Sample records for tasks multiple regression

  1. Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

    ERIC Educational Resources Information Center

    Williams, Ryan T.

    2012-01-01

    Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

  2. Multiple Correlation versus Multiple Regression.

    ERIC Educational Resources Information Center

    Huberty, Carl J.

    2003-01-01

    Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

  3. Application of Multi-task Lasso Regression in the Stellar Parametrization

    NASA Astrophysics Data System (ADS)

    Chang, L. N.; Zhang, P. A.

    2015-01-01

    The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only makes different physical parameters share the common features, but also can effectively preserve their own peculiar features. Experiments were done based on the ELODIE data simulated with the stellar atmospheric simulation model, and on the SDSS data released by the American large survey Sloan. The precision of the model is better than those of the methods in the related literature, especially for the acceleration of gravity (lg g) and the chemical abundance ([Fe/H]). In the experiments, we changed the resolution of the spectrum, and applied the noises with different signal-to-noise ratio (SNR) to the spectrum, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, has a strong stability, and also can improve the overall accuracy of the model.

  4. 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.

  5. Predicting MHC-II binding affinity using multiple instance regression

    PubMed Central

    EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

    2011-01-01

    Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

  6. Application of Multi-task Lasso Regression in the Parametrization of Stellar Spectra

    NASA Astrophysics Data System (ADS)

    Chang, Li-Na; Zhang, Pei-Ai

    2015-07-01

    The multi-task learning approaches have attracted the increasing attention in the fields of machine learning, computer vision, and artificial intelligence. By utilizing the correlations in tasks, learning multiple related tasks simultaneously is better than learning each task independently. An efficient multi-task Lasso (Least Absolute Shrinkage Selection and Operator) regression algorithm is proposed in this paper to estimate the physical parameters of stellar spectra. It not only can obtain the information about the common features of the different physical parameters, but also can preserve effectively their own peculiar features. Experiments were done based on the ELODIE synthetic spectral data simulated with the stellar atmospheric model, and on the SDSS data released by the American large-scale survey Sloan. The estimation precision of our model is better than those of the methods in the related literature, especially for the estimates of the gravitational acceleration (lg g) and the chemical abundance ([Fe/H]). In the experiments we changed the spectral resolution, and applied the noises with different signal-to-noise ratios (SNRs) to the spectral data, so as to illustrate the stability of the model. The results show that the model is influenced by both the resolution and the noise. But the influence of the noise is larger than that of the resolution. In general, the multi-task Lasso regression algorithm is easy to operate, it has a strong stability, and can also improve the overall prediction accuracy of the model.

  7. Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

    PubMed Central

    Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

    2012-01-01

    Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

  8. Multiple-Instance Regression with Structured Data

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

    2008-01-01

    We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

  9. An efficient approach to understanding and predicting the effects of multiple task characteristics on performance.

    PubMed

    Richardson, Miles

    2017-04-01

    In ergonomics there is often a need to identify and predict the separate effects of multiple factors on performance. A cost-effective fractional factorial approach to understanding the relationship between task characteristics and task performance is presented. The method has been shown to provide sufficient independent variability to reveal and predict the effects of task characteristics on performance in two domains. The five steps outlined are: selection of performance measure, task characteristic identification, task design for user trials, data collection, regression model development and task characteristic analysis. The approach can be used for furthering knowledge of task performance, theoretical understanding, experimental control and prediction of task performance. Practitioner Summary: A cost-effective method to identify and predict the separate effects of multiple factors on performance is presented. The five steps allow a better understanding of task factors during the design process.

  10. Incremental Net Effects in Multiple Regression

    ERIC Educational Resources Information Center

    Lipovetsky, Stan; Conklin, Michael

    2005-01-01

    A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

  11. Managing Multiple Tasks in Complex, Dynamic Environments

    NASA Technical Reports Server (NTRS)

    Freed, Michael; Null, Cynthia H. (Technical Monitor)

    1998-01-01

    Sketchy planners are designed to achieve goals in realistically complex, time-pressured, and uncertain task environments. However, the ability to manage multiple, potentially interacting tasks in such environments requires extensions to the functionality these systems typically provide. This paper identifies a number of factors affecting how interacting tasks should be prioritized, interrupted, and resumed, and then describes a sketchy planner called APEX that takes account of these factors when managing multiple tasks.

  12. Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

    PubMed

    Ng, Kar Yong; Awang, Norhashidah

    2018-01-06

    Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

  13. Testing Different Model Building Procedures Using Multiple Regression.

    ERIC Educational Resources Information Center

    Thayer, Jerome D.

    The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

  14. Multiple Regression: A Leisurely Primer.

    ERIC Educational Resources Information Center

    Daniel, Larry G.; Onwuegbuzie, Anthony J.

    Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

  15. Multiple-object tracking while driving: the multiple-vehicle tracking task.

    PubMed

    Lochner, Martin J; Trick, Lana M

    2014-11-01

    Many contend that driving an automobile involves multiple-object tracking. At this point, no one has tested this idea, and it is unclear how multiple-object tracking would coordinate with the other activities involved in driving. To address some of the initial and most basic questions about multiple-object tracking while driving, we modified the tracking task for use in a driving simulator, creating the multiple-vehicle tracking task. In Experiment 1, we employed a dual-task methodology to determine whether there was interference between tracking and driving. Findings suggest that although it is possible to track multiple vehicles while driving, driving reduces tracking performance, and tracking compromises headway and lane position maintenance while driving. Modified change-detection paradigms were used to assess whether there were change localization advantages for tracked targets in multiple-vehicle tracking. When changes occurred during a blanking interval, drivers were more accurate (Experiment 2a) and ~250 ms faster (Experiment 2b) at locating the vehicle that changed when it was a target rather than a distractor in tracking. In a more realistic driving task where drivers had to brake in response to the sudden onset of brake lights in one of the lead vehicles, drivers were more accurate at localizing the vehicle that braked if it was a tracking target, although there was no advantage in terms of braking response time. Overall, results suggest that multiple-object tracking is possible while driving and perhaps even advantageous in some situations, but further research is required to determine whether multiple-object tracking is actually used in day-to-day driving.

  16. Age-Related Differences in Multiple Task Monitoring

    PubMed Central

    Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

    2014-01-01

    Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age. PMID:25215609

  17. Age-related differences in multiple task monitoring.

    PubMed

    Todorov, Ivo; Del Missier, Fabio; Mäntylä, Timo

    2014-01-01

    Coordinating multiple tasks with narrow deadlines is particularly challenging for older adults because of age related decline in cognitive control functions. We tested the hypothesis that multiple task performance reflects age- and gender-related differences in executive functioning and spatial ability. Young and older adults completed a multitasking session with four monitoring tasks as well as separate tasks measuring executive functioning and spatial ability. For both age groups, men exceeded women in multitasking, measured as monitoring accuracy. Individual differences in executive functioning and spatial ability were independent predictors of young adults' monitoring accuracy, but only spatial ability was related to sex differences. For older adults, age and executive functioning, but not spatial ability, predicted multitasking performance. These results suggest that executive functions contribute to multiple task performance across the adult life span and that reliance on spatial skills for coordinating deadlines is modulated by age.

  18. RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

    DTIC Science & Technology

    This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

  19. Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

    PubMed

    Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

    2008-01-01

    This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

  20. 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.

  1. The Geometry of Enhancement in Multiple Regression

    ERIC Educational Resources Information Center

    Waller, Niels G.

    2011-01-01

    In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

  2. General Nature of Multicollinearity in Multiple Regression Analysis.

    ERIC Educational Resources Information Center

    Liu, Richard

    1981-01-01

    Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

  3. Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

    PubMed

    Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

    2016-11-15

    There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression

    PubMed Central

    Steiner, Genevieve Z.; Barry, Robert J.; Gonsalvez, Craig J.

    2016-01-01

    In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies. PMID:27445774

  5. Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression.

    PubMed

    Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

    2016-01-01

    In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies.

  6. Enhance-Synergism and Suppression Effects in Multiple Regression

    ERIC Educational Resources Information Center

    Lipovetsky, Stan; Conklin, W. Michael

    2004-01-01

    Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

  7. The M Word: Multicollinearity in Multiple Regression.

    ERIC Educational Resources Information Center

    Morrow-Howell, Nancy

    1994-01-01

    Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

  8. Efficient multitasking: parallel versus serial processing of multiple tasks.

    PubMed

    Fischer, Rico; Plessow, Franziska

    2015-01-01

    In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling.

  9. Efficient multitasking: parallel versus serial processing of multiple tasks

    PubMed Central

    Fischer, Rico; Plessow, Franziska

    2015-01-01

    In the context of performance optimizations in multitasking, a central debate has unfolded in multitasking research around whether cognitive processes related to different tasks proceed only sequentially (one at a time), or can operate in parallel (simultaneously). This review features a discussion of theoretical considerations and empirical evidence regarding parallel versus serial task processing in multitasking. In addition, we highlight how methodological differences and theoretical conceptions determine the extent to which parallel processing in multitasking can be detected, to guide their employment in future research. Parallel and serial processing of multiple tasks are not mutually exclusive. Therefore, questions focusing exclusively on either task-processing mode are too simplified. We review empirical evidence and demonstrate that shifting between more parallel and more serial task processing critically depends on the conditions under which multiple tasks are performed. We conclude that efficient multitasking is reflected by the ability of individuals to adjust multitasking performance to environmental demands by flexibly shifting between different processing strategies of multiple task-component scheduling. PMID:26441742

  10. Categorical Variables in Multiple Regression: Some Cautions.

    ERIC Educational Resources Information Center

    O'Grady, Kevin E.; Medoff, Deborah R.

    1988-01-01

    Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

  11. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

    PubMed

    He, Dan; Kuhn, David; Parida, Laxmi

    2016-06-15

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

  12. Simultaneity, Sequentiality, and Speed: Organizational Messages about Multiple-Task Completion

    ERIC Educational Resources Information Center

    Stephens, Keri K.; Cho, Jaehee K.; Ballard, Dawna I.

    2012-01-01

    Workplace norms for task completion increasingly value speed and the ability to accomplish multiple tasks at once. This study situates this popularized issue of multitasking within the context of chronemics scholarship by addressing related issues of simultaneity, sequentiality, and speed. Ultimately, we consider 2 multiple-task completion…

  13. Floating Data and the Problem with Illustrating Multiple Regression.

    ERIC Educational Resources Information Center

    Sachau, Daniel A.

    2000-01-01

    Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

  14. Predictive performance models and multiple task performance

    NASA Technical Reports Server (NTRS)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  15. Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution.

    PubMed

    Schrum, Jacob; Miikkulainen, Risto

    2016-01-01

    Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.

  16. Efficient Symbolic Task Planning for Multiple Mobile Robots

    DTIC Science & Technology

    2016-12-13

    Efficient Symbolic Task Planning for Multiple Mobile Robots Yuqian Jiang December 13, 2016 Abstract Symbolic task planning enables a robot to make...high-level deci- sions toward a complex goal by computing a sequence of actions with minimum expected costs. This thesis builds on a single- robot ...time complexity of optimal planning for multiple mobile robots . In this thesis we first investigate the performance of the state-of-the-art solvers of

  17. 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.

  18. The Challenge of Multiple Perspectives: Multiple Solution Tasks for Students Incorporating Diverse Tools and Representation Systems

    ERIC Educational Resources Information Center

    Kordaki, Maria

    2015-01-01

    This study focuses on the role of multiple solution tasks (MST) incorporating multiple learning tools and representation systems (MTRS) in encouraging each student to develop multiple perspectives on the learning concepts under study and creativity of thought. Specifically, two types of MST were used, namely tasks that allowed and demanded…

  19. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

    PubMed

    Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

    2012-05-01

    Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  20. Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

    PubMed Central

    Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

    2012-01-01

    While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

  1. Tools to support interpreting multiple regression in the face of multicollinearity.

    PubMed

    Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

    2012-01-01

    While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

  2. Multiple imputation for cure rate quantile regression with censored data.

    PubMed

    Wu, Yuanshan; Yin, Guosheng

    2017-03-01

    The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

  3. The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

    ERIC Educational Resources Information Center

    Jaccard, James; And Others

    1990-01-01

    Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

  4. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    PubMed

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  5. Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

    ERIC Educational Resources Information Center

    Warne, Russell T.

    2011-01-01

    Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

  6. Distinguishing Schemes and Tasks in Children's Development of Multiplicative Reasoning

    ERIC Educational Resources Information Center

    Tzur, Ron; Johnson, Heather L.; McClintock, Evan; Kenney, Rachael H.; Xin, Yan P.; Si, Luo; Woordward, Jerry; Hord, Casey; Jin, Xianyan

    2013-01-01

    We present a synthesis of findings from constructivist teaching experiments regarding six schemes children construct for reasoning multiplicatively and tasks to promote them. We provide a task-generating platform game, depictions of each scheme, and supporting tasks. Tasks must be distinguished from children's thinking, and learning situations…

  7. Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

    PubMed Central

    Liu, Yufeng; Wu, Yichao

    2011-01-01

    Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

  8. Multiple regression for physiological data analysis: the problem of multicollinearity.

    PubMed

    Slinker, B K; Glantz, S A

    1985-07-01

    Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

  9. 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.

  10. Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

    PubMed

    Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

    2016-04-01

    To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

  11. Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

    ERIC Educational Resources Information Center

    Kromrey, Jeffrey D.; Hines, Constance V.

    1995-01-01

    The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

  12. MULGRES: a computer program for stepwise multiple regression analysis

    Treesearch

    A. Jeff Martin

    1971-01-01

    MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

  13. "Photographing money" task pricing

    NASA Astrophysics Data System (ADS)

    Jia, Zhongxiang

    2018-05-01

    "Photographing money" [1]is a self-service model under the mobile Internet. The task pricing is reasonable, related to the success of the commodity inspection. First of all, we analyzed the position of the mission and the membership, and introduced the factor of membership density, considering the influence of the number of members around the mission on the pricing. Multivariate regression of task location and membership density using MATLAB to establish the mathematical model of task pricing. At the same time, we can see from the life experience that membership reputation and the intensity of the task will also affect the pricing, and the data of the task success point is more reliable. Therefore, the successful point of the task is selected, and its reputation, task density, membership density and Multiple regression of task positions, according to which a nhew task pricing program. Finally, an objective evaluation is given of the advantages and disadvantages of the established model and solution method, and the improved method is pointed out.

  14. A Rich Assessment Task as a Window into Students' Multiplicative Reasoning

    ERIC Educational Resources Information Center

    Downton, Ann; Wright, Vince

    2016-01-01

    This study explored the potential of a rich assessment task to reveal students' multiplicative thinking in respect to a hypothetical learning trajectory. Thirty pairs of students in grades 5 and 6 attempted the task. Twenty-two pairs applied multiplicative structure to find the number of items in arrays. However counting and computational errors…

  15. Estimating air drying times of lumber with multiple regression

    Treesearch

    William T. Simpson

    2004-01-01

    In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

  16. 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…

  17. Task allocation among multiple intelligent robots

    NASA Technical Reports Server (NTRS)

    Gasser, L.; Bekey, G.

    1987-01-01

    Researchers describe the design of a decentralized mechanism for allocating assembly tasks in a multiple robot assembly workstation. Currently, the approach focuses on distributed allocation to explore its feasibility and its potential for adaptability to changing circumstances, rather than for optimizing throughput. Individual greedy robots make their own local allocation decisions using both dynamic allocation policies which propagate through a network of allocation goals, and local static and dynamic constraints describing which robots are elibible for which assembly tasks. Global coherence is achieved by proper weighting of allocation pressures propagating through the assembly plan. Deadlock avoidance and synchronization is achieved using periodic reassessments of local allocation decisions, ageing of allocation goals, and short-term allocation locks on goals.

  18. Analysis and Interpretation of Findings Using Multiple Regression Techniques

    ERIC Educational Resources Information Center

    Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

    2006-01-01

    Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

  19. False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

    ERIC Educational Resources Information Center

    Shear, Benjamin R.; Zumbo, Bruno D.

    2013-01-01

    Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

  20. Examining Response Confidence in Multiple Text Tasks

    ERIC Educational Resources Information Center

    List, Alexandra; Alexander, Patricia A.

    2015-01-01

    Students' confidence in their responses to a multiple text-processing task and their justifications for those confidence ratings were investigated. Specifically, 215 undergraduates responded to two academic questions, differing by type (i.e., discrete and open-ended) and by domain (i.e., developmental psychology and astrophysics), using a digital…

  1. Source Evaluation, Comprehension, and Learning in Internet Science Inquiry Tasks

    ERIC Educational Resources Information Center

    Wiley, Jennifer; Goldman, Susan R.; Graesser, Arthur C.; Sanchez, Christopher A.; Ash, Ivan K.; Hemmerich, Joshua A.

    2009-01-01

    In two experiments, undergraduates' evaluation and use of multiple Internet sources during a science inquiry task were examined. In Experiment 1, undergraduates had the task of explaining what caused the eruption of Mt. St. Helens using the results of an Internet search. Multiple regression analyses indicated that source evaluation significantly…

  2. 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…

  3. Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks

    PubMed Central

    Caywood, Matthew S.; Roberts, Daniel M.; Colombe, Jeffrey B.; Greenwald, Hal S.; Weiland, Monica Z.

    2017-01-01

    There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model’s predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy. PMID:28123359

  4. Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

    PubMed

    Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

    2014-12-01

    This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

  5. Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

    PubMed

    Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

    2017-02-01

    In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

  6. Endogenous Task Shift Processes in Relapsing-Remitting Multiple Sclerosis

    ERIC Educational Resources Information Center

    Stablum, F.; Meligrana, L.; Sgaramella, T.; Bortolon, F.; Toso, V.

    2004-01-01

    This paper reports a study that was aimed to evaluate executive functions in relapsing-remitting multiple sclerosis patients. The groups tested comprised 22 relapsing-remitting multiple sclerosis patients, and 22 non-brain damaged controls. When one is engaged in two speeded tasks, not simultaneously but with some form of alternation, it is slower…

  7. A Solution to Separation and Multicollinearity in Multiple Logistic Regression

    PubMed Central

    Shen, Jianzhao; Gao, Sujuan

    2010-01-01

    In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

  8. A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

    PubMed

    Shen, Jianzhao; Gao, Sujuan

    2008-10-01

    In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

  9. Two SPSS programs for interpreting multiple regression results.

    PubMed

    Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

    2010-02-01

    When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

  10. Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

    PubMed

    Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

    2017-05-01

    This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

  11. Lifespan development of pro- and anti-saccades: multiple regression models for point estimates.

    PubMed

    Klein, Christoph; Foerster, Friedrich; Hartnegg, Klaus; Fischer, Burkhart

    2005-12-07

    The comparative study of anti- and pro-saccade task performance contributes to our functional understanding of the frontal lobes, their alterations in psychiatric or neurological populations, and their changes during the life span. In the present study, we apply regression analysis to model life span developmental effects on various pro- and anti-saccade task parameters, using data of a non-representative sample of 327 participants aged 9 to 88 years. Development up to the age of about 27 years was dominated by curvilinear rather than linear effects of age. Furthermore, the largest developmental differences were found for intra-subject variability measures and the anti-saccade task parameters. Ageing, by contrast, had the shape of a global linear decline of the investigated saccade functions, lacking the differential effects of age observed during development. While these results do support the assumption that frontal lobe functions can be distinguished from other functions by their strong and protracted development, they do not confirm the assumption of disproportionate deterioration of frontal lobe functions with ageing. We finally show that the regression models applied here to quantify life span developmental effects can also be used for individual predictions in applied research contexts or clinical practice.

  12. Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

    ERIC Educational Resources Information Center

    Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

    2010-01-01

    Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

  13. Cross Validation of Selection of Variables in Multiple Regression.

    DTIC Science & Technology

    1979-12-01

    55 vii CROSS VALIDATION OF SELECTION OF VARIABLES IN MULTIPLE REGRESSION I Introduction Background Long term DoD planning gcals...028545024 .31109000 BF * SS - .008700618 .0471961 Constant - .70977903 85.146786 55 had adequate predictive capabilities; the other two models (the...71ZCO F111D Control 54 73EGO FlIID Computer, General Purpose 55 73EPO FII1D Converter-Multiplexer 56 73HAO flllD Stabilizer Platform 57 73HCO F1ID

  14. 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

  15. Studying Different Tasks of Implicit Learning across Multiple Test Sessions Conducted on the Web

    PubMed Central

    Sævland, Werner; Norman, Elisabeth

    2016-01-01

    Implicit learning is usually studied through individual performance on a single task, with the most common tasks being the Serial Reaction Time (SRT) task, the Dynamic System Control (DSC) task, and Artificial Grammar Learning (AGL). Few attempts have been made to compare performance across different implicit learning tasks within the same study. The current study was designed to explore the relationship between performance on the DSC Sugar factory task and the Alternating Serial Reaction Time (ASRT) task. We also addressed another limitation of traditional implicit learning experiments, namely that implicit learning is usually studied in laboratory settings over a restricted time span lasting for less than an hour. In everyday situations, implicit learning is assumed to involve a gradual accumulation of knowledge across several learning episodes over a longer time span. One way to increase the ecological validity of implicit learning experiments could be to present the learning material repeatedly across shorter test sessions. This can most easily be done by using a web-based setup in which participants can access the material from home. We therefore created an online web-based system for measuring implicit learning that could be administered in either single or multiple sessions. Participants (n = 66) were assigned to either a single session or a multiple session condition. Learning occurred on both tasks, and awareness measures suggested that acquired knowledge was not fully conscious on either of the tasks. Learning and the degree of conscious awareness of the learned regularities were compared across conditions and tasks. On the DSC task, performance was not affected by whether learning had taken place in one or over multiple sessions. On the ASRT task, RT improvement across blocks was larger in the multiple-session condition. Learning in the two tasks was not related. PMID:27375512

  16. Estimating Optimal Transformations for Multiple Regression and Correlation.

    DTIC Science & Technology

    1982-07-01

    S w.EECTli1Z"", , J OCT 0 11982 u! !for Public its... .. . ESTIMATING OPTIMAL TRANSFORMATIONS FOR MULTIPLE REGRESSION AND CORRELATION by Leo...in the plot lb of *(yk) versus 1 < k < 200. Figure lc is a plot of $*(xk) versus xk. These plots clearly suggest the transformati " s 6(y) = log(y) and...direct .814 .022 ACE .808 .031 -13- Figure la6L ’ ’ I . . . S " ’ ’ . . I ’ 6- - - .4...... Co o • . o ’ 0 0.2 0.4 0.5 0.8 1 Fi gure lb2 2 2 // II / / -/

  17. EPIC Computational Models of Psychological Refractory-Period Effects in Human Multiple-Task Performance.

    ERIC Educational Resources Information Center

    Meyer, David E.; Kieras, David E.

    Perceptual-motor and cognitive processes whereby people perform multiple concurrent tasks have been studied through an overlapping-tasks procedure in which two successive choice-reaction tasks are performed with a variable interval (stimulus onset asynchrony, or SOA) between the beginning of the first and second tasks. The increase in subjects'…

  18. The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

    NASA Astrophysics Data System (ADS)

    Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

    2014-09-01

    In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

  19. Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

    NASA Astrophysics Data System (ADS)

    Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

    2011-11-01

    SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

  20. BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

    PubMed Central

    Zhu, Xiang; Stephens, Matthew

    2017-01-01

    Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

  1. Human performance in a multiple-task environment: effects of automation reliability on visual attention allocation.

    PubMed

    Cullen, Ralph H; Rogers, Wendy A; Fisk, Arthur D

    2013-11-01

    Diagnostic automation has been posited to alleviate the high demands of multiple-task environments; however, mixed effects have been found pertaining to performance aid success. To better understand these effects, attention allocation must be studied directly. We developed a multiple-task environment to study the effects of automation on visual attention. Participants interacted with a system providing varying levels of automation and automation reliability and then were transferred to a system with no support. Attention allocation was measured by tracking the number of times each task was viewed. We found that participants receiving automation allocated their time according to the task frequency and that tasks that benefited most from automation were most harmed when it was removed. The results suggest that the degree to which automation affects multiple-task performance is dependent on the relative attributes of the tasks involved. Moreover, there is an inverse relationship between support and cost when automation fails. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  2. RRegrs: an R package for computer-aided model selection with multiple regression models.

    PubMed

    Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

    2015-01-01

    Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR

  3. Understanding and Integrating Multiple Science Texts: Summary Tasks Are Sometimes Better than Argument Tasks

    ERIC Educational Resources Information Center

    Gil, Laura; Braten, Ivar; Vidal-Abarca, Eduardo; Stromso, Helge I.

    2010-01-01

    One of the major challenges of a knowledge society is that students as well as other citizens must learn to understand and integrate information from multiple textual sources. Still, task and reader characteristics that may facilitate or constrain such intertextual processes are not well understood by researchers. In this study, we compare the…

  4. Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

    Treesearch

    John W. Edwards; Susan C. Loeb; David C. Guynn

    1994-01-01

    Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

  5. Optimization of fixture layouts of glass laser optics using multiple kernel regression.

    PubMed

    Su, Jianhua; Cao, Enhua; Qiao, Hong

    2014-05-10

    We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

  6. Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

    PubMed Central

    O’Brien, Liam M.; Fitzmaurice, Garrett M.

    2006-01-01

    We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

  7. Cox regression analysis with missing covariates via nonparametric multiple imputation.

    PubMed

    Hsu, Chiu-Hsieh; Yu, Mandi

    2018-01-01

    We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program.

  8. [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.

  9. Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

    ERIC Educational Resources Information Center

    Williams, Ryan

    2013-01-01

    The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

  10. Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

    NASA Astrophysics Data System (ADS)

    Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

    2011-08-01

    In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

  11. Predicting dual-task performance with the Multiple Resources Questionnaire (MRQ).

    PubMed

    Boles, David B; Bursk, Jonathan H; Phillips, Jeffrey B; Perdelwitz, Jason R

    2007-02-01

    The objective was to assess the validity of the Multiple Resources Questionnaire (MRQ) in predicting dual-task interference. Subjective workload measures such as the Subjective Workload Assessment Technique (SWAT) and NASA Task Load Index are sensitive to single-task parameters and dual-task loads but have not attempted to measure workload in particular mental processes. An alternative is the MRQ. In Experiment 1, participants completed simple laboratory tasks and the MRQ after each. Interference between tasks was then correlated to three different task similarity metrics: profile similarity, based on r(2) between ratings; overlap similarity, based on summed minima; and overall demand, based on summed ratings. Experiment 2 used similar methods but more complex computer-based games. In Experiment 1 the MRQ moderately predicted interference (r = +.37), with no significant difference between metrics. In Experiment 2 the metric effect was significant, with overlap similarity excelling in predicting interference (r = +.83). Mean ratings showed high diagnosticity in identifying specific mental processing bottlenecks. The MRQ shows considerable promise as a cognitive-process-sensitive workload measure. Potential applications of the MRQ include the identification of dual-processing bottlenecks as well as process overloads in single tasks, preparatory to redesign in areas such as air traffic management, advanced flight displays, and medical imaging.

