 preterm and 46 control infants were recruited for magnetic resonance imaging. Working memory and early mathematics skills were assessed at 5 years (n = 195 preterm; n = 40 controls) and 7 years (n = 197 preterm; n = 43 controls). In the preterm group, results identified localized regions around the insula and putamen in the neonatal Jacobian map that were positively associated with early mathematics at 5 and 7 years (both P < 0.05), even after covarying for important perinatal clinical factors using general linear model but not support vector regression. The neonatal Jacobian map showed the same trend for association with working memory at 7 years (models ranging from P = 0.07 to P = 0.05). Neonatal fractional anisotropy was positively associated with working memory and early mathematics at 5 years (both P < 0.001) even after covarying for clinical factors using support vector regression but not general linear model. These significant relationships were not observed in the control group. In summary, we identified, in the preterm brain, regions around the insula and putamen using neonatal deformation-based morphometry, and brain microstructural organization using neonatal diffusion tensor imaging, associated with skills important for childhood mathematical achievement. Results contribute to the growing evidence for the clinical utility of neonatal magnetic resonance imaging for early identification of preterm infants at risk for childhood cognitive and academic impairment. PMID:26329284

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.

Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate

PubMed Central

Motulsky, Harvey J; Brown, Ronald E

2006-01-01

Background Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Conclusion Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives. PMID:16526949

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.

Exploring the association between parental rearing styles and medical students' critical thinking disposition in China.

PubMed

Huang, Lei; Wang, Zhaoxin; Yao, Yuhong; Shan, Chang; Wang, Haojie; Zhu, Mengyi; Lu, Yuan; Sun, Pengfei; Zhao, Xudong

2015-05-14

Critical thinking is an essential ability for medical students. However, the relationship between parental rearing styles and medical students' critical thinking disposition has rarely been considered. The aim of this study was to investigate whether parental rearing styles were significant predictors of critical thinking disposition among Chinese medical students. 1,075 medical students from the first year to the fifth year attending one of three medical schools in China were recruited via multistage stratified cluster sampling. The Chinese Critical Thinking Disposition Inventory(CTDI-CV) and The Egna Minnen av Barndoms Uppfostran (EMBU) questionnaire were applied to collect data and to conduct descriptive analysis. Stepwise multiple linear regression was used to analyze the data. The critical thinking disposition average mean score was 287.44 with 632 participants (58.79%) demonstrating positive critical thinking disposition. Stepwise multiple linear regression analysis revealed that the rearing styles of fathers, including "overprotection", "emotional warmth and understanding", "rejection" and "over-interference" were significant predictors of medical students' critical thinking disposition that explained 79.0% of the variance in critical thinking ability. Rearing styles of mothers including "emotional warmth and understanding", "punishing" and "rejection" were also found to be significant predictors, and explained 77.0% of the variance. Meaningful association has been evidenced between parental rearing styles and Chinese medical students' critical thinking disposition. Parental rearing styles should be considered as one of the many potential determinant factors that contribute to the cultivation of medical students' critical thinking capability. Positive parental rearing styles should be encouraged in the cultivation of children's critical thinking skills.

Residents' engagement in everyday activities and its association with thriving in nursing homes.

PubMed

Björk, Sabine; Lindkvist, Marie; Wimo, Anders; Juthberg, Christina; Bergland, Ådel; Edvardsson, David

2017-08-01

To describe the prevalence of everyday activity engagement for older people in nursing homes and the extent to which engagement in everyday activities is associated with thriving. Research into residents' engagement in everyday activities in nursing homes has focused primarily on associations with quality of life and prevention and management of neuropsychiatric symptoms. However, the mere absence of symptoms does not necessarily guarantee experiences of well-being. The concept of thriving encapsulates and explores experiences of well-being in relation to the place where a person lives. A cross-sectional survey. A national survey of 172 Swedish nursing homes (2013-2014). Resident (n = 4831) symptoms, activities and thriving were assessed by staff using a study survey based on established questionnaires. Descriptive statistics, simple and multiple linear regression, and linear stepwise multiple regression were performed. The most commonly occurring everyday activities were receiving hugs and physical touch, talking to relatives/friends and receiving visitors, having conversation with staff not related to care and grooming. The least commonly occurring everyday activities were going to the cinema, participating in an educational program, visiting a restaurant and doing everyday chores. Positive associations were found between activity engagement and thriving, where engagement in an activity program, dressing nicely and spending time with someone the resident likes had the strongest positive association with resident thriving. Engagement in everyday activities can support personhood and thriving and can be conceptualized and implemented as nursing interventions to enable residents to thrive in nursing homes. © 2017 John Wiley & Sons Ltd.

Association between birthweight and later body mass index: an individual-based pooled analysis of 27 twin cohorts participating in the CODATwins project

PubMed Central

Jelenkovic, Aline; Yokoyama, Yoshie; Sund, Reijo; Pietiläinen, Kirsi H; Hur, Yoon-Mi; Willemsen, Gonneke; Bartels, Meike; van Beijsterveldt, Toos CEM; Ooki, Syuichi; Saudino, Kimberly J; Stazi, Maria A; Fagnani, Corrado; D’Ippolito, Cristina; Nelson, Tracy L; Whitfield, Keith E; Knafo-Noam, Ariel; Mankuta, David; Abramson, Lior; Heikkilä, Kauko; Cutler, Tessa L; Hopper, John L; Wardle, Jane; Llewellyn, Clare H; Fisher, Abigail; Corley, Robin P; Huibregtse, Brooke M; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth JF; Bjerregaard-Andersen, Morten; Beck-Nielsen, Henning; Sodemann, Morten; Tarnoki, Adam D; Tarnoki, David L; Burt, S Alexandra; Klump, Kelly L; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Dubois, Lise; Boivin, Michel; Brendgen, Mara; Dionne, Ginette; Vitaro, Frank; Harris, Jennifer R; Brandt, Ingunn; Nilsen, Thomas Sevenius; Craig, Jeffrey M; Saffery, Richard; Rasmussen, Finn; Tynelius, Per; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Haworth, Claire MA; Plomin, Robert; Ji, Fuling; Ning, Feng; Pang, Zengchang; Rebato, Esther; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Boomsma, Dorret I; Sørensen, Thorkild IA; Kaprio, Jaakko; Silventoinen, Karri

2017-01-01

Abstract Background There is evidence that birthweight is positively associated with body mass index (BMI) in later life, but it remains unclear whether this is explained by genetic factors or the intrauterine environment. We analysed the association between birthweight and BMI from infancy to adulthood within twin pairs, which provides insights into the role of genetic and environmental individual-specific factors. Methods This study is based on the data from 27 twin cohorts in 17 countries. The pooled data included 78 642 twin individuals (20 635 monozygotic and 18 686 same-sex dizygotic twin pairs) with information on birthweight and a total of 214 930 BMI measurements at ages ranging from 1 to 49 years. The association between birthweight and BMI was analysed at both the individual and within-pair levels using linear regression analyses. Results At the individual level, a 1-kg increase in birthweight was linearly associated with up to 0.9 kg/m2 higher BMI (P < 0.001). Within twin pairs, regression coefficients were generally greater (up to 1.2 kg/m2 per kg birthweight, P < 0.001) than those from the individual-level analyses. Intra-pair associations between birthweight and later BMI were similar in both zygosity groups and sexes and were lower in adulthood. Conclusions These findings indicate that environmental factors unique to each individual have an important role in the positive association between birthweight and later BMI, at least until young adulthood. PMID:28369451

Validation of alternative indicators of social support in perinatal outcomes research using quality of the partner relationship.

PubMed

Kruse, Julie A; Low, Lisa Kane; Seng, Julia S

2013-07-01

To test alternatives to the current research and clinical practice of assuming that married or partnered status is a proxy for positive social support. Having a partner is assumed to relate to better health status via the intermediary process of social support. However, women's health research indicates that having a partner is not always associated with positive social support. An exploratory post hoc analysis focused on posttraumatic stress and childbearing was conducted using a large perinatal database from 2005-2009. To operationalize partner relationship, four variables were analysed: partner ('yes' or 'no'), intimate partner violence ('yes' or 'no'), the combination of those two factors, and the woman's appraisal of the quality of her partner relationship via a single item. Construct validity of these four alternative variables was assessed in relation to appraisal of the partner's social support in labour and the postpartum using linear regression standardized betas and adjusted R-squares. Predictive validity was assessed using unadjusted and adjusted linear regression modelling. Four groups were compared. Married, abused women differed most from married, not abused women in relation to the social support, and depression outcomes used for validity checks. The variable representing the women's appraisals of their partner relationships accounts for the most variance in predicting depression scores. Our results support the validity of operationalizing the impact of the partner relationship on outcomes using a combination of partnered status and abuse status or using a subjective rating of quality of the partner relationship. © 2012 Blackwell Publishing Ltd.

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.

Family and home characteristics correlate with mold in homes.

PubMed

Reponen, Tiina; Levin, Linda; Zheng, Shu; Vesper, Stephen; Ryan, Patrick; Grinshpun, Sergey A; LeMasters, Grace

2013-07-01

Previously, we demonstrated that infants residing in homes with higher Environmental Relative Moldiness Index were at greater risk for developing asthma by age seven. The purpose of this analysis was to identify the family and home characteristics associated with higher moldiness index values in infants' homes at age one. Univariate linear regression of each characteristic determined that family factors associated with moldiness index were race and income. Home characteristics associated with the moldiness index values were: air conditioning, carpet, age of the home, season of home assessment, and house dust mite allergen. Parental history of asthma, use of dehumidifier, visible mold, dog and cat allergen levels were not associated with moldiness index. Results of multiple linear regression showed that older homes had 2.9 units higher moldiness index (95% confidence interval [CI]=0.4, 5.4), whereas homes with central air conditioning had 2.5 units lower moldiness index (95% CI=-4.7, -0.4). In addition, higher dust mite allergen levels and carpeting were positively and negatively associated with higher moldiness index, respectively. Because older homes and lack of air conditioning were also correlated with race and lower income, whereas carpeting was associated with newer homes, the multivariate analyses suggests that lower overall socioeconomic position is associated with higher moldiness index values. This may lead to increased asthma risk in homes inhabited by susceptible, vulnerable population subgroups. Further, age of the home was a surrogate of income, race and carpeting in our population; thus the use of these factors should carefully be evaluated in future studies. Copyright © 2013 Elsevier Inc. All rights reserved.

Association between red and processed meat consumption and chronic diseases: the confounding role of other dietary factors.

PubMed

Fogelholm, M; Kanerva, N; Männistö, S

2015-09-01

High consumption of meat has been linked with the risk for obesity and chronic diseases. This could partly be explained by the association between meat and lower-quality diet. We studied whether high intake of red and processed meat was associated with lower-quality dietary habits, assessed against selected nutrients, other food groups and total diet. Moreover, we studied whether meat consumption was associated with obesity, after adjustment for all identified associations between meat and food consumption. The nationally representative cross-sectional study population consisted of 2190 Finnish men and 2530 women, aged 25-74 years. Food consumption over the previous 12 months was assessed using a validated 131-item Food Frequency Questionnaire. Associations between nutrients, foods, a modified Baltic Sea Diet Score and meat consumption (quintile classification) were analysed using linear regression. The models were adjusted for age and energy intake and additionally for education, physical activity and smoking. High consumption of red and processed meat was inversely associated with fruits, whole grain and nuts, and positively with potatoes, oil and coffee in both sexes. Results separately for the two types of meat were essentially similar. In a linear regression analysis, high consumption of meat was positively associated with body mass index in both men and women, even when using a model adjusted for all foods with a significant association with meat consumption in both sexes identified in this study. The association between meat consumption and a lower-quality diet may complicate studies on meat and health.

Perceived stress and smoking-related behaviors and symptomatology in male and female smokers.

PubMed

Lawless, Michael H; Harrison, Katherine A; Grandits, Gregory A; Eberly, Lynn E; Allen, Sharon S

2015-12-01

Stress has been found to be a significant risk factor for cigarette smoking. Stress affects males and females differently, as does the use of smoking for stress reduction. Few studies have examined gender differences with the interrelation of perceived stress and smoking behaviors and nicotine related symptomatology. Our study investigates this association, as well as the influence of sociodemographic variables. This is a retrospective analysis of 62 smokers (41 males, 21 females) enrolled in a smoking cessation study. At the screening visit sociodemographic information, smoking behaviors and survey measures were completed. These included the Perceived Stress Scale (PSS), Minnesota Nicotine Withdrawal Scale (MNWS), and others. Analyses were conducted using multiple linear regression models. PSS score was found to have a negative association with number of cigarettes smoked in males (slope -0.29±0.08; p=0.0009) and females (slope -0.20±0.18; p=0.26) with no difference in effect between genders (p=0.64). Linear regression of MNWS on PSS revealed a positive association for both males (slope 0.41±0.068; p<0.0001) and females (slope 0.73±0.14; p<0.0001). There was a significant difference in effect between genders (p=0.04). A strong positive association was observed between perceived stress and nicotine withdrawal symptomatology in smokers of both sexes, with a larger effect seen in women. These findings emphasize the importance of stress reduction in smokers, which may lead to fewer withdrawal symptoms and more effective smoking cessation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Self-Reported Adherence among Individuals at High Risk of Metabolic Syndrome: Effect of Knowledge and Attitude.