  12. Inhibition Plasticity in Older Adults: Practice and Transfer Effects Using a Multiple Task Approach.

    PubMed

    Wilkinson, Andrea J; Yang, Lixia

    2016-01-01

    OBJECTIVE. To examine plasticity of inhibition, as indexed by practice effects of inhibition tasks and the associated transfer effects, using a multiple task approach in healthy older adults. METHOD. Forty-eight healthy older adults were evenly assigned to either a practice group or a no-contact control group. All participants completed pretest (2.5 hours) and posttest (2 hours) sessions, with a 2-week interval in between. During the 2-week interval, only the practice group completed six 30-minute practice sessions (three sessions per week for two consecutive weeks) of three lab-based inhibition tasks. RESULTS. All three inhibition tasks demonstrated significant improvement across practice sessions, suggesting practice-induced plasticity. The benefit, however, only transferred to near-near tasks. The results are inconclusive with regard to the near-far and far-far transfer effects. DISCUSSION. This study further extends literature on practice effects of inhibition in older adults by using a multiple task approach. Together with previous work, the current study suggests that older adults are able to improve inhibition performance through practice and transfer the practice gains to tasks that overlap in both target cognitive ability and task structure (i.e., near-near tasks).

  13. Inhibition Plasticity in Older Adults: Practice and Transfer Effects Using a Multiple Task Approach

    PubMed Central

    Wilkinson, Andrea J.; Yang, Lixia

    2016-01-01

    Objective. To examine plasticity of inhibition, as indexed by practice effects of inhibition tasks and the associated transfer effects, using a multiple task approach in healthy older adults. Method. Forty-eight healthy older adults were evenly assigned to either a practice group or a no-contact control group. All participants completed pretest (2.5 hours) and posttest (2 hours) sessions, with a 2-week interval in between. During the 2-week interval, only the practice group completed six 30-minute practice sessions (three sessions per week for two consecutive weeks) of three lab-based inhibition tasks. Results. All three inhibition tasks demonstrated significant improvement across practice sessions, suggesting practice-induced plasticity. The benefit, however, only transferred to near-near tasks. The results are inconclusive with regard to the near-far and far-far transfer effects. Discussion. This study further extends literature on practice effects of inhibition in older adults by using a multiple task approach. Together with previous work, the current study suggests that older adults are able to improve inhibition performance through practice and transfer the practice gains to tasks that overlap in both target cognitive ability and task structure (i.e., near-near tasks). PMID:26885407

  14. Functional connectivity of task context representations in prefrontal nodes of the multiple demand network.

    PubMed

    Stiers, Peter; Goulas, Alexandros

    2018-06-01

    A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange.

  15. Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

    PubMed

    Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

    2012-12-01

    Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

  16. Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

    PubMed

    Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

    2017-09-01

    To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

  17. 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...

  18. Imitating actions on objects in early-onset and regressive autism: Effects and implications of task characteristics on performance

    PubMed Central

    Rogers, Sally J.; Young, Gregory S.; Cook, Ian; Giolzetti, Angelo; Ozonoff, Sally

    2010-01-01

    This study was designed to examine the nature of object imitation performance in early autism. We hypothesized that imitation would be relatively preserved when behaviors on objects resulted in salient instrumental effects. We designed tasks in which, in one condition, the motor action resulted in a salient, meaningful effect on an object, whereas in the other condition, the same action resulted in a less salient effect because of differing object characteristics. The motor aspects of the tasks did not vary across conditions. Four participant groups of 2- to 5-year-olds were examined: 17 children with early-onset autism, 24 children with regressive onset autism, 22 children with developmental delays, and 22 children with typical development. Groups were matched on nonverbal skills, and differences in verbal development were examined as a moderator of imitative ability. Results revealed an interaction of group by condition, with the combined autism group failing more tasks than the combined comparison groups, and failing more tasks in the less salient condition than in the more salient condition, as hypothesized. Analyses of autism subgroups revealed these effects were primarily because of the regression onset group. Accuracy of motor performance was examined by analyzing errors. Among children passing imitative acts, there were no group differences and no condition effects in the number, type, or pattern of performance errors. Among children passing the tasks, the group with autism did not demonstrate more emulation errors (imitating the goal but not the means) than other groups. There was no evidence that either motor or attentional aspects of the tasks contributed to the poorer imitative performance of the children with autism. PMID:20102648

  19. Delay and probability discounting of multiple commodities in smokers and never-smokers using multiple-choice tasks.

    PubMed

    Poltavski, Dmitri V; Weatherly, Jeffrey N

    2013-12-01

    The purpose of the present study was to investigate temporal and probabilistic discounting in smokers and never-smokers, across a number of commodities, using a multiple-choice method. One hundred and eighty-two undergraduate university students, of whom 90 had never smoked, 73 were self-reported light smokers (<10 cigarettes/day), and 17 were heavy smokers (10+cigarettes/day), completed computerized batteries of delay and probability discounting questions pertaining to a total of eight commodities and administered in a multiple-choice format. In addition to cigarettes, monetary rewards, and health outcomes, the tasks included novel commodities such as ideal dating partner and retirement income. The results showed that heavy smokers probability discounted commodities at a significantly shallower rate than never-smokers, suggesting greater risk-taking. No effect of smoking status was observed for delay discounting questions. The only commodity that was probability discounted significantly less than others was 'finding an ideal dating partner'. The results suggest that probability discounting tasks using the multiple-choice format can discriminate between non-abstaining smokers and never-smokers and could be further explored in the context of behavioral and drug addictions.

  20. A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks.

    PubMed

    Wu, Guanlin; Bao, Weidong; Zhu, Xiaomin; Zhang, Xiongtao

    2018-05-23

    The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks' scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority.

  1. Predicting flight delay based on multiple linear regression

    NASA Astrophysics Data System (ADS)

    Ding, Yi

    2017-08-01

    Delay of flight has been regarded as one of the toughest difficulties in aviation control. How to establish an effective model to handle the delay prediction problem is a significant work. To solve the problem that the flight delay is difficult to predict, this study proposes a method to model the arriving flights and a multiple linear regression algorithm to predict delay, comparing with Naive-Bayes and C4.5 approach. Experiments based on a realistic dataset of domestic airports show that the accuracy of the proposed model approximates 80%, which is further improved than the Naive-Bayes and C4.5 approach approaches. The result testing shows that this method is convenient for calculation, and also can predict the flight delays effectively. It can provide decision basis for airport authorities.

  2. Multiple-Object Tracking in Children: The "Catch the Spies" Task

    ERIC Educational Resources Information Center

    Trick, L.M.; Jaspers-Fayer, F.; Sethi, N.

    2005-01-01

    Multiple-object tracking involves simultaneously tracking positions of a number of target-items as they move among distractors. The standard version of the task poses special challenges for children, demanding extended concentration and the ability to distinguish targets from identical-looking distractors, and may thus underestimate children's…

  3. Interpret with caution: multicollinearity in multiple regression of cognitive data.

    PubMed

    Morrison, Catriona M

    2003-08-01

    Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

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

    PubMed

    Marill, Keith A

    2004-01-01

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

  5. The Role of CLEAR Thinking in Learning Science from Multiple-Document Inquiry Tasks

    ERIC Educational Resources Information Center

    Griffin, Thomas D.; Wiley, Jennifer; Britt, M. Anne; Salas, Carlos R.

    2012-01-01

    The main goal for the current study was to investigate whether individual differences in domain-general thinking dispositions might affect learning from multiple-document inquiry tasks in science. Middle school students were given a set of documents and were tasked with understanding how and why recent patterns in global temperature might be…

  6. Evidencing the association between swimming capacities and performance indicators in water polo: a multiple regression study.

    PubMed

    Kontic, Dean; Zenic, Natasa; Uljevic, Ognjen; Sekulic, Damir; Lesnik, Blaz

    2017-06-01

    Swimming capacities are hypothesized to be important determinants of water polo performance but there is an evident lack of studies examining different swimming capacities in relation to specific offensive and defensive performance variables in this sport. The aim of this study was to determine the relationship between five swimming capacities and six performance determinants in water polo. The sample comprised 79 high-level youth water polo players (all males, 17-18 years of age). The variables included six performance-related variables (agility in offence and defense, efficacy in offence and defense, polyvalence in offence and defense), and five swimming-capacity tests (water polo sprint test [15 m], swimming sprint test [25 m], short-distance [100 m], aerobic endurance [400 m] and an anaerobic lactate endurance test [4× 50 m]). First, multiple regressions were calculated for one-half of the sample of subjects which were then validated with the remaining half of the sample. The 25-m swim was not included in the regression analyses due to the multicollinearity with other predictors. The originally calculated regression models were validated for defensive agility (R=0.67 and R=0.55 for the original regression calculation and validation subsample, respectively) offensive agility (R=0.59 and R=0.61), and offensive efficacy (R=0.64 and R=0.58). Anaerobic lactate endurance is a significant predictor of offensive and defensive agility, while 15 m sprint significantly contributes to offensive efficacy. Swimming capacities are not found to be related to the polyvalence of the players. The most superior offensive performance can be expected from those players with a high level of anaerobic lactate endurance and advanced sprinting capacity, while anaerobic lactate endurance is recognized as most important quality in defensive duties. Future studies should observe players' polyvalence in relation to (theoretical) knowledge of technical and tactical tasks. Results reinforce

  7. Comparing the index-flood and multiple-regression methods using L-moments

    NASA Astrophysics Data System (ADS)

    Malekinezhad, H.; Nachtnebel, H. P.; Klik, A.

    In arid and semi-arid regions, the length of records is usually too short to ensure reliable quantile estimates. Comparing index-flood and multiple-regression analyses based on L-moments was the main objective of this study. Factor analysis was applied to determine main influencing variables on flood magnitude. Ward’s cluster and L-moments approaches were applied to several sites in the Namak-Lake basin in central Iran to delineate homogeneous regions based on site characteristics. Homogeneity test was done using L-moments-based measures. Several distributions were fitted to the regional flood data and index-flood and multiple-regression methods as two regional flood frequency methods were compared. The results of factor analysis showed that length of main waterway, compactness coefficient, mean annual precipitation, and mean annual temperature were the main variables affecting flood magnitude. The study area was divided into three regions based on the Ward’s method of clustering approach. The homogeneity test based on L-moments showed that all three regions were acceptably homogeneous. Five distributions were fitted to the annual peak flood data of three homogeneous regions. Using the L-moment ratios and the Z-statistic criteria, GEV distribution was identified as the most robust distribution among five candidate distributions for all the proposed sub-regions of the study area, and in general, it was concluded that the generalised extreme value distribution was the best-fit distribution for every three regions. The relative root mean square error (RRMSE) measure was applied for evaluating the performance of the index-flood and multiple-regression methods in comparison with the curve fitting (plotting position) method. In general, index-flood method gives more reliable estimations for various flood magnitudes of different recurrence intervals. Therefore, this method should be adopted as regional flood frequency method for the study area and the Namak-Lake basin

  8. Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

    ERIC Educational Resources Information Center

    Li, Spencer D.

    2011-01-01

    Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

  9. Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

    PubMed

    Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

    2005-09-01

    To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

  10. Balancing the Demands of Two Tasks: An Investigation of Cognitive-Motor Dual-Tasking in Relapsing Remitting Multiple Sclerosis.

    PubMed

    Butchard-MacDonald, Emma; Paul, Lorna; Evans, Jonathan J

    2018-03-01

    People with relapsing remitting multiple sclerosis (PwRRMS) suffer disproportionate decrements in gait under dual-task conditions, when walking and a cognitive task are combined. There has been much less investigation of the impact of cognitive demands on balance. This study investigated whether: (1) PwRRMS show disproportionate decrements in postural stability under dual-task conditions compared to healthy controls, and (2) dual-task decrements are associated with everyday dual-tasking difficulties. The impact of mood, fatigue, and disease severity on dual-tasking was also examined. A total of 34 PwRRMS and 34 matched controls completed cognitive (digit span) and balance (movement of center of pressure on Biosway on stable and unstable surfaces) tasks under single- and dual-task conditions. Everyday dual-tasking was measured using the Dual-Tasking Questionnaire. Mood was measured by the Hospital Anxiety & Depression Scale. Fatigue was measured via the Modified Fatigue Index Scale. No differences in age, gender, years of education, estimated pre-morbid IQ, or baseline digit span between groups. Compared with controls, PwRRMS showed significantly greater decrement in postural stability under dual-task conditions on an unstable surface (p=.007), but not a stable surface (p=.679). Balance decrement scores were not correlated with everyday dual-tasking difficulties or fatigue. Stable surface balance decrement scores were significantly associated with levels of anxiety (rho=0.527; p=.001) and depression (rho=0.451; p=.007). RRMS causes dual-tasking difficulties, impacting balance under challenging conditions, which may contribute to increased risk of gait difficulties and falls. The relationship between anxiety/depression and dual-task decrement suggests that emotional factors may be contributing to dual-task difficulties. (JINS, 2018, 24, 247-258).

  11. 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…

  12. Classification Systems for Individual Differences in Multiple-task Performance and Subjective Estimates of Workload

    NASA Technical Reports Server (NTRS)

    Damos, D. L.

    1984-01-01

    Human factors practitioners often are concerned with mental workload in multiple-task situations. Investigations of these situations have demonstrated repeatedly that individuals differ in their subjective estimates of workload. These differences may be attributed in part to individual differences in definitions of workload. However, after allowing for differences in the definition of workload, there are still unexplained individual differences in workload ratings. The relation between individual differences in multiple-task performance, subjective estimates of workload, information processing abilities, and the Type A personality trait were examined.

  13. Multiple linear regression analysis

    NASA Technical Reports Server (NTRS)

    Edwards, T. R.

    1980-01-01

    Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

  14. The Effects of Tasks on Integrating Information from Multiple Documents

    ERIC Educational Resources Information Center

    Cerdan, Raquel; Vidal-Abarca, Eduardo

    2008-01-01

    The authors examine 2 issues: (a) how students integrate information from multiple scientific documents to describe and explain a physical phenomenon that represents a subset of the information in the documents; and (b) the role of 2 sorts of tasks to achieve this type of integration, either writing an essay on a question requiring integration…

  15. MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

    EPA Science Inventory

    A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

  16. Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

    ERIC Educational Resources Information Center

    Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

    1999-01-01

    A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

  17. Effects of automation and task load on task switching during human supervision of multiple semi-autonomous robots in a dynamic environment.

    PubMed

    Squire, P N; Parasuraman, R

    2010-08-01

    The present study assessed the impact of task load and level of automation (LOA) on task switching in participants supervising a team of four or eight semi-autonomous robots in a simulated 'capture the flag' game. Participants were faster to perform the same task than when they chose to switch between different task actions. They also took longer to switch between different tasks when supervising the robots at a high compared to a low LOA. Task load, as manipulated by the number of robots to be supervised, did not influence switch costs. The results suggest that the design of future unmanned vehicle (UV) systems should take into account not simply how many UVs an operator can supervise, but also the impact of LOA and task operations on task switching during supervision of multiple UVs. The findings of this study are relevant for the ergonomics practice of UV systems. This research extends the cognitive theory of task switching to inform the design of UV systems and results show that switching between UVs is an important factor to consider.

  18. Should metacognition be measured by logistic regression?

    PubMed

    Rausch, Manuel; Zehetleitner, Michael

    2017-03-01

    Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. 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…

  20. Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

    ERIC Educational Resources Information Center

    Rowell, R. Kevin

    In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

  1. Multiple Regression Redshift Calibration for Clusters of Galaxies

    NASA Astrophysics Data System (ADS)

    Kalinkov, M.; Kuneva, I.; Valtchanov, I.

    A new procedure for calibration of distances to ACO (Abell et al.1989) clusters of galaxies has been developed. In the previous version of the Reference Catalog of ACO Clusters of Galaxies (Kalinkov & Kuneva 1992) an attempt has been made to compare various calibration schemes. For the Version 93 we have made some refinements. Many improvements from the early days of the photometric calibration have been made --- from Rowan-Robinson (1972), Corwin (1974), Kalinkov & Kuneva (1975), Mills Hoskins (1977) to more complicated --- Leir & van den Bergh (1977), Postman et al.(1985), Kalinkov Kuneva (1985, 1986, 1990), Scaramella et al.(1991), Zucca et al. (1993). It was shown that it is impossible to use the same calibration relation for northern (A) and southern (ACO) clusters of galaxies. Therefore the calibration have to be made separately for both catalogs. Moreover it is better if one could find relations for the 274 A-clusters, studied by the authors of ACO. We use the luminosity distance for H0=100km/s/Mpc and q0 = 0.5 and we have 1200 clusters with measured redshifts. The first step is to fit log(z) on m10 (magnitude of the tenth rank galaxy) for A-clusters and on m1, m3 and m10 for ACO clusters. The second step is to take into account the K-correction and the Scott effect (Postman et al.1985) with iterative process. To avoid the initial errors of the redshift estimates in A- and ACO catalogs we adopt Hubble's law for the apparent radial distribution of galaxies in clusters. This enable us to calculate a new cluster richness from preliminary redshift estimate. This is the third step. Further continues the study of the correlation matrix between log(z) and prospective predictors --- new richness groups, BM, RS and A types, radio and X-ray fluxes, apparent separations between the first three brightest galaxies, mean population (gal/sq.deg), Multiple linear as well as nonlinear regression estimators are found. Many clusters that deviate by more than 2.5 sigmas are

  2. Explorative Function in Williams Syndrome Analyzed through a Large-Scale Task with Multiple Rewards

    ERIC Educational Resources Information Center

    Foti, F.; Petrosini, L.; Cutuli, D.; Menghini, D.; Chiarotti, F.; Vicari, S.; Mandolesi, L.

    2011-01-01

    This study aimed to evaluate spatial function in subjects with Williams syndrome (WS) by using a large-scale task with multiple rewards and comparing the spatial abilities of WS subjects with those of mental age-matched control children. In the present spatial task, WS participants had to explore an open space to search nine rewards placed in…

  3. 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.

  4. 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.)

  5. 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...

  6. Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

    PubMed

    Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

    2018-06-02

    This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

  7. A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

    ERIC Educational Resources Information Center

    Martin, David

    2008-01-01

    This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

  8. Optimizing and Interpreting Insular Functional Connectivity Maps Obtained During Acute Experimental Pain: The Effects of Global Signal and Task Paradigm Regression.

    PubMed

    Ibinson, James W; Vogt, Keith M; Taylor, Kevin B; Dua, Shiv B; Becker, Christopher J; Loggia, Marco; Wasan, Ajay D

    2015-12-01

    The insula is uniquely located between the temporal and parietal cortices, making it anatomically well-positioned to act as an integrating center between the sensory and affective domains for the processing of painful stimulation. This can be studied through resting-state functional connectivity (fcMRI) imaging; however, the lack of a clear methodology for the analysis of fcMRI complicates the interpretation of these data during acute pain. Detected connectivity changes may reflect actual alterations in low-frequency synchronous neuronal activity related to pain, may be due to changes in global cerebral blood flow or the superimposed task-induced neuronal activity. The primary goal of this study was to investigate the effects of global signal regression (GSR) and task paradigm regression (TPR) on the changes in functional connectivity of the left (contralateral) insula in healthy subjects at rest and during acute painful electric nerve stimulation of the right hand. The use of GSR reduced the size and statistical significance of connectivity clusters and created negative correlation coefficients for some connectivity clusters. TPR with cyclic stimulation gave task versus rest connectivity differences similar to those with a constant task, suggesting that analysis which includes TPR is more accurately reflective of low-frequency neuronal activity. Both GSR and TPR have been inconsistently applied to fcMRI analysis. Based on these results, investigators need to consider the impact GSR and TPR have on connectivity during task performance when attempting to synthesize the literature.

  9. Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

    PubMed

    Krasikova, Dina V; Le, Huy; Bachura, Eric

    2018-06-01

    To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  10. Time-localized wavelet multiple regression and correlation

    NASA Astrophysics Data System (ADS)

    Fernández-Macho, Javier

    2018-02-01

    This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.

  11. Contingent interaction during work and play tasks for mothers with multiple sclerosis and their daughters.

    PubMed

    Crist, P

    1993-02-01

    Occupational therapy has focused on activity as a catalyst for understanding human roles and interactions, regardless of whether disability or chronic illness is present. Parenting is an important interactional activity accompanied by specific role expectations. This investigation examined the interaction patterns of mothers with multiple sclerosis and their daughters. Thirty-one mothers with multiple sclerosis and their daughters aged 8 to 12 years were compared with 34 mothers without disabilities and their daughters aged 8 to 12 years. Videotaped mother-daughter interactions during a work task and a play task were scored by two raters for 11 different behaviors. These behaviors were collapsed into three behavioral composites--receptiveness, directiveness, and dissuasiveness--for statistical analysis. Statistical analysis revealed no significant differences between the two groups on the behavioral composites for either mothers or their daughters. The two tasks stimulated a different pattern of mother-daughter interactions. For both members of the dyad, interactions during the work task were more directive and less dissuasive than those in the play task. The clinical implication of this finding indicates the importance of understanding the influence of the task selected when observing interaction. Because of recent social and legal changes, understanding parenting and chronic illness is critical.

  12. Overcoming multicollinearity in multiple regression using correlation coefficient

    NASA Astrophysics Data System (ADS)

    Zainodin, H. J.; Yap, S. J.

    2013-09-01

    Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

  13. Multiple Ordinal Regression by Maximizing the Sum of Margins

    PubMed Central

    Hamsici, Onur C.; Martinez, Aleix M.

    2016-01-01

    Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

  14. Multiple paths in complex tasks

    NASA Technical Reports Server (NTRS)

    Galanter, Eugene; Wiegand, Thomas; Mark, Gloria

    1987-01-01

    The relationship between utility judgments of subtask paths and the utility of the task as a whole was examined. The convergent validation procedure is based on the assumption that measurements of the same quantity done with different methods should covary. The utility measures of the subtasks were obtained during the performance of an aircraft flight controller navigation task. Analyses helped decide among various models of subtask utility combination, whether the utility ratings of subtask paths predict the whole tasks utility rating, and indirectly, whether judgmental models need to include the equivalent of cognitive noise.

  15. [Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

    PubMed

    Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

    2011-12-01

    To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

  16. The Use of Digital Technology in Finding Multiple Paths to Solve and Extend an Equilateral Triangle Task

    ERIC Educational Resources Information Center

    Santos-Trigo, Manuel; Reyes-Rodriguez, Aaron

    2016-01-01

    Mathematical tasks are crucial elements for teachers to orient, foster and assess students' processes to comprehend and develop mathematical knowledge. During the process of working and solving a task, searching for or discussing multiple solution paths becomes a powerful strategy for students to engage in mathematical thinking. A simple task that…

  17. [Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

    PubMed

    Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

    2016-05-01

    Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

  18. Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

    PubMed

    Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

    2017-02-01

    The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism

  19. The source of dual-task limitations: Serial or parallel processing of multiple response selections?

    PubMed Central

    Marois, René

    2014-01-01

    Although it is generally recognized that the concurrent performance of two tasks incurs costs, the sources of these dual-task costs remain controversial. The serial bottleneck model suggests that serial postponement of task performance in dual-task conditions results from a central stage of response selection that can only process one task at a time. Cognitive-control models, by contrast, propose that multiple response selections can proceed in parallel, but that serial processing of task performance is predominantly adopted because its processing efficiency is higher than that of parallel processing. In the present study, we empirically tested this proposition by examining whether parallel processing would occur when it was more efficient and financially rewarded. The results indicated that even when parallel processing was more efficient and was incentivized by financial reward, participants still failed to process tasks in parallel. We conclude that central information processing is limited by a serial bottleneck. PMID:23864266

  20. Correlation Weights in Multiple Regression

    ERIC Educational Resources Information Center

    Waller, Niels G.; Jones, Jeff A.

    2010-01-01

    A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…

  1. Formal Derivation of Lotka-Volterra-Haken Amplitude Equations of Task-Related Brain Activity in Multiple, Consecutively Performed Tasks

    NASA Astrophysics Data System (ADS)

    Frank, T. D.

    The Lotka-Volterra-Haken equations have been frequently used in ecology and pattern formation. Recently, the equations have been proposed by several research groups as amplitude equations for task-related patterns of brain activity. In this theoretical study, the focus is on the circular causality aspect of pattern formation systems as formulated within the framework of synergetics. Accordingly, the stable modes of a pattern formation system inhibit the unstable modes, whereas the unstable modes excite the stable modes. Using this circular causality principle it is shown that under certain conditions the Lotka-Volterra-Haken amplitude equations can be derived from a general model of brain activity akin to the Wilson-Cowan model. The model captures the amplitude dynamics for brain activity patterns in experiments involving several consecutively performed multiple-choice tasks. This is explicitly demonstrated for two-choice tasks involving grasping and walking. A comment on the relevance of the theoretical framework for clinical psychology and schizophrenia is given as well.

  2. Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

    PubMed Central

    Chen, Jianhui; Liu, Ji; Ye, Jieping

    2013-01-01

    We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms. PMID:24077658

  3. Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.

    PubMed

    Chen, Jianhui; Liu, Ji; Ye, Jieping

    2012-02-01

    We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.

  4. Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

    PubMed

    Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

    2017-05-01

    This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

  5. Multiplicative Multitask Feature Learning

    PubMed Central

    Wang, Xin; Bi, Jinbo; Yu, Shipeng; Sun, Jiangwen; Song, Minghu

    2016-01-01

    We investigate a general framework of multiplicative multitask feature learning which decomposes individual task’s model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. PMID:28428735

  6. Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

    ERIC Educational Resources Information Center

    Anderson, Joan L.

    2006-01-01

    Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

  7. 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…

  8. Dual task cost of walking is related to fall risk in persons with multiple sclerosis.

    PubMed

    Wajda, Douglas A; Motl, Robert W; Sosnoff, Jacob J

    2013-12-15

    Persons with multiple sclerosis (MS) commonly have walking and cognitive impairments. While walking with a simultaneous cognitive task, persons with MS experience a greater decline in walking performance than healthy controls. This change in performance is termed dual task cost or dual task interference and has been associated with fall risk in older adults. We examined whether dual task cost during walking was related to fall risk in persons with MS. Thirty-three ambulatory persons with MS performed walking tasks with and without a concurrent cognitive task (dual task condition) as well as underwent a fall risk assessment. Dual task cost was operationalized as the percent change in velocity from normal walking conditions to dual task walking conditions. Fall risk was quantified using the Physiological Profile Assessment. A Spearman correlation analysis revealed a significant positive correlation between dual task cost of walking velocity and fall risk as well as dual task cost of stride length and fall risk. Overall, the findings indicate that dual task cost is associated with fall risk and may be an important target for falls prevention strategies. © 2013.

  9. ERP correlates of word production predictors in picture naming: a trial by trial multiple regression analysis from stimulus onset to response.