PubMed

Alefishat, Eman A; Abu Farha, Rana K; Al-Debei, Mutaz M

2017-01-01

This study aimed to evaluate factors that affect adherence in individuals at high risk of metabolic syndrome, with a focus on knowledge and attitude effect. A sample of 900 high-risk individuals with metabolic syndrome was recruited in this cross-sectional study. During the study period, all participants filled in validated structured questionnaires to evaluate the adherence to different management options of metabolic syndrome, knowledge about the syndrome, and health-related attitude. Simple linear regression followed by multiple linear regression analysis were used to evaluate the effect of knowledge, attitude, and other factors on participants' adherence to both medications and lifestyle changes. Of the 900 participants, 436 (48.4%) were nonadherent to medications and 813 (90.3%) were nonadherent to lifestyle changes. Increasing age (r = 0.140, p = 0.000), the presence of hypertension (r = 0.075, p = 0.036), and a more positive attitude toward health (r = 0.230, p = 0.000) were significantly associated with increasing adherence to medications. Higher educational level (r = 0.085, p = 0.023), higher knowledge score (r = 0.135, p = 0.001), and more positive attitude toward health (r = 0.183, p = 0.000) were found to significantly increase the adherence to lifestyle changes, while central obesity (r = -0.106, p = 0.003) was found to significantly decrease the adherence to lifestyle changes. Patients' knowledge about metabolic syndrome and attitude to health affected adherence rates in patients at high risk of metabolic syndrome. Hence, we suggest the need to incorporate patients' educational programs into current management of metabolic syndrome. © 2016 S. Karger AG, Basel.

Physiological Aldosterone Concentrations Are Associated with Alterations of Lipid Metabolism: Observations from the General Population.

PubMed

Hannich, M; Wallaschofski, H; Nauck, M; Reincke, M; Adolf, C; Völzke, H; Rettig, R; Hannemann, A

2018-01-01

Aldosterone and high-density lipoprotein cholesterol (HDL-C) are involved in many pathophysiological processes that contribute to the development of cardiovascular diseases. Previously, associations between the concentrations of aldosterone and certain components of the lipid metabolism in the peripheral circulation were suggested, but data from the general population is sparse. We therefore aimed to assess the associations between aldosterone and HDL-C, low-density lipoprotein cholesterol (LDL-C), total cholesterol, triglycerides, or non-HDL-C in the general adult population. Data from 793 men and 938 women aged 25-85 years who participated in the first follow-up of the Study of Health in Pomerania were obtained. The associations of aldosterone with serum lipid concentrations were assessed in multivariable linear regression models adjusted for sex, age, body mass index (BMI), estimated glomerular filtration rate (eGFR), and HbA1c. The linear regression models showed statistically significant positive associations of aldosterone with LDL-C ( β -coefficient = 0.022, standard error = 0.010, p = 0.03) and non-HDL-C ( β -coefficient = 0.023, standard error = 0.009, p = 0.01) as well as an inverse association of aldosterone with HDL-C ( β -coefficient = -0.022, standard error = 0.011, p = 0.04). The present data show that plasma aldosterone is positively associated with LDL-C and non-HDL-C and inversely associated with HDL-C in the general population. Our data thus suggests that aldosterone concentrations within the physiological range may be related to alterations of lipid metabolism.

Influence of specific obsessive-compulsive symptom dimensions on strategic planning in patients with obsessive-compulsive disorder.

PubMed

Pinto, Paula Sanders Pereira; Iego, Sandro; Nunes, Samantha; Menezes, Hemanny; Mastrorosa, Rosana Sávio; Oliveira, Irismar Reis de; Rosário, Maria Conceição do

2011-03-01

This study investigates obsessive-compulsive disorder patients in terms of strategic planning and its association with specific obsessive-compulsive symptom dimensions. We evaluated 32 obsessive-compulsive disorder patients. Strategic planning was assessed by the Rey-Osterrieth Complex Figure Test, and the obsessive-compulsive dimensions were assessed by the Dimensional Yale-Brown Obsessive-Compulsive Scale. In the statistical analyses, the level of significance was set at 5%. We employed linear regression, including age, intelligence quotient, number of comorbidities, the Yale-Brown Obsessive-Compulsive Scale score, and the Dimensional Yale-Brown Obsessive-Compulsive Scale. The Dimensional Yale-Brown Obsessive-Compulsive Scale "worst-ever" score correlated significantly with the planning score on the copy portion of the Rey-Osterrieth Complex Figure Test (r = 0.4, p = 0.04) and was the only variable to show a significant association after linear regression (β = 0.55, t = 2.1, p = 0.04). Compulsive hoarding correlated positively with strategic planning (r = 0.44, p = 0.03). None of the remaining symptom dimensions presented any significant correlations with strategic planning. We found the severity of obsessive-compulsive symptoms to be associated with strategic planning. In addition, there was a significant positive association between the planning score on the copy portion of the Rey-Osterrieth Complex Figure Test copy score and the hoarding dimension score on the Dimensional Yale-Brown Obsessive-Compulsive Scale. Our results underscore the idea that obsessive-compulsive disorder is a heterogeneous disorder and suggest that the hoarding dimension has a specific neuropsychological profile. Therefore, it is important to assess the peculiarities of each obsessive-compulsive symptom dimension.

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.

The impact of intrinsic and extrinsic factors on the job satisfaction of dentists.

PubMed

Goetz, K; Campbell, S M; Broge, B; Dörfer, C E; Brodowski, M; Szecsenyi, J

2012-10-01

The Two-Factor Theory of job satisfaction distinguishes between intrinsic-motivation (i.e. recognition, responsibility) and extrinsic-hygiene (i.e. job security, salary, working conditions) factors. The presence of intrinsic-motivation facilitates higher satisfaction and performance, whereas the absences of extrinsic factors help mitigate against dissatisfaction. The consideration of these factors and their impact on dentists' job satisfaction is essential for the recruitment and retention of dentists. The objective of the study is to assess the level of job satisfaction of German dentists and the factors that are associated with it. This cross-sectional study was based on a job satisfaction survey. Data were collected from 147 dentists working in 106 dental practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Organizational characteristics were measured with two items. Linear regression analyses were performed in which each of the nine items of the job satisfaction scale (excluding overall satisfaction) were handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction as the dependent outcome variable, the nine items of job satisfaction and the two items of organizational characteristics controlled for age and gender as predictors. The response rate was 95.0%. Dentists were satisfied with 'freedom of working method' and mostly dissatisfied with their 'income'. Both variables are extrinsic factors. The regression analyses identified five items that were significantly associated with each item of the job satisfaction scale: 'age', 'mean weekly working time', 'period in the practice', 'number of dentist's assistant' and 'working atmosphere'. Within the stepwise linear regression analysis the intrinsic factor 'opportunity to use abilities' (β = 0.687) showed the highest score of explained variance (R(2) = 0.468) regarding overall job satisfaction. With respect to the Two-Factor Theory of job satisfaction both components, intrinsic and extrinsic, are essential for dentists but the presence of intrinsic motivating factors like the opportunity to use abilities has most positive impact on job satisfaction. The findings of this study will be helpful for further activities to improve the working conditions of dentists and to ensure quality of care. © 2012 John Wiley & Sons A/S.

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

Correlation of Respirator Fit Measured on Human Subjects and a Static Advanced Headform

PubMed Central

Bergman, Michael S.; He, Xinjian; Joseph, Michael E.; Zhuang, Ziqing; Heimbuch, Brian K.; Shaffer, Ronald E.; Choe, Melanie; Wander, Joseph D.

2015-01-01

This study assessed the correlation of N95 filtering face-piece respirator (FFR) fit between a Static Advanced Headform (StAH) and 10 human test subjects. Quantitative fit evaluations were performed on test subjects who made three visits to the laboratory. On each visit, one fit evaluation was performed on eight different FFRs of various model/size variations. Additionally, subject breathing patterns were recorded. Each fit evaluation comprised three two-minute exercises: “Normal Breathing,” “Deep Breathing,” and again “Normal Breathing.” The overall test fit factors (FF) for human tests were recorded. The same respirator samples were later mounted on the StAH and the overall test manikin fit factors (MFF) were assessed utilizing the recorded human breathing patterns. Linear regression was performed on the mean log10-transformed FF and MFF values to assess the relationship between the values obtained from humans and the StAH. This is the first study to report a positive correlation of respirator fit between a headform and test subjects. The linear regression by respirator resulted in R2 = 0.95, indicating a strong linear correlation between FF and MFF. For all respirators the geometric mean (GM) FF values were consistently higher than those of the GM MFF. For 50% of respirators, GM FF and GM MFF values were significantly different between humans and the StAH. For data grouped by subject/respirator combinations, the linear regression resulted in R2 = 0.49. A weaker correlation (R2 = 0.11) was found using only data paired by subject/respirator combination where both the test subject and StAH had passed a real-time leak check before performing the fit evaluation. For six respirators, the difference in passing rates between the StAH and humans was < 20%, while two respirators showed a difference of 29% and 43%. For data by test subject, GM FF and GM MFF values were significantly different for 40% of the subjects. Overall, the advanced headform system has potential for assessing fit for some N95 FFR model/sizes. PMID:25265037

Methane exchange at the peatland forest floor - automatic chamber system exposes the dynamics of small fluxes

NASA Astrophysics Data System (ADS)

Korkiakoski, Mika; Tuovinen, Juha-Pekka; Aurela, Mika; Koskinen, Markku; Minkkinen, Kari; Ojanen, Paavo; Penttilä, Timo; Rainne, Juuso; Laurila, Tuomas; Lohila, Annalea

2017-04-01

We measured methane (CH4) exchange rates with automatic chambers at the forest floor of a nutrient-rich drained peatland in 2011-2013. The fen, located in southern Finland, was drained for forestry in 1969 and the tree stand is now a mixture of Scots pine, Norway spruce, and pubescent birch. Our measurement system consisted of six transparent chambers and stainless steel frames, positioned on a number of different field and moss layer compositions. Gas concentrations were measured with an online cavity ring-down spectroscopy gas analyzer. Fluxes were calculated with both linear and exponential regression. The use of linear regression resulted in systematically smaller CH4 fluxes by 10-45 % as compared to exponential regression. However, the use of exponential regression with small fluxes ( < 2.5 µg CH4 m-2 h-1) typically resulted in anomalously large absolute fluxes and high hour-to-hour deviations. Therefore, we recommend that fluxes are initially calculated with linear regression to determine the threshold for low fluxes and that higher fluxes are then recalculated using exponential regression. The exponential flux was clearly affected by the length of the fitting period when this period was < 190 s, but stabilized with longer periods. Thus, we also recommend the use of a fitting period of several minutes to stabilize the results and decrease the flux detection limit. There were clear seasonal dynamics in the CH4 flux: the forest floor acted as a CH4 sink particularly from early summer until the end of the year, while in late winter the flux was very small and fluctuated around zero. However, the magnitude of fluxes was relatively small throughout the year, ranging mainly from -130 to +100 µg CH4 m-2 h-1. CH4 emission peaks were observed occasionally, mostly in summer during heavy rainfall events. Diurnal variation, showing a lower CH4 uptake rate during the daytime, was observed in all of the chambers, mainly in the summer and late spring, particularly in dry conditions. It was attributed more to changes in wind speed than air or soil temperature, which suggest that physical rather than biological phenomena are responsible for the observed variation. The annual net CH4 exchange varied from -104 ± 30 to -505 ± 39 mg CH4 m-2 yr-1 among the six chambers, with an average of -219 mg CH4 m-2 yr-1 over the 2-year measurement period.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Appreciation of historical events and characters: their relationship with national identity and collective self-esteem in a sample of public school teachers from the city of Lima.

PubMed

Rottenbacher de Rojas, Jan Marc

2010-11-01

This study analyzes the relation between national identity and the appreciation of the characters and events of Peruvian history in a sample of public school teachers from the city of Lima (N = 99). Adapted versions of the NATID Scale (Keillor et al., 1996) and the CSES Scale (Luhtanen & Crocker, 1992) are used as measures of national identity. National pride and interest in knowing about Peruvian history are variables also included in this study. The study shows that appreciation of historical characters is more positive than appreciation of historical events. There is a positive association between national identity and appreciation of Peruvian historical characters. A multiple linear regression model is proposed; this model shows that appreciation of cultural heritage and national pride has a positive impact on the appreciation of characters of Peruvian history.

The prevalence of Campylobacter pylori gastritis among asymptomatic adults.