    PubMed

    Valente, Andrea; Bürki, Audrey; Laganaro, Marina

    2014-01-01

    A major effort in cognitive neuroscience of language is to define the temporal and spatial characteristics of the core cognitive processes involved in word production. One approach consists in studying the effects of linguistic and pre-linguistic variables in picture naming tasks. So far, studies have analyzed event-related potentials (ERPs) during word production by examining one or two variables with factorial designs. Here we extended this approach by investigating simultaneously the effects of multiple theoretical relevant predictors in a picture naming task. High density EEG was recorded on 31 participants during overt naming of 100 pictures. ERPs were extracted on a trial by trial basis from picture onset to 100 ms before the onset of articulation. Mixed-effects regression models were conducted to examine which variables affected production latencies and the duration of periods of stable electrophysiological patterns (topographic maps). Results revealed an effect of a pre-linguistic variable, visual complexity, on an early period of stable electric field at scalp, from 140 to 180 ms after picture presentation, a result consistent with the proposal that this time period is associated with visual object recognition processes. Three other variables, word Age of Acquisition, Name Agreement, and Image Agreement influenced response latencies and modulated ERPs from ~380 ms to the end of the analyzed period. These results demonstrate that a topographic analysis fitted into the single trial ERPs and covering the entire processing period allows one to associate the cost generated by psycholinguistic variables to the duration of specific stable electrophysiological processes and to pinpoint the precise time-course of multiple word production predictors at once.

  10. Multiple network-constrained regressions expand insights into influenza vaccination responses.

    PubMed

    Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

    2017-07-15

    Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https

  11. Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

    PubMed Central

    Almedeij, Jaber

    2012-01-01

    Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

  12. Walking and talking: an investigation of cognitive-motor dual tasking in multiple sclerosis.

    PubMed

    Hamilton, F; Rochester, L; Paul, L; Rafferty, D; O'Leary, C P; Evans, J J

    2009-10-01

    Deficits in motor functioning, including walking, and in cognitive functions, including attention, are known to be prevalent in multiple sclerosis (MS), though little attention has been paid to how impairments in these areas of functioning interact. This study investigated the effects of performing a concurrent cognitive task when walking in people with MS. Level of task demand was manipulated to investigate whether this affected level of dual-task decrement. Eighteen participants with MS and 18 healthy controls took part. Participants completed walking and cognitive tasks under single- and dual-task conditions. Compared to healthy controls, MS participants showed greater decrements in performance under dual-task conditions in cognitive task performance, walking speed and swing time variability. In the MS group, the degree of decrement under dual-task conditions was related to levels of fatigue, a measure of general cognitive functioning and self-reported everyday cognitive errors, but not to measures of disease severity or duration. Difficulty with walking and talking in MS may be a result of a divided attention deficit or of overloading of the working memory system, and further investigation is needed. We suggest that difficulty with walking and talking in MS may lead to practical problems in everyday life, including potentially increasing the risk of falls. Clinical tools to assess cognitive-motor dual-tasking ability are needed.

  13. Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

    DTIC Science & Technology

    2017-03-23

    PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

  14. A CNN Regression Approach for Real-Time 2D/3D Registration.

    PubMed

    Shun Miao; Wang, Z Jane; Rui Liao

    2016-05-01

    In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.

  15. Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.

    PubMed

    Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Ferrigno, Giancarlo; D'Angelo, Egidio; Pedrocchi, Alessandra

    2015-01-01

    The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

  16. 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.

  17. Multiple regression analysis in nomogram development for myopic wavefront laser in situ keratomileusis: Improving astigmatic outcomes.

    PubMed

    Allan, Bruce D; Hassan, Hala; Ieong, Alvin

    2015-05-01

    To describe and evaluate a new multiple regression-derived nomogram for myopic wavefront laser in situ keratomileusis (LASIK). Moorfields Eye Hospital, London, United Kingdom. Prospective comparative case series. Multiple regression modeling was used to derive a simplified formula for adjusting attempted spherical correction in myopic LASIK. An adaptation of Thibos' power vector method was then applied to derive adjustments to attempted cylindrical correction in eyes with 1.0 diopter (D) or more of preoperative cylinder. These elements were combined in a new nomogram (nomogram II). The 3-month refractive results for myopic wavefront LASIK (spherical equivalent ≤11.0 D; cylinder ≤4.5 D) were compared between 299 consecutive eyes treated using the earlier nomogram (nomogram I) in 2009 and 2010 and 414 eyes treated using nomogram II in 2011 and 2012. There was no significant difference in treatment accuracy (variance in the postoperative manifest refraction spherical equivalent error) between nomogram I and nomogram II (P = .73, Bartlett test). Fewer patients treated with nomogram II had more than 0.5 D of residual postoperative astigmatism (P = .0001, Fisher exact test). There was no significant coupling between adjustments to the attempted cylinder and the achieved sphere (P = .18, t test). Discarding marginal influences from a multiple regression-derived nomogram for myopic wavefront LASIK had no clinically significant effect on treatment accuracy. Thibos' power vector method can be used to guide adjustments to the treatment cylinder alongside nomograms designed to optimize postoperative spherical equivalent results in myopic LASIK. mentioned. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

  18. Multiple regression technique for Pth degree polynominals with and without linear cross products

    NASA Technical Reports Server (NTRS)

    Davis, J. W.

    1973-01-01

    A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

  19. Multiple task performance as a predictor of the potential of air traffic controller trainees.

    DOT National Transportation Integrated Search

    1972-01-01

    Two hundred and twenty-nine air traffic controller trainees were tested on the CAMI Multiple Task Performance Battery. The battery provides objective measures of monitoring, arithmetical skills, visual discrimination, and group problem solving. The c...

  20. Hierarchical multiple regression modelling on predictors of behavior and sexual practices at Takoradi Polytechnic, Ghana.

    PubMed

    Turkson, Anthony Joe; Otchey, James Eric

    2015-01-14

    Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices. A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices. The variables that were extracted from the multiple regression were; for the constant; Beta=14.202, t=2.279, p=0.023, variable is significant; for the marital status; Beta=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; Beta=0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one's marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs., Y(Behavior and sexual practies)= Beta0+Beta1(Marital status)+Beta2(Knowledge on HIV/AIDs issues)+Beta3(Attitude towards HIV/AIDs issues) Beta0, Beta1, Beta2 and Beta3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better. Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.

  1. Deep ensemble learning of sparse regression models for brain disease diagnosis

    PubMed Central

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2018-01-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394

  2. 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.

  3. The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

    ERIC Educational Resources Information Center

    Campbell, S. Duke; Greenberg, Barry

    The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

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

    PubMed

    Beckstead, Jason W

    2012-03-30

    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 strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.

  5. 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.

  6. Single versus multiple sets of resistance exercise: a meta-regression.

    PubMed

    Krieger, James W

    2009-09-01

    There has been considerable debate over the optimal number of sets per exercise to improve musculoskeletal strength during a resistance exercise program. The purpose of this study was to use hierarchical, random-effects meta-regression to compare the effects of single and multiple sets per exercise on dynamic strength. English-language studies comparing single with multiple sets per exercise, while controlling for other variables, were considered eligible for inclusion. The analysis comprised 92 effect sizes (ESs) nested within 30 treatment groups and 14 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.26 +/- 0.05; confidence interval [CI]: 0.15, 0.37; p < 0.0001). In a dose-response model, 2 to 3 sets per exercise were associated with a significantly greater ES than 1 set (difference = 0.25 +/- 0.06; CI: 0.14, 0.37; p = 0.0001). There was no significant difference between 1 set per exercise and 4 to 6 sets per exercise (difference = 0.35 +/- 0.25; CI: -0.05, 0.74; p = 0.17) or between 2 to 3 sets per exercise and 4 to 6 sets per exercise (difference = 0.09 +/- 0.20; CI: -0.31, 0.50; p = 0.64). There were no interactions between set volume and training program duration, subject training status, or whether the upper or lower body was trained. Sensitivity analysis revealed no highly influential studies, and no evidence of publication bias was observed. In conclusion, 2 to 3 sets per exercise are associated with 46% greater strength gains than 1 set, in both trained and untrained subjects.

  7. 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)

  8. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

    PubMed

    Agha, Salah R; Alnahhal, Mohammed J

    2012-11-01

    The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  9. Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

    PubMed

    Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

    2017-08-01

    Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

  10. The Role of Multiple Solution Tasks in Developing Knowledge and Creativity in Geometry

    ERIC Educational Resources Information Center

    Levav-Waynberg, Anat; Leikin, Roza

    2012-01-01

    This paper describes changes in students' geometrical knowledge and their creativity associated with implementation of Multiple Solution Tasks (MSTs) in school geometry courses. Three hundred and three students from 14 geometry classes participated in the study, of whom 229 students from 11 classes learned in an experimental environment that…

  11. Predicting social and communicative ability in school-age children with autism spectrum disorder: A pilot study of the Social Attribution Task, Multiple Choice.

    PubMed

    Burger-Caplan, Rebecca; Saulnier, Celine; Jones, Warren; Klin, Ami

    2016-11-01

    The Social Attribution Task, Multiple Choice is introduced as a measure of implicit social cognitive ability in children, addressing a key challenge in quantification of social cognitive function in autism spectrum disorder, whereby individuals can often be successful in explicit social scenarios, despite marked social adaptive deficits. The 19-question Social Attribution Task, Multiple Choice, which presents ambiguous stimuli meant to elicit social attribution, was administered to children with autism spectrum disorder (N = 23) and to age-matched and verbal IQ-matched typically developing children (N = 57). The Social Attribution Task, Multiple Choice performance differed between autism spectrum disorder and typically developing groups, with typically developing children performing significantly better than children with autism spectrum disorder. The Social Attribution Task, Multiple Choice scores were positively correlated with age (r = 0.474) while being independent from verbal IQ (r = 0.236). The Social Attribution Task, Multiple Choice was strongly correlated with Vineland Adaptive Behavior Scales Communication (r = 0.464) and Socialization (r = 0.482) scores, but not with Daily Living Skills scores (r = 0.116), suggesting that the implicit social cognitive ability underlying performance on the Social Attribution Task, Multiple Choice is associated with real-life social adaptive function. © The Author(s) 2016.

  12. Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

    PubMed

    Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

    2006-06-01

    This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

  13. Retro-regression--another important multivariate regression improvement.

    PubMed

    Randić, M

    2001-01-01

    We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

  14. 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…

  15. Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

    ERIC Educational Resources Information Center

    Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

    2015-01-01

    Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

  16. Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

    PubMed Central

    Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

    2011-01-01

    Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that

  17. Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks.

    PubMed

    Hewitt, Angela L; Popa, Laurentiu S; Pasalar, Siavash; Hendrix, Claudia M; Ebner, Timothy J

    2011-11-01

    Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of R(adj)(2)), followed by position (28 ± 24% of R(adj)(2)) and speed (11 ± 19% of R(adj)(2)). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower R(adj)(2) values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward

  18. Cognitive evaluation by tasks in a virtual reality environment in multiple sclerosis.

    PubMed

    Lamargue-Hamel, Delphine; Deloire, Mathilde; Saubusse, Aurore; Ruet, Aurélie; Taillard, Jacques; Philip, Pierre; Brochet, Bruno

    2015-12-15

    The assessment of cognitive impairment in multiple sclerosis (MS) requires large neuropsychological batteries that assess numerous domains. The relevance of these assessments to daily cognitive functioning is not well established. Cognitive ecological evaluation has not been frequently studied in MS. The aim of this study was to determine the interest of cognitive evaluation in a virtual reality environment in a sample of persons with MS with cognitive deficits. Thirty persons with MS with at least moderate cognitive impairment were assessed with two ecological evaluations, an in-house developed task in a virtual reality environment (Urban DailyCog®) and a divided attention task in a driving simulator. Classical neuropsychological testing was also used. Fifty-two percent of the persons with MS failed the driving simulator task and 80% failed the Urban DailyCog®. Virtual reality assessments are promising in identifying cognitive impairment in MS. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

    EPA Science Inventory

    Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

  20. Confidence intervals for distinguishing ordinal and disordinal interactions in multiple regression.

    PubMed

    Lee, Sunbok; Lei, Man-Kit; Brody, Gene H

    2015-06-01

    Distinguishing between ordinal and disordinal interaction in multiple regression is useful in testing many interesting theoretical hypotheses. Because the distinction is made based on the location of a crossover point of 2 simple regression lines, confidence intervals of the crossover point can be used to distinguish ordinal and disordinal interactions. This study examined 2 factors that need to be considered in constructing confidence intervals of the crossover point: (a) the assumption about the sampling distribution of the crossover point, and (b) the possibility of abnormally wide confidence intervals for the crossover point. A Monte Carlo simulation study was conducted to compare 6 different methods for constructing confidence intervals of the crossover point in terms of the coverage rate, the proportion of true values that fall to the left or right of the confidence intervals, and the average width of the confidence intervals. The methods include the reparameterization, delta, Fieller, basic bootstrap, percentile bootstrap, and bias-corrected accelerated bootstrap methods. The results of our Monte Carlo simulation study suggest that statistical inference using confidence intervals to distinguish ordinal and disordinal interaction requires sample sizes more than 500 to be able to provide sufficiently narrow confidence intervals to identify the location of the crossover point. (c) 2015 APA, all rights reserved).

  1. A Multiple-Choice Task with Changes of Mind

    PubMed Central

    Albantakis, Larissa; Branzi, Francesca M.; Costa, Albert; Deco, Gustavo

    2012-01-01

    The role of changes of mind and multiple choices has recently received increased attention in the study of perceptual decision-making. Previously, these extensions to standard two-alternative tasks have been studied separately. Here we explored how changes of mind depend on the number of choice-alternatives. To this end, we tested 14 human subjects on a 2- and 4-alternative direction-discrimination task. Changes of mind in the participants' movement trajectories could be observed for two and for four choice alternatives. With fewer alternatives, participants responded faster and more accurately. The frequency of changes of mind, however, did not significantly differ for the different numbers of choice alternatives. Nevertheless, mind-changing improved the participants' final performance, particularly for intermediate difficulty levels, in both experimental conditions. Moreover, the mean reaction times of individual participants were negatively correlated with their overall tendency to make changes of mind. We further reproduced these findings with a multi-alternative attractor model for decision-making, while a simple race model could not account for the experimental data. Our experiment, combined with the theoretical models allowed us to shed light on: (1) the differences in choice behavior between two and four alternatives, (2) the differences between the data of our human subjects and previous monkey data, (3) individual differences between participants, and (4) the inhibitory interaction between neural representations of choice alternatives. PMID:22916216

  2. Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

    ERIC Educational Resources Information Center

    Hafner, Lawrence E.

    A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

  3. Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

    ERIC Educational Resources Information Center

    Choi, Kilchan

    2011-01-01

    This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

  4. A Powerful Test for Comparing Multiple Regression Functions.

    PubMed

    Maity, Arnab

    2012-09-01

    In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

  5. Deep ensemble learning of sparse regression models for brain disease diagnosis.

    PubMed

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2017-04-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

    PubMed

    NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

    2017-08-01

    Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

  7. Variation at work: alternations between physically and mentally demanding tasks in blue-collar occupations.

    PubMed

    Jahncke, Helena; Hygge, Staffan; Mathiassen, Svend Erik; Hallman, David; Mixter, Susanna; Lyskov, Eugene

    2017-09-01

    The aims of this questionnaire study were to describe the occurrence and desired number of alternations between mental and physical tasks in industrial and non-industrial blue-collar work, and determine to which extent selected personal and occupational factors influence these conditions. On average, the 122 participating workers (55 females) reported to have close to four alternations per day between mental and physical tasks, and to desire more alternations than they actually had. They also expressed a general preference for performing a physical task after a mental task and vice versa. In univariate regression models, the desired change in task alternations was significantly associated with gender, age, occupation, years with current work tasks and perceived job control, while occupation was the only significant determinant in a multiple regression model including all factors. Our results suggest that alternations between productive physical and mental tasks could be a viable option in future job rotation. Practitioner Summary: We addressed attitudes among blue-collar workers to alternations between physically and mentally demanding tasks. More alternations were desired than those occurring in the job, and workers preferred performing a physical task after a mental and vice versa. Alternating physical and mental tasks could, thus, be a viable option in job rotation.

  8. The Influence of Math Anxiety, Math Performance, Worry, and Test Anxiety on the Iowa Gambling Task and Balloon Analogue Risk Task.

    PubMed

    Buelow, Melissa T; Barnhart, Wesley R

    2017-01-01

    Multiple studies have shown that performance on behavioral decision-making tasks, such as the Iowa Gambling Task (IGT) and Balloon Analogue Risk Task (BART), is influenced by external factors, such as mood. However, the research regarding the influence of worry is mixed, and no research has examined the effect of math or test anxiety on these tasks. The present study investigated the effects of anxiety (including math anxiety) and math performance on the IGT and BART in a sample of 137 undergraduate students. Math performance and worry were not correlated with performance on the IGT, and no variables were correlated with BART performance. Linear regressions indicated math anxiety, physiological anxiety, social concerns/stress, and test anxiety significantly predicted disadvantageous selections on the IGT during the transition from decision making under ambiguity to decision making under risk. Implications for clinical evaluation of decision making are discussed. © The Author(s) 2015.

  9. Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

    2018-01-01

    Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

  10. Model selection with multiple regression on distance matrices leads to incorrect inferences.

    PubMed

    Franckowiak, Ryan P; Panasci, Michael; Jarvis, Karl J; Acuña-Rodriguez, Ian S; Landguth, Erin L; Fortin, Marie-Josée; Wagner, Helene H

    2017-01-01

    In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

  11. The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

    ERIC Educational Resources Information Center

    Fraas, John W.; Newman, Isadore

    1996-01-01

    In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

  12. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

    NASA Astrophysics Data System (ADS)

    Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

    2013-10-01

    In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

  13. The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

    PubMed

    Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

    2013-01-01

    Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

  14. A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

    PubMed

    Bersabé, Rosa; Rivas, Teresa

    2010-05-01

    The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

  15. Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

    NASA Astrophysics Data System (ADS)

    Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

    2018-01-01

    Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

  16. Genotype-phenotype association study via new multi-task learning model

    PubMed Central

    Huo, Zhouyuan; Shen, Dinggang

    2018-01-01

    Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896

  17. Context matters: the structure of task goals affects accuracy in multiple-target visual search.

    PubMed

    Clark, Kait; Cain, Matthew S; Adcock, R Alison; Mitroff, Stephen R

    2014-05-01

    Career visual searchers such as radiologists and airport security screeners strive to conduct accurate visual searches, but despite extensive training, errors still occur. A key difference between searches in radiology and airport security is the structure of the search task: Radiologists typically scan a certain number of medical images (fixed objective), and airport security screeners typically search X-rays for a specified time period (fixed duration). Might these structural differences affect accuracy? We compared performance on a search task administered either under constraints that approximated radiology or airport security. Some displays contained more than one target because the presence of multiple targets is an established source of errors for career searchers, and accuracy for additional targets tends to be especially sensitive to contextual conditions. Results indicate that participants searching within the fixed objective framework produced more multiple-target search errors; thus, adopting a fixed duration framework could improve accuracy for career searchers. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  18. Building Regression Models: The Importance of Graphics.

    ERIC Educational Resources Information Center

    Dunn, Richard

    1989-01-01

    Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

  19. Screening for ketosis using multiple logistic regression based on milk yield and composition

    PubMed Central

    KAYANO, Mitsunori; KATAOKA, Tomoko

    2015-01-01

    Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. PMID:26074408

  20. Screening for ketosis using multiple logistic regression based on milk yield and composition.

    PubMed

    Kayano, Mitsunori; Kataoka, Tomoko

    2015-11-01

    Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF - 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

  1. A methodology for the design of experiments in computational intelligence with multiple regression models.

    PubMed

    Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  2. A methodology for the design of experiments in computational intelligence with multiple regression models

    PubMed Central

    Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

  3. Practical Session: Multiple Linear Regression

    NASA Astrophysics Data System (ADS)

    Clausel, M.; Grégoire, G.

    2014-12-01

    Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).

  4. Statistical research using the multiple regression analysis in areas of the cast hipereutectoid steel rolls manufacturing

    NASA Astrophysics Data System (ADS)

    Kiss, I.; Alexa, V.; Serban, S.; Rackov, M.; Čavić, M.

    2018-01-01

    The cast hipereutectoid steel (usually named Adamite) is a roll manufacturing destined material, having mechanical, chemical properties and Carbon [C] content of which stands between steelandiron, along-withitsalloyelements such as Nickel [Ni], Chrome [Cr], Molybdenum [Mo] and/or other alloy elements. Adamite Rolls are basically alloy steel rolls (a kind of high carbon steel) having hardness ranging from 40 to 55 degrees Shore C, with Carbon [C] percentage ranging from 1.35% until to 2% (usually between 1.2˜2.3%), the extra Carbon [C] and the special alloying element giving an extra wear resistance and strength. First of all the Adamite roll’s prominent feature is the small variation in hardness of the working surface, and has a good abrasion resistance and bite performance. This paper reviews key aspects of roll material properties and presents an analysis of the influences of chemical composition upon the mechanical properties (hardness) of the cast hipereutectoid steel rolls (Adamite). Using the multiple regression analysis (the double and triple regression equations), some mathematical correlations between the cast hipereutectoid steel rolls’ chemical composition and the obtained hardness are presented. In this work several results and evidence obtained by actual experiments are presented. Thus, several variation boundaries for the chemical composition of cast hipereutectoid steel rolls, in view the obtaining the proper values of the hardness, are revealed. For the multiple regression equations, correlation coefficients and graphical representations the software Matlab was used.

  5. 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.

  6. A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

    PubMed

    Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

    2017-09-01

    Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  7. Multiplicative Forests for Continuous-Time Processes.

    PubMed

    Weiss, Jeremy C; Natarajan, Sriraam; Page, David

    2012-01-01

    Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability.

  8. Reduction of interferences in graphite furnace atomic absorption spectrometry by multiple linear regression modelling

    NASA Astrophysics Data System (ADS)

    Grotti, Marco; Abelmoschi, Maria Luisa; Soggia, Francesco; Tiberiade, Christian; Frache, Roberto

    2000-12-01

    The multivariate effects of Na, K, Mg and Ca as nitrates on the electrothermal atomisation of manganese, cadmium and iron were studied by multiple linear regression modelling. Since the models proved to efficiently predict the effects of the considered matrix elements in a wide range of concentrations, they were applied to correct the interferences occurring in the determination of trace elements in seawater after pre-concentration of the analytes. In order to obtain a statistically significant number of samples, a large volume of the certified seawater reference materials CASS-3 and NASS-3 was treated with Chelex-100 resin; then, the chelating resin was separated from the solution, divided into several sub-samples, each of them was eluted with nitric acid and analysed by electrothermal atomic absorption spectrometry (for trace element determinations) and inductively coupled plasma optical emission spectrometry (for matrix element determinations). To minimise any other systematic error besides that due to matrix effects, accuracy of the pre-concentration step and contamination levels of the procedure were checked by inductively coupled plasma mass spectrometric measurements. Analytical results obtained by applying the multiple linear regression models were compared with those obtained with other calibration methods, such as external calibration using acid-based standards, external calibration using matrix-matched standards and the analyte addition technique. Empirical models proved to efficiently reduce interferences occurring in the analysis of real samples, allowing an improvement of accuracy better than for other calibration methods.

  9. 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

  10. Multi-Target Regression via Robust Low-Rank Learning.

    PubMed

    Zhen, Xiantong; Yu, Mengyang; He, Xiaofei; Li, Shuo

    2018-02-01

    Multi-target regression has recently regained great popularity due to its capability of simultaneously learning multiple relevant regression tasks and its wide applications in data mining, computer vision and medical image analysis, while great challenges arise from jointly handling inter-target correlations and input-output relationships. In this paper, we propose Multi-layer Multi-target Regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general framework via robust low-rank learning. Specifically, the MMR can explicitly encode inter-target correlations in a structure matrix by matrix elastic nets (MEN); the MMR can work in conjunction with the kernel trick to effectively disentangle highly complex nonlinear input-output relationships; the MMR can be efficiently solved by a new alternating optimization algorithm with guaranteed convergence. The MMR leverages the strength of kernel methods for nonlinear feature learning and the structural advantage of multi-layer learning architectures for inter-target correlation modeling. More importantly, it offers a new multi-layer learning paradigm for multi-target regression which is endowed with high generality, flexibility and expressive ability. Extensive experimental evaluation on 18 diverse real-world datasets demonstrates that our MMR can achieve consistently high performance and outperforms representative state-of-the-art algorithms, which shows its great effectiveness and generality for multivariate prediction.

  11. 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.

  12. Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

    PubMed

    Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

    2018-01-01

    Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

  13. Predictive Factors of Household Task Participation in Brazilian Children and Adolescents.

    PubMed

    Drummond, Adriana de França; Gomes, Ana Maria Rabelo; Coster, Wendy J; Mancini, Marisa Cotta

    2015-04-01

    Home environment is an important setting for child participation. This study investigated the participation of Brazilian children and adolescents in household self-care (SC) and family-care (FC) tasks. Interviews were conducted with 109 caregivers of children and adolescents ages 6 to 14 years residing in Belo Horizonte (Brazil). Multiple regression models revealed that a greater number of FC tasks were performed by children and adolescents (R2 = .23) from families who did not have a housekeeper and those in which the mothers did not work outside of the home; children and adolescents from this subgroup also received less assistance from the caregivers (R2 = .21) and showed greater independence in task performance (R2 = .20). On average, Brazilian children and adolescents participate in about half of the SC and 25% the FC household tasks. Factors related to family structure and child's age were associated with task performance, caregiver assistance, and child and adolescent independence in household tasks.

  14. Multi-task functional MRI in multiple sclerosis patients without clinical disability.

    PubMed

    Colorado, René A; Shukla, Karan; Zhou, Yuxiang; Wolinsky, Jerry S; Narayana, Ponnada A

    2012-01-02

    While the majority of individuals with multiple sclerosis (MS) develop significant clinical disability, a subset experiences a disease course with minimal impairment even in the presence of significant apparent tissue damage on magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) in MS patients with low disability suggests that increased use of the cognitive control system may limit the clinical manifestation of the disease. The current fMRI studies tested the hypothesis that nondisabled MS patients show increased recruitment of cognitive control regions while performing sensory, motor and cognitive tasks. Twenty two patients with relapsing-remitting MS and an Expanded Disability Status Scale (EDSS) score of ≤1.5 and 23 matched healthy controls were recruited. Subjects underwent fMRI while observing flashing checkerboards, performing right or left hand movements, or executing the 2-back working memory task. Compared to control subjects, patients demonstrated increased activation of the right dorsolateral prefrontal cortex and anterior cingulate cortex during the performance of the working memory task. This pattern of functional recruitment also was observed during the performance of non-dominant hand movements. These results support the mounting evidence of increased functional recruitment of cognitive control regions in the working memory system of MS patients with low disability and provide new evidence for the role of increased cognitive control recruitment in the motor system. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

    PubMed

    Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

    2013-08-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

  16. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

    PubMed Central

    Kim, Yoonsang; Emery, Sherry

    2013-01-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

  17. Relationships between Spontaneous Note-Taking, Self-Reported Strategies and Comprehension When Reading Multiple Texts in Different Task Conditions

    ERIC Educational Resources Information Center

    Hagen, Åste M.; Braasch, Jason L. G.; Bråten, Ivar

    2014-01-01

    This study investigated note-taking during multiple-text reading across two different task conditions in relation to comprehension performance and self-reports of strategy use. Forty-four undergraduates read multiple texts about climate change to write an argument or a summary. Analysis of students' spontaneous note-taking during reading…

  18. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

    USGS Publications Warehouse

    Ohlmacher, G.C.; Davis, J.C.

    2003-01-01

    Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

  19. A task-oriented circuit training in multiple sclerosis: a feasibility study

    PubMed Central

    2014-01-01

    Background The aim of this study was to evaluate the safety, feasibility and preliminary effects of a high-intensity rehabilitative task-oriented circuit training (TOCT) in a sample of multiple sclerosis (MS) subjects on walking competency, mobility, fatigue and health-related quality of life (HRQoL). Methods 24 MS subjects (EDSS 4.89 ± 0.54, 17 female and 7 male, 52.58 ± 11.21 years, MS duration 15.21 ± 8.68 years) have been enrolled and randomly assigned to 2 treatment groups: (i) experimental group received 10 TOCT sessions over 2 weeks (2 hours/each session) followed by a 3 months home exercise program, whereas control group did not receive any specific rehabilitation intervention. A feasibility patient-reported questionnaire was administered after TOCT. Functional outcome measures were: walking endurance (Six Minute Walk Test), gait speed (10 Meter Walk Test), mobility (Timed Up and Go test) and balance (Dynamic Gait Index). Furthermore, self-reported questionnaire of motor fatigue (Fatigue Severity Scale), walking ability (Multiple Sclerosis Walking Scale – 12) and health-related quality of life (Multiple Sclerosis Impact Scale – 29) were included. Subjects’ assessments were delivered at baseline (T0), after TOCT (T1) and 3 months of home-based exercise program (T2). Results After TOCT subjects reported a positive global rating on the received treatment. At 3 months, we found a 58.33% of adherence to the home-exercise program. After TOCT, walking ability and health-related quality of life were improved (p < 0.05) with minor retention after 3 months. The control group showed no significant changes in any variables. Conclusions This two weeks high-intensity task-oriented circuit class training followed by a three months home-based exercise program seems feasible and safe in MS people with moderate mobility impairments; moreover it might improve walking abilities. Trial registration NCT01464749 PMID:24906545

  20. 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.

  1. Development of multiple regression analysis instruments to predict success in advanced placement chemistry

    NASA Astrophysics Data System (ADS)

    Wagner, Kurt Collins

    2001-10-01

    This research asks the fundamental question: "What is the profile of the successful AP chemistry student?" Two populations of students are studied. The first population is comprised of students who attend or attended the South Carolina Governor's School for Science and Mathematics, a specialized high school for high ability students, and who have taken the Advanced Placement (AP) chemistry examination in the past five years. The second population is comprised of the 581 South Carolina public school students at 46 high schools who took the AP chemistry examination in 2000. The first part of the study is intended to be useful in recruitment and placement decisions for schools in the National Consortium for Specialized Secondary Schools of Mathematics, Science and Technology. The second part of the study is intended to facilitate AP chemistry recruitment in South Carolina public schools. The first part of the study was conducted by ex post facto searches of teacher and school records at the South Carolina Governor's School for Science and Mathematics. The second part of the study was conducted by obtaining school participation information from the SC Department of Education and soliciting data from the public schools. Data were collected from 440 of 581 (75.7%) of students in 35 of 46 (76.1%) of schools. Intercorrelational and Multiple Regression Analyses (MRA) have yielded different results for these two populations. For the specialized school population, the significant predictors for success in AP chemistry are PSAT Math, placement test, and PSAT Writing. For the population of SC students, significant predictors for success are PSAT Math, count of prior science courses, and PSAT Writing. Multiple Regressions have been successfully developed for the two populations studied. Recommendations for their application are made.

  2. Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

    NASA Astrophysics Data System (ADS)

    Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

    2018-02-01

    The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

  3. Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study

    PubMed Central

    Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

    2015-01-01

    The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200–400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. PMID:25005037

  4. Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

    USGS Publications Warehouse

    Schilling, K.E.; Wolter, C.F.

    2005-01-01

    Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

  5. Multiplicative Forests for Continuous-Time Processes

    PubMed Central

    Weiss, Jeremy C.; Natarajan, Sriraam; Page, David

    2013-01-01

    Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability. PMID:25284967

  6. Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

    USGS Publications Warehouse

    Brown, C. Erwin

    1993-01-01

    Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

  7. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  8. Logistic Regression Modeling for Predicting Task-Related ICT Use in Teaching

    ERIC Educational Resources Information Center

    Askar, Petek; Usluel, Yasemin Kocak; Mumcu, Filiz Kuskaya

    2006-01-01

    The main goal of this study is to estimate the extent to which perceived innovation characteristics are associated with the probability of task related ICT use among secondary school teachers. The tasks were categorized as teaching preparation, teaching delivery, and management. Four hundred and sixteen teachers from secondary schools in Turkey,…

  9. Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

    NASA Astrophysics Data System (ADS)

    Gao, Wei; Li, Xiang-ru

    2017-07-01

    The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.

  10. Effects of Text-Belief Consistency and Reading Task on the Strategic Validation of Multiple Texts

    ERIC Educational Resources Information Center

    Maier, Johanna; Richter, Tobias

    2016-01-01

    In the comprehension of multiple controversial scientific texts, readers with strong prior beliefs tend to construct a one-sided mental representation that is biased towards belief-consistent information. In the present study, we examined whether an argument in contrast to a summary task instruction can increase the resource allocation to and…

  11. Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

    PubMed

    Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

    2013-09-01

    Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

  12. EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD WHILE PERFORMING MULTIPLE TASKS.

    PubMed

    Hsu, Bin-Wei; Wang, Mao-Jiun J; Chen, Chi-Yuan; Chen, Fang

    2015-08-01

    This study identified several physiological indices that can accurately monitor mental workload while participants performed multiple tasks with the strategy of maintaining stable performance and maximizing accuracy. Thirty male participants completed three 10-min. simulated multitasks: MATB (Multi-Attribute Task Battery) with three workload levels. Twenty-five commonly used mental workload measures were collected, including heart rate, 12 HRV (heart rate variability), 10 EEG (electroencephalography) indices (α, β, θ, α/θ, θ/β from O1-O2 and F4-C4), and two subjective measures. Analyses of index sensitivity showed that two EEG indices, θ and α/θ (F4-C4), one time-domain HRV-SDNN (standard deviation of inter-beat intervals), and four frequency-domain HRV: VLF (very low frequency), LF (low frequency), %HF (percentage of high frequency), and LF/HF were sensitive to differentiate high workload. EEG α/θ (F4-C4) and LF/HF were most effective for monitoring high mental workload. LF/HF showed the highest correlations with other physiological indices. EEG α/θ (F4-C4) showed strong correlations with subjective measures across different mental workload levels. Operation strategy would affect the sensitivity of EEG α (F4-C4) and HF.

  13. Contingent attentional capture across multiple feature dimensions in a temporal search task.

    PubMed

    Ito, Motohiro; Kawahara, Jun I

    2016-01-01

    The present study examined whether attention can be flexibly controlled to monitor two different feature dimensions (shape and color) in a temporal search task. Specifically, we investigated the occurrence of contingent attentional capture (i.e., interference from task-relevant distractors) and resulting set reconfiguration (i.e., enhancement of single task-relevant set). If observers can restrict searches to a specific value for each relevant feature dimension independently, the capture and reconfiguration effect should only occur when the single relevant distractor in each dimension appears. Participants identified a target letter surrounded by a non-green square or a non-square green frame. The results revealed contingent attentional capture, as target identification accuracy was lower when the distractor contained a target-defining feature than when it contained a nontarget feature. Resulting set reconfiguration was also obtained in that accuracy was superior when the current target's feature (e.g., shape) corresponded to the defining feature of the present distractor (shape) than when the current target's feature did not match the distractor's feature (color). This enhancement was not due to perceptual priming. The present study demonstrated that the principles of contingent attentional capture and resulting set reconfiguration held even when multiple target feature dimensions were monitored. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

    PubMed

    Brown, Angus M

    2006-04-01

    The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

  15. Are factors related to dual-task performance in people with Parkinson's disease dependent on the type of dual task?

    PubMed

    Strouwen, Carolien; Molenaar, Esther A L M; Keus, Samyra H J; Münks, Liesbeth; Heremans, Elke; Vandenberghe, Wim; Bloem, Bastiaan R; Nieuwboer, Alice

    2016-02-01

    Impaired dual-task performance significantly impacts upon functional mobility in people with Parkinson's disease (PD). The aim of this study was to identify determinants of dual-task performance in people with PD in three different dual tasks to assess their possible task-dependency. We recruited 121 home-dwelling patients with PD (mean age 65.93 years; mean disease duration 8.67 years) whom we subjected to regular walking (control condition) and to three dual-task conditions: walking combined with a backwards Digit Span task, an auditory Stroop task and a Mobile Phone task. We measured dual-task gait velocity using the GAITRite mat and dual-task reaction times and errors on the concurrent tasks as outcomes. Motor, cognitive and descriptive variables which correlated to dual-task performance (p < 0.20) were entered into a stepwise forward multiple linear regression model. Single-task gait velocity and executive function, tested by the alternating intake test, was significantly associated with gait velocity during the Digit Span (R(2) = 0.65; p < 0.001), the Stroop (R(2) = 0.73; p < 0.001) and the Mobile Phone task (R(2) = 0.62; p < 0.001). In addition, disease severity proved correlated to gait velocity during the Stroop task. Age was a surplus determinant of gait velocity while using a mobile phone. Single-task gait velocity and executive function as measured by a verbal fluency switching task were independent determinants of dual-task gait performance in people with PD. In contrast to expectation, these factors were the same across different tasks, supporting the robustness of the findings. Future study needs to determine whether these factors predict dual-task abnormalities prospectively. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

    PubMed Central

    Zhang, Meiyan; Zheng, Yahong Rosa

    2017-01-01

    This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X−Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem. PMID:28696377

  17. Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.

    PubMed

    Cai, Wenyu; Zhang, Meiyan; Zheng, Yahong Rosa

    2017-07-11

    This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X - Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

  18. Exploring General Versus Task-Specific Assessments of Metacognition in University Chemistry Students: A Multitrait-Multimethod Analysis

    NASA Astrophysics Data System (ADS)

    Wang, Chia-Yu

    2015-08-01

    The purpose of this study was to use multiple assessments to investigate the general versus task-specific characteristics of metacognition in dissimilar chemistry topics. This mixed-method approach investigated the nature of undergraduate general chemistry students' metacognition using four assessments: a self-report questionnaire, assessment of concurrent metacognitive skills, confidence judgment, and calibration accuracy. Data were analyzed using a multitrait-multimethod correlation matrix, supplemented with regression analyses, and qualitative interpretation. Significant correlations among task performance, calibration accuracy, and concurrent metacognition within a task suggest a converging relationship. Confidence judgment, however, was not associated with task performance or the other metacognitive measurements. The results partially support hypotheses of both general and task-specific metacognition. However, general and task-specific properties of metacognition were detected using different assessments. Case studies were constructed for two participants to illustrate how concurrent metacognition varied within different task demands. Considerations of how each assessment may appropriate different metacognitive constructs and the importance of the alignment of analytical constructs when using multiple assessments are discussed. These results may help lead to improvements in metacognition assessment and may provide insights into designs of effective metacognitive instruction.

  19. Stepwise versus Hierarchical Regression: Pros and Cons

    ERIC Educational Resources Information Center

    Lewis, Mitzi

    2007-01-01

    Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

  20. Dual Task of Fine Motor Skill and Problem Solving in Individuals With Multiple Sclerosis: A Pilot Study.

    PubMed

    Goverover, Y; Sandroff, B M; DeLuca, J

    2018-04-01

    To (1) examine and compare dual-task performance in patients with multiple sclerosis (MS) and healthy controls (HCs) using mathematical problem-solving questions that included an everyday competence component while performing an upper extremity fine motor task; and (2) examine whether difficulties in dual-task performance are associated with problems in performing an everyday internet task. Pilot study, mixed-design with both a within and between subjects' factor. A nonprofit rehabilitation research institution and the community. Participants (N=38) included persons with MS (n=19) and HCs (n=19) who were recruited from a nonprofit rehabilitation research institution and from the community. Not applicable. Participant were presented with 2 testing conditions: (1) solving mathematical everyday problems or placing bolts into divots (single-task condition); and (2) solving problems while putting bolts into divots (dual-task condition). Additionally, participants were required to perform a test of everyday internet competence. As expected, dual-task performance was significantly worse than either of the single-task tasks (ie, number of bolts into divots or correct answers, and time to answer the questions). Cognitive but not motor dual-task cost was associated with worse performance in activities of everyday internet tasks. Cognitive dual-task cost is significantly associated with worse performance of everyday technology. This was not observed in the motor dual-task cost. The implications of dual-task costs on everyday activity are discussed. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  1. Analysis of components of variance in multiple-reader studies of computer-aided diagnosis with different tasks

    NASA Astrophysics Data System (ADS)

    Beiden, Sergey V.; Wagner, Robert F.; Campbell, Gregory; Metz, Charles E.; Chan, Heang-Ping; Nishikawa, Robert M.; Schnall, Mitchell D.; Jiang, Yulei

    2001-06-01

    In recent years, the multiple-reader, multiple-case (MRMC) study paradigm has become widespread for receiver operating characteristic (ROC) assessment of systems for diagnostic imaging and computer-aided diagnosis. We review how MRMC data can be analyzed in terms of the multiple components of the variance (case, reader, interactions) observed in those studies. Such information is useful for the design of pivotal studies from results of a pilot study and also for studying the effects of reader training. Recently, several of the present authors have demonstrated methods to generalize the analysis of multiple variance components to the case where unaided readers of diagnostic images are compared with readers who receive the benefit of a computer assist (CAD). For this case it is necessary to model the possibility that several of the components of variance might be reduced when readers incorporate the computer assist, compared to the unaided reading condition. We review results of this kind of analysis on three previously published MRMC studies, two of which were applications of CAD to diagnostic mammography and one was an application of CAD to screening mammography. The results for the three cases are seen to differ, depending on the reader population sampled and the task of interest. Thus, it is not possible to generalize a particular analysis of variance components beyond the tasks and populations actually investigated.

  2. Multiple task performance as a predictor of the potential of air traffic controller trainees : a followup study.

    DOT National Transportation Integrated Search

    1974-11-01

    Two hundred and twenty-nine air traffic controller trainees were tested on the CAMI Multiple Task Performance Battery. The battery provides objective measures of monitoring, arithmetical skills, visual discrimination, and group problem solving. The c...

  3. 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…

  4. Influence of Significant Others on High School Students' Expectancies of Success and Task Value in Physical Education

    ERIC Educational Resources Information Center

    Williams, Skip M.; Weiss, Windee

    2018-01-01

    This study examined the perceived influence of significant others' beliefs on students' expectancies of success and task value in physical education (PE). PE students (N = 231) between Grades 9 and 12 participated. Multiple regressions examined the influence of perceived parents', teachers', and classmates' beliefs on students' ability…

  5. Regression Analysis: Legal Applications in Institutional Research

    ERIC Educational Resources Information Center

    Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

    2008-01-01

    This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

  6. Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Shrivastava, Prashant Kumar; Pandey, Arun Kumar

    2018-06-01

    Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

  7. The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

    ERIC Educational Resources Information Center

    Le, Huy; Marcus, Justin

    2012-01-01

    This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

  8. Effects of dual-task balance training on postural performance in patients with Multiple Sclerosis: a double-blind, randomized controlled pilot trial.

    PubMed

    Monjezi, Saeideh; Negahban, Hossein; Tajali, Shirin; Yadollahpour, Nava; Majdinasab, Nastaran

    2017-02-01

    To investigate the effects of dual-task balance training on postural performance in patients with multiple sclerosis as compared with single-task balance training. Double-blind, pretest-posttest, randomized controlled pilot trial. Local Multiple Sclerosis Society. A total of 47 patients were randomly assigned to two equal groups labeled as single-task training and dual-task training groups. All patients received supervised balance training sessions, 3 times per week for 4 weeks. The patients in the single-task group performed balance activities, alone. However, patients in dual-task group practiced balance activities while simultaneously performing cognitive tasks. The 10-Meter Walk Test and Timed Up-and-Go under single-task and dual-task conditions, in addition to Activities-specific Balance Confidence, Berg Balance Scale, and Functional Gait Assessment were assessed pre-, and post intervention and also 6-weeks after the end of intervention. Only 38 patients completed the treatment plan. There was no difference in the amount of improvement seen between the two study groups. In both groups there was a significant effect of time for dual-10 Meter Walk Test (F 1, 36 =11.33, p=0.002) and dual-Timed Up-and-Go (F 1, 36 =14.27, p=0.001) but not for their single-tasks. Moreover, there was a significant effect of time for Activities-specific Balance Confidence, Berg Balance Scale, and Functional Gait Assessment ( P<0.01). This pilot study did not show more benefits from undertaking dual-task training than single-task training. A power analysis showed 71 patients per group would be needed to determine whether there was a clinically relevant difference for dual-task gait speed between the groups.

  9. Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

    DTIC Science & Technology

    1981-09-01

    corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

  10. A rotor optimization using regression analysis

    NASA Technical Reports Server (NTRS)

    Giansante, N.

    1984-01-01

    The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

  11. Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

    PubMed

    Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

    2015-05-01

    Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Factors associated with preventable infant death: a multiple logistic regression.

    PubMed

    Vidal E Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

    2018-01-01

    OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight.

  13. Factors associated with preventable infant death: a multiple logistic regression

    PubMed Central

    Vidal e Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

    2018-01-01

    ABSTRACT OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight. PMID:29723389

  14. Application-level regression testing framework using Jenkins

    DOE PAGES

    Budiardja, Reuben; Bouvet, Timothy; Arnold, Galen

    2017-09-26

    Monitoring and testing for regression of large-scale systems such as the NCSA's Blue Waters supercomputer are challenging tasks. In this paper, we describe the solution we came up with to perform those tasks. The goal was to find an automated solution for running user-level regression tests to evaluate system usability and performance. Jenkins, an automation server software, was chosen for its versatility, large user base, and multitude of plugins including collecting data and plotting test results over time. We also describe our Jenkins deployment to launch and monitor jobs on remote HPC system, perform authentication with one-time password, and integratemore » with our LDAP server for its authorization. We show some use cases and describe our best practices for successfully using Jenkins as a user-level system-wide regression testing and monitoring framework for large supercomputer systems.« less

  15. Application-level regression testing framework using Jenkins

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

    Budiardja, Reuben; Bouvet, Timothy; Arnold, Galen

    Monitoring and testing for regression of large-scale systems such as the NCSA's Blue Waters supercomputer are challenging tasks. In this paper, we describe the solution we came up with to perform those tasks. The goal was to find an automated solution for running user-level regression tests to evaluate system usability and performance. Jenkins, an automation server software, was chosen for its versatility, large user base, and multitude of plugins including collecting data and plotting test results over time. We also describe our Jenkins deployment to launch and monitor jobs on remote HPC system, perform authentication with one-time password, and integratemore » with our LDAP server for its authorization. We show some use cases and describe our best practices for successfully using Jenkins as a user-level system-wide regression testing and monitoring framework for large supercomputer systems.« less

  16. 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

  17. Transcranial direct current stimulation over multiple days enhances motor performance of a grip task.

    PubMed

    Fan, Julie; Voisin, Julien; Milot, Marie-Hélène; Higgins, Johanne; Boudrias, Marie-Hélène

    2017-09-01

    Recovery of handgrip is critical after stroke since it is positively related to upper limb function. To boost motor recovery, transcranial direct current stimulation (tDCS) is a promising, non-invasive brain stimulation technique for the rehabilitation of persons with stroke. When applied over the primary motor cortex (M1), tDCS has been shown to modulate neural processes involved in motor learning. However, no studies have looked at the impact of tDCS on the learning of a grip task in both stroke and healthy individuals. To assess the use of tDCS over multiple days to promote motor learning of a grip task using a learning paradigm involving a speed-accuracy tradeoff in healthy individuals. In a double-blinded experiment, 30 right-handed subjects (mean age: 22.1±3.3 years) participated in the study and were randomly assigned to an anodal (n=15) or sham (n=15) stimulation group. First, subjects performed the grip task with their dominant hand while following the pace of a metronome. Afterwards, subjects trained on the task, at their own pace, over 5 consecutive days while receiving sham or anodal tDCS over M1. After training, subjects performed de novo the metronome-assisted task. The change in performance between the pre and post metronome-assisted task was used to assess the impact of the grip task and tDCS on learning. Anodal tDCS over M1 had a significant effect on the speed-accuracy tradeoff function. The anodal tDCS group showed significantly greater improvement in performance (39.28±15.92%) than the sham tDCS group (24.06±16.35%) on the metronome-assisted task, t(28)=2.583, P=0.015 (effect size d=0.94). Anodal tDCS is effective in promoting grip motor learning in healthy individuals. Further studies are warranted to test its potential use for the rehabilitation of fine motor skills in stroke patients. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  18. 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.

  19. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

    PubMed

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

  20. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

    PubMed Central

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning. PMID:27445958

  1. Regression in autistic spectrum disorders.

    PubMed

    Stefanatos, Gerry A

    2008-12-01

    A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

  2. Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

    PubMed

    Suresh, Arumuganainar; Choi, Hong Lim

    2011-10-01

    Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Task-Dependent Behavioral Dynamics Make the Case for Temporal Integration in Multiple Strategies during Odor Processing

    PubMed Central

    Brown, Austin; Mehta, Nisarg; Vujovic, Mark; Amina, Tasneem; Fixsen, Bethany

    2017-01-01

    are very similar. Rats may extend sampling time to integrate odor information up to ∼0.5 s (2–6 sniffs). Such factors as task, task parameters, and training history affect decision times and performance, making it important to use multiple tasks when making inferences about sensory or cognitive processing. PMID:28336570

  4. Computerized assessment of sustained attention: interactive effects of task demand, noise, and anxiety.

    PubMed

    Ballard, J C

    1996-12-01

    In a sample of 163 college undergraduates, the effects of task demand, noise, and anxiety on Continuous Performance Test (CPT) errors were evaluated with multiple regression and multivariate analysis of variance. Results indicated significantly more omission errors on the difficult task. Complex interaction effects of noise and self-reported anxiety yielded more omissions in quiet intermittent white noise, particularly for high-anxious subjects performing the difficult task. Anxiety levels tended to increase from pretest to posttest, particularly for low-anxious subjects in the quiet, difficult-task condition, while a decrease was seen for high-anxious subjects in the loud, easy-task condition. Commission errors were unrelated to any predictor variables, suggesting that "attention" cannot be considered a unitary phenomenon. The variety of direct and interactive effects on vigilance performance underscore the need for clinicians to use a variety of measures to assess attentional skills, to avoid diagnosis of attention deficits on the basis of a single computerized task performance, and to rule out anxiety and other contributors to poor vigilance task performance.

  5. When Law Students Read Multiple Documents about Global Warming: Examining the Role of Topic-Specific Beliefs about the Nature of Knowledge and Knowing

    ERIC Educational Resources Information Center

    Braten, Ivar; Stromso, Helge I.

    2010-01-01

    In this study, law students (n = 49) read multiple authentic documents presenting conflicting information on the topic of climate change and responded to verification tasks assessing their superficial as well as their deeper-level within- and across-documents comprehension. Hierarchical multiple regression analyses showed that even after variance…

  6. Precision Efficacy Analysis for Regression.

    ERIC Educational Resources Information Center

    Brooks, Gordon P.

    When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

  7. Information Scent Determines Attention Allocation and Link Selection among Multiple Information Patches on a Webpage

    ERIC Educational Resources Information Center

    Blackmon, Marilyn Hughes

    2012-01-01

    This paper draws from cognitive psychology and cognitive neuroscience to develop a preliminary similarity-choice theory of how people allocate attention among information patches on webpages while completing search tasks in complex informational websites. Study 1 applied stepwise multiple regression to a large dataset and showed that success rate…

  8. Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

    ERIC Educational Resources Information Center

    Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

    2012-01-01

    In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

  9. Rules and more rules: the effects of multiple tasks, extensive training, and aging on task-switching performance.

    PubMed

    Buchler, Norbou G; Hoyer, William J; Cerella, John

    2008-06-01

    Task-switching performance was assessed in young and older adults as a function of the number of task sets to be actively maintained in memory (varied from 1 to 4) over the course of extended training (5 days). Each of the four tasks required the execution of a simple computational algorithm, which was instantaneously cued by the color of the two-digit stimulus. Tasks were presented in pure (task set size 1) and mixed blocks (task set sizes 2, 3, 4), and the task sequence was unpredictable. By considering task switching beyond two tasks, we found evidence for a cognitive control system that is not overwhelmed by task set size load manipulations. Extended training eliminated age effects in task-switching performance, even when the participants had to manage the execution of up to four tasks. The results are discussed in terms of current theories of cognitive control, including task set inertia and production system postulates.

  10. 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.

  11. Considerations of Numbers Used in Tasks for Promoting Multiplicative Reasoning in Students with Learning Difficulties in Mathematics

    ERIC Educational Resources Information Center

    Risley, Rachael Ann

    2016-01-01

    This study explored the impact of numbers used in instructional tasks on the construction and generalization of multiplicative reasoning by fourth grade students designated as having learning difficulties or disabilities in mathematics (SLDs). In particular, this study addressed the following research questions: (1) In what ways do SLDs'…

  12. Effects of Goal Relations on Self-Regulated Learning in Multiple Goal Pursuits: Performance, the Self-Regulatory Process, and Task Enjoyment

    ERIC Educational Resources Information Center

    Lee, Hyunjoo

    2012-01-01

    The purpose of this study was to investigate the effects of goal relations on self-regulation in the pursuit of multiple goals, focusing on self-regulated performance, the self-regulatory process, and task enjoyment. The effect of multiple goal relations on self-regulation was explored in a set of three studies. Goal relations were divided into…

  13. Task deactivation reductions and atrophy within parietal default mode regions are overlapping but only weakly correlated in mild cognitive impairment

    PubMed Central

    Threlkeld, Zachary D.; Jicha, Greg A.; Smith, Charles D.; Gold, Brian T.

    2012-01-01

    Reduced task deactivation within regions of the default mode network (DMN) has been frequently reported in Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI). As task deactivations reductions become increasingly used in the study of early AD states, it is important to understand their relationship to atrophy. To address this issue, the present study compared task deactivation reductions during a lexical decision task and atrophy in aMCI, using a series of parallel voxel-wise and region-wise analyses of fMRI and structural data. Our results identified multiple regions within parietal cortex as convergence areas of task deactivation and atrophy in aMCI. Relationships between parietal regions showing overlapping task deactivation reductions and atrophy in aMCI were then explored. Regression analyses demonstrated minimal correlation between task deactivation reductions and either local or global atrophy in aMCI. In addition, a logistic regression model which combined task deactivation reductions and atrophy in parietal DMN regions showed higher classificatory accuracy of aMCI than separate task deactivation or atrophy models. Results suggest that task deactivation reductions and atrophy in parietal regions provide complementary rather than redundant information in aMCI. Future longitudinal studies will be required to assess the utility of combining task deactivation reductions and atrophy in the detection of early AD. PMID:21860094

  14. Self-regulated learning processes of medical students during an academic learning task.

    PubMed

    Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

    2016-10-01

    This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated

  15. Detection of epistatic effects with logic regression and a classical linear regression model.

    PubMed

    Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

    2014-02-01

    To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

  16. The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

    PubMed

    Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

    2017-11-01

    Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

  17. Divided visual attention: A comparison of patients with multiple sclerosis and controls, assessed with an optokinetic nystagmus suppression task.