PubMed Central

Gregson, D B; Low, D E; Cohen, M M; Cooter, N B; Connon, J J; Wolman, S L; Simor, A E

1989-01-01

To determine the prevalence of Campylobacter pylori colonization in the healthy population we studied 54 asymptomatic volunteers and 65 patients referred because of gastrointestinal symptoms. All subjects underwent gastroscopy and gastric biopsy. C. pylori was isolated from 6 volunteers (11%) and 36 patients (55%). Histologic evidence of inflammation was present in 98% of the culture-positive subjects. Linear regression analysis revealed that the prevalence of C. pylori colonization increased with age. There was no difference in the isolation rate between the two groups when adjusted for age. Four of the six culture-positive volunteers underwent repeat endoscopy and gastric biopsy 1 year later; despite remaining asymptomatic, all still had positive culture results and histologic evidence of gastritis. We conclude that the prevalence of C. pylori-associated gastritis among symptomatic patients increases with age and that the organism may be present in the gastrointestinal tract for prolonged periods without symptoms or evidence of disease progression. PMID:2785841

Computerized scintigraphic technique for the evaluation of adult respiratory distress syndrome: initial clinical trials

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

Tatum, J.L.; Burke, T.S.; Sugerman, H.T.

1982-04-01

Eleven patients with suspected adult respiratory distress syndrome (ARDS) and five control patients were studied using a computerized gamma imaging and analysis technique and /sup 99m/Tc-labeled human serum albumin. The heart and right lung were imaged, lung:heart ratio was plotted vs. time, and a linear regression was fitted to the data points displayed. The slope of this fit was termed the ''slope index.'' An index value of 2 standard deviations greater than the control mean was considered positive. Radiographs from the six positive studies revealed typical diffuse air-space disease. Radiographs from two of the five negative studies demonstrated air-space consolidation.more » Both of these patients had elevated pulmonary capillary wedge pressure, cardiomegaly, and clinical course consistent with cardiogenic pulmonary edema. These preliminary data demonstrated a good correlation between positive slope index and clinical ARDS.« less

Computerized scintigraphic technique for the evaluation of adult respiratory distress syndrome: initial clinical trials

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

Tatum, J.L.; Burke, T.S.; Sugerman, H.J.

1982-04-01

Eleven patients with suspected adult respiratory distress syndrome (ARDS) and five control patients were studied using a computerized gamma imaging and analysis technique and 99mTc-labeled human serum albumin. The heart and right lung were imaged, lung:heart ratio was plotted vs. time, and a linear regression was fitted to the data points displayed. The slope of this fit was termed the ''slope index.'' An index value of 2 standard deviations greater than the control mean was considered positive. Radiographs from the six positive studies revealed typical diffuse air-space disease. Radiographs from two of the five negative studies demonstrated air-space consolidation. Bothmore » of these patients had elevated pulmonary capillary wedge pressure, cardiomegaly, and clinical course consistent with cardiogenic pulmonary edema. These preliminary data demonstrated a good correlation between positive slope index and clinical ARDS.« less

Body appreciation and attitudes toward menstruation.

PubMed

Chrisler, Joan C; Marván, Maria Luisa; Gorman, Jennifer A; Rossini, Meghan

2015-01-01

Menstruation is an important function of the female body, yet it has rarely been included in research on body image. As women's attitudes toward menstruation are so often negative, this study was designed to examine whether women with positive body image would have more positive attitudes toward menstruation. Seventy-two American women, ages 18-45 years, were recruited online to complete the Body Appreciation Scale (Avalos et al., 2005) and the Beliefs about and Attitudes toward Menstruation Scale (Marván et al., 2006) and to answer some questions about their interest in menstrual suppression. Linear regressions showed that higher scores on body appreciation predicted more positive attitudes toward and beliefs about menstruation, but were not related to interest in menstrual suppression. Our findings may be useful in designing interventions to increase women's comfort with their bodies and bodily functions. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

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…

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

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

2017-12-01

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

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.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

A positional misalignment correction method for Fourier ptychographic microscopy based on simulated annealing

NASA Astrophysics Data System (ADS)

Sun, Jiasong; Zhang, Yuzhen; Chen, Qian; Zuo, Chao

2017-02-01

Fourier ptychographic microscopy (FPM) is a newly developed super-resolution technique, which employs angularly varying illuminations and a phase retrieval algorithm to surpass the diffraction limit of a low numerical aperture (NA) objective lens. In current FPM imaging platforms, accurate knowledge of LED matrix's position is critical to achieve good recovery quality. Furthermore, considering such a wide field-of-view (FOV) in FPM, different regions in the FOV have different sensitivity of LED positional misalignment. In this work, we introduce an iterative method to correct position errors based on the simulated annealing (SA) algorithm. To improve the efficiency of this correcting process, large number of iterations for several images with low illumination NAs are firstly implemented to estimate the initial values of the global positional misalignment model through non-linear regression. Simulation and experimental results are presented to evaluate the performance of the proposed method and it is demonstrated that this method can both improve the quality of the recovered object image and relax the LED elements' position accuracy requirement while aligning the FPM imaging platforms.

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)

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.

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.

Adolescent romance and depressive symptoms: the moderating effects of positive coping and perceived friendship competence.

PubMed

Szwedo, David E; Chango, Joanna M; Allen, Joseph P

2015-01-01

Youths' ability to positively cope with negative emotions and their self-perceived friendship competence were examined as potential moderators of links between multiple aspects of romantic relationships and residualized increases in depressive symptoms from late adolescence into early adulthood. Participants included 184 teens (46% male; 42% non-White) assessed at ages 15 to 19 and 21, as well as a subsample of 62 romantic partners of participants assessed when teens were 18. Results of hierarchical linear regressions showed that positive coping served as a buffer against depressive symptoms for romantically involved adolescents and also for teens receiving more intense emotional support from their romantic partners, but not for youth whose relationship had ended and had not been replaced by a new relationship. Higher perceived friendship competence served as a buffer against depressive symptoms for youth enduring the dissolution and nonreplacement of their romantic relationship. Greater use of positive coping skills and higher perceived friendship competence may help protect adolescents from depressive symptoms in different types of romantic experiences.

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...

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.

Estimation of stature from the foot and its segments in a sub-adult female population of North India

PubMed Central

2011-01-01

Background Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. Methods The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. Results The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. Conclusions The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults. PMID:22104433

Estimation of stature from the foot and its segments in a sub-adult female population of North India.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam

2011-11-21

Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults.

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.

Jobs and the resource curse in the sun: The effects of oil production on female labor force participation in California counties from 1980-2010

NASA Astrophysics Data System (ADS)

Zavala, Gabriel

This study aims to evaluate the relationship between oil income and the female labor force participation rate in California for the years of 1980, 1990, 2000 and 2010 using panel linear regression models. This study also aims to visualize the spatial patterns of both variables in California through Hot Spot analysis at the county level for the same years. The regression found no sign of a relationship between oil income and female labor force participation rate but did find evidence of a positive relationship between two income control variables and the female labor force participation rate. The hot spot analysis also found that female labor force participation cold spots are not spatially correlated with oil production hot spots. These findings contribute new methodologies at a finer scale to the very nuanced discussion of the resource curse in the United States.

Physician leadership styles and effectiveness: an empirical study.

PubMed

Xirasagar, Sudha; Samuels, Michael E; Stoskopf, Carleen H

2005-12-01

The authors study the association between physician leadership styles and leadership effectiveness. Executive directors of community health centers were surveyed (269 respondents; response rate = 40.9 percent) for their perceptions of the medical director's leadership behaviors and effectiveness, using an adapted Multifactor Leadership Questionnaire (43 items on a 0-4 point Likert-type scale), with additional questions on demographics and the center's clinical goals and achievements. The authors hypothesize that transformational leadership would be more positively associated with executive directors' ratings of effectiveness, satisfaction with the leader, and subordinate extra effort, as well as the center's clinical goal achievement, than transactional or laissez-faire leadership. Separate ordinary least squares regressions were used to model each of the effectiveness measures, and general linear model regression was used to model clinical goal achievement. Results support the hypothesis and suggest that physician leadership development using the transformational leadership model may result in improved health care quality and cost control.

The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA.

PubMed

Al-Hamdan, Ashraf Z; Albashaireh, Reem N; Al-Hamdan, Mohammad Z; Crosson, William L

2017-05-12

This study aimed to assess the association between exposure to fine particulate matter (PM 2.5 ) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM 2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM 2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM 2.5 concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM 2.5 and RSC incidence rate per 1 μg/m 3 PM 2.5 increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R 2 = 0.36), 2.14 (R 2 = 0.14), 3.92 (R 2 = 0.23), 5.02 (R 2 = 0.21), and 4.15 (R 2 = 0.28). Similarly, the logistic regression odds ratios per 10 μg/m 3 increase of PM 2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM 2.5 is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56-3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09-2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43-3.14) than for Whites (OR = 1.72, 95% CI = 1.23-2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM 2.5 .

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

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.

Relationship between mechanical factors and pelvic tilt in adults with and without low back pain.

PubMed

Król, Anita; Polak, Maciej; Szczygieł, Elżbieta; Wójcik, Paweł; Gleb, Klaudia

2017-01-01

The assessment of the lumbo-pelvic complex parameters is the basic procedure during the examination of the patients with low back pain syndrome (LBP). The aim of the study was to define the relationship between pelvic tilt and following factors: age, BMI, ability to activate deep abdominal muscles, iliopsoas and hamstrings muscles length, lumbar lordosis and thoracic kyphosis angle value, in adults with and without low back pain. The study covered a group of 60 female students aged 20-26. Average age was 22 years ± 1.83 (median = 22.5 years). In order to investigate the relationship between the anterior pelvic tilt and the analysed variables, simple linear regression and multiple linear regression were carried out. Individuals with and without pain differed significantly in terms of age, p < 0.001. There was a statistically significant relationship between the anterior pelvic tilt and the LBP (R2 = 0.07, p = 0.049) and the lumbar lordosis (R2 = 0.13, p = 0.02). The position of the pelvis depends on age, angle value of lumbar lordosis and BMI. Individuals with and without pain differed significantly in terms of the anterior pelvic tilt. The risk of LBP incidence increased with age in the study group.

Effect of postprandial thermogenesis on the cutaneous vasodilatory response during exercise.

PubMed

Hayashi, Keiji; Ito, Nozomi; Ichikawa, Yoko; Suzuki, Yuichi

2014-08-01

To examine the effect of postprandial thermogenesis on the cutaneous vasodilatory response, 10 healthy male subjects exercised for 30 min on a cycle ergometer at 50% of peak oxygen uptake, with and without food intake. Mean skin temperature, mean body temperature (Tb), heart rate, oxygen uptake, carbon dioxide elimination, and respiratory quotient were all significantly higher at baseline in the session with food intake than in the session without food intake. To evaluate the cutaneous vasodilatory response, relative laser Doppler flowmetry values were plotted against esophageal temperature (Tes) and Tb. Regression analysis revealed that the [Formula: see text] threshold for cutaneous vasodilation tended to be higher with food intake than without it, but there were no significant differences in the sensitivity. To clarify the effect of postprandial thermogenesis on the threshold for cutaneous vasodilation, the between-session difference in the Tes threshold and the Tb threshold were plotted against the between-session difference in baseline Tes and baseline Tb, respectively. Linear regression analysis of the resultant plot showed significant positive linear relationships (Tes: r = 0.85, P < 0.01; Tb: r = 0.67, P < 0.05). These results suggest that postprandial thermogenesis increases baseline body temperature, which raises the body temperature threshold for cutaneous vasodilation during exercise.

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

Pass rates on the American Board of Family Medicine Certification Exam by residency location and size.

PubMed

Falcone, John L; Middleton, Donald B

2013-01-01

The Accreditation Council for Graduate Medical Education (ACGME) sets residency performance standards for the American Board of Family Medicine Certification Examination. This study aims are to describe the compliance of residency programs with ACGME standards and to determine whether residency pass rates depend on program size and location. In this retrospective cohort study, residency performance from 2007 to 2011 was compared with the ACGME performance standards. Simple linear regression was performed to see whether program pass rates were dependent on program size. Regional differences in performance were compared with χ(2) tests, using an α level of 0.05. Of 429 total residency programs, there were 205 (47.8%) that violate ACGME performance standards. Linear regression showed that program pass rates were positively correlated and dependent on program size (P < .001). The median pass rate per state was 86.4% (interquartile range, 82.0-90.8. χ(2) Tests showed that states in the West performed higher than the other 3 US Census Bureau Regions (all P < .001). Approximately half of the family medicine training programs do not meet the ACGME examination performance standards. Pass rates are associated with residency program size, and regional variation occurs. These findings have the potential to affect ACGME policy and residency program application patterns.

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.

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.

Climbing fibers predict movement kinematics and performance errors.

PubMed

Streng, Martha L; Popa, Laurentiu S; Ebner, Timothy J

2017-09-01

Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey ( Macaca mulatta ). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to "events," either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control. NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each parameter. In contrast with the view that CSs carry feedback signals, the CSs are predominantly predictive of upcoming position errors and kinematics. Therefore, climbing fibers carry multiple and predictive signals for online motor control. Copyright © 2017 the American Physiological Society.

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.

Left atrial volume index in patients with heart failure and severely impaired left ventricular systolic function: the role of established echocardiographic parameters, circulating cystatin C and galectin-3.

PubMed

Zivlas, Christos; Triposkiadis, Filippos; Psarras, Stelios; Giamouzis, Gregory; Skoularigis, Ioannis; Chryssanthopoulos, Stavros; Kapelouzou, Alkistis; Ramcharitar, Steve; Barnes, Edward; Papasteriadis, Evangelos; Cokkinos, Dennis

2017-11-01

Backround: Left atrial (LA) enlargement plays an important role in the development of heart failure (HF) and is a robust prognostic factor. Fibrotic processes have also been advocated to evoke HF through finite signalling proteins. We examined the association of two such proteins, cystatin C (CysC) and galectin-3 (Gal-3), and other clinical, echocardiographic and biochemical parameters with LA volume index (LAVi) in patients with HF with severely impaired left ventricular ejection fraction (LVEF). Severe renal, liver, autoimmune disease and cancer were exclusion criteria. A total of 40 patients with HF (31 men, age 66.6 ± 1.7) with LVEF = 25.4 ± 0.9% were divided into two groups according to the mean LAVi (51.03 ± 2.9 ml/m 2 ) calculated by two-dimensional transthoracic echocardiography. Greater LAVi was positively associated with LV end-diastolic volume ( p = 0.017), LV end-systolic volume ( p = 0.025), mitral regurgitant volume (MRV) ( p = 0.001), right ventricular systolic pressure (RVSP) ( p < 0.001), restrictive diastolic filling pattern ( p = 0.003) and atrial fibrillation ( p = 0.005). Plasma CysC was positively correlated with LAVi ( R 2 = 0.135, p = 0.019) and log-transformed plasma Gal-3 ( R 2 = 0.109, p = 0.042) by simple linear regression analysis. Stepwise multiple linear regression analysis showed that only MRV ( t = 2.236, p = 0.032), CysC ( t = 2.467, p = 0.019) and RVSP ( t = 2.155, p = 0.038) were significant predictors of LAVi. Apart from known determinants of LAVi, circulating CysC and Gal-3 were associated with greater LA dilatation in patients with HF with reduced LVEF. Interestingly, the correlation between these two fibrotic proteins was positive.

The effects of practice on movement distance and final position reproduction: implications for the equilibrium-point control of movements.

PubMed

Jaric, S; Corcos, D M; Gottlieb, G L; Ilic, D B; Latash, M L

1994-01-01

Predictions of two views on single-joint motor control, namely programming of muscle force patterns and equilibrium-point control, were compared with the results of experiments with reproduction of movement distance and final location during fast unidirectional elbow flexions. Two groups of subjects were tested. The first group practiced movements over a fixed distance (36 degrees), starting from seven different initial positions (distance group, DG). The second group practiced movements from the same seven initial positions to a fixed final location (location group, LG). Later, all the subjects were tested at the practiced task with their eyes closed, and then, unexpectedly for the subjects, they were tested at the other, unpracticed task. In both groups, the task to reproduce final position had lower indices of final position variability than the task to reproduce movement distance. Analysis of the linear regression lines between initial position and final position (or movement distance) also demonstrated a better (more accurate) performance during final position reproduction than during distance reproduction. The data are in a good correspondence with the predictions of the equilibrium-point hypothesis, but not with the predictions of the force-pattern control approach.

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.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

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.

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.

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.

Quantitative analysis of triacylglycerol regioisomers in fats and oils using reversed-phase high-performance liquid chromatography and atmospheric pressure chemical ionization mass spectrometry.

PubMed

Fauconnot, Laëtitia; Hau, Jörg; Aeschlimann, Jean-Marc; Fay, Laurent-Bernard; Dionisi, Fabiola

2004-01-01

Positional distribution of fatty acyl chains of triacylglycerols (TGs) in vegetable oils and fats (palm oil, cocoa butter) and animal fats (beef, pork and chicken fats) was examined by reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to atmospheric pressure chemical ionization using a quadrupole mass spectrometer. Quantification of regioisomers was achieved for TGs containing two different fatty acyl chains (palmitic (P), stearic (S), oleic (O), and/or linoleic (L)). For seven pairs of 'AAB/ABA'-type TGs, namely PPS/PSP, PPO/POP, SSO/SOS, POO/OPO, SOO/OSO, PPL/PLP and LLS/LSL, calibration curves were established on the basis of the difference in relative abundances of the fragment ions produced by preferred losses of the fatty acid from the 1/3-position compared to the 2-position. In practice the positional isomers AAB and ABA yield mass spectra showing a significant difference in relative abundance ratios of the ions AA(+) to AB(+). Statistical analysis of the validation data obtained from analysis of TG standards and spiked oils showed that, under repeatability conditions, least-squares regression can be used to establish calibration curves for all pairs. The regression models show linear behavior that allow the determination of the proportion of each regioisomer in an AAB/ABA pair, within a working range from 10 to 1000 microg/mL and a 95% confidence interval of +/-3% for three replicates. Copyright 2003 John Wiley & Sons, Ltd.

CO-occurring exposure to perchlorate, nitrate and thiocyanate alters thyroid function in healthy pregnant women.

PubMed

Horton, Megan K; Blount, Benjamin C; Valentin-Blasini, Liza; Wapner, Ronald; Whyatt, Robin; Gennings, Chris; Factor-Litvak, Pam

2015-11-01

Adequate maternal thyroid function during pregnancy is necessary for normal fetal brain development, making pregnancy a critical window of vulnerability to thyroid disrupting insults. Sodium/iodide symporter (NIS) inhibitors, namely perchlorate, nitrate, and thiocyanate, have been shown individually to competitively inhibit uptake of iodine by the thyroid. Several epidemiologic studies examined the association between these individual exposures and thyroid function. Few studies have examined the effect of this chemical mixture on thyroid function during pregnancy We examined the cross sectional association between urinary perchlorate, thiocyanate and nitrate concentrations and thyroid function among healthy pregnant women living in New York City using weighted quantile sum (WQS) regression. We measured thyroid stimulating hormone (TSH) and free thyroxine (FreeT4) in blood samples; perchlorate, thiocyanate, nitrate and iodide in urine samples collected from 284 pregnant women at 12 (±2.8) weeks gestation. We examined associations between urinary analyte concentrations and TSH or FreeT4 using linear regression or WQS adjusting for gestational age, urinary iodide and creatinine. Individual analyte concentrations in urine were significantly correlated (Spearman's r 0.4-0.5, p<0.001). Linear regression analyses did not suggest associations between individual concentrations and thyroid function. The WQS revealed a significant positive association between the weighted sum of urinary concentrations of the three analytes and increased TSH. Perchlorate had the largest weight in the index, indicating the largest contribution to the WQS. Co-exposure to perchlorate, nitrate and thiocyanate may alter maternal thyroid function, specifically TSH, during pregnancy. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Co-occurring exposure to perchlorate, nitrate and thiocyanate alters thyroid function in healthy pregnant women

PubMed Central

Horton, Megan K.; Blount, Benjamin C.; Valentin-Blasini, Liza; Wapner, Ronald; Whyatt, Robin; Gennings, Chris; Factor-Litvak, Pam

2015-01-01

Background Adequate maternal thyroid function during pregnancy is necessary for normal fetal brain development, making pregnancy a critical window of vulnerability to thyroid disrupting insults. Sodium/iodide symporter (NIS) inhibitors, namely perchlorate, nitrate, and thiocyanate, have been shown individually to competitively inhibit uptake of iodine by the thyroid. Several epidemiologic studies examined the association between these individual exposures and thyroid function. Few studies have examined the effect of this chemical mixture on thyroid function during pregnancy. Objectives We examined the cross sectional association between urinary perchlorate, thiocyanate and nitrate concentrations and thyroid function among healthy pregnant women living in New York City using weighted quantile sum (WQS) regression. Methods We measured thyroid stimulating hormone (TSH) and free thyroxine (FreeT4) in blood samples; perchlorate, thiocyanate, nitrate and iodide in urine samples collected from 284 pregnant women at 12 (± 2.8) weeks gestation. We examined associations between urinary analyte concentrations and TSH or FreeT4 using linear regression or WQS adjusting for gestational age, urinary iodide and creatinine. Results Individual analyte concentrations in urine were significantly correlated (Spearman’s r 0.4–0.5, p < 0.001). Linear regression analyses did not suggest associations between individual concentrations and thyroid function. The WQS revealed a significant positive association between the weighted sum of urinary concentrations of the three analytes and increased TSH. Perchlorate had the largest weight in the index, indicating the largest contribution to the WQS. Conclusions Co-exposure to perchlorate, nitrate and thiocyanate may alter maternal thyroid function, specifically TSH, during pregnancy. PMID:26408806

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.

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.

Polymorphisms of human leukocyte antigen B*27 on clinical phenotype of spondyloarthritis in Chinese.

PubMed

Ma, Hai-Jun; Yin, Qing-Feng; Liu, Yun; Wu, Yin; Zhu, Tie-Chui; Guo, Ming-Hao

2018-02-01

In recent years, an ever-increasing number of alleles of human leukocyte antigen B*27 (HLA-B*27) have been identified. This study aimed to establish an updated method for HLA-B*27 subtyping, and to investigate the impact of HLA-B*27 polymorphisms on the clinical phenotype of spondyloarthritis (SpA). Overall, 184 SpA patients were recruited for analyzing diversity of HLA-B*27 via an updated high-resolution polymerase chain reaction amplification with sequence specific primers (PCR-SSP). The prevalence of HLA-B*27 was 94.0%, and four subtypes were identified including HLA-B*2704 (77.5%), B*2705 (20.2%), B*2707 (1.7%), and B*2724 (0.6%). There was an obvious male predominance (P=.05) and markedly elevated C-reaction protein (CRP) in B*27 positive SpA (P<.01). In multivariate linear regression analysis, the elevated CRP was positively associated with HLA-B*27 positivity (regression coefficient B=46.1, P=.0003), grade of sacroiliitis (B=47.5, P=.0032), and male gender (B=20.4, P=.0041). Notably, a male predilection was also found in B*2705 positive SpA while B*2707 was associated with older age, higher positive family history, and higher prevalence of extra-articular features (all P<.05). In this study, an updated PCR-SSP technique to identify increasing alleles of HLA-B*27 was developed and their different effects on clinical manifestations of SpA were demonstrated. Genotyping of HLA-B*27 would shed light on our understanding of the pathogenesis of SpA. © 2017 Wiley Periodicals, Inc.

Effect of the Time to Change anti-stigma campaign on trends in mental-illness-related public stigma among the English population in 2003-13: an analysis of survey data.

PubMed

Evans-Lacko, Sara; Corker, Elizabeth; Williams, Paul; Henderson, Claire; Thornicroft, Graham

2014-07-01

Understanding trends and effective mechanisms that are likely to reduce public stigma and discrimination towards people with mental illness is important. We aimed to assess changes in public stigma in England after the introduction of the Time to Change anti-stigma campaign. We used data from the 2003 and 2007-13 national Attitudes to Mental Illness surveys to investigate 10-year trends in public attitudes across England before and during the Time to Change anti-stigma campaign. We present annual mean scores for attitude items related to prejudice and exclusion, and tolerance and support for community care. We also present an extrapolated linear trend line for the years 2009-13 and estimate population attitude scores without the campaign. We present unadjusted and adjusted linear regression models. In addition, we used multivariable linear regression models fitted to data aggregated by region to investigate whether a dose-effect response exists between campaign awareness and regional outcomes related to knowledge, attitudes, and intended behaviour. About 1700 respondents were surveyed each year. Significant increases in positive attitudes related to prejudice and exclusion occurred after the Time to Change campaign. In the multivariable analysis, we noted a significant increase in positive attitudes in relation to prejudice and exclusion after the launch of Time to Change (reverse-coded Z score 0·02, 95% CI 0·01 to 0·05; p=0·01), but not for tolerance and support for community care (Z score 0·01, -0·01 to 0·03; p=0·27). We also found evidence for a dose-effect relation between campaign awareness and regional improvement in knowledge (p=0·004) and attitudes (tolerance and support p<0·0001; prejudice and exclusion p=0·001), but not intended behaviour (p=0·20). The positive effects of Time to Change seem to be significant and moderate. Although attitudes are probably more at risk of deterioration during times of economic hardship, anti-stigma programmes might still play an active part in long-term reduction of stigma and discrimination, especially in relation to prejudice and exclusion of people with mental health problems. UK Department of Health, Comic Relief, Big Lottery. Copyright © 2014 Elsevier Ltd. All rights reserved.