    PubMed

    Williams, Isla M; Schofield, Peter; Khade, Neha; Abel, Larry A

    2016-12-01

    Multiple sclerosis (MS) frequently causes impairment of cognitive function. We compared patients with MS with controls on divided visual attention tasks. The MS patients' and controls' stare optokinetic nystagmus (OKN) was recorded in response to a 24°/s full field stimulus. Suppression of the OKN response, judged by the gain, was measured during tasks dividing visual attention between the fixation target and a second stimulus, central or peripheral, static or dynamic. All participants completed the Audio Recorded Cognitive Screen. MS patients had lower gain on the baseline stare OKN. OKN suppression in divided attention tasks was the same in MS patients as in controls but in both groups was better maintained in static than in dynamic tasks. In only dynamic tasks, older age was associated with less effective OKN suppression. MS patients had lower scores on a timed attention task and on memory. There was no significant correlation between attention or memory and eye movement parameters. Attention, a complex multifaceted construct, has different neural combinations for each task. Despite impairments on some measures of attention, MS patients completed the divided visual attention tasks normally. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Understanding logistic regression analysis.

    PubMed

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

  19. Individual strategy ratings improve the control for task difficulty effects in arithmetic problem solving paradigms.

    PubMed

    Tschentscher, Nadja; Hauk, Olaf

    2015-01-01

    Mental arithmetic is a powerful paradigm to study problem solving using neuroimaging methods. However, the evaluation of task complexity varies significantly across neuroimaging studies. Most studies have parameterized task complexity by objective features such as the number size. Only a few studies used subjective rating procedures. In fMRI, we provided evidence that strategy self-reports control better for task complexity across arithmetic conditions than objective features (Tschentscher and Hauk, 2014). Here, we analyzed the relative predictive value of self-reported strategies and objective features for performance in addition and multiplication tasks, by using a paradigm designed for neuroimaging research. We found a superiority of strategy ratings as predictor of performance above objective features. In a Principal Component Analysis on reaction times, the first component explained over 90 percent of variance and factor loadings reflected percentages of self-reported strategies well. In multiple regression analyses on reaction times, self-reported strategies performed equally well or better than objective features, depending on the operation type. A Receiver Operating Characteristic (ROC) analysis confirmed this result. Reaction times classified task complexity better when defined by individual ratings. This suggests that participants' strategy ratings are reliable predictors of arithmetic complexity and should be taken into account in neuroimaging research.

  20. Individual strategy ratings improve the control for task difficulty effects in arithmetic problem solving paradigms

    PubMed Central

    Tschentscher, Nadja; Hauk, Olaf

    2015-01-01

    Mental arithmetic is a powerful paradigm to study problem solving using neuroimaging methods. However, the evaluation of task complexity varies significantly across neuroimaging studies. Most studies have parameterized task complexity by objective features such as the number size. Only a few studies used subjective rating procedures. In fMRI, we provided evidence that strategy self-reports control better for task complexity across arithmetic conditions than objective features (Tschentscher and Hauk, 2014). Here, we analyzed the relative predictive value of self-reported strategies and objective features for performance in addition and multiplication tasks, by using a paradigm designed for neuroimaging research. We found a superiority of strategy ratings as predictor of performance above objective features. In a Principal Component Analysis on reaction times, the first component explained over 90 percent of variance and factor loadings reflected percentages of self-reported strategies well. In multiple regression analyses on reaction times, self-reported strategies performed equally well or better than objective features, depending on the operation type. A Receiver Operating Characteristic (ROC) analysis confirmed this result. Reaction times classified task complexity better when defined by individual ratings. This suggests that participants’ strategy ratings are reliable predictors of arithmetic complexity and should be taken into account in neuroimaging research. PMID:26321997

  1. Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

    NASA Astrophysics Data System (ADS)

    Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

    2018-05-01

    Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

  2. Melanin and blood concentration in human skin studied by multiple regression analysis: experiments

    NASA Astrophysics Data System (ADS)

    Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

    2001-09-01

    Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

  3. All for one but not one for all: how multiple number representations are recruited in one numerical task.

    PubMed

    Wood, Guilherme; Nuerk, Hans-Christoph; Moeller, Korbinian; Geppert, Barbara; Schnitker, Ralph; Weber, Jochen; Willmes, Klaus

    2008-01-02

    Number processing recruits a complex network of multiple numerical representations. Usually the components of this network are examined in a between-task approach with the disadvantage of relying upon different instructions, tasks, and inhomogeneous stimulus sets across different studies. A within-task approach may avoid these disadvantages and access involved numerical representations more specifically. In the present study we employed a within-task approach to investigate numerical representations activated in the number bisection task (NBT) using parametric rapid event-related fMRI. Participants were to judge whether the central number of a triplet was also its arithmetic mean (e.g. 23_26_29) or not (e.g. 23_25_29). Activation in the left inferior parietal cortex was associated with the deployment of arithmetic fact knowledge, while activation of the intraparietal cortex indicated more intense magnitude processing, instrumental aspects of calculation and integration of the base-10 structure of two-digit numbers. These results replicate evidence from the literature. Furthermore, activation in the dorsolateral and ventrolateral prefrontal cortex revealed mechanisms of feature monitoring and inhibition as well as allocation of cognitive resources recruited to solve a specific triplet. We conclude that the network of numerical representations should rather be studied in a within-task approach than in varying between-task approaches.

  4. Strategic predictors of performance in a divided attention task

    PubMed Central

    Faragó, Kinga Bettina; Lőrincz, András

    2018-01-01

    In this study we investigate the strategies of subjects in a complex divided attention task. We conducted a series of experiments with ten participants and evaluated their performance. After an extensive analysis, we identified four strategic measures that justify the achievement of the participants, by highlighting the individual differences and predicting performance in a regression analysis using generalized estimating equations. Selecting the more urgent task and user action between multiple simultaneous possibilities form two of the strategic decisions, respectively. The third one refers to choosing a response within the same task when the opportunity is present. The fourth and most important measure of strategy involves thinking ahead and executing an action before a situation would become critical. This latter one has the effect of reducing later cognitive load or timing constraints and it is shown to explain almost as much variance in performance as the other three, more straightforward predictors together. In addition to determining these strategic predictors, we also show how manipulating task difficulty induces a shift in strategy, thus impairing human performance in the rehearsed task. The results of this study indicate that considerable differences in the divided attention ability of normal subjects can be identified early and with simple measurements. The importance of describing and analyzing strategies is also emphasized, which can substantially influence performance in complex tasks and may serve training needs. PMID:29621292

  5. Strategic predictors of performance in a divided attention task.

    PubMed

    Rill, Róbert Adrian; Faragó, Kinga Bettina; Lőrincz, András

    2018-01-01

    In this study we investigate the strategies of subjects in a complex divided attention task. We conducted a series of experiments with ten participants and evaluated their performance. After an extensive analysis, we identified four strategic measures that justify the achievement of the participants, by highlighting the individual differences and predicting performance in a regression analysis using generalized estimating equations. Selecting the more urgent task and user action between multiple simultaneous possibilities form two of the strategic decisions, respectively. The third one refers to choosing a response within the same task when the opportunity is present. The fourth and most important measure of strategy involves thinking ahead and executing an action before a situation would become critical. This latter one has the effect of reducing later cognitive load or timing constraints and it is shown to explain almost as much variance in performance as the other three, more straightforward predictors together. In addition to determining these strategic predictors, we also show how manipulating task difficulty induces a shift in strategy, thus impairing human performance in the rehearsed task. The results of this study indicate that considerable differences in the divided attention ability of normal subjects can be identified early and with simple measurements. The importance of describing and analyzing strategies is also emphasized, which can substantially influence performance in complex tasks and may serve training needs.

  6. NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

    PubMed

    Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

    2014-01-01

    One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

  7. NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

    PubMed Central

    Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

    2014-01-01

    One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available

  8. [Ocra Method: development of a new procedure for analysis of multiple tasks subject to infrequent rotation].

    PubMed

    Occhipinti, E; Colombini, Daniela; Occhipinti, M

    2008-01-01

    In the Ocra methods (Ocra index and Ocra Checklist), when computing the final indices (Ocra index or checklist score), in the case of more than one repetitive task a "traditional" procedure was already proposed, the results of which could be defined as "time-weighted average". This approach appears to be appropriate when considering rotations among tasks that are performed very frequently, for instance almost once every hour (or for shorter periods). However, when rotation among repetitive tasks is less frequent (i.e. once every 1 1/2 or more hours), the "time-weighted average" approach could result in an underestimation of the exposure level (as it practically flattens peaks of high exposures). For those scenarios an alternative approach based on the "most stressful task as minimum" might be more realistic. This latter approach has already been included in the NIOSH approach for multiple sequential lifting tasks and, given the recent availability in the Ocra method of more detailed duration multipliers (practically one different Du(M) for each different step of one hour of duration of the repetitive task), it is now possible to define a particular procedure to compute the complex Ocra Multitask Index (cOCRA) and the complex Checklist Score (cCHESCO) for the analysis of two or more repetitive tasks when rotations are infrequent (rotations every 1 1/2 hours or more). The result of this approach will be at least equal to the index of the most stressful task considered for its individual daily duration and at the most equal to the index of the most stressful task when it is (only theoretically) considered as lasting for the overall daily duration of all examined repetitive tasks. The procedure is based on the following formula: Complex Ocra Multitask Index = Ocra(1(Dum1) + (Delta ocra1xK) where 1,2,3,...,N = repetitive tasks ordered by ocra index values (1 = highest; N = lowest) computed considering respective real duration multipliers (Dum(i)). ocra1 = ocra index of

  9. Personality and attention: Levels of neuroticism and extraversion can predict attentional performance during a change detection task.

    PubMed

    Hahn, Sowon; Buttaccio, Daniel R; Hahn, Jungwon; Lee, Taehun

    2015-01-01

    The present study demonstrates that levels of extraversion and neuroticism can predict attentional performance during a change detection task. After completing a change detection task built on the flicker paradigm, participants were assessed for personality traits using the Revised Eysenck Personality Questionnaire (EPQ-R). Multiple regression analyses revealed that higher levels of extraversion predict increased change detection accuracies, while higher levels of neuroticism predict decreased change detection accuracies. In addition, neurotic individuals exhibited decreased sensitivity A' and increased fixation dwell times. Hierarchical regression analyses further revealed that eye movement measures mediate the relationship between neuroticism and change detection accuracies. Based on the current results, we propose that neuroticism is associated with decreased attentional control over the visual field, presumably due to decreased attentional disengagement. Extraversion can predict increased attentional performance, but the effect is smaller than the relationship between neuroticism and attention.

  10. Design-Build-Write: Increasing the Impact of English for Specific Purposes Learning and Teaching in Aeronautical Engineering Education through Multiple Intelligences Task Design

    ERIC Educational Resources Information Center

    Tatzl, Dietmar

    2011-01-01

    This article presents an English for Specific Purposes (ESP) task developed for teaching aeronautical engineering students. The task Design-Build-Write rests on the assumption that engineering students are skilled at mathematical reasoning, problem solving, drawing and constructing. In Gardner's 1983 Multiple Intelligences (MI) theory, these…

  11. Exploring inter-task transfer following a CO-OP approach with four children with DCD: A single subject multiple baseline design.

    PubMed

    Capistran, Julie; Martini, Rose

    2016-10-01

    Cognitive Orientation to daily Occupational Performance (CO-OP) approach has been shown to be effective for improving the performance of tasks worked on in therapy and the use of cognitive strategies. No study to date seems to have explored its effectiveness for improving performance of untrained tasks (inter-task transfer) in children with Developmental Coordination Disorder (DCD). This study aimed to determine whether CO-OP leads to improved performance in an untrained task. A single-subject design with multiple baselines across skills was adopted, with three replications. Four children with DCD (7-12years) received 10 sessions of CO-OP intervention where each child worked on three tasks during therapy sessions and a fourth task was identified, but not worked on, to verify inter-task transfer. Task performance was rated over four phases (baseline, intervention, post-intervention, follow-up) using the Performance Quality Rating Scale (PQRS-OD). Graphed data was statistically analyzed using a two or three standard deviation band method. Significant improvement was obtained for 11 of 12 tasks worked on during therapy and for two of the four untrained tasks. These results indicate that the effectiveness of CO-OP to improve untrained tasks in children merit further exploration. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. An Effect Size for Regression Predictors in Meta-Analysis

    ERIC Educational Resources Information Center

    Aloe, Ariel M.; Becker, Betsy Jane

    2012-01-01

    A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

  13. Virtual street-crossing performance in persons with multiple sclerosis: Feasibility and task performance characteristics.

    PubMed

    Stratton, M E; Pilutti, L A; Crowell, J A; Kaczmarski, H; Motl, R W

    2017-01-02

    Multiple sclerosis (MS) is a neurological disease that commonly results in physical and cognitive dysfunction. Accordingly, MS might impact the ability to safely cross the street. The purpose of this study was to examine the feasibility of a simulated street-crossing task in persons with MS and to determine differences in street-crossing performance between persons with MS and non-MS controls. 26 participants with MS (median Expanded Disability Status Scale [EDSS] score = 3.5) and 19 controls completed 40 trials of a virtual street-crossing task. There were 2 crossing conditions (i.e., no distraction and phone conversation), and participants performed 20 trials per condition. Participants were instructed that the goal of the task was to cross the street successfully (i.e., without being hit be a vehicle). The primary outcome was task feasibility, assessed as completion and adverse events. Secondary outcomes were measures of street-crossing performance. Overall, the simulated street-crossing task was feasible (i.e., 90% completion, no adverse events) in participants with MS. Participants with MS waited longer and were less attentive to traffic before entering the street compared with controls (all P < .05). Participants with MS also took longer to cross the street and were closer to oncoming vehicles when exiting the street compared to controls (all P < .05). When distracted, all participants took longer to initiate crossing, took longer to cross the street, and made more head turns while crossing (all P < .05). There were no significant group by condition interaction effects (all P > .05). A virtual street-crossing task is feasible for studying street-crossing behavior in persons with mild MS and most individuals with moderate MS. Virtual street-crossing performance is impaired in persons with MS compared to controls; however, persons with MS do not appear to be more vulnerable to a distracting condition. The virtual reality environment presents a safe and useful

  14. Gait initiation time is associated with the risk of multiple falls-A population-based study.

    PubMed

    Callisaya, Michele L; Blizzard, Leigh; Martin, Kara; Srikanth, Velandai K

    2016-09-01

    In a population-based study of older people to examine whether 1) overall gait initiation (GI) time or its components are associated with falls and 2) GI under dual-task is a stronger predictor of falls risk than under single-task. Participants aged 60-85 years were randomly selected from the electoral roll. GI was obtained with a force platform under both single and dual-task conditions. Falls were ascertained prospectively over a 12-month period. Log multinomial regression was used to examine the association between GI time (total and its components) and risk of single and multiple falls. Age, sex and physiological and cognitive falls risk factors were considered as confounders. The mean age of the sample (n=124) was 71.0 (SD 6.8) years and 58.9% (n=73) were male. Over 12 months 21.8% (n=27) of participants reported a single fall and 16.1% (n=20) reported multiple falls. Slower overall GI time under both single (RR all per 100ms 1.28, 95%CI 1.03, 1.58) and dual-task (RR 1.14, 95%CI 1.02, 1.27) was associated with increased risk of multiple, but not single falls (p<0.05). Multiple falls were also associated with slower time to first lateral movement under single-task (RR 1.90 95%CI 0.59, 1.51) and swing time under dual-task condition (RR 1.44 95%CI 1.08, 1.94). Slower GI time is associated with the risk of multiple falls independent of other risk factors, suggesting it could be used as part of a comprehensive falls assessment. Time to the first lateral movement under single-task may be the best measures of this risk. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks

    PubMed Central

    Chen, Jianhui; Tang, Lei; Liu, Jun; Ye, Jieping

    2013-01-01

    In this paper, we consider the problem of learning from multiple related tasks for improved generalization performance by extracting their shared structures. The alternating structure optimization (ASO) algorithm, which couples all tasks using a shared feature representation, has been successfully applied in various multitask learning problems. However, ASO is nonconvex and the alternating algorithm only finds a local solution. We first present an improved ASO formulation (iASO) for multitask learning based on a new regularizer. We then convert iASO, a nonconvex formulation, into a relaxed convex one (rASO). Interestingly, our theoretical analysis reveals that rASO finds a globally optimal solution to its nonconvex counterpart iASO under certain conditions. rASO can be equivalently reformulated as a semidefinite program (SDP), which is, however, not scalable to large datasets. We propose to employ the block coordinate descent (BCD) method and the accelerated projected gradient (APG) algorithm separately to find the globally optimal solution to rASO; we also develop efficient algorithms for solving the key subproblems involved in BCD and APG. The experiments on the Yahoo webpages datasets and the Drosophila gene expression pattern images datasets demonstrate the effectiveness and efficiency of the proposed algorithms and confirm our theoretical analysis. PMID:23520249

  16. Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

    PubMed

    Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

    2017-10-07

    The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

  17. Test-Task Authenticity: The Multiple Perspectives

    ERIC Educational Resources Information Center

    Gan, Zhengdong

    2012-01-01

    Leung and Lewkowicz remind us that the debate over the past two decades that is most relevant to ELT (English languge teaching) pedagogy and curriculum concerns test-task authenticity. This paper first reviews how the authenticity debate in the literature of second language acquisition, pedagogy and testing has evolved. Drawing on a body of…

  18. Normative data for distal line bisection and baking tray task.

    PubMed

    Facchin, Alessio; Beschin, Nicoletta; Pisano, Alessia; Reverberi, Cristina

    2016-09-01

    Line bisection is one of the tests used to diagnose unilateral spatial neglect (USN). Despite its wide application, no procedure or norms were available for the distal variant when the task was performed at distance with a laser pointer. Furthermore, the baking tray task was an ecological test aimed at diagnosing USN in a more natural context. The aim of this study was to collect normative values for these two tests in an Italian population. We recruited a sample of 191 healthy subjects with ages ranging from 20 to 89 years. They performed line bisection with a laser pointer on three different line lengths (1, 1.5, and 2 m) at a distance of 3 m. After this task, the subjects performed the baking tray task and a second repetition of line bisection to test the reliability of measurement. Multiple regression analysis revealed no significant effects of demographic variables on the performance of both tests. Normative cut-off values for the two tests were developed using non-parametric tolerance intervals. The results formed the basis for clinical use of these two tools for assessing lateralized performance of patients with brain injury and for diagnosing USN.

  19. A Verbal-Instruction System to Help Persons with Multiple Disabilities Perform Complex Food- and Drink-Preparation Tasks Independently

    ERIC Educational Resources Information Center

    Lancioni, Giulio E.; Singh, Nirbhay N.; O'Reilly, Mark F.; Sigafoos, Jeff; Oliva, Doretta; Smaldone, Angela; La Martire, Maria L.; Alberti, Gloria; Scigliuzzo, Francesca

    2011-01-01

    In a recent single-case study, we showed that a new verbal-instruction system, ensuring the automatic presentation of step instructions, was beneficial for promoting the task performance of a woman with multiple disabilities (including blindness). The present study was aimed at replicating and extending the aforementioned investigation with three…

  20. 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).

  1. A new semantic vigilance task: vigilance decrement, workload, and sensitivity to dual-task costs.

    PubMed

    Epling, Samantha L; Russell, Paul N; Helton, William S

    2016-01-01

    Cognitive resource theory is a common explanation for both the performance decline in vigilance tasks, known as the vigilance decrement, and the limited ability to perform multiple tasks simultaneously. The limited supply of cognitive resources may be utilized faster than they are replenished resulting in a performance decrement, or may need to be allocated among multiple tasks with some performance cost. Researchers have proposed both domain-specific, for example spatial versus verbal processing resources, and domain general cognitive resources. One challenge in testing the domain specificity of cognitive resources in vigilance is the current lack of difficult semantic vigilance tasks which reliably produce a decrement. In the present research, we investigated whether the vigilance decrement was found in a new abbreviated semantic discrimination vigilance task, and whether there was a performance decrement in said vigilance task when paired with a word recall task, as opposed to performed individually. As hypothesized, a vigilance decrement in the semantic vigilance task was found in both the single-task and dual-task conditions, along with reduced vigilance performance in the dual-task condition and reduced word recall in the dual-task condition. This is consistent with cognitive resource theory. The abbreviated semantic vigilance task will be a useful tool for researchers interested in determining the specificity of cognitive resources utilized in vigilance tasks.

  2. Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

    PubMed

    Campos-Filho, N; Franco, E L

    1989-02-01

    A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

  3. Sample size determination for logistic regression on a logit-normal distribution.

    PubMed

    Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

    2017-06-01

    Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

  4. Contributions to "k"-Means Clustering and Regression via Classification Algorithms

    ERIC Educational Resources Information Center

    Salman, Raied

    2012-01-01

    The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…

  5. Regression Simulation of Turbine Engine Performance - Accuracy Improvement (TASK IV)

    DTIC Science & Technology

    1978-09-30

    33 21 Generalized Form of the Regression Equation for the Optimized Polynomial Exponent M ethod...altitude, Mach number and power setting combinations were generated during the ARES evaluation. The orthogonal Latin Square selection procedure...pattern. In data generation , the low (L), mid (M), and high (H) values of a variable are not always the same. At some of the corner points where

  6. Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and performance of everyday life tasks: Actual Reality.

    PubMed

    Goverover, Yael; Chiaravalloti, Nancy; DeLuca, John

    2016-04-01

    Recently, a brief cognitive assessment (Brief International Cognitive Assessment for Multiple Sclerosis: BICAMS) has been recommended for use with patients diagnosed with multiple sclerosis (MS) to screen for cognitive impairments. However, the relationship between the BICAMS and everyday life activity has not been examined. The aim of this study was to examine whether the BICAMS can predict performance of activities of daily living using Actual Reality(TM) (AR) in persons with MS. A between-subjects design was utilized to compare 41 individuals with MS and 32 healthy controls (HC) performing BICAMS and an AR task. Participants were asked to access the internet to purchase a flight ticket or cookies, and were administered the BICAMS and questionnaires to assess quality of life (QOL), affect symptomatology, and prior internet experience. Participants with MS performed significantly worse than HC on the BICAMS and the AR. Additionally, better BICAMS performance was associated with more independent AR performance. Self-reports of QOL were not correlated with AR or BICAMS performance. Individuals with MS have greater problems with actual everyday life tasks as compared to HC. The BICAMS is a promising cognitive screening tool to predict actual functional performance in participants with MS. © The Author(s), 2015.

  7. The Effects of Multiple Exemplar Training on a Working Memory Task Involving Sequential Responding in Children with Autism

    ERIC Educational Resources Information Center

    Baltruschat, Lisa; Hasselhorn, Marcus; Tarbox, Jonathan; Dixon, Dennis R.; Najdowski, Adel; Mullins, Ryan David; Gould, Evelyn

    2012-01-01

    This study is part of a programmatic line of research into the use of basic positive reinforcement procedures for improving working memory in children with autism spectrum disorders. The authors evaluated the effects of multiple exemplar training, utilizing positive reinforcement, on performance of a "digit span backwards" task--a test of working…

  8. Walking and cognition, but not symptoms, correlate with dual task cost of walking in multiple sclerosis.

    PubMed

    Motl, Robert W; Sosnoff, Jacob J; Dlugonski, Deirdre; Pilutti, Lara A; Klaren, Rachel; Sandroff, Brian M

    2014-03-01

    Performing a cognitive task while walking results in a reduction of walking performance among persons with MS. To date, very little is known about correlates of this dual task cost (DTC) of walking in MS. We examined walking performance, cognitive processing speed, and symptoms of fatigue, depression, anxiety, and pain as correlates of DTC of walking in MS. 82 persons with MS undertook a 6-min walk test (6MWT) and completed the Symbol Digit Modalities Test (SDMT), Fatigue Severity Scale (FSS), Short-form of the McGill Pain Questionnaire (SF-MPQ), Hospital Anxiety and Depression Scale (HADS), and self-reported Expanded Disability Status Scale (SR-EDSS). The participants completed 4 trials of walking at a self-selected pace on an electronic walkway that recorded spatiotemporal parameters of gait. The first 2 trials were performed without a cognitive task, whereas the second 2 trials were completed while performing a modified Word List Generation task. There were significant and large declines in gait performance with the addition of a cognitive task for velocity (p<.001, η2=.52), cadence (p<.001, η2=.49), and step length (p<.001, η2=.23). 6MWT and SDMT scores correlated with DTC for velocity (r=-.41, p<.001 and r=-.32, p<.001, respectively) and step length (r=-.45, p<.001 and r=-.37, p<.001, respectively); there were no significant associations between FSS, SF-MPQ, and HADS scores with the DTC of walking. Regression analyses indicated that 6MW, but not SDMT, explained variance in DTC for velocity (ΔR2=.11, p<.001) and step length (ΔR2=.13, p<.001), after controlling for SR-EDSS scores. Walking performance might be a target of interventions for reducing the DTC of walking in MS. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

    NASA Astrophysics Data System (ADS)

    Tsumori, Kenji; Ozawa, Seiichi

    When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  10. Rapid gist perception of meaningful real-life scenes: Exploring individual and gender differences in multiple categorization tasks

    PubMed Central

    Vanmarcke, Steven; Wagemans, Johan

    2015-01-01

    In everyday life, we are generally able to dynamically understand and adapt to socially (ir)elevant encounters, and to make appropriate decisions about these. All of this requires an impressive ability to directly filter and obtain the most informative aspects of a complex visual scene. Such rapid gist perception can be assessed in multiple ways. In the ultrafast categorization paradigm developed by Simon Thorpe et al. (1996), participants get a clear categorization task in advance and succeed at detecting the target object of interest (animal) almost perfectly (even with 20 ms exposures). Since this pioneering work, follow-up studies consistently reported population-level reaction time differences on different categorization tasks, indicating a superordinate advantage (animal versus dog) and effects of perceptual similarity (animals versus vehicles) and object category size (natural versus animal versus dog). In this study, we replicated and extended these separate findings by using a systematic collection of different categorization tasks (varying in presentation time, task demands, and stimuli) and focusing on individual differences in terms of e.g., gender and intelligence. In addition to replicating the main findings from the literature, we find subtle, yet consistent gender differences (women faster than men). PMID:26034569

  11. Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

    ERIC Educational Resources Information Center

    Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

    2012-01-01

    Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

  12. Relationship between postoperative refractive outcomes and cataract density: multiple regression analysis.

    PubMed

    Ueda, Tetsuo; Ikeda, Hitoe; Ota, Takeo; Matsuura, Toyoaki; Hara, Yoshiaki

    2010-05-01

    To evaluate the relationship between cataract density and the deviation from the predicted refraction. Department of Ophthalmology, Nara Medical University, Kashihara, Japan. Axial length (AL) was measured in eyes with mainly nuclear cataract using partial coherence interferometry (IOLMaster). The postoperative AL was measured in pseudophakic mode. The AL difference was calculated by subtracting the postoperative AL from the preoperative AL. Cataract density was measured with the pupil dilated using anterior segment Scheimpflug imaging (EAS-1000). The predicted postoperative refraction was calculated using the SRK/T formula. The subjective refraction 3 months postoperatively was also measured. The mean absolute prediction error (MAE) (mean of absolute difference between predicted postoperative refraction and spherical equivalent of postoperative subjective refraction) was calculated. The relationship between the MAE and cataract density, age, preoperative visual acuity, anterior chamber depth, corneal radius of curvature, and AL difference was evaluated using multiple regression analysis. In the 96 eyes evaluated, the MAE was correlated with cataract density (r = 0.37, P = .001) and the AL difference (r = 0.34, P = .003) but not with the other parameters. The AL difference was correlated with cataract density (r = 0.53, P<.0001). The postoperative refractive outcome was affected by cataract density. This should be taken into consideration in eyes with a higher density cataract. (c) 2010 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

  13. Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

    NASA Technical Reports Server (NTRS)

    Ohring, G.