No increase in small-solute transport in peritoneal dialysis patients treated without hypertonic glucose for fifty-four months.

PubMed

Pagniez, Dominique; Duhamel, Alain; Boulanger, Eric; Lessore de Sainte Foy, Celia; Beuscart, Jean-Baptiste

2017-08-31

Glucose is widely used as an osmotic agent in peritoneal dialysis (PD), but exerts untoward effects on the peritoneum. The potential protective effect of a reduced exposure to hypertonic glucose has never been investigated. The cohort of PD patients attending our center which tackled the challenge of a restricted use of hypertonic glucose solutions has been prospectively followed since 1992. Small-solute transport was assessed using an equivalent of the glucose peritoneal equilibration test after 6 months, and then every year. Study was stopped on July 1st, 2008, before use of biocompatible solutions. Repeated measures in patients treated with PD for 54 months were analyzed by using (1) the slopes of the linear regression for D 4 /D 0 ratios over time computed for each individual, and (2) a linear mixed model. In the study period, 44 patients were treated for a total of 2376 months, 2058 without hypertonic glucose. There was one episode of peritoneal infection every 18 patient-months. The mean of slopes of the linear regression for D 4 /D 0 ratios was found to be significantly positive (Student's test, p < .001) and the results of the mixed model reflected a similar significant increase for D 4 /D 0 ratios over time. These results reflected a significant decrease of small-solute transport. In this large series, minimizing the use of hypertonic glucose solutions was associated in patients on long term PD with an overall decrease of small-solute transport within 54 months, despite a high rate of peritoneal infection.

Simulation of multi-stage nonlinear bone remodeling induced by fixed partial dentures of different configurations: a comparative clinical and numerical study.

PubMed

Liao, Zhipeng; Yoda, Nobuhiro; Chen, Junning; Zheng, Keke; Sasaki, Keiichi; Swain, Michael V; Li, Qing

2017-04-01

This paper aimed to develop a clinically validated bone remodeling algorithm by integrating bone's dynamic properties in a multi-stage fashion based on a four-year clinical follow-up of implant treatment. The configurational effects of fixed partial dentures (FPDs) were explored using a multi-stage remodeling rule. Three-dimensional real-time occlusal loads during maximum voluntary clenching were measured with a piezoelectric force transducer and were incorporated into a computerized tomography-based finite element mandibular model. Virtual X-ray images were generated based on simulation and statistically correlated with clinical data using linear regressions. The strain energy density-driven remodeling parameters were regulated over the time frame considered. A linear single-stage bone remodeling algorithm, with a single set of constant remodeling parameters, was found to poorly fit with clinical data through linear regression (low [Formula: see text] and R), whereas a time-dependent multi-stage algorithm better simulated the remodeling process (high [Formula: see text] and R) against the clinical results. The three-implant-supported and distally cantilevered FPDs presented noticeable and continuous bone apposition, mainly adjacent to the cervical and apical regions. The bridged and mesially cantilevered FPDs showed bone resorption or no visible bone formation in some areas. Time-dependent variation of bone remodeling parameters is recommended to better correlate remodeling simulation with clinical follow-up. The position of FPD pontics plays a critical role in mechanobiological functionality and bone remodeling. Caution should be exercised when selecting the cantilever FPD due to the risk of overloading bone resorption.

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.

Analysis of Learning Curve Fitting Techniques.

DTIC Science & Technology

1987-09-01

1986. 15. Neter, John and others. Applied Linear Regression Models. Homewood IL: Irwin, 19-33. 16. SAS User’s Guide: Basics, Version 5 Edition. SAS... Linear Regression Techniques (15:23-52). Random errors are assumed to be normally distributed when using -# ordinary least-squares, according to Johnston...lot estimated by the improvement curve formula. For a more detailed explanation of the ordinary least-squares technique, see Neter, et. al., Applied

On vertical profile of ozone at Syowa

NASA Technical Reports Server (NTRS)

Chubachi, Shigeru

1994-01-01

The difference in the vertical ozone profile at Syowa between 1966-1981 and 1982-1988 is shown. The month-height cross section of the slope of the linear regressions between ozone partial pressure and 100-mb temperature is also shown. The vertically integrated values of the slopes are in close agreement with the slopes calculated by linear regression of Dobson total ozone on 100-mb temperature in the period of 1982-1988.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Nonlinear isochrones in murine left ventricular pressure-volume loops: how well does the time-varying elastance concept hold?

PubMed

Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P

2006-04-01

The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) < 0.75) during isovolumic relaxation and early filling; and 4) logarithmic regression is superior in estimating the fixed volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

PubMed

Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M

2017-06-01

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Application of General Regression Neural Network to the Prediction of LOD Change

NASA Astrophysics Data System (ADS)

Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

2012-01-01

Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

Influence of Heart Rate in Non-linear HRV Indices as a Sampling Rate Effect Evaluated on Supine and Standing.

PubMed

Bolea, Juan; Pueyo, Esther; Orini, Michele; Bailón, Raquel

2016-01-01

The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position ( p < 0.05), concomitant with a 28% increase in mean HR ( p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Familial associations with paratuberculosis ELISA results in Texas Longhorn cattle.

PubMed

Osterstock, Jason B; Fosgate, Geoffrey T; Cohen, Noah D; Derr, James N; Manning, Elizabeth J B; Collins, Michael T; Roussel, Allen J

2008-05-25

The objective of this cross-sectional study was to estimate familial associations with paratuberculosis ELISA status in beef cattle. Texas Longhorn cattle (n=715) greater than 2years of age were sampled for paratuberculosis testing using ELISA and fecal culture. Diagnostic test results were indicative of substantial numbers of false-positive serological reactions consistent with environmental exposure to non-MAP Mycobacterium spp. Associations between ancestors and paratuberculosis ELISA status of offspring were assessed using conditional logistic regression. The association between ELISA status of the dam and her offspring was assessed using linear mixed-effect models. Significant associations were identified between some ancestors and offspring ELISA status. The odds of being classified as "suspect" or greater based on ELISA results were 4.6 times greater for offspring of dams with similarly increased S:P ratios. A significant positive linear association was also observed between dam and offspring log-transformed S:P ratios. Results indicate that there is familial aggregation of paratuberculosis ELISA results in beef cattle and suggest that genetic selection based on paratuberculosis ELISA status may decrease seroprevalence. However, genetic selection may have minimal effect on paratuberculosis control in herds with exposure to non-MAP Mycobacterium spp.

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Factors influencing medication adherence in patients with gout: A descriptive correlational study.

PubMed

Chua, Xin Hui Jasmine; Lim, Siriwan; Lim, Fui Ping; Lim, Yee Nah Anita; He, Hong-Gu; Teng, Gim Gee

2018-01-01

To examine the factors influencing adherence to urate-lowering therapy in patients with gout in Singapore. Gout is the most common type of chronic inflammatory arthritis. Urate-lowering therapy is used to treat gout by reducing serum uric acid levels. However, adherence to urate-lowering therapy among patients remains poor. To date, there have been no available studies based on a conceptual framework that examined factors influencing medication adherence in patients with gout. Cross-sectional, descriptive correlational study. A convenience sample of outpatients (n = 108) was recruited between October 2014-January 2015 from a tertiary hospital in Singapore. Outcomes were measured by relevant valid and reliable instruments. Descriptive statistics and parametric tests including multiple linear regression were used to analyse the data. Although 44.4% of the participants were high adherers to urate-lowering therapy, the mean adherence level was moderate. Significant differences in medication adherence scores were found among the subgroups of gender, ethnicity, marital status, employment status and presence of comorbidity. Medication adherence was positively significantly correlated with age, number of comorbidities and beliefs about medicines. Linear regression showed that higher level of beliefs about medicines, presence of comorbidity and being married were factors positively influencing medication adherence. This study revealed moderate adherence to urate-lowering therapy in patients with gout in Singapore, indicating the need for strategies to improve adherence by considering its main influencing factors. Future research should be conducted to develop interventions targeted at modifying patients' beliefs about medicines in order to improve medication adherence. Findings from this study allow healthcare providers to quickly and easily identify patients who may have low adherence. Nurses should take the lead in educating patients on the mechanism of urate-lowering therapy and highlight the importance of adhering to it. © 2017 John Wiley & Sons Ltd.

The impact of days off between cases on perioperative outcomes for robotic-assisted laparoscopic prostatectomy.

PubMed

Pearce, Shane M; Pariser, Joseph J; Patel, Sanjay G; Anderson, Blake B; Eggener, Scott E; Zagaja, Gregory P

2016-02-01

To examine the effect of days off between cases on perioperative outcomes for robotic-assisted laparoscopic prostatectomy (RALP). We analyzed a single-surgeon series of 2036 RALP cases between 2003 and 2014. Days between cases (DBC) was calculated as the number of days elapsed since the surgeon's previous RALP with the second start cases assigned 0 DBC. Surgeon experience was assessed by dividing sequential case experience into cases 0-99, cases 100-249, cases 250-999, and cases 1000+ based on previously reported learning curve data for RALP. Outcomes included estimated blood loss (EBL), operative time (OT), and positive surgical margins (PSMs). Multiple linear regression was used to assess the impact of the DBC and surgeon experience on EBL, OT, and PSM, while controlling for patient characteristics, surgical technique, and pathologic variables. Overall median DBC was 1 day (0-3) and declined with increasing surgeon case experience. Multiple linear regression demonstrated that each additional DBC was independently associated with increased EBL [β = 3.7, 95% CI (1.3-6.2), p < 0.01] and OT [β = 2.3 (1.4-3.2), p < 0.01], but was not associated with rate of PSM [β = 0.004 (-0.003-0.010), p = 0.2]. Increased experience was also associated with reductions in EBL and OT (p < 0.01). Surgeon experience of 1000+ cases was associated with a 10% reduction in PSM rate (p = 0.03) compared to cases 0-99. In a large single-surgeon RALP series, DBC was associated with increased blood loss and operative time, but not associated with positive surgical margins, when controlling for surgeon experience.

Evaluation of the association between Hispanic ethnicity and disease activity and severity in a large cohort of patients with juvenile idiopathic arthritis.

PubMed

Pelajo, Christina F; Angeles-Han, Sheila T; Prahalad, Sampath; Sgarlat, Caitlin M; Davis, Trevor E; Miller, Laurie C; Lopez-Benitez, Jorge M

2013-10-01

To examine the association between ethnicity and disease activity in patients with juvenile idiopathic arthritis (JIA), and to determine the association of ethnicity with disease severity and disability in this population. CARRAnet, a US database containing information (collected between May 2010 and June 2011) on almost 3,000 subjects with JIA, was used. Demographic variables were compared between Hispanic patients and non-Hispanic patients. Mann-Whitney and chi-square tests were used to compare indicators of disease activity, as well as imaging evidence of joint damage, and Childhood Health Assessment Questionnaire (CHAQ) scores between ethnicities. Two linear regression models were used to determine the association of ethnicity with number of active joints in JIA, and the association between ethnicity and disability (CHAQ scores). A total of 2,704 patients with JIA (277 Hispanic; 2,427 non-Hispanic) were included. Income and health insurance coverage were higher in non-Hispanics. RF-positive polyarticular JIA, positive RF and anti-CCP, as well as use of systemic steroids were more frequent in Hispanics. Imaging evidence of joint damage was present in 32 % of the Hispanic patients compared to 24 % of the non-Hispanic patients (p = 0.008). In multivariate linear regression analyses, the number of active joints was significantly higher in Hispanics than in non-Hispanics (p = 0.03), as well as CHAQ scores (p = 0.003), after adjusting for confounders. Hispanic patients with JIA had higher disease activity than non-Hispanic patients, as well as higher disease severity and disability. Since ethnicity influences disease activity, severity, and disability, different management and treatment plans should be planned accordingly.

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.

Association between birthweight and later body mass index: an individual-based pooled analysis of 27 twin cohorts participating in the CODATwins project.