    1972-01-01

    Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

  14. Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

    PubMed

    Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

    2018-06-05

    Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

  15. Shared processing in multiple object tracking and visual working memory in the absence of response order and task order confounds

    PubMed Central

    Howe, Piers D. L.

    2017-01-01

    To understand how the visual system represents multiple moving objects and how those representations contribute to tracking, it is essential that we understand how the processes of attention and working memory interact. In the work described here we present an investigation of that interaction via a series of tracking and working memory dual-task experiments. Previously, it has been argued that tracking is resistant to disruption by a concurrent working memory task and that any apparent disruption is in fact due to observers making a response to the working memory task, rather than due to competition for shared resources. Contrary to this, in our experiments we find that when task order and response order confounds are avoided, all participants show a similar decrease in both tracking and working memory performance. However, if task and response order confounds are not adequately controlled for we find substantial individual differences, which could explain the previous conflicting reports on this topic. Our results provide clear evidence that tracking and working memory tasks share processing resources. PMID:28410383

  16. Shared processing in multiple object tracking and visual working memory in the absence of response order and task order confounds.

    PubMed

    Lapierre, Mark D; Cropper, Simon J; Howe, Piers D L

    2017-01-01

    To understand how the visual system represents multiple moving objects and how those representations contribute to tracking, it is essential that we understand how the processes of attention and working memory interact. In the work described here we present an investigation of that interaction via a series of tracking and working memory dual-task experiments. Previously, it has been argued that tracking is resistant to disruption by a concurrent working memory task and that any apparent disruption is in fact due to observers making a response to the working memory task, rather than due to competition for shared resources. Contrary to this, in our experiments we find that when task order and response order confounds are avoided, all participants show a similar decrease in both tracking and working memory performance. However, if task and response order confounds are not adequately controlled for we find substantial individual differences, which could explain the previous conflicting reports on this topic. Our results provide clear evidence that tracking and working memory tasks share processing resources.

  17. 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.

  18. Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking.

    PubMed

    Sanbonmatsu, David M; Strayer, David L; Medeiros-Ward, Nathan; Watson, Jason M

    2013-01-01

    The present study examined the relationship between personality and individual differences in multi-tasking ability. Participants enrolled at the University of Utah completed measures of multi-tasking activity, perceived multi-tasking ability, impulsivity, and sensation seeking. In addition, they performed the Operation Span in order to assess their executive control and actual multi-tasking ability. The findings indicate that the persons who are most capable of multi-tasking effectively are not the persons who are most likely to engage in multiple tasks simultaneously. To the contrary, multi-tasking activity as measured by the Media Multitasking Inventory and self-reported cell phone usage while driving were negatively correlated with actual multi-tasking ability. Multi-tasking was positively correlated with participants' perceived ability to multi-task ability which was found to be significantly inflated. Participants with a strong approach orientation and a weak avoidance orientation--high levels of impulsivity and sensation seeking--reported greater multi-tasking behavior. Finally, the findings suggest that people often engage in multi-tasking because they are less able to block out distractions and focus on a singular task. Participants with less executive control--low scorers on the Operation Span task and persons high in impulsivity--tended to report higher levels of multi-tasking activity.

  19. Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

    USDA-ARS?s Scientific Manuscript database

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

  20. Modeling the energy content of combustible ship-scrapping waste at Alang-Sosiya, India, using multiple regression analysis.

    PubMed

    Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K

    2005-01-01

    Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.

  1. Multiple Criteria Decision Analysis for Health Care Decision Making--Emerging Good Practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force.

    PubMed

    Marsh, Kevin; IJzerman, Maarten; Thokala, Praveen; Baltussen, Rob; Boysen, Meindert; Kaló, Zoltán; Lönngren, Thomas; Mussen, Filip; Peacock, Stuart; Watkins, John; Devlin, Nancy

    2016-01-01

    Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  2. Using cognitive task analysis to develop simulation-based training for medical tasks.

    PubMed

    Cannon-Bowers, Jan; Bowers, Clint; Stout, Renee; Ricci, Katrina; Hildabrand, Annette

    2013-10-01

    Pressures to increase the efficacy and effectiveness of medical training are causing the Department of Defense to investigate the use of simulation technologies. This article describes a comprehensive cognitive task analysis technique that can be used to simultaneously generate training requirements, performance metrics, scenario requirements, and simulator/simulation requirements for medical tasks. On the basis of a variety of existing techniques, we developed a scenario-based approach that asks experts to perform the targeted task multiple times, with each pass probing a different dimension of the training development process. In contrast to many cognitive task analysis approaches, we argue that our technique can be highly cost effective because it is designed to accomplish multiple goals. The technique was pilot tested with expert instructors from a large military medical training command. These instructors were employed to generate requirements for two selected combat casualty care tasks-cricothyroidotomy and hemorrhage control. Results indicated that the technique is feasible to use and generates usable data to inform simulation-based training system design. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

  3. Multiple regression analysis of factors influencing dominant hand grip strength in an adult Malaysian population.

    PubMed

    Hossain, M G; Zyroul, R; Pereira, B P; Kamarul, T

    2012-01-01

    Grip strength is an important measure used to monitor the progression of a condition, and to evaluate outcomes of treatment. We assessed how various physical and social factors predict normal grip strength in an adult Malaysian population of mixed Asian ethnicity (254 men, 246 women). Grip strength was recorded using the Jamar dynamometer. The mean grip strength for the dominant hand was 29.8 kg for men and 17.6 kg for women. Multiple regression analysis demonstrated that the dominant hand grip strength was positively associated with height and body mass index, and negatively associated with age for both sexes. Dominant hand grip strength was related to work status for men (p < 0.05) but not for women. However, there was no difference in grip strength among ethnic groups.

  4. Ridge: a computer program for calculating ridge regression estimates

    Treesearch

    Donald E. Hilt; Donald W. Seegrist

    1977-01-01

    Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

  5. The semantic category-based grouping in the Multiple Identity Tracking task.

    PubMed

    Wei, Liuqing; Zhang, Xuemin; Li, Zhen; Liu, Jingyao

    2018-01-01

    In the Multiple Identity Tracking (MIT) task, categorical distinctions between targets and distractors have been found to facilitate tracking (Wei, Zhang, Lyu, & Li in Frontiers in Psychology, 7, 589, 2016). The purpose of this study was to further investigate the reasons for the facilitation effect, through six experiments. The results of Experiments 1-3 excluded the potential explanations of visual distinctiveness, attentional distribution strategy, and a working memory mechanism, respectively. When objects' visual information was preserved and categorical information was removed, the facilitation effect disappeared, suggesting that the visual distinctiveness between targets and distractors was not the main reason for the facilitation effect. Moreover, the facilitation effect was not the result of strategically shifting the attentional distribution, because the targets received more attention than the distractors in all conditions. Additionally, the facilitation effect did not come about because the identities of targets were encoded and stored in visual working memory to assist in the recovery from tracking errors; when working memory was disturbed by the object identities changing during tracking, the facilitation effect still existed. Experiments 4 and 5 showed that observers grouped targets together and segregated them from distractors on the basis of their categorical information. By doing this, observers could largely avoid distractor interference with tracking and improve tracking performance. Finally, Experiment 6 indicated that category-based grouping is not an automatic, but a goal-directed and effortful, strategy. In summary, the present findings show that a semantic category-based target-grouping mechanism exists in the MIT task, which is likely to be the major reason for the tracking facilitation effect.

  6. Better dual-task processing in simultaneous interpreters

    PubMed Central

    Strobach, Tilo; Becker, Maxi; Schubert, Torsten; Kühn, Simone

    2015-01-01

    Simultaneous interpreting (SI) is a highly complex activity and requires the performance and coordination of multiple, simultaneous tasks: analysis and understanding of the discourse in a first language, reformulating linguistic material, storing of intermediate processing steps, and language production in a second language among others. It is, however, an open issue whether persons with experience in SI possess superior skills in coordination of multiple tasks and whether they are able to transfer these skills to lab-based dual-task situations. Within the present study, we set out to explore whether interpreting experience is associated with related higher-order executive functioning in the context of dual-task situations of the Psychological Refractory Period (PRP) type. In this PRP situation, we found faster reactions times in participants with experience in simultaneous interpretation in contrast to control participants without such experience. Thus, simultaneous interpreters possess superior skills in coordination of multiple tasks in lab-based dual-task situations. PMID:26528232

  7. New paradigm for task switching strategies while performing multiple tasks: entropy and symbolic dynamics analysis of voluntary patterns.

    PubMed

    Guastello, Stephen J; Gorin, Hillary; Huschen, Samuel; Peters, Natalie E; Fabisch, Megan; Poston, Kirsten

    2012-10-01

    It has become well established in laboratory experiments that switching tasks, perhaps due to interruptions at work, incur costs in response time to complete the next task. Conditions are also known that exaggerate or lessen the switching costs. Although switching costs can contribute to fatigue, task switching can also be an adaptive response to fatigue. The present study introduces a new research paradigm for studying the emergence of voluntary task switching regimes, self-organizing processes therein, and the possibly conflicting roles of switching costs and minimum entropy. Fifty-four undergraduates performed 7 different computer-based cognitive tasks producing sets of 49 responses under instructional conditions requiring task quotas or no quotas. The sequences of task choices were analyzed using orbital decomposition to extract pattern types and lengths, which were then classified and compared with regard to Shannon entropy, topological entropy, number of task switches involved, and overall performance. Results indicated that similar but different patterns were generated under the two instructional conditions, and better performance was associated with lower topological entropy. Both entropy metrics were associated with the amount of voluntary task switching. Future research should explore conditions affecting the trade-off between switching costs and entropy, levels of automaticity between task elements, and the role of voluntary switching regimes on fatigue.

  8. Automatic identification of variables in epidemiological datasets using logic regression.

    PubMed

    Lorenz, Matthias W; Abdi, Negin Ashtiani; Scheckenbach, Frank; Pflug, Anja; Bülbül, Alpaslan; Catapano, Alberico L; Agewall, Stefan; Ezhov, Marat; Bots, Michiel L; Kiechl, Stefan; Orth, Andreas

    2017-04-13

    For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

  9. Regression models for predicting peak and continuous three-dimensional spinal loads during symmetric and asymmetric lifting tasks.

    PubMed

    Fathallah, F A; Marras, W S; Parnianpour, M

    1999-09-01

    Most biomechanical assessments of spinal loading during industrial work have focused on estimating peak spinal compressive forces under static and sagittally symmetric conditions. The main objective of this study was to explore the potential of feasibly predicting three-dimensional (3D) spinal loading in industry from various combinations of trunk kinematics, kinetics, and subject-load characteristics. The study used spinal loading, predicted by a validated electromyography-assisted model, from 11 male participants who performed a series of symmetric and asymmetric lifts. Three classes of models were developed: (a) models using workplace, subject, and trunk motion parameters as independent variables (kinematic models); (b) models using workplace, subject, and measured moments variables (kinetic models); and (c) models incorporating workplace, subject, trunk motion, and measured moments variables (combined models). The results showed that peak 3D spinal loading during symmetric and asymmetric lifting were predicted equally well using all three types of regression models. Continuous 3D loading was predicted best using the combined models. When the use of such models is infeasible, the kinematic models can provide adequate predictions. Finally, lateral shear forces (peak and continuous) were consistently underestimated using all three types of models. The study demonstrated the feasibility of predicting 3D loads on the spine under specific symmetric and asymmetric lifting tasks without the need for collecting EMG information. However, further validation and development of the models should be conducted to assess and extend their applicability to lifting conditions other than those presented in this study. Actual or potential applications of this research include exposure assessment in epidemiological studies, ergonomic intervention, and laboratory task assessment.

  10. Estimating Optimal Transformations for Multiple Regression and Correlation.

    DTIC Science & Technology

    1982-07-01

    algorithm; we minimize (2.4) e2 (,,, ...,) = E[e(Y) - 1I (X 2 j=l j 2holding EO =1, E6 = E0, =.-. =Ecp = 0, through a series of single function minimizations...X, x = INU = lIVe . Then (5.16) THEOREM. If 6*, p* is an optimal transformation for regression, then = ue*o Conversely, if e satisfies Xe = U6, Nll1...Stanford University, Tech. Report ORIONOO6. Gasser, T. and Rosenblatt, M. (eds.) (1979). Smoothing Techniques for Curve Estimation, in Lecture Notes in

  11. Factors Associated With Community Health Worker Performance Differ by Task in a Multi-Tasked Setting in Rural Zimbabwe.

    PubMed

    Kambarami, Rukundo A; Mbuya, Mduduzi Nn; Pelletier, David; Fundira, Dadirai; Tavengwa, Naume V; Stoltzfus, Rebecca J

    2016-06-20

    Zimbabwe, like most low-income countries, faces health worker shortages. Community health workers (CHWs) bridge this gap by delivering essential health services and nutrition interventions to communities. However, as workloads increase, CHWs' ability to provide quality services may be compromised. We studied influences upon CHWs' performance related to pregnancy surveillance and nutrition and hygiene education in rural Zimbabwe. In the context of a cluster-randomized trial conducted in 2 rural districts between November 2012 and March 2015, 342 government-employed CHWs identified and referred pregnant women for early antenatal care and delivered household-level behavior change lessons about infant feeding and hygiene to more than 5,000 women. In 2013, we conducted a survey among 322 of the CHWs to assess the association between demographic and work characteristics and task performance. Exploratory factor analyses of the Likert-type survey questions produced 8 distinct and reliable constructs of job satisfaction and motivation, supervision, peer support, and feedback (Cronbach α range, 0.68 to 0.92). Pregnancy surveillance performance was assessed from pregnancy referrals, and nutrition and hygiene education performance was assessed by taking the average summative score (range, 5 to 30) of lesson delivery observations completed by a nurse supervisor using a 6-item Likert-type checklist. Poisson and multiple linear regressions were used to test associations between CHW demographic and work characteristics and performance. CHWs who referred more pregnant women were female, unmarried, under 40 years old, from larger households, and of longer tenure. They also perceived work resources to be adequate and received positive feedback from supervisors and the community, but they were less satisfied with remuneration. CHWs with high scores on behavior change lesson delivery were from smaller households, and they received more supportive supervision but less operational

  12. Factors Associated With Community Health Worker Performance Differ by Task in a Multi-Tasked Setting in Rural Zimbabwe

    PubMed Central

    Kambarami, Rukundo A; Mbuya, Mduduzi NN; Pelletier, David; Fundira, Dadirai; Tavengwa, Naume V; Stoltzfus, Rebecca J

    2016-01-01

    ABSTRACT Background: Zimbabwe, like most low-income countries, faces health worker shortages. Community health workers (CHWs) bridge this gap by delivering essential health services and nutrition interventions to communities. However, as workloads increase, CHWs’ ability to provide quality services may be compromised. We studied influences upon CHWs’ performance related to pregnancy surveillance and nutrition and hygiene education in rural Zimbabwe. Methods: In the context of a cluster-randomized trial conducted in 2 rural districts between November 2012 and March 2015, 342 government-employed CHWs identified and referred pregnant women for early antenatal care and delivered household-level behavior change lessons about infant feeding and hygiene to more than 5,000 women. In 2013, we conducted a survey among 322 of the CHWs to assess the association between demographic and work characteristics and task performance. Exploratory factor analyses of the Likert-type survey questions produced 8 distinct and reliable constructs of job satisfaction and motivation, supervision, peer support, and feedback (Cronbach α range, 0.68 to 0.92). Pregnancy surveillance performance was assessed from pregnancy referrals, and nutrition and hygiene education performance was assessed by taking the average summative score (range, 5 to 30) of lesson delivery observations completed by a nurse supervisor using a 6-item Likert-type checklist. Poisson and multiple linear regressions were used to test associations between CHW demographic and work characteristics and performance. Results: CHWs who referred more pregnant women were female, unmarried, under 40 years old, from larger households, and of longer tenure. They also perceived work resources to be adequate and received positive feedback from supervisors and the community, but they were less satisfied with remuneration. CHWs with high scores on behavior change lesson delivery were from smaller households, and they received more

  13. Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

    NASA Astrophysics Data System (ADS)

    Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

    2017-05-01

    The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for

  14. Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

    DOE PAGES

    Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

    2016-04-07

    The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

  15. Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

    Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

    The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

  16. A Modified Double Multiple Nonlinear Regression Constitutive Equation for Modeling and Prediction of High Temperature Flow Behavior of BFe10-1-2 Alloy

    NASA Astrophysics Data System (ADS)

    Cai, Jun; Wang, Kuaishe; Shi, Jiamin; Wang, Wen; Liu, Yingying

    2018-01-01

    Constitutive analysis for hot working of BFe10-1-2 alloy was carried out by using experimental stress-strain data from isothermal hot compression tests, in a wide range of temperature of 1,023 1,273 K, and strain rate range of 0.001 10 s-1. A constitutive equation based on modified double multiple nonlinear regression was proposed considering the independent effects of strain, strain rate, temperature and their interrelation. The predicted flow stress data calculated from the developed equation was compared with the experimental data. Correlation coefficient (R), average absolute relative error (AARE) and relative errors were introduced to verify the validity of the developed constitutive equation. Subsequently, a comparative study was made on the capability of strain-compensated Arrhenius-type constitutive model. The results showed that the developed constitutive equation based on modified double multiple nonlinear regression could predict flow stress of BFe10-1-2 alloy with good correlation and generalization.

  17. 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.

  18. Survival Data and Regression Models

    NASA Astrophysics Data System (ADS)

    Grégoire, G.

    2014-12-01

    We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

  19. A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

    PubMed Central

    Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

    2015-01-01

    Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

  20. A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

    PubMed

    Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

    2015-01-01

    Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

  1. Detection of reduced interhemispheric cortical communication during task execution in multiple sclerosis patients using functional near-infrared spectroscopy

    NASA Astrophysics Data System (ADS)

    Jimenez, Jon J.; Yang, Runze; Nathoo, Nabeela; Varshney, Vishal P.; Golestani, Ali-Mohammad; Goodyear, Bradley G.; Metz, Luanne M.; Dunn, Jeff F.

    2014-07-01

    Multiple sclerosis (MS) impairs brain activity through demyelination and loss of axons. Increased brain activity is accompanied by increases in microvascular hemoglobin oxygen saturation (oxygenation) and total hemoglobin, which can be measured using functional near-infrared spectroscopy (fNIRS). Due to the potentially reduced size and integrity of the white matter tracts within the corpus callosum, it may be expected that MS patients have reduced functional communication between the left and right sides of the brain; this could potentially be an indicator of disease progression. To assess interhemispheric communication in MS, we used fNIRS during a unilateral motor task and the resting state. The magnitude of the change in hemoglobin parameters in the motor cortex was significantly reduced in MS patients during the motor task relative to healthy control subjects. There was also a significant decrease in interhemispheric communication between the motor cortices (expressed as coherence) in MS patients compared to controls during the motor task, but not during the resting state. fNIRS assessment of interhemispheric coherence during task execution may be a useful marker in disorders with white matter damage or axonal loss, including MS.

  2. Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

    PubMed

    Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

    2017-06-01

    We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. Examining the Effect of Multiple Writing Tasks on Year 10 Biology Students' Understandings of Cell and Molecular Biology Concepts

    ERIC Educational Resources Information Center

    Hand, Brian; Hohenshell, Liesl; Prain, Vaughan

    2007-01-01

    This paper reports on a study that examined the cumulative effects on students' learning of science, and perceptions of the role of writing in learning, when the students engaged in multiple writing tasks with planning strategy support. The study was conducted with Year 10 biology students who completed two consecutive units on Cells and Molecular…

  4. Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

    PubMed

    McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

    2017-02-01

    Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

  5. Parallel processing considerations for image recognition tasks

    NASA Astrophysics Data System (ADS)

    Simske, Steven J.

    2011-01-01

    Many image recognition tasks are well-suited to parallel processing. The most obvious example is that many imaging tasks require the analysis of multiple images. From this standpoint, then, parallel processing need be no more complicated than assigning individual images to individual processors. However, there are three less trivial categories of parallel processing that will be considered in this paper: parallel processing (1) by task; (2) by image region; and (3) by meta-algorithm. Parallel processing by task allows the assignment of multiple workflows-as diverse as optical character recognition [OCR], document classification and barcode reading-to parallel pipelines. This can substantially decrease time to completion for the document tasks. For this approach, each parallel pipeline is generally performing a different task. Parallel processing by image region allows a larger imaging task to be sub-divided into a set of parallel pipelines, each performing the same task but on a different data set. This type of image analysis is readily addressed by a map-reduce approach. Examples include document skew detection and multiple face detection and tracking. Finally, parallel processing by meta-algorithm allows different algorithms to be deployed on the same image simultaneously. This approach may result in improved accuracy.

  6. Weighted regression analysis and interval estimators

    Treesearch

    Donald W. Seegrist

    1974-01-01

    A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

  7. On the origins of the task mixing cost in the cuing task-switching paradigm.

    PubMed

    Rubin, Orit; Meiran, Nachshon

    2005-11-01

    Poorer performance in conditions involving task repetition within blocks of mixed tasks relative to task repetition within blocks of single task is called mixing cost (MC). In 2 experiments exploring 2 hypotheses regarding the origins of MC, participants either switched between cued shape and color tasks, or they performed them as single tasks. Experiment 1 supported the hypothesis that mixed-tasks trials require the resolution of task ambiguity by showing that MC existed only with ambiguous stimuli that afforded both tasks and not with unambiguous stimuli affording only 1 task. Experiment 2 failed to support the hypothesis that holding multiple task sets in working memory (WM) generates MC by showing that systematic manipulation of the number of stimulus-response rules in WM did not affect MC. The results emphasize the role of competition management between task sets during task control.

  8. Regression modeling of ground-water flow

    USGS Publications Warehouse

    Cooley, R.L.; Naff, R.L.

    1985-01-01

    Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

  9. Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

    PubMed Central

    Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

    2009-01-01

    Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

  10. The role of verbal memory in regressions during reading.

    PubMed

    Guérard, Katherine; Saint-Aubin, Jean; Maltais, Marilyne

    2013-01-01

    During reading, participants generally move their eyes rightward on the line. A number of eye movements, called regressions, are made leftward, to words that have already been fixated. In the present study, we investigated the role of verbal memory during regressions. In Experiment 1, participants were asked to read sentences for comprehension. After reading, they were asked to make a regression to a target word presented auditorily. The results revealed that their regressions were guided by memory, as they differed from those of a control group who did not read the sentences. The role of verbal memory during regressions was then investigated by combining the reading task with articulatory suppression (Exps. 2 and 3). The results showed that articulatory suppression affected the size and the accuracy of the initial regression but had a minimal effect on corrective saccades. This suggests that verbal memory plays an important role in determining the location of the initial saccade during regressions.

  11. Multiple regression based imputation for individualizing template human model from a small number of measured dimensions.

    PubMed

    Nohara, Ryuki; Endo, Yui; Murai, Akihiko; Takemura, Hiroshi; Kouchi, Makiko; Tada, Mitsunori

    2016-08-01

    Individual human models are usually created by direct 3D scanning or deforming a template model according to the measured dimensions. In this paper, we propose a method to estimate all the necessary dimensions (full set) for the human model individualization from a small number of measured dimensions (subset) and human dimension database. For this purpose, we solved multiple regression equation from the dimension database given full set dimensions as the objective variable and subset dimensions as the explanatory variables. Thus, the full set dimensions are obtained by simply multiplying the subset dimensions to the coefficient matrix of the regression equation. We verified the accuracy of our method by imputing hand, foot, and whole body dimensions from their dimension database. The leave-one-out cross validation is employed in this evaluation. The mean absolute errors (MAE) between the measured and the estimated dimensions computed from 4 dimensions (hand length, breadth, middle finger breadth at proximal, and middle finger depth at proximal) in the hand, 2 dimensions (foot length, breadth, and lateral malleolus height) in the foot, and 1 dimension (height) and weight in the whole body are computed. The average MAE of non-measured dimensions were 4.58% in the hand, 4.42% in the foot, and 3.54% in the whole body, while that of measured dimensions were 0.00%.

  12. Quantile Regression in the Study of Developmental Sciences

    ERIC Educational Resources Information Center

    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…

  13. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

    PubMed

    Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

    2013-01-01

    The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  14. Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain's parallel processing systems.

    PubMed

    Zeki, Semir

    2016-10-01

    Results from a variety of sources, some many years old, lead ineluctably to a re-appraisal of the twin strategies of hierarchical and parallel processing used by the brain to construct an image of the visual world. Contrary to common supposition, there are at least three 'feed-forward' anatomical hierarchies that reach the primary visual cortex (V1) and the specialized visual areas outside it, in parallel. These anatomical hierarchies do not conform to the temporal order with which visual signals reach the specialized visual areas through V1. Furthermore, neither the anatomical hierarchies nor the temporal order of activation through V1 predict the perceptual hierarchies. The latter shows that we see (and become aware of) different visual attributes at different times, with colour leading form (orientation) and directional visual motion, even though signals from fast-moving, high-contrast stimuli are among the earliest to reach the visual cortex (of area V5). Parallel processing, on the other hand, is much more ubiquitous than commonly supposed but is subject to a barely noticed but fundamental aspect of brain operations, namely that different parallel systems operate asynchronously with respect to each other and reach perceptual endpoints at different times. This re-assessment leads to the conclusion that the visual brain is constituted of multiple, parallel and asynchronously operating task- and stimulus-dependent hierarchies (STDH); which of these parallel anatomical hierarchies have temporal and perceptual precedence at any given moment is stimulus and task related, and dependent on the visual brain's ability to undertake multiple operations asynchronously. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  15. Interleaved Practice in Multi-Dimensional Learning Tasks: Which Dimension Should We Interleave?