PubMed

Jelenkovic, Aline; Yokoyama, Yoshie; Sund, Reijo; Pietiläinen, Kirsi H; Hur, Yoon-Mi; Willemsen, Gonneke; Bartels, Meike; van Beijsterveldt, Toos C E M; Ooki, Syuichi; Saudino, Kimberly J; Stazi, Maria A; Fagnani, Corrado; D'Ippolito, Cristina; Nelson, Tracy L; Whitfield, Keith E; Knafo-Noam, Ariel; Mankuta, David; Abramson, Lior; Heikkilä, Kauko; Cutler, Tessa L; Hopper, John L; Wardle, Jane; Llewellyn, Clare H; Fisher, Abigail; Corley, Robin P; Huibregtse, Brooke M; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth J F; Bjerregaard-Andersen, Morten; Beck-Nielsen, Henning; Sodemann, Morten; Tarnoki, Adam D; Tarnoki, David L; Burt, S Alexandra; Klump, Kelly L; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Dubois, Lise; Boivin, Michel; Brendgen, Mara; Dionne, Ginette; Vitaro, Frank; Harris, Jennifer R; Brandt, Ingunn; Nilsen, Thomas Sevenius; Craig, Jeffrey M; Saffery, Richard; Rasmussen, Finn; Tynelius, Per; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Haworth, Claire M A; Plomin, Robert; Ji, Fuling; Ning, Feng; Pang, Zengchang; Rebato, Esther; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Boomsma, Dorret I; Sørensen, Thorkild I A; Kaprio, Jaakko; Silventoinen, Karri

2017-10-01

There is evidence that birthweight is positively associated with body mass index (BMI) in later life, but it remains unclear whether this is explained by genetic factors or the intrauterine environment. We analysed the association between birthweight and BMI from infancy to adulthood within twin pairs, which provides insights into the role of genetic and environmental individual-specific factors. This study is based on the data from 27 twin cohorts in 17 countries. The pooled data included 78 642 twin individuals (20 635 monozygotic and 18 686 same-sex dizygotic twin pairs) with information on birthweight and a total of 214 930 BMI measurements at ages ranging from 1 to 49 years. The association between birthweight and BMI was analysed at both the individual and within-pair levels using linear regression analyses. At the individual level, a 1-kg increase in birthweight was linearly associated with up to 0.9 kg/m2 higher BMI (P < 0.001). Within twin pairs, regression coefficients were generally greater (up to 1.2 kg/m2 per kg birthweight, P < 0.001) than those from the individual-level analyses. Intra-pair associations between birthweight and later BMI were similar in both zygosity groups and sexes and were lower in adulthood. These findings indicate that environmental factors unique to each individual have an important role in the positive association between birthweight and later BMI, at least until young adulthood. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association

A generic sun-tracking algorithm for on-axis solar collector in mobile platforms

NASA Astrophysics Data System (ADS)

Lai, An-Chow; Chong, Kok-Keong; Lim, Boon-Han; Ho, Ming-Cheng; Yap, See-Hao; Heng, Chun-Kit; Lee, Jer-Vui; King, Yeong-Jin

2015-04-01

This paper proposes a novel dynamic sun-tracking algorithm which allows accurate tracking of the sun for both non-concentrated and concentrated photovoltaic systems located on mobile platforms to maximize solar energy extraction. The proposed algorithm takes not only the date, time, and geographical information, but also the dynamic changes of coordinates of the mobile platforms into account to calculate the sun position angle relative to ideal azimuth-elevation axes in real time using general sun-tracking formulas derived by Chong and Wong. The algorithm acquires data from open-loop sensors, i.e. global position system (GPS) and digital compass, which are readily available in many off-the-shelf portable gadgets, such as smart phone, to instantly capture the dynamic changes of coordinates of mobile platforms. Our experiments found that a highly accurate GPS is not necessary as the coordinate changes of practical mobile platforms are not fast enough to produce significant differences in the calculation of the incident angle. On the contrary, it is critical to accurately identify the quadrant and angle where the mobile platforms are moving toward in real time, which can be resolved by using digital compass. In our implementation, a noise filtering mechanism is found necessary to remove unexpected spikes in the readings of the digital compass to ensure stability in motor actuations and effectiveness in continuous tracking. Filtering mechanisms being studied include simple moving average and linear regression; the results showed that a compound function of simple moving average and linear regression produces a better outcome. Meanwhile, we found that a sampling interval is useful to avoid excessive motor actuations and power consumption while not sacrificing the accuracy of sun-tracking.

Associations of Genetically Determined Continental Ancestry with CD4+ Count and Plasma HIV-1 RNA beyond Self-Reported Race and Ethnicity

PubMed Central

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X.; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M.; Spector, Stephen A.

2015-01-01

Background Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4+ counts in HIV-positive children in the US. Methods 41 AIMs, developed to distinguish 7 continental regions, were detected by real-time-PCR in 994 HIV-positive, antiretroviral naïve children. GDA was estimated comparing each individual’s genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4+ count and log10 RNA, adjusting for SRR and age. Results Subjects were 61% Black, 25% Hispanic, 13% White and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4+ count 981 cells/mm3, and mean log10 RNA 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: (0.03, 0.62), p=0.028) and 253 CD4+ cells /mm3 lower (95% CI: (−517, 11), p = 0.06) in CD4+ count, compared to subjects with 100% African ancestry. Conclusion Marked continental admixture was found among this cohort of HIV-infected children from the US. GDA contributed to differences in RNA and CD4+ counts beyond SRR, and should be considered when outcomes associated with HIV infection are likely to have a genetic component. PMID:26536313

[Association between urinary microalbumin-to-creatinine ratio and brachial-ankle pulse wave velocity in hypertensive patients].

PubMed

Zhu, Hang; Xue, Hao; Wang, Guangyi; Fu, Zhenhong; Liu, Jie; Shi, Yajun

2015-04-01

To explore the association between urinary microalbumin-to-creatinine ratio (ACR) and brachial-ankle pulse wave velocity (baPWV) in hypertensive patients. A total of 877 primary hypertension patients were enrolled in this trial from September 2009 to December 2012, and were randomly recruited and patients were divided into normal ACR group (ACR < 30 mg/g, n = 723), micro-albuminuria group (30 mg/g ≤ ACR < 300 mg/g, n = 136) and macro-albuminuria group (ACR ≥ 300 mg/g, n = 18). baPWV was measure by automatic pulse wave velocity measuring system. The baPWV values in patients of micro-albuminuria group and macro-albuminuria group were significantly higher than in the normal ACR group (all P < 0.05). The baPWV value of macro-albuminuria group was significantly higher than in the micro-albuminuria group (P < 0.05). Linear correlation analysis revealed that ACR was positively correlated with baPWV (r = 0.413, P < 0.01). Multiple linear regression analysis showed that ACR independently correlated with baPWV in patients with primary hypertension (β = 0.29, R(2) = 0.112, P < 0.01) after adjusting for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, blood glucose, total cholesterol, low density lipoprotein, high density lipoprotein and triglyceride. Using ACR < 30 mg/g and ACR ≥ 30 mg/g as dichotomous variable, binary logistic regression analysis showed that ACR ≥ 30 mg/g was also a risk factor of the ascending baPWV in primary hypertension patients (OR: 1.73, 95% CI: 1.62-2.98) after adjusting the traditional cardiovascular risk factors. ACR is positively correlated to baPWV in primary hypertension patients, and the ascending baPWV is a risk factor of early renal dysfunction in primary hypertension patients.

Association of Admission Glucose Level and Improvement in Pulmonary Artery Pressure in Patients with Submassive-type Acute Pulmonary Embolism.

PubMed

Gohbara, Masaomi; Hayakawa, Keigo; Hayakawa, Azusa; Akazawa, Yusuke; Yamaguchi, Yukihiro; Furihata, Shuta; Kondo, Ai; Fukushima, Yusuke; Tomari, Sakie; Mitsuhashi, Takayuki; Endo, Tsutomu; Kimura, Kazuo

2018-03-01

Objective The admission glucose level is a predictor of mortality even in patients with acute pulmonary embolism (APE). However, whether or not the admission glucose level is associated with the severity of APE itself or the underlying disease of APE is unclear. Methods This study was a retrospective observational study. A pulmonary artery (PA) catheter was used to accurately evaluate the severity of APE. The percentage changes in the mean PA pressure (PAPm) upon placement and removal of the inferior vena cava filter (IVCF) were evaluated. We hypothesized that the admission glucose level was associated with the improvement in the PA pressure in patients with APE. Patients A total of consecutive 22 patients with submassive APE who underwent temporary or retrievable IVCF insertion on admission and repetitive PA catheter measurements upon placement and removal of IVCFs were enrolled. Results There was a significant positive correlation between the admission glucose levels and the percentage changes in the PAPm (r=0.543, p=0.009). A univariate linear regression analysis showed that the admission glucose level was the predictor of the percentage change in PAPm (β coefficient=0.169 per 1 mg/dL; 95% confidence interval, 0.047-0.291; p=0.009). A multivariate linear regression analysis with the forced inclusion model showed that the admission glucose level was the predictor of the percentage change in PAPm independent of diabetes mellitus, PAPm on admission, troponin positivity, and brain natriuretic peptide level (all p<0.05). Conclusion The admission glucose level was associated with the improvement in the PAPm in patients with submassive-type APE.

Associations between food insecurity, supplemental nutrition assistance program (SNAP) benefits, and body mass index among adult females.

PubMed

Jilcott, Stephanie B; Wall-Bassett, Elizabeth D; Burke, Sloane C; Moore, Justin B

2011-11-01

Obesity disproportionately affects low-income and minority individuals and has been linked with food insecurity, particularly among women. More research is needed to examine potential mechanisms linking obesity and food insecurity. Therefore, this study's purpose was to examine cross-sectional associations between food insecurity, Supplemental Nutrition Assistance Program (SNAP) benefits per household member, perceived stress, and body mass index (BMI) among female SNAP participants in eastern North Carolina (n=202). Women were recruited from the Pitt County Department of Social Services between October 2009 and April 2010. Household food insecurity was measured using the validated US Department of Agriculture 18-item food security survey module. Perceived stress was measured using the 14-item Cohen's Perceived Stress Scale. SNAP benefits and number of children in the household were self-reported and used to calculate benefits per household member. BMI was calculated from measured height and weight (as kg/m(2)). Multivariate linear regression was used to examine associations between BMI, SNAP benefits, stress, and food insecurity while adjusting for age and physical activity. In adjusted linear regression analyses, perceived stress was positively related to food insecurity (P<0.0001), even when SNAP benefits were included in the model. BMI was positively associated with food insecurity (P=0.04). Mean BMI was significantly greater among women receiving <$150 in SNAP benefits per household member vs those receiving ≥$150 in benefits per household member (35.8 vs 33.1; P=0.04). Results suggest that provision of adequate SNAP benefits per household member might partially ameliorate the negative effects of food insecurity on BMI. Copyright © 2011 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

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.

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.

Associations of Genetically Determined Continental Ancestry With CD4+ Count and Plasma HIV-1 RNA Beyond Self-Reported Race and Ethnicity.

PubMed

Brummel, Sean S; Singh, Kumud K; Maihofer, Adam X; Farhad, Mona; Qin, Min; Fenton, Terry; Nievergelt, Caroline M; Spector, Stephen A

2016-04-15

Ancestry informative markers (AIMs) measure genetic admixtures within an individual beyond self-reported racial/ethnic (SRR) groups. Here, we used genetically determined ancestry (GDA) across SRR groups and examine associations between GDA and HIV-1 RNA and CD4 counts in HIV-positive children in the United States. Forty-one AIMs, developed to distinguish 7 continental regions, were detected by real-time PCR in 994 HIV-positive, antiretroviral naive children. GDA was estimated comparing each individual's genotypes to allele frequencies found in a large set of reference individuals originating from global populations using STRUCTURE. The means of GDA were calculated for each category of SRR. Linear regression was used to model GDA on CD4 count and log10 RNA, adjusting for SRR and age. Subjects were 61% black, 25% Hispanic, 13% white, and 1.3% Unknown. The mean age was 2.3 years (45% male), mean CD4 count of 981 cells per cubic millimeter, and mean log10 RNA of 5.11. Marked heterogeneity was found for all SRR groups with high admixture for Hispanics. In adjusted linear regression models, subjects with 100% European ancestry were estimated to have 0.33 higher log10 RNA levels (95% CI: 0.03 to 0.62, P = 0.028) and 253 CD4 cells per cubic millimeter lower (95% CI: -517 to 11, P = 0.06) in CD4 count, compared to subjects with 100% African ancestry. Marked continental admixture was found among this cohort of HIV-infected children from the United States. GDA contributed to differences in RNA and CD4 counts beyond SRR and should be considered when outcomes associated with HIV infection are likely to have a genetic component.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

40 CFR 1066.220 - Linearity verification for chassis dynamometer systems.

Code of Federal Regulations, 2014 CFR

2014-07-01

... dynamometer speed and torque at least as frequently as indicated in Table 1 of § 1066.215. The intent of... linear regression and the linearity criteria specified in Table 1 of this section. (b) Performance requirements. If a measurement system does not meet the applicable linearity criteria in Table 1 of this...

A Learning Progression Should Address Regression: Insights from Developing Non-Linear Reasoning in Ecology

ERIC Educational Resources Information Center

Hovardas, Tasos

2016-01-01

Although ecological systems at varying scales involve non-linear interactions, learners insist thinking in a linear fashion when they deal with ecological phenomena. The overall objective of the present contribution was to propose a hypothetical learning progression for developing non-linear reasoning in prey-predator systems and to provide…

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Estimation of the laser cutting operating cost by support vector regression methodology

NASA Astrophysics Data System (ADS)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

Cultural Competence of Obstetric and Neonatal Nurses.