    ERIC Educational Resources Information Center

    Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

    2013-01-01

    Research shows that multiple representations can enhance student learning. Many curricula use multiple representations across multiple task types. The temporal sequence of representations and task types is likely to impact student learning. Research on contextual interference shows that interleaving learning tasks leads to better learning results…

  16. Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

    PubMed

    Kolasa-Wiecek, Alicja

    2015-04-01

    The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.

  17. 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…

  18. Evaluating Multiple Perspectives: Approaching the Synthesis Task through Assessing Credibility

    ERIC Educational Resources Information Center

    Lafferty, Karen Elizabeth; Summers, Amy; Tanaka, Stephanie; Cavanagh, Jeanne

    2016-01-01

    Introduction of the Common Core State Standards and assessments like the synthesis performance task pose new challenges for secondary English teachers. As students of all ability levels engage with complex text and in tasks that target higher level cognitive skills, teachers need strategies to support their understanding. This article describes…

  19. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

    USGS Publications Warehouse

    Lee, L.; Helsel, D.

    2005-01-01

    Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

  20. Regression Commonality Analysis: A Technique for Quantitative Theory Building

    ERIC Educational Resources Information Center

    Nimon, Kim; Reio, Thomas G., Jr.

    2011-01-01

    When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

  1. Task Description Language

    NASA Technical Reports Server (NTRS)

    Simmons, Reid; Apfelbaum, David

    2005-01-01

    Task Description Language (TDL) is an extension of the C++ programming language that enables programmers to quickly and easily write complex, concurrent computer programs for controlling real-time autonomous systems, including robots and spacecraft. TDL is based on earlier work (circa 1984 through 1989) on the Task Control Architecture (TCA). TDL provides syntactic support for hierarchical task-level control functions, including task decomposition, synchronization, execution monitoring, and exception handling. A Java-language-based compiler transforms TDL programs into pure C++ code that includes calls to a platform-independent task-control-management (TCM) library. TDL has been used to control and coordinate multiple heterogeneous robots in projects sponsored by NASA and the Defense Advanced Research Projects Agency (DARPA). It has also been used in Brazil to control an autonomous airship and in Canada to control a robotic manipulator.

  2. Adjusted variable plots for Cox's proportional hazards regression model.

    PubMed

    Hall, C B; Zeger, S L; Bandeen-Roche, K J

    1996-01-01

    Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

  3. The Multiple Roles of the Task Design Mediator in Telecollaboration

    ERIC Educational Resources Information Center

    Fuchs, Carolin; Snyder, Bill; Tung, Bruce; Jung Han, Yu

    2017-01-01

    This case study explores how a Chinese-American novice teacher acted as mediator in a telecollaboration with student teacher (ST) peers in the USA who designed tasks for his English as a foreign language (EFL) learners in China. The novice teacher was instrumental in mediating the student teachers' task design process by providing feedback…

  4. The Social Attribution Task - Multiple Choice (SAT-MC): Psychometric comparison with social cognitive measures for schizophrenia research.

    PubMed

    Johannesen, Jason K; Fiszdon, Joanna M; Weinstein, Andrea; Ciosek, David; Bell, Morris D

    2018-04-01

    The Social Attribution Task-Multiple Choice (SAT-MC) tests the ability to extract social themes from viewed object motion. This form of animacy perception is thought to aid the development of social inference, but appears impaired in schizophrenia. The current study was undertaken to examine psychometric equivalence of two forms of the SAT-MC and to compare their performance against social cognitive tests recommended for schizophrenia research. Thirty-two schizophrenia (SZ) and 30 substance use disorder (SUD) participants completed both SAT-MC forms, the Bell-Lysaker Emotion Recognition Task (BLERT), Hinting Task, The Awareness of Social Inference Test (TASIT), Ambiguous Intentions and Hostility Questionnaire (AIHQ) and questionnaire measures of interpersonal function. Test sensitivity, construct and external validity, test-retest reliability, and internal consistency were evaluated. SZ scored significantly lower than SUD on both SAT-MC forms, each classifying ~60% of SZ as impaired, compared with ~30% of SUD. SAT-MC forms demonstrated good test-retest and parallel form reliability, minimal practice effect, high internal consistency, and similar patterns of correlation with social cognitive and external validity measures. The SAT-MC compared favorably to recommended social cognitive tests across psychometric features and, with exception of TASIT, was most sensitive to impairment in schizophrenia when compared to a chronic substance use sample. Published by Elsevier B.V.

  5. Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

    ERIC Educational Resources Information Center

    Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

    2010-01-01

    Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

  6. Ca analysis: An Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis☆

    PubMed Central

    Greensmith, David J.

    2014-01-01

    Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908

  7. Regression modeling and prediction of road sweeping brush load characteristics from finite element analysis and experimental results.

    PubMed

    Wang, Chong; Sun, Qun; Wahab, Magd Abdel; Zhang, Xingyu; Xu, Limin

    2015-09-01

    Rotary cup brushes mounted on each side of a road sweeper undertake heavy debris removal tasks but the characteristics have not been well known until recently. A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design. However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system. This study develops a mathematical regression model to summarize the FE modeled results. The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc. The data were then fitted by a multiple variable regression model using the maximum likelihood method. The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. The methodology may also be used in the design and optimization of rotary brush tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Cognitive task demands, self-control demands and the mental well-being of office workers.

    PubMed

    Bridger, Robert S; Brasher, Kate

    2011-09-01

    The cognitive task demands of office workers and the self-control demands of their work roles were measured in a sample of 196 employees in two different office layouts using a self-report questionnaire, which was circulated electronically. Multiple linear regression analysis revealed that both factors were associated with mental well-being, but not with physical well-being, while controlling for exposure to psychosocial stressors. The interaction between cognitive task demands and self-control demands had the strongest association with mental well-being, suggesting that the deleterious effect of one was greater when the other was present. An exploratory analysis revealed that the association was stronger for employees working in a large open-plan office than for those working in smaller offices with more privacy. Frustration of work goals was the cognitive task demand having the strongest negative impact on mental well-being. Methodological limitations and scale psychometrics (particularly the use of the NASA Task Load Index) are discussed. STATEMENT OF RELEVANCE: Modern office work has high mental demands and low physical demands and there is a need to design offices to prevent adverse psychological reactions. It is shown that cognitive task demands interact with self-control demands to degrade mental well-being. The association was stronger in an open-plan office.

  9. Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

    PubMed

    Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

    2017-02-01

    Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

  10. Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

    PubMed

    Henrard, S; Speybroeck, N; Hermans, C

    2015-11-01

    Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

  11. Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards

    NASA Astrophysics Data System (ADS)

    Chiu, Tina

    This dissertation includes three studies that analyze a new set of assessment tasks developed by the Learning Progressions in Middle School Science (LPS) Project. These assessment tasks were designed to measure science content knowledge on the structure of matter domain and scientific argumentation, while following the goals from the Next Generation Science Standards (NGSS). The three studies focus on the evidence available for the success of this design and its implementation, generally labelled as "validity" evidence. I use explanatory item response models (EIRMs) as the overarching framework to investigate these assessment tasks. These models can be useful when gathering validity evidence for assessments as they can help explain student learning and group differences. In the first study, I explore the dimensionality of the LPS assessment by comparing the fit of unidimensional, between-item multidimensional, and Rasch testlet models to see which is most appropriate for this data. By applying multidimensional item response models, multiple relationships can be investigated, and in turn, allow for a more substantive look into the assessment tasks. The second study focuses on person predictors through latent regression and differential item functioning (DIF) models. Latent regression models show the influence of certain person characteristics on item responses, while DIF models test whether one group is differentially affected by specific assessment items, after conditioning on latent ability. Finally, the last study applies the linear logistic test model (LLTM) to investigate whether item features can help explain differences in item difficulties.

  12. Investigating bias in squared regression structure coefficients

    PubMed Central

    Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce

    2015-01-01

    The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients. PMID:26217273

  13. 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

  14. CAHOST: An Excel Workbook for Facilitating the Johnson-Neyman Technique for Two-Way Interactions in Multiple Regression.

    PubMed

    Carden, Stephen W; Holtzman, Nicholas S; Strube, Michael J

    2017-01-01

    When using multiple regression, researchers frequently wish to explore how the relationship between two variables is moderated by another variable; this is termed an interaction. Historically, two approaches have been used to probe interactions: the pick-a-point approach and the Johnson-Neyman (JN) technique. The pick-a-point approach has limitations that can be avoided using the JN technique. Currently, the software available for implementing the JN technique and creating corresponding figures lacks several desirable features-most notably, ease of use and figure quality. To fill this gap in the literature, we offer a free Microsoft Excel 2013 workbook, CAHOST (a concatenation of the first two letters of the authors' last names), that allows the user to seamlessly create publication-ready figures of the results of the JN technique.

  15. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

    NASA Technical Reports Server (NTRS)

    Barrett, C. A.

    1985-01-01

    Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

  16. Community hoarding task forces: a comparative case study of five task forces in the United States.

    PubMed

    Bratiotis, Christiana

    2013-05-01

    During the past decade, many community task forces have formed to address hoarding problems that come to public attention. Such task forces provide a societal-level intervention to assist people with the most severe cases of hoarding, who do not voluntarily seek or want help for their hoarding behaviour. This qualitative study of five U.S. hoarding task forces included sites selected for their diversity of purpose, approaches to hoarding intervention and community geography, composition and resources. Data were collected during the period of September 2007-March 2008. The case study methodology used multiple forms of data, including semi-structured interviews, analysis of documents, small group interviews and investigator observation. This study captured the perspectives of public and private sector service providers such as mental health, housing, social service, public health agencies and community enforcement organisations (fire, police, legal, animal control) to examine how task forces organise and operate and the emerging practice and policy changes. Study findings suggest that structural factors (e.g. leadership, purpose, funding and membership) impact hoarding task force viability, that participation on a task force influences practice and policy decisions about hoarding, and that social work can expand its role in task force leadership. Task forces may be a mechanism for improving community policies about hoarding and mechanisms for addressing other social problems across multiple sectors. © 2012 Blackwell Publishing Ltd.

  17. The Effects of Study Tasks in a Computer-Based Chemistry Learning Environment

    NASA Astrophysics Data System (ADS)

    Urhahne, Detlef; Nick, Sabine; Poepping, Anna Christin; Schulz, Sarah Jayne

    2013-12-01

    The present study examines the effects of different study tasks on the acquisition of knowledge about acids and bases in a computer-based learning environment. Three different task formats were selected to create three treatment conditions: learning with gap-fill and matching tasks, learning with multiple-choice tasks, and learning only from text and figures without any additional tasks. Participants were 196 ninth-grade students who learned with a self-developed multimedia program in a pretest-posttest control group design. Research results reveal that gap-fill and matching tasks were most effective in promoting knowledge acquisition, followed by multiple-choice tasks, and no tasks at all. The findings are in line with previous research on this topic. The effects can possibly be explained by the generation-recognition model, which predicts that gap-fill and matching tasks trigger more encompassing learning processes than multiple-choice tasks. It is concluded that instructional designers should incorporate more challenging study tasks for enhancing the effectiveness of computer-based learning environments.

  18. Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

    ERIC Educational Resources Information Center

    Kim, Rae Seon

    2011-01-01

    When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

  19. Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

    PubMed

    Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

    2015-11-18

    Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

  20. Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

    PubMed Central

    Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

    2016-01-01

    Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

  1. 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.

  2. Agile Task Tracking Tool

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

    Duke, Roger T.; Crump, Thomas Vu

    The work was created to provide a tool for the purpose of improving the management of tasks associated with Agile projects. Agile projects are typically completed in an iterative manner with many short duration tasks being performed as part of iterations. These iterations are generally referred to as sprints. The objective of this work is to create a single tool that enables sprint teams to manage all of their tasks in multiple sprints and automatically produce all standard sprint performance charts with minimum effort. The format of the printed work is designed to mimic a standard Kanban board. The workmore » is developed as a single Excel file with worksheets capable of managing up to five concurrent sprints and up to one hundred tasks. It also includes a summary worksheet providing performance information from all active sprints. There are many commercial project management systems typically designed with features desired by larger organizations with many resources managing multiple programs and projects. The audience for this work is the small organizations and Agile project teams desiring an inexpensive, simple, user-friendly, task management tool. This work uses standard readily available software, Excel, requiring minimum data entry and automatically creating summary charts and performance data. It is formatted to print out and resemble standard flip charts and provide the visuals associated with this type of work.« less

  3. Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

    PubMed

    Choi, Jae-Seok; Kim, Munchurl

    2017-03-01

    Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

  4. Lung nodule malignancy prediction using multi-task convolutional neural network

    NASA Astrophysics Data System (ADS)

    Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

    2017-03-01

    In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

  5. Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits.

    PubMed

    Fu, Liya; Wang, You-Gan

    2011-02-15

    Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which clearly demonstrates the advantages of the rank regression models.

  6. Which Feedback Is More Effective for Pursuing Multiple Goals of Differing Importance? The Interaction Effects of Goal Importance and Performance Feedback Type on Self-Regulation and Task Achievement

    ERIC Educational Resources Information Center

    Lee, Hyunjoo

    2016-01-01

    This study examined how performance feedback type (progress vs. distance) affects Korean college students' self-regulation and task achievement according to relative goal importance in the pursuit of multiple goals. For this study, 146 students participated in a computerised task. The results showed the interaction effects of goal importance and…

  7. A task-invariant cognitive reserve network.

    PubMed

    Stern, Yaakov; Gazes, Yunglin; Razlighi, Qolomreza; Steffener, Jason; Habeck, Christian

    2018-05-14

    The concept of cognitive reserve (CR) can explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or disease-related brain changes. Epidemiologic evidence indicates that CR helps maintain performance in the face of pathology across multiple cognitive domains. We therefore tried to identify a single, "task-invariant" CR network that is active during the performance of many disparate tasks. In imaging data acquired from 255 individuals age 20-80 while performing 12 different cognitive tasks, we used an iterative approach to derive a multivariate network that was expressed during the performance of all tasks, and whose degree of expression correlated with IQ, a proxy for CR. When applied to held out data or forward applied to fMRI data from an entirely different activation task, network expression correlated with IQ. Expression of the CR pattern accounted for additional variance in fluid reasoning performance over and above the influence of cortical thickness, and also moderated between cortical thickness and reasoning performance, consistent with the behavior of a CR network. The identification of a task-invariant CR network supports the idea that life experiences may result in brain processing differences that might provide reserve against age- or disease-related changes across multiple tasks. Copyright © 2018. Published by Elsevier Inc.

  8. Introduction to the use of regression models in epidemiology.

    PubMed

    Bender, Ralf

    2009-01-01

    Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

  9. Multiple Criteria Decision Analysis for Health Care Decision Making--An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force.

    PubMed

    Thokala, Praveen; Devlin, Nancy; Marsh, Kevin; Baltussen, Rob; Boysen, Meindert; Kalo, Zoltan; Longrenn, Thomas; Mussen, Filip; Peacock, Stuart; Watkins, John; Ijzerman, Maarten

    2016-01-01

    Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making. Copyright © 2016. Published by Elsevier Inc.

  10. Cactus: An Introduction to Regression

    ERIC Educational Resources Information Center

    Hyde, Hartley

    2008-01-01

    When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…

  11. Multiple memory systems as substrates for multiple decision systems

    PubMed Central

    Doll, Bradley B.; Shohamy, Daphna; Daw, Nathaniel D.

    2014-01-01

    It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an “internal model.” Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. Here we investigated the relationship between model-based RL and

  12. Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

    PubMed

    Greensmith, David J

    2014-01-01

    Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

  13. [Multiple mere exposure effect: category evaluation measured in the Go/No-go association task (GNAT)].

    PubMed

    Kawakami, Naoaki; Yoshida, Fujio

    2011-12-01

    The effect on likability of multiple subliminal exposures to the same person was investigated. Past studies on the mere exposure effect indicated a correlation between the frequency of repeated exposure to the same stimulus and likability. We proposed that exposure to various stimuli of the same person would have a stronger effect on likability. Participants were subliminally exposed to photographs of a person's face taken from seven angles (multi-angle-exposure) three times each (Experiment 1), or photographs of a person with seven facial expressions (multi-expression-exposure) three times each (Experiment 2). Then, the likability toward the exposed person was measured using the Go/No-go Association Task. The results indicated that the effect of the multiple exposures from various angles was equivalent to exposure to only one full-face photograph shown 21 times (Experiment 1). Moreover, likability was significantly higher in the case of exposure to various facial expressions than for exposure to only a single facial expression (Experiment 2). The results suggest that exposure to various stimuli in a category is more effective than repeated exposure to a single stimulus for increasing likability.

  14. Interquantile Shrinkage in Regression Models

    PubMed Central

    Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.

    2012-01-01

    Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546

  15. Flexible spatial perspective-taking: conversational partners weigh multiple cues in collaborative tasks.

    PubMed

    Galati, Alexia; Avraamides, Marios N

    2013-01-01

    Research on spatial perspective-taking often focuses on the cognitive processes of isolated individuals as they adopt or maintain imagined perspectives. Collaborative studies of spatial perspective-taking typically examine speakers' linguistic choices, while overlooking their underlying processes and representations. We review evidence from two collaborative experiments that examine the contribution of social and representational cues to spatial perspective choices in both language and the organization of spatial memory. Across experiments, speakers organized their memory representations according to the convergence of various cues. When layouts were randomly configured and did not afford intrinsic cues, speakers encoded their partner's viewpoint in memory, if available, but did not use it as an organizing direction. On the other hand, when the layout afforded an intrinsic structure, speakers organized their spatial memories according to the person-centered perspective reinforced by the layout's structure. Similarly, in descriptions, speakers considered multiple cues whether available a priori or at the interaction. They used partner-centered expressions more frequently (e.g., "to your right") when the partner's viewpoint was misaligned by a small offset or coincided with the layout's structure. Conversely, they used egocentric expressions more frequently when their own viewpoint coincided with the intrinsic structure or when the partner was misaligned by a computationally difficult, oblique offset. Based on these findings we advocate for a framework for flexible perspective-taking: people weigh multiple cues (including social ones) to make attributions about the relative difficulty of perspective-taking for each partner, and adapt behavior to minimize their collective effort. This framework is not specialized for spatial reasoning but instead emerges from the same principles and memory-depended processes that govern perspective-taking in non-spatial tasks.

  16. The comparison of robust partial least squares regression with robust principal component regression on a real

    NASA Astrophysics Data System (ADS)

    Polat, Esra; Gunay, Suleyman

    2013-10-01

    One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

  17. All eyes on relevance: strategic allocation of attention as a result of feature-based task demands in multiple object tracking.

    PubMed

    Brockhoff, Alisa; Huff, Markus

    2016-10-01

    Multiple object tracking (MOT) plays a fundamental role in processing and interpreting dynamic environments. Regarding the type of information utilized by the observer, recent studies reported evidence for the use of object features in an automatic, low- level manner. By introducing a novel paradigm that allowed us to combine tracking with a noninterfering top-down task, we tested whether a voluntary component can regulate the deployment of attention to task-relevant features in a selective manner. In four experiments we found conclusive evidence for a task-driven selection mechanism that guides attention during tracking: The observers were able to ignore or prioritize distinct objects. They marked the distinct (cued) object (target/distractor) more or less often than other objects of the same type (targets /distractors)-but only when they had received an identification task that required them to actively process object features (cues) during tracking. These effects are discussed with regard to existing theoretical approaches to attentive tracking, gaze-cue usability as well as attentional readiness, a term that originally stems from research on attention capture and visual search. Our findings indicate that existing theories of MOT need to be adjusted to allow for flexible top-down, voluntary processing during tracking.

  18. Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression

    NASA Astrophysics Data System (ADS)

    Beh, W. L.; Yong, M. K. Au

    2017-04-01

    This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.

  19. 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.

  20. Real estate value prediction using multivariate regression models

    NASA Astrophysics Data System (ADS)

    Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

    2017-11-01

    The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

  1. Age-related decrements in dual-task performance: Comparison of different mobility and cognitive tasks. A cross sectional study.

    PubMed

    Brustio, Paolo Riccardo; Magistro, Daniele; Zecca, Massimiliano; Rabaglietti, Emanuela; Liubicich, Monica Emma

    2017-01-01

    This cross-sectional study investigated the age-related differences in dual-task performance both in mobility and cognitive tasks and the additive dual-task costs in a sample of older, middle-aged and young adults. 74 older adults (M = 72.63±5.57 years), 58 middle-aged adults (M = 46.69±4.68 years) and 63 young adults (M = 25.34±3.00 years) participated in the study. Participants performed different mobility and subtraction tasks under both single- and dual-task conditions. Linear regressions, repeated-measures and one-way analyses of covariance were used, The results showed: significant effects of the age on the dual and mobility tasks (p<0.05) and differences among the age-groups in the combined dual-task costs (p<0.05); significant decreases in mobility performance under dual-task conditions in all groups (p<0.05) and a decrease in cognitive performance in the older group (p<0.05). Dual-task activity affected mobility and cognitive performance, especially in older adults who showed a higher dual-task cost, suggesting that dual-tasks activities are affected by the age and consequently also mobility and cognitive tasks are negatively influenced.

  2. Age-related decrements in dual-task performance: Comparison of different mobility and cognitive tasks. A cross sectional study

    PubMed Central

    Brustio, Paolo Riccardo; Zecca, Massimiliano; Rabaglietti, Emanuela; Liubicich, Monica Emma

    2017-01-01

    This cross-sectional study investigated the age-related differences in dual-task performance both in mobility and cognitive tasks and the additive dual-task costs in a sample of older, middle-aged and young adults. 74 older adults (M = 72.63±5.57 years), 58 middle-aged adults (M = 46.69±4.68 years) and 63 young adults (M = 25.34±3.00 years) participated in the study. Participants performed different mobility and subtraction tasks under both single- and dual-task conditions. Linear regressions, repeated-measures and one-way analyses of covariance were used, The results showed: significant effects of the age on the dual and mobility tasks (p<0.05) and differences among the age-groups in the combined dual-task costs (p<0.05); significant decreases in mobility performance under dual-task conditions in all groups (p<0.05) and a decrease in cognitive performance in the older group (p<0.05). Dual-task activity affected mobility and cognitive performance, especially in older adults who showed a higher dual-task cost, suggesting that dual-tasks activities are affected by the age and consequently also mobility and cognitive tasks are negatively influenced. PMID:28732080

  3. Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

    PubMed

    Tan, Li Kuo; Liew, Yih Miin; Lim, Einly; McLaughlin, Robert A

    2017-07-01

    Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Predicting Social and Communicative Ability in School-Age Children with Autism Spectrum Disorder: A Pilot Study of the Social Attribution Task, Multiple Choice

    ERIC Educational Resources Information Center

    Burger-Caplan, Rebecca; Saulnier, Celine; Jones, Warren; Klin, Ami

    2016-01-01

    The Social Attribution Task, Multiple Choice is introduced as a measure of implicit social cognitive ability in children, addressing a key challenge in quantification of social cognitive function in autism spectrum disorder, whereby individuals can often be successful in explicit social scenarios, despite marked social adaptive deficits. The…

  5. Reduced dual-task gait speed is associated with visual Go/No-Go brain network activation in children and adolescents with concussion.

    PubMed

    Howell, David R; Meehan, William P; Barber Foss, Kim D; Reches, Amit; Weiss, Michal; Myer, Gregory D

    2018-05-31

    To investigate the association between dual-task gait performance and brain network activation (BNA) using an electroencephalography (EEG)-based Go/No-Go paradigm among children and adolescents with concussion. Participants with a concussion completed a visual Go/No-Go task with collection of electroencephalogram brain activity. Data were treated with BNA analysis, which involves an algorithmic approach to EEG-ERP activation quantification. Participants also completed a dual-task gait assessment. The relationship between dual-task gait speed and BNA was assessed using multiple linear regression models. Participants (n = 20, 13.9 ± 2.3 years of age, 50% female) were tested at a mean of 7.0 ± 2.5 days post-concussion and were symptomatic at the time of testing (post-concussion symptom scale = 40.4 ± 21.9). Slower dual-task average gait speed (mean = 82.2 ± 21.0 cm/s) was significantly associated with lower relative time BNA scores (mean = 39.6 ± 25.8) during the No-Go task (β = 0.599, 95% CI = 0.214, 0.985, p = 0.005, R 2  = 0.405), while controlling for the effect of age and gender. Among children and adolescents with a concussion, slower dual-task gait speed was independently associated with lower BNA relative time scores during a visual Go/No-Go task. The relationship between abnormal gait behaviour and brain activation deficits may be reflective of disruption to multiple functional abilities after concussion.

  6. Multiple injections of electroporated autologous T cells expressing a chimeric antigen receptor mediate regression of human disseminated tumor.

    PubMed

    Zhao, Yangbing; Moon, Edmund; Carpenito, Carmine; Paulos, Chrystal M; Liu, Xiaojun; Brennan, Andrea L; Chew, Anne; Carroll, Richard G; Scholler, John; Levine, Bruce L; Albelda, Steven M; June, Carl H

    2010-11-15

    Redirecting T lymphocyte antigen specificity by gene transfer can provide large numbers of tumor-reactive T lymphocytes for adoptive immunotherapy. However, safety concerns associated with viral vector production have limited clinical application of T cells expressing chimeric antigen receptors (CAR). T lymphocytes can be gene modified by RNA electroporation without integration-associated safety concerns. To establish a safe platform for adoptive immunotherapy, we first optimized the vector backbone for RNA in vitro transcription to achieve high-level transgene expression. CAR expression and function of RNA-electroporated T cells could be detected up to a week after electroporation. Multiple injections of RNA CAR-electroporated T cells mediated regression of large vascularized flank mesothelioma tumors in NOD/scid/γc(-/-) mice. Dramatic tumor reduction also occurred when the preexisting intraperitoneal human-derived tumors, which had been growing in vivo for >50 days, were treated by multiple injections of autologous human T cells electroporated with anti-mesothelin CAR mRNA. This is the first report using matched patient tumor and lymphocytes showing that autologous T cells from cancer patients can be engineered to provide an effective therapy for a disseminated tumor in a robust preclinical model. Multiple injections of RNA-engineered T cells are a novel approach for adoptive cell transfer, providing flexible platform for the treatment of cancer that may complement the use of retroviral and lentiviral engineered T cells. This approach may increase the therapeutic index of T cells engineered to express powerful activation domains without the associated safety concerns of integrating viral vectors. Copyright © 2010 AACR.

  7. Multiple injections of electroporated autologous T cells expressing a chimeric antigen receptor mediate regression of human disseminated tumor

    PubMed Central

    Zhao, Yangbing; Moon, Edmund; Carpenito, Carmine; Paulos, Chrystal M.; Liu, Xiaojun; Brennan, Andrea L; Chew, Anne; Carroll, Richard G.; Scholler, John; Levine, Bruce L.; Albelda, Steven M.; June, Carl H.

    2010-01-01

    Redirecting T lymphocyte antigen specificity by gene transfer can provide large numbers of tumor reactive T lymphocytes for adoptive immunotherapy. However, safety concerns associated with viral vector production have limited clinical application of T cells expressing chimeric antigen receptors (CARs). T lymphocytes can be gene modified by RNA electroporation without integration-associated safety concerns. To establish a safe platform for adoptive immunotherapy, we first optimized the vector backbone for RNA in vitro transcription to achieve high level transgene expression. CAR expression and function of RNA-electroporated T cells could be detected up to a week post electroporation. Multiple injections of RNA CAR electroporated T cells mediated regression of large vascularized flank mesothelioma tumors in NOD/scid/γc(−/−) mice. Dramatic tumor reduction also occurred when the pre-existing intraperitoneal human-derived tumors, that had been growing in vivo for over 50 days, were treated by multiple injections of autologous human T cells electroporated with anti-mesothelin CAR mRNA. This is the first report using matched patient tumor and lymphocytes demonstrating that autologous T cells from cancer patients can be engineered to provide an effective therapy for a disseminated tumor in a robust preclinical model. Multiple injections of RNA engineered T cells are a novel approach for adoptive cell transfer, providing flexible platform for the treatment of cancer that may complement the use of retroviral and lentiviral engineered T cells. This approach may increase the therapeutic index of T cells engineered to express powerful activation domains without the associated safety concerns of integrating viral vectors. PMID:20926399

  8. Distributed task coding throughout the multiple demand network of the human frontal-insular cortex.

    PubMed

    Stiers, Peter; Mennes, Maarten; Sunaert, Stefan

    2010-08-01

    The large variety of tasks that humans can perform is governed by a small number of key frontal-insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal-insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multi-voxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. Copyright 2010 Elsevier Inc. All rights reserved.

  9. Interference between a fast-paced spatial puzzle task and verbal memory demands.

    PubMed

    Epling, Samantha L; Blakely, Megan J; Russell, Paul N; Helton, William S

    2017-06-01

    Research continues to provide evidence that people are poor multi-taskers. Cognitive resource theory is a common explanation for the inability to efficiently perform multiple tasks at the same time. This theory proposes that one's limited supply of cognitive resources can be utilized faster than it is replenished, which results in a performance decline, particularly when these limited resources must be allocated among multiple tasks. Researchers have proposed both domain-specific, for example, spatial versus verbal processing resources, and domain general cognitive resources. In the present research, we investigated whether a spatial puzzle task performed simultaneously with a verbal recall task would impair performance in either task or both tasks, compared to performance on the tasks individually. As hypothesized, a reduction in word recall was found when dual-tasking, though performance on the puzzle task did not significantly differ between the single- and dual-task conditions. This is consistent, in part, with both a general resource theory and a Multiple Resource Theory, but further work is required to better understand the cognitive processing system. The employment of the recall task in the dual-task paradigm with a variety of secondary tasks will help to continue mapping out the specificity (or lack thereof) of cognitive resources utilized in various mental and physical tasks.

  10. Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

    ERIC Educational Resources Information Center

    Petrocelli, John V.

    2003-01-01

    A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

  11. Using Task Clarification, Graphic Feedback, And Verbal Feedback To Increase Closing-Task Completion In A Privately Owned Restaurant

    PubMed Central

    Weatherly, Nic L; Gravina, Nicole E

    2005-01-01

    An informant functional assessment was used to evaluate closing-task completion by servers and dishwashers at a restaurant. Based on the functional assessment results, an intervention consisting of task clarification, posted graphic feedback, and verbal feedback was implemented and evaluated with a multiple baseline design across two groups of employees. Results showed an increase of 15% and 38% in task completion for the two groups. PMID:15898481

  12. Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

    PubMed

    Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

    2011-01-01

    Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

  13. Multiple Logistic Regression Analysis of Cigarette Use among High School Students

    ERIC Educational Resources Information Center

    Adwere-Boamah, Joseph

    2011-01-01

    A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

  14. Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

    USGS Publications Warehouse

    Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

    2015-01-01

    Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

  15. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

    NASA Astrophysics Data System (ADS)

    Sahoo, Sasmita; Jha, Madan K.

    2013-12-01

    The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

  16. Fast Quantitative Analysis Of Museum Objects Using Laser-Induced Breakdown Spectroscopy And Multiple Regression Algorithms

    NASA Astrophysics Data System (ADS)

    Lorenzetti, G.; Foresta, A.; Palleschi, V.; Legnaioli, S.

    2009-09-01

    The recent development of mobile instrumentation, specifically devoted to in situ analysis and study of museum objects, allows the acquisition of many LIBS spectra in very short time. However, such large amount of data calls for new analytical approaches which would guarantee a prompt analysis of the results obtained. In this communication, we will present and discuss the advantages of statistical analytical methods, such as Partial Least Squares Multiple Regression algorithms vs. the classical calibration curve approach. PLS algorithms allows to obtain in real time the information on the composition of the objects under study; this feature of the method, compared to the traditional off-line analysis of the data, is extremely useful for the optimization of the measurement times and number of points associated with the analysis. In fact, the real time availability of the compositional information gives the possibility of concentrating the attention on the most `interesting' parts of the object, without over-sampling the zones which would not provide useful information for the scholars or the conservators. Some example on the applications of this method will be presented, including the studies recently performed by the researcher of the Applied Laser Spectroscopy Laboratory on museum bronze objects.

  17. Flexible spatial perspective-taking: conversational partners weigh multiple cues in collaborative tasks

    PubMed Central

    Galati, Alexia; Avraamides, Marios N.

    2013-01-01

    Research on spatial perspective-taking often focuses on the cognitive processes of isolated individuals as they adopt or maintain imagined perspectives. Collaborative studies of spatial perspective-taking typically examine speakers' linguistic choices, while overlooking their underlying processes and representations. We review evidence from two collaborative experiments that examine the contribution of social and representational cues to spatial perspective choices in both language and the organization of spatial memory. Across experiments, speakers organized their memory representations according to the convergence of various cues. When layouts were randomly configured and did not afford intrinsic cues, speakers encoded their partner's viewpoint in memory, if available, but did not use it as an organizing direction. On the other hand, when the layout afforded an intrinsic structure, speakers organized their spatial memories according to the person-centered perspective reinforced by the layout's structure. Similarly, in descriptions, speakers considered multiple cues whether available a priori or at the interaction. They used partner-centered expressions more frequently (e.g., “to your right”) when the partner's viewpoint was misaligned by a small offset or coincided with the layout's structure. Conversely, they used egocentric expressions more frequently when their own viewpoint coincided with the intrinsic structure or when the partner was misaligned by a computationally difficult, oblique offset. Based on these findings we advocate for a framework for flexible perspective-taking: people weigh multiple cues (including social ones) to make attributions about the relative difficulty of perspective-taking for each partner, and adapt behavior to minimize their collective effort. This framework is not specialized for spatial reasoning but instead emerges from the same principles and memory-depended processes that govern perspective-taking in non-spatial tasks

  18. A comparative analysis of multiple-choice and student performance-task assessment in the high school biology classroom

    NASA Astrophysics Data System (ADS)

    Cushing, Patrick Ryan

    This study compared the performance of high school students on laboratory assessments. Thirty-four high school students who were enrolled in the second semester of a regular biology class or had completed the biology course the previous semester participated in this study. They were randomly assigned to examinations of two formats, performance-task and traditional multiple-choice, from two content areas, using a compound light microscope and diffusion. Students were directed to think-aloud as they performed the assessments. Additional verbal data were obtained during interviews following the assessment. The tape-recorded narrative data were analyzed for type and diversity of knowledge and skill categories, and percentage of in-depth processing demonstrated. While overall mean scores on the assessments were low, elicited statements provided additional insight into student cognition. Results indicated that a greater diversity of knowledge and skill categories was elicited by the two microscope assessments and by the two performance-task assessments. In addition, statements demonstrating in-depth processing were coded most frequently in narratives elicited during clinical interviews following the diffusion performance-task assessment. This study calls for individual teachers to design authentic assessment practices and apply them to daily classroom routines. Authentic assessment should be an integral part of the learning process and not merely an end result. In addition, teachers are encouraged to explicitly identify and model, through think-aloud methods, desired cognitive behaviors in the classroom.

  19. On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning.

    PubMed

    Grossberg, Stephen; Markowitz, Jeffrey; Cao, Yongqiang

    2011-12-01

    Visual object recognition is an essential accomplishment of advanced brains. Object recognition needs to be tolerant, or invariant, with respect to changes in object position, size, and view. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise in stages due to processes across multiple cortical areas. These processes include the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features with different levels of vigilance. The model predicts that data from the tradeoff and image morph tasks emerge from different levels of vigilance in the animals performing them. This result illustrates how different vigilance requirements of a task may change the course of category learning, notably the critical features that are attended and incorporated into learned category prototypes. The model outlines a path for developing an animal model of how defective vigilance control can lead to symptoms of various mental disorders, such as autism and amnesia. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Comparison of continuously acquired resting state and extracted analogues from active tasks.

    PubMed

    Ganger, Sebastian; Hahn, Andreas; Küblböck, Martin; Kranz, Georg S; Spies, Marie; Vanicek, Thomas; Seiger, René; Sladky, Ronald; Windischberger, Christian; Kasper, Siegfried; Lanzenberger, Rupert

    2015-10-01

    Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting-state data, the application to task-specific fMRI has received growing attention. Three major methods for extraction of resting-state data from task-related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in-between task blocks. Despite widespread application in current research, consensus on which method best resembles resting-state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting-state, two different task paradigms were assessed (emotion discrimination and right finger-tapping) and five well-described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting-state (Dice, Intraclass correlation coefficient (ICC), R(2) ) showed that regression against task effects yields functional connectivity networks most alike to resting-state. However, all methods exhibited significant differences when compared to continuous resting-state and similarity metrics were lower than test-retest of two resting-state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting-state when extracting signals from task designs, although functional connectivity computed from task-specific data may indeed yield interesting information. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  1. Differentiating regressed melanoma from regressed lichenoid keratosis.

    PubMed

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  2. Accurate Segmentation of CT Male Pelvic Organs via Regression-based Deformable Models and Multi-task Random Forests

    PubMed Central

    Gao, Yaozong; Shao, Yeqin; Lian, Jun; Wang, Andrew Z.; Chen, Ronald C.

    2016-01-01

    Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation. PMID:26800531

  3. Robot training of upper limb in multiple sclerosis: comparing protocols with or without manipulative task components.

    PubMed

    Carpinella, Ilaria; Cattaneo, Davide; Bertoni, Rita; Ferrarin, Maurizio

    2012-05-01

    In this pilot study, we compared two protocols for robot-based rehabilitation of upper limb in multiple sclerosis (MS): a protocol involving reaching tasks (RT) requiring arm transport only and a protocol requiring both objects' reaching and manipulation (RMT). Twenty-two MS subjects were assigned to RT or RMT group. Both protocols consisted of eight sessions. During RT training, subjects moved the handle of a planar robotic manipulandum toward circular targets displayed on a screen. RMT protocol required patients to reach and manipulate real objects, by moving the robotic arm equipped with a handle which left the hand free for distal tasks. In both trainings, the robot generated resistive and perturbing forces. Subjects were evaluated with clinical and instrumental tests. The results confirmed that MS patients maintained the ability to adapt to the robot-generated forces and that the rate of motor learning increased across sessions. Robot-therapy significantly reduced arm tremor and improved arm kinematics and functional ability. Compared to RT, RMT protocol induced a significantly larger improvement in movements involving grasp (improvement in Grasp ARAT sub-score: RMT 77.4%, RT 29.5%, p=0.035) but not precision grip. Future studies are needed to evaluate if longer trainings and the use of robotic handles would significantly improve also fine manipulation.

  4. Using Task Clarification, Graphic Feedback, and Verbal Feedback to Increase Closing-Task Completion in a Privately Owned Restaurant.

    ERIC Educational Resources Information Center

    Austin, John; Weatherly, Nic L.; Gravina, Nicole E.

    2005-01-01

    An informant functional assessment was used to evaluate closing-task completion by servers and dishwashers at a restaurant. Based on the functional assessment results, an intervention consisting of task clarification, posted graphic feedback, and verbal feedback was implemented and evaluated with a multiple baseline design across two groups of…

  5. Interaction Models for Functional Regression.

    PubMed

    Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

    2016-02-01

    A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.

  6. Limits of Spatial Attention in Three-Dimensional Space and Dual-task Driving Performance

    PubMed Central

    Andersen, George J.; Ni, Rui; Bian, Zheng; Kang, Julie

    2010-01-01

    The present study examined the limits of spatial attention while performing two driving relevant tasks that varied in depth. The first task was to maintain a fixed headway distance behind a lead vehicle that varied speed. The second task was to detect a light-change target in an array of lights located above the roadway. In Experiment 1 the light detection task required drivers to encode color and location. The results indicated that reaction time to detect a light-change target increased and accuracy decreased as a function of the horizontal location of the light-change target and as a function of the distance from the driver. In a second experiment the light change task was changed to a singleton search (detect the onset of a yellow light) and the workload of the car following task was systematically varied. The results of Experiment 2 indicated that RT increased as a function of task workload, the 2D position of the light-change target and the distance of the light-change target. A multiple regression analysis indicated that the effect of distance on light detection performance was not due to changes in the projected size of the light target. In Experiment 3 we found that the distance effect in detecting a light change could not be explained by the location of eye fixations. The results demonstrate that when drivers attend to a roadway scene attention is limited in three-dimensional space. These results have important implications for developing tests for assessing crash risk among drivers as well as the design of in vehicle technologies such as head-up displays. PMID:21094336

  7. Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.

    PubMed

    Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan

    2017-02-20

    The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

  8. Visual event-related potential changes in multiple system atrophy: delayed N2 latency in selective attention to a color task.

    PubMed

    Kamitani, Toshiaki; Kuroiwa, Yoshiyuki

    2009-01-01

    Recent studies demonstrated an altered P3 component and prolonged reaction time during the visual discrimination tasks in multiple system atrophy (MSA). In MSA, however, little is known about the N2 component which is known to be closely related to the visual discrimination process. We therefore compared the N2 component as well as the N1 and P3 components in 17 MSA patients with these components in 10 normal controls, by using a visual selective attention task to color or to shape. While the P3 in MSA was significantly delayed in selective attention to shape, the N2 in MSA was significantly delayed in selective attention to color. N1 was normally preserved both in attention to color and in attention to shape. Our electrophysiological results indicate that the color discrimination process during selective attention is impaired in MSA.

  9. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    NASA Astrophysics Data System (ADS)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  10. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    NASA Astrophysics Data System (ADS)

    dos Santos, T. S.; Mendes, D.; Torres, R. R.

    2015-08-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  12. Risk factors for autistic regression: results of an ambispective cohort study.

    PubMed

    Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu

    2012-08-01

    A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.

  13. The Association between Dual-Task Gait after Concussion and Prolonged Symptom Duration.

    PubMed

    Howell, David R; Brilliant, Anna; Berkstresser, Brant; Wang, Francis; Fraser, Joana; Meehan, William P

    2017-12-01

    Quantitative gait measurements can identify persistent postconcussion impairments. However, their prognostic utility after injury to identify the likelihood of prolonged concussion symptoms remains unknown. Our objective was to examine if dual-task gait performance measures are independently associated with persistent (> 28 days) concussion symptoms among a sample of athletes. Sixty individuals diagnosed with a sport-related concussion were assessed within 10 days of their injury. Each participant completed a postconcussion symptom scale, an injury history questionnaire, and a single/dual-task gait examination. They were followed until they no longer reported symptoms, and the duration of time required for symptom resolution was calculated. A binary multivariable logistic regression model determined the independent association between dual-task gait and symptom duration (≤ 28 days vs. >28 days) while controlling for the effect of gender, age, symptom severity, injury-to-examination time, and history of concussion. Seventeen (28%) participants reported a symptom duration >28 days. The dual-task cost for average gait speed (-25.9 ± 9.5% vs. -19.8 ± 8.9%; p = 0.027) and cadence (-18.0 ± 2.9% vs. -12.0 ± 7.7%; p = 0.029) was significantly greater among participants who experienced symptoms for >28 days. After adjusting for potential confounding variables, greater dual-task average gait speed costs were independently associated with prolonged symptom duration (aOR = 0.908; 95% CI = 0.835-0.987). Examinations of dual-task gait may provide useful information during multifaceted concussion examinations. Quantitative assessments that simultaneously test multiple domains, such as dual tasks, may be clinically valuable after a concussion to identify those more likely to experience symptoms for >28 days after injury.

  14. Valid Statistical Analysis for Logistic Regression with Multiple Sources

    NASA Astrophysics Data System (ADS)

    Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

    Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

  15. Watershed Regressions for Pesticides (WARP) models for predicting stream concentrations of multiple pesticides

    USGS Publications Warehouse

    Stone, Wesley W.; Crawford, Charles G.; Gilliom, Robert J.

    2013-01-01

    Watershed Regressions for Pesticides for multiple pesticides (WARP-MP) are statistical models developed to predict concentration statistics for a wide range of pesticides in unmonitored streams. The WARP-MP models use the national atrazine WARP models in conjunction with an adjustment factor for each additional pesticide. The WARP-MP models perform best for pesticides with application timing and methods similar to those used with atrazine. For other pesticides, WARP-MP models tend to overpredict concentration statistics for the model development sites. For WARP and WARP-MP, the less-than-ideal sampling frequency for the model development sites leads to underestimation of the shorter-duration concentration; hence, the WARP models tend to underpredict 4- and 21-d maximum moving-average concentrations, with median errors ranging from 9 to 38% As a result of this sampling bias, pesticides that performed well with the model development sites are expected to have predictions that are biased low for these shorter-duration concentration statistics. The overprediction by WARP-MP apparent for some of the pesticides is variably offset by underestimation of the model development concentration statistics. Of the 112 pesticides used in the WARP-MP application to stream segments nationwide, 25 were predicted to have concentration statistics with a 50% or greater probability of exceeding one or more aquatic life benchmarks in one or more stream segments. Geographically, many of the modeled streams in the Corn Belt Region were predicted to have one or more pesticides that exceeded an aquatic life benchmark during 2009, indicating the potential vulnerability of streams in this region.

  16. Penalized nonparametric scalar-on-function regression via principal coordinates

    PubMed Central

    Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

    2016-01-01

    A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

  17. Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

    PubMed

    Yang, Lixue; Chen, Kean

    2015-11-01

    To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

  18. Creativity and inductive reasoning: the relationship between divergent thinking and performance on Wasons 246 task.

    PubMed

    Vartanian, Oshin; Martindale, Colin; Kwiatkowski, Jonna

    2003-05-01

    This study was an investigation of the relationship between potential creativityas measured by fluency scores on the Alternate Uses Testand performance on Wasons 246 task. As hypothesized, participants who were successful in discovering the rule had significantly higher fluency scores. Successful participants also generated higher frequencies of confirmatory and disconfirmatory hypotheses, but a multiple regression analysis using the stepwise method revealed that the frequency of generating disconfirmatory hypotheses and fluency scores were the only two significant factors in task outcome. The results also supported earlier studies where disconfirmation was shown to play a more important role in the later stages of hypothesis testing. This was especially true of successful participants, who employed a higher frequency of disconfirmatory hypotheses after receiving feedback on the first announcement. These results imply that successful participants benefited from the provision of feedback on the first announcement by switching to a more successful strategy in the hypothesis-testing sequence.

  19. Parts-Per-Billion Mass Measurement Accuracy Achieved through the Combination of Multiple Linear Regression and Automatic Gain Control in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

    PubMed Central

    Williams, D. Keith; Muddiman, David C.

    2008-01-01

    Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument while allowing mean MMA values to remain less than 1 ppm. In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level. PMID:17539605

  20. Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

    NASA Astrophysics Data System (ADS)

    Shastri, Niket; Pathak, Kamlesh

    2018-05-01

    The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

  1. Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

    PubMed

    Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

    2017-03-01

    The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

  2. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

    ERIC Educational Resources Information Center

    Smith, Kent W.; Sasaki, M. S.

    1979-01-01

    A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

  3. Clustered Multi-Task Learning for Automatic Radar Target Recognition

    PubMed Central

    Li, Cong; Bao, Weimin; Xu, Luping; Zhang, Hua

    2017-01-01

    Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. PMID:28953267

  4. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

    PubMed

    Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

    2016-10-01

    We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. Efficient Regressions via Optimally Combining Quantile Information*

    PubMed Central

    Zhao, Zhibiao; Xiao, Zhijie

    2014-01-01

    We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods. PMID:25484481

  6. The prediction of intelligence in preschool children using alternative models to regression.

    PubMed

    Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

    2011-12-01

    Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

  7. Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

    NASA Astrophysics Data System (ADS)

    Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

    2014-12-01

    This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

  8. Task-driven dictionary learning.

    PubMed

    Mairal, Julien; Bach, Francis; Ponce, Jean

    2012-04-01

    Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

  9. Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

    PubMed

    Liu, Han; Wang, Lie; Zhao, Tuo

    2015-08-01

    We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

  10. Effects of Task and Category Membership on Representation Stability

    ERIC Educational Resources Information Center

    Gaillard, Audrey; Urdapilleta, Isabel; Houix, Olivier; Manetta, Celine

    2011-01-01

    This study examined the within-subject stability of 150 participants who performed both a sorting task and a property-generation task over multiple sessions, focusing on three concrete concept categories (food, animals and bathroom products). We hypothesized that (1) the within-subject stability would be higher in the sorting task than in the…

  11. 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.

  12. The effect of processing code, response modality and task difficulty on dual task performance and subjective workload in a manual system

    NASA Technical Reports Server (NTRS)

    Liu, Yili; Wickens, Christopher D.

    1987-01-01

    This paper reports on the first experiment of a series studying the effect of task structure and difficulty demand on time-sharing performance and workload in both automated and corresponding manual systems. The experimental task involves manual control time-shared with spatial and verbal decisions tasks of two levels of difficulty and two modes of response (voice or manual). The results provide strong evidence that tasks and processes competing for common processing resources are time shared less effecively and have higher workload than tasks competing for separate resources. Subjective measures and the structure of multiple resources are used in conjunction to predict dual task performance. The evidence comes from both single-task and from dual-task performance.

  13. Lesion symptom map of cognitive-postural interference in multiple sclerosis.

    PubMed

    Ruggieri, Serena; Fanelli, Fulvia; Castelli, Letizia; Petsas, Nikolaos; De Giglio, Laura; Prosperini, Luca

    2018-04-01

    To investigate the disease-altered structure-function relationship underlying the cognitive-postural interference (CPI) phenomenon in multiple sclerosis (MS). We measured postural sway of 96 patients and 48 sex-/age-matched healthy controls by force platform in quiet standing (single-task (ST)) while performing the Stroop test (dual-task (DT)) to estimate the dual-task cost (DTC) of balance. In patient group, binary T2 and T1 lesion masks and their corresponding lesion volumes were obtained from magnetic resonance imaging (MRI) of brain. Normalized brain volume (NBV) was also estimated by SIENAX. Correlations between DTC and lesion location were determined by voxel-based lesion symptom mapping (VLSM) analyses. Patients had greater DTC than controls ( p < 0.001). Among whole brain MRI metrics, only T1 lesion volume correlated with DTC ( r = -0.27; p < 0.01). However, VLSM analysis did not reveal any association with DTC using T1 lesion masks. By contrast, we found clusters of T2 lesions in distinct anatomical regions (anterior and superior corona radiata, bilaterally) to be correlated with DTC ( p < 0.01 false discovery rate (FDR)-corrected). A multivariable stepwise regression model confirmed findings from VLSM analysis. NBV did not contribute to fit the model. Our findings suggest that the CPI phenomenon in MS can be explained by disconnection along specific areas implicated in task-switching abilities and divided attention.

  14. Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

    2016-10-01

    Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

  15. Convex Regression with Interpretable Sharp Partitions

    PubMed Central

    Petersen, Ashley; Simon, Noah; Witten, Daniela

    2016-01-01

    We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set. PMID:27635120

  16. Reliability of spatial-temporal gait parameters during dual-task interference in people with multiple sclerosis. A cross-sectional study.

    PubMed

    Monticone, Marco; Ambrosini, Emilia; Fiorentini, Roberta; Rocca, Barbara; Liquori, Valentina; Pedrocchi, Alessandra; Ferrante, Simona

    2014-09-01

    To evaluate the reliability and minimum detectable change (MDC) of spatial-temporal gait parameters in subjects with multiple sclerosis (MS) during dual tasking. This cross-sectional study involved 25 healthy subjects (mean age 49.9 ± 15.8 years) and 25 people with MS (mean age 49.2 ± 11.5 years). Gait under motor-cognitive and motor-motor dual tasking conditions was evaluated in two sessions separated by a one-day interval using the GAITRite Walkway System. Test-retest reliability was assessed using intraclass correlation coefficients (ICCs), standard errors of measurement (SEM), and coefficients of variation (CV). MDC scores were computed for the velocity, cadence, step and stride length, step and stride time, double support time, the % of gait cycle for single support and stance phase, and base of support. All of the gait parameters reported good to excellent ICCs under both conditions, with healthy subject values of >0.69 and MS subject values of >0.84. SEM values were always below 18% for both groups of subjects. The gait patterns of the people with MS were slightly more variable than those of the normal controls (CVs: 5.88-41.53% vs 2.84-30.48%). The assessment of quantitative gait parameters in healthy subjects and people with MS is highly reliable under both of the investigated dual tasking conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Decreased cerebellar-cerebral connectivity contributes to complex task performance

    PubMed Central

    Knops, André

    2016-01-01

    The cerebellum's role in nonmotor processes is now well accepted, but cerebellar interaction with cerebral targets is not well understood. Complex cognitive tasks activate cerebellar, parietal, and frontal regions, but the effective connectivity between these regions has never been tested. To this end, we used psycho-physiological interactions (PPI) analysis to test connectivity changes of cerebellar and parietal seed regions in complex (2-digit by 1-digit multiplication, e.g., 12 × 3) vs. simple (1-digit by 1-digit multiplication, e.g., 4 × 3) task conditions (“complex − simple”). For cerebellar seed regions (lobule VI, hemisphere and vermis), we found significantly decreased cerebellar-parietal, cerebellar-cingulate, and cerebellar-frontal connectivity in complex multiplication. For parietal seed regions (PFcm, PFop, PFm) we found significantly increased parietal-parietal and parietal-frontal connectivity in complex multiplication. These results suggest that decreased cerebellar-cerebral connectivity contributes to complex task performance. Interestingly, BOLD activity contrasts revealed partially overlapping parietal areas of increased BOLD activity but decreased cerebellar-parietal PPI connectivity. PMID:27334957

  18. Human interaction with an intelligent computer in multi-task situations

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1975-01-01

    A general formulation of human decision making in multiple task situations is presented. It includes a description of the state, event, and action space in which the multiple task supervisor operates. A specific application to a failure detection and correction situation is discussed and results of a simulation experiment presented. Issues considered include static vs. dynamic allocation of responsibility and competitive vs. cooperative intelligence.

  19. Task switching costs in preschool children and adults.

    PubMed

    Peng, Anna; Kirkham, Natasha Z; Mareschal, Denis

    2018-08-01

    Past research investigating cognitive flexibility has shown that preschool children make many perseverative errors in tasks