PubMed

Heitzler, Ella T

To measure the cultural competence level of obstetric and neonatal nurses, explore relationships among cultural competence and selected sociodemographic variables, and identify factors related to cultural competence. Descriptive correlational study. Online survey. A convenience sample of 132 obstetric and neonatal registered nurses practicing in the United States. Nurse participants completed the Cultural Competence Assessment (CCA) instrument, which included Cultural Awareness and Sensitivity (CAS) and Cultural Competence Behaviors (CCB) subscales, and a sociodemographic questionnaire. Correlation and regression analyses were conducted. The average CCA score was 5.38 (possible range = 1.00-7.00). CCA scores were negatively correlated with age and positively correlated with self-ranked cultural competence, years of nursing experience, years of experience within the specialty area, and number of types of previous cultural diversity training. CCB subscale scores were correlated positively with age, years of nursing experience, years of experience within the specialty area, and number of types of previous diversity training. CAS subscale scores were positively correlated with number of types of previous diversity training. Standard multiple linear regression explained approximately 10%, 12%, and 11% of the variance in CCA, CAS, and CCB scores, respectively. Obstetric and neonatal registered nurses should continue to work toward greater cultural competence. Exposing nurses to more types of cultural diversity training may help achieve greater cultural competence. Copyright © 2017 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses. Published by Elsevier Inc. All rights reserved.

Using Carl Rogers' person-centered model to explain interpersonal relationships at a school of nursing.

PubMed

Bryan, Venise D; Lindo, Jascinth; Anderson-Johnson, Pauline; Weaver, Steve

2015-01-01

Faculty members are viewed as nurturers within the academic setting and may be able to influence students' behaviors through the formation of positive interpersonal relationships. Faculty members' attributes that best facilitated positive interpersonal relationships according to Carl Rogers' Person-Centered Model was studied. Students (n = 192) enrolled in a 3-year undergraduate nursing program in urban Jamaica were randomly selected to participate in this descriptive cross-sectional study. A 38-item questionnaire on interpersonal relationships with nursing faculty and students' perceptions of their teachers was utilized to collect data. Factor analysis was used to create factors of realness, prizing, and empathetic understanding. Multiple linear regression analysis on the interaction of the 3 factors and interpersonal relationship scores was performed while controlling for nursing students' study year and age. One hundred sixty-five students (mean age: 23.18 ± 4.51years; 99% female) responded. The regression model explained over 46% of the variance. Realness (β = 0.50, P < .001) was the only significant predictor of the interpersonal relationship scores assigned by the nursing students. Of the total number of respondents, 99 students (60%) reported satisfaction with the interpersonal relationships shared with faculty. Nursing students' perception of faculty members' realness appeared to be the most significant attribute in fostering positive interpersonal relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

NASA Astrophysics Data System (ADS)

Sreekanth, V.; Mahesh, B.; Niranjan, K.

2017-11-01

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy's air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Mental chronometry with simple linear regression.

PubMed

Chen, J Y

1997-10-01

Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Identification of the most significant electrode positions in electromyographic evaluation of swallowing-related movements in humans.

PubMed

Zaretsky, E; Pluschinski, P; Sader, R; Birkholz, P; Neuschaefer-Rube, C; Hey, Christiane

2017-02-01

Surface electromyography (sEMG) is a well-established procedure for recording swallowing-related muscle activities. Because the use of a large number of sEMG channels is time consuming and technically sophisticated, the aim of this study was to identify the most significant electrode positions associated with oropharyngeal swallowing activities. Healthy subjects (N = 16) were tested with a total of 42 channels placed in M. masseter, M. orbicularis oris, submental and paralaryngeal regions. Each test subject swallowed 10 ml of water five times. After having identified 16 optimal electrode positions, that is, positions with the strongest signals quantified by the highest integral values, differences to 26 other ones were determined by a Mann-Whitney U test. Kruskal-Wallis H test was utilized for the analysis of differences between single subjects, subject subgroups, and single electrode positions. Factors associated with sEMG signals were examined in a linear regression. Sixteen electrode positions were chosen by a simple ranking of integral values. These positions delivered significantly higher signals than the other 26 positions. Differences between single electrode positions and between test subjects were also significant. Sixteen most significant positions were identified which represent swallowing-related muscle potentials in healthy subjects.

[Comparison of arterial stiffness in non-hypertensive and hypertensive population of various age groups].

PubMed

Zhang, Y J; Wu, S L; Li, H Y; Zhao, Q H; Ning, C H; Zhang, R Y; Yu, J X; Li, W; Chen, S H; Gao, J S

2018-01-24

Objective: To investigate the impact of blood pressure and age on arterial stiffness in general population. Methods: Participants who took part in 2010, 2012 and 2014 Kailuan health examination were included. Data of brachial ankle pulse wave velocity (baPWV) examination were analyzed. According to the WHO criteria of age, participants were divided into 3 age groups: 18-44 years group ( n= 11 608), 45-59 years group ( n= 12 757), above 60 years group ( n= 5 002). Participants were further divided into hypertension group and non-hypertension group according to the diagnostic criteria for hypertension (2010 Chinese guidelines for the managemengt of hypertension). Multiple linear regression analysis was used to analyze the association between systolic blood pressure (SBP) with baPWV in the total participants and then stratified by age groups. Multivariate logistic regression model was used to analyze the influence of blood pressure on arterial stiffness (baPWV≥1 400 cm/s) of various groups. Results: (1)The baseline characteristics of all participants: 35 350 participants completed 2010, 2012 and 2014 Kailuan examinations and took part in baPWV examination. 2 237 participants without blood pressure measurement values were excluded, 1 569 participants with history of peripheral artery disease were excluded, we also excluded 1 016 participants with history of cardiac-cerebral vascular disease. Data from 29 367 participants were analyzed. The age was (48.0±12.4) years old, 21 305 were males (72.5%). (2) Distribution of baPWV in various age groups: baPWV increased with aging. In non-hypertension population, baPWV in 18-44 years group, 45-59 years group, above 60 years group were as follows: 1 299.3, 1 428.7 and 1 704.6 cm/s, respectively. For hypertension participants, the respective values of baPWV were: 1 498.4, 1 640.7 and 1 921.4 cm/s. BaPWV was significantly higher in hypertension group than non-hypertension group of respective age groups ( P< 0.05). (3) Multiple linear regression analysis defined risk factors of baPWV: Multivariate linear regression analysis showed that baPWV was positively correlated with SBP( t= 39.30, P< 0.001), and same results were found in the sub-age groups ( t -value was 37.72, 27.30, 9.15, all P< 0.001, respectively) after adjustment for other confounding factors, including age, sex, pulse pressure(PP), body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), smoking, drinking, physical exercise, antihypertensive medications, lipid-lowering medication. (4) Multivariate logistic regression analysis of baPWV-related factors: After adjustment for other confounding factors, including age, sex, PP, BMI, FBG, TC, smoking, drinking, physical exercise, antihypertensive medication, lipid-lowering medication, multivariate logistic regression analysis showed that risks for increased arterial stiffness in hypertension group were higher than those in non-hypertension group, the OR in participants with hypertension was 2.54 (2.35-2.74) in the total participants, and same results were also found in sub-age groups, the OR s were 3.22(2.86-3.63), 2.48(2.23-2.76), and 1.91(1.42-2.56), respectively, in each sub-age group. Conclusion: SBP is positively related to arterial stiffness in different age groups, and hypertension is a risk factor for increased arterial stiffness in different age groups. Clinical Trial Registry Chinese Clinical Trial Registry, ChiCTR-TNC-11001489.

Distance correction system for localization based on linear regression and smoothing in ambient intelligence display.

PubMed

Kim, Dae-Hee; Choi, Jae-Hun; Lim, Myung-Eun; Park, Soo-Jun

2008-01-01

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression and smoothing method, by which distance information of a user who approaches to the display can he accurately output even in an unanticipated condition using a passive infrared VIR) sensor and an ultrasonic device. The developed system consists of an ambient intelligence display and an ultrasonic transmitter, and a sensor gateway. Each module communicates with each other through RF (Radio frequency) communication. The ambient intelligence display includes an ultrasonic receiver and a PIR sensor for motion detection. In particular, this system selects and processes algorithms such as smoothing or linear regression for current input data processing dynamically through judgment process that is determined using the previous reliable data stored in a queue. In addition, we implemented GUI software with JAVA for real time location tracking and an ambient intelligence display.

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER’S DISEASE*

PubMed Central

Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

2015-01-01

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM. PMID:26900412

Liquid electrolyte informatics using an exhaustive search with linear regression.

PubMed

Sodeyama, Keitaro; Igarashi, Yasuhiko; Nakayama, Tomofumi; Tateyama, Yoshitaka; Okada, Masato

2018-06-14

Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the "prediction accuracy" and "calculation cost" of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the "accuracy" and "cost" when the search of a huge amount of new materials was carried out.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

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.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

PubMed

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso

PubMed Central

Kong, Shengchun; Nan, Bin

2013-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328

A cross-sectional examination of psychological distress, positive mental health and their predictors in medical students in their clinical clerkships.

PubMed

van Dijk, Inge; Lucassen, Peter L B J; van Weel, Chris; Speckens, Anne E M

2017-11-17

Medical students can experience the transition from theory to clinical clerkships as stressful. Scientific literature on the mental health of clinical clerkship students is scarce and mental health is usually defined as absence of psychological distress without assessing psychological, emotional and social wellbeing, together called 'positive mental health'. This cross-sectional study examines the prevalence of psychological distress and positive mental health and explores possible predictors in a Dutch sample of clinical clerkship students. Fourth-year medical students in their first year of clinical clerkships were invited to complete an online questionnaire assessing demographics, psychological distress (Brief Symptom Inventory), positive mental health (Mental Health Continuum- SF), dysfunctional cognitions (Irrational Beliefs Inventory) and dispositional mindfulness skills (Five Facet Mindfulness Questionnaire). Multiple linear regression analysis was used to explore relationships between psychological distress, positive mental health (dependent variables) and demographics, dysfunctional cognitions and dispositional mindfulness skills (predictors). Of 454 eligible students, 406 (89%) completed the assessment of whom 21% scored in the clinical range of psychological distress and 41% reported a flourishing mental health. These proportions partially overlap each other. Female students reported a significantly higher mean level of psychological distress than males. In the regression analysis the strongest predictors of psychological distress were 'acting with awareness' (negative) and 'worrying' (positive). Strongest predictors of positive mental health were 'problem avoidance' (negative) and 'emotional irresponsibility' (negative). The prevalence of psychopathology in our sample of Dutch clinical clerkship students is slightly higher than in the general population. Our results support conclusions of previous research that psychological distress and positive mental health are not two ends of one continuum but partially overlap. Although no conclusion on causality can be drawn, this study supports the idea that self-awareness and active, nonavoidant coping strategies are related to lower distress and higher positive mental health.

Vitamin D is linked to carotid intima-media thickness and immune reconstitution in HIV-positive individuals.

PubMed

Ross, Allison C; Judd, Suzanne; Kumari, Meena; Hileman, Corrilynn; Storer, Norma; Labbato, Danielle; Tangpricha, Vin; McComsey, Grace A

2011-01-01

Patients with HIV infection are at increased risk of cardiovascular disease (CVD). Vitamin D insufficiency has been associated with increased CVD risk in non-HIV populations. This study sought to determine the relationship between vitamin D status and markers of CVD and HIV-related factors in HIV-positive patients. Patients with HIV infection on antiretroviral therapy and healthy controls were prospectively enrolled. Fasting lipids, glucose, insulin, inflammatory markers (soluble tumour necrosis factor-α receptor I, interleukin-6 and high-sensitivity C-reactive protein) and endothelial markers (soluble intercellular adhesion molecule-1 and soluble vascular cell adhesion molecule-1) were measured. Fasting 25-hydroxyvitamin D (25(OH)D) was measured from stored serum samples. The internal carotid artery and common carotid artery (CCA) intima-media thickness (IMT) were measured in a subset of HIV-positive patients. Baseline cross-sectional data were analysed. A total of 149 HIV-positive patients (56 with carotid IMT) and 34 controls were included. Controls had higher adjusted mean 25(OH)D levels than HIV-positive patients (P=0.02). In multivariable linear regression among the HIV-positive patients, 25(OH)D was positively associated with CD4(+) T-cell restoration after antiretroviral therapy (ΔCD4 = current - nadir CD4(+) T-cell; P<0.01), but was not associated with inflammatory or endothelial markers. In multivariable logistic regression, odds of having CCA IMT above the median were more than 10× higher in those with lower 25(OH)D levels (OR=10.62, 95% CI 1.37-82.34; P<0.01). Vitamin D status in HIV-positive patients was positively associated with improved immune restoration after antiretroviral therapy and negatively associated with CCA IMT. These findings suggest that vitamin D may play a role in HIV-related CVD and in immune reconstitution after antiretroviral therapy.

Climate change at upper treeline: How do trees on the edge react to increasing temperatures?

NASA Astrophysics Data System (ADS)

Jochner, Matthias; Bugmann, Harald; Nötzli, Magdalena; Bigler, Christof

2017-04-01

Treeline ecotones are thought to be particularly sensitive to climate warming, and an alteration of their growth conditions may have important implications for the ecosystem services they supply in mountain regions. We use a novel approach to quantify effects of a changing climate on tree growth, using case studies in the European Alps. We compiled tree-ring data from almost 600 trees of four species at treeline in three climate regions of Switzerland. Temperature loggers installed along transects provided data for a precise interpolation of temperatures experienced by the sampled trees. To assess the influence of temperature on annual growth, we used linear mixed-effects models, allowing us to quantify effect sizes and to account for between-tree growth variability. After removing biological growth trends, we isolated temporal trends of ring-width indices. Furthermore, we fitted non-linear regression models to radial growth rates of individual years with temperature and tree age as predicting covariates for a fine-scale investigation of the temperature dependency of tree growth. For all species, climate-growth linear mixed-effects models indicated strong positive responses of ring-width indices to temperature in early summer and previous year's autumn, featuring considerable between-tree variability. All species showed positive ring-width index trends at treeline but different interactions with elevation: Larix decidua exhibited a declining ring-width index trend with decreasing elevation, whereas Picea abies, Pinus cembra and Pinus mugo showed increasing and/or stable trends. Not only reflected our findings the effects of ameliorated growth conditions, they might have also revealed suspected negative and positive feedbacks of climate change on growth, and increased the knowledge about the functional form and parameterization of the temperature dependency of tree growth.

Duration of U.S. residence and suicidality among racial/ethnic minority immigrants.

PubMed

Brown, Monique J; Cohen, Steven A; Mezuk, Briana

2015-02-01

The immigration experience embodies a range of factors including different cultural norms and expectations, which may be particularly important for groups who become racial/ethnic minorities when they migrate to the U.S. However, little is known about the correlates of mental health indicators among these groups. The primary and secondary aims were to determine the association between duration of U.S. residence and suicidality, and 12-month mood, anxiety, and substance use disorders, respectively, among racial/ethnic minority immigrants. Data were obtained from the National Survey of American Life and the National Latino and Asian American Survey. Multivariable logistic regression was used to determine the association between duration of US residence, and suicidality and 12-month psychopathology. Among Afro-Caribbeans, there was a modest positive association between duration of U.S. residence and 12-month psychopathology (P linear trend = 0.016). Among Asians there was a modest positive association between duration of US residence and suicidal ideation and attempts (P linear trend = 0.018, 0.063, respectively). Among Latinos, there was a positive association between duration of US residence, and suicidal ideation, attempts and 12-month psychopathology (P linear trend = 0.001, 0.012, 0.002, respectively). Latinos who had been in the U.S. for >20 years had 2.6 times greater likelihood of suicidal ideation relative to those who had been in the U.S. for <5 years (95% CI 1.01-6.78). The association between duration of US residence and suicidality and psychopathology varies across racial/ethnic minority groups. The results for Latino immigrants are broadly consistent with the goal-striving or acculturation stress hypothesis.

Effects of early nightmares on the development of sleep disturbances in motor vehicle accident victims.

PubMed

Kobayashi, Ihori; Sledjeski, Eve M; Spoonster, Eileen; Fallon, William F; Delahanty, Douglas L

2008-12-01

The present study prospectively examined the extent to which trauma-related nightmares affected the subsequent development of insomnia symptoms in 314 motor vehicle accident (MVA) victims. Participants were assessed in-hospital and at 2 weeks, 6 weeks, 3 months, and 1 year post-MVA. Hierarchical linear regression analyses showed that 6-week PTSD symptoms (PTSS) and 3-month nightmares, but not 2-week nightmares were positively associated with sleep onset and maintenance problems reported at 3-month post-MVA. Nightmares reported at 3-months post-MVA were positively associated with 1-year sleep maintenance problems. These findings highlight the dynamic relationship between PTSS and sleep problems as well as the potential importance of early intervention for trauma-related nightmares as a means to prevent sleep problems after a traumatic experience.

Quantitative work demands, emotional demands, and cognitive stress symptoms in surgery nurses.

PubMed

Elfering, Achim; Grebner, Simone; Leitner, Monika; Hirschmüller, Anja; Kubosch, Eva Johanna; Baur, Heiner

2017-06-01

In surgery, cognitive stress symptoms, including problems in concentrating, deciding, memorising, and reflecting are risks to patient safety. Recent evidence points to social stressors as antecedents of cognitive stress symptoms in surgery personnel. The current study tests whether cognitive stress symptoms are positively associated with emotional abuse, emotional- and task-related demands and resources in surgery work. Forty-eight surgery nurses from two hospitals filled out the Copenhagen Psychosocial Questionnaire in its German version. Task-related and emotional demands were positively related to cognitive stress symptoms. In a stepwise, multiple, linear regression of cognitive stress symptoms on task-related and emotional demands, emotional abuse and emotional demands were unique predictors (p < .05). Efforts to increase patient safety should address emotional abuse, emotional demands, and, therefore, communication and cooperation team climate in surgery personnel.

Prenatal and childhood perfluoroalkyl substances exposures and children's reading skills at ages 5 and 8years.

PubMed

Zhang, Hongmei; Yolton, Kimberly; Webster, Glenys M; Ye, Xiaoyun; Calafat, Antonia M; Dietrich, Kim N; Xu, Yingying; Xie, Changchun; Braun, Joseph M; Lanphear, Bruce P; Chen, Aimin

2018-02-01

Exposure to perfluoroalkyl substances (PFASs) may impact children's neurodevelopment. To examine the association of prenatal and early childhood serum PFAS concentrations with children's reading skills at ages 5 and 8years. We used data from 167 mother-child pairs recruited during pregnancy (2003-2006) in Cincinnati, OH, quantified prenatal serum PFAS concentrations at 16±3weeks of gestation and childhood sera at ages 3 and 8years. We assessed children's reading skills using Woodcock-Johnson Tests of Achievement III at age 5years and Wide Range Achievement Test-4 at age 8years. We used general linear regression to quantify the covariate-adjusted associations between natural log-transformed PFAS concentrations and reading skills, and used multiple informant model to identify the potential windows of susceptibility. Median serum PFASs concentrations were PFOS>PFOA>PFHxS>PFNA in prenatal, 3-year, and 8-year children. The covariate-adjusted general linear regression identified positive associations between serum PFOA, PFOS and PFNA concentrations and children's reading scores at ages 5 and 8years, but no association between any PFHxS concentration and reading skills. The multiple informant model showed: a) Prenatal PFOA was positively associated with higher children's scores in Reading Composite (β: 4.0, 95% CI: 0.6, 7.4 per a natural log unit increase in exposure) and Sentence Comprehension (β: 4.2, 95% CI: 0.5, 8.0) at age 8years; b) 3-year PFOA was positively associated with higher children's scores in Brief Reading (β: 7.3, 95% CI: 0.9, 13.8), Letter Word Identification (β: 6.6, 95% CI: 1.1, 12.0), and Passage Comprehension (β: 5.9, 95% CI: 1.5, 10.2) at age 5years; c) 8-year PFOA was positively associated with higher children's Word Reading scores (β: 5.8, 95% CI: 0.8, 10.7) at age 8years. Prenatal PFOS and PFNA were positively associated with children's reading abilities at age 5years, but not at age 8years; 3-year PFOS and PFNA were positively associated with reading scores at age 5years. But PFHxS concentrations, at any exposure windows, were not associated with reading skills. Prenatal and childhood serum PFOA, PFOS and PFNA concentrations were positively associated with better children's reading skills at ages 5 and 8years, but no association was found between serum PFHxS and reading skills. Copyright © 2017 Elsevier Ltd. All rights reserved.

Outcomes and resource utilization associated with underage drinking at a level I trauma center.

PubMed

Psoter, Kevin J; Roudsari, Bahman S; Mack, Christopher; Vavilala, Monica S; Jarvik, Jeffrey G

2014-08-01

To examine the association of blood alcohol content (BAC) on hospital-based outcomes and imaging utilization for patients <21 years admitted to a level I trauma center. Retrospective analysis of alcohol-involved injuries in patients 13-20 years, admitted to a level I trauma center from 1996 to 2010. An injury was considered alcohol involved if the patient had a BAC > 0. Multivariable logistic regression was used to compare mortality, discharge destination (home and skilled nursing facility), intensive care unit admission, and operating room use between patients with and without positive BAC for patients 13-15, 16-17, and 18-20 years. Multivariable linear regression was used to compare length of hospitalization. Finally, multivariable negative binomial regression evaluated radiology resource utilization (x-ray, computed tomography [CT], and magnetic resonance imaging). A total of 7,663 patients, 13-20 years old, were admitted over the study period. A positive BAC was reported in 19% of these patients. In general, the presence of alcohol was not associated with mortality rate, length of hospitalization, intensive care unit, and operating room use or discharge status for any age group. However, the presence of alcohol was associated with higher utilization of head (incidence rate ratio [IRR] 1.13, 95% confidence interval [CI] 1.02-1.26), cervical spine (IRR 1.10, 95% CI 1.01-1.22), and thoracic (IRR 1.30, 95% CI 1.05-1.63) CTs in young adults 18-20 years. No differences in CT use were observed in patients 13-15 or 16-17 years. Positive BAC was not significantly associated with adverse outcomes or resource utilization in younger trauma patients. However, the use of certain body region CTs was associated with positive BAC in patients 18-20 years. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Six-year follow-up on work force and finances of the United States anesthesiology training programs: 2000 to 2006.

PubMed

Kheterpal, Sachin; Tremper, Kevin K; Shanks, Amy; Morris, Michelle

2009-01-01

In the mid 1990s, interest in the field of anesthesiology decreased significantly among medical students, resulting in a decreasing resident class size and, subsequently, fewer anesthesiologists entering the United States workforce. This apparent practitioner shortage was associated with increased salary demands, which placed anesthesiology training departments in financial jeopardy. Starting in 1999, a survey was sent to the department chairs of the United States anesthesiology training programs to assess the status of faculty and finances of their departments. Follow-up surveys have been conducted each year thereafter. We present the results of the 2006 survey and 7 yr trend data. Surveys were distributed by e-mail in September 2006 to anesthesiology department chairs of the United States training programs. The responses were received by e-mail. Descriptive statistics were performed on responder data. In addition, a linear regression model to predict institutional support was developed. One-hundred-eighteen departments were surveyed with a response rate of 61%. There were an average of 4 open faculty positions in the 71% of the departments reporting open faculty positions. This would imply an overall 5% open position rate, down from 10% in 2000. Of the 96% of departments who employ certified registered nurse anesthetists, 70% had an average of 4 open positions, or approximately 11% shortage. The average department received $5,500,000 in total institutional support annually ($120,000/faculty). When the portion of this support provided for certified registered nurse anesthetists was removed, the average amount received was $4,600,000 or $100,000/faculty. This is a 10% increase over the previous year and an approximate 300% increase over the year 2000. Faculty academic time averaged 18% (where 20% is 1 day per week). The departments billed an average of 12,200 U/faculty/year. The average anesthesia unit value collected was $31/unit, while departments would require $46/unit to meet expenses. In a linear regression model, clinical revenue per unit billed minus expenses per unit billed predicted faculty support per full-time equivalent. This current survey reveals a continuing need for institutional support to keep anesthesiology training departments financially solvent. The amount of support is associated with the reimbursement for anesthesia work. There is also a continuing, but decreasing, number of open faculty anesthesiologist positions nationwide.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

PubMed

Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A

2005-04-15

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.

Effects of the Maytiv positive psychology school program on early adolescents' well-being, engagement, and achievement.

PubMed

Shoshani, Anat; Steinmetz, Sarit; Kanat-Maymon, Yaniv

2016-08-01

As positive psychology is a nascent area of research, there are very few empirical studies assessing the impact and sustained effects of positive psychology school interventions. The current study presents a 2-year longitudinal evaluation of the effects of a school-based positive psychology program on students' subjective well-being, school engagement, and academic achievements. The study investigated the effectiveness of the Maytiv school program using a positive psychology-based classroom-level intervention with 2517 seventh- to ninth-grade students in 70 classrooms, from six schools in the center of Israel. The classes were randomly assigned to intervention and control conditions, which were comparable in terms of students' age, gender, and socio-economic status. Hierarchical linear regression analyses revealed positive intervention effects on positive emotions, peer relations, emotional engagement in school, cognitive engagement, and grade point average scores (Cohen's ds 0.16-0.71). In the control group, there were significant decreases in positive emotions and cognitive engagement, and no significant changes in peer relations, emotional engagement or school achievements. These findings demonstrate the significant socio-emotional and academic benefits of incorporating components of positive psychology into school curricula. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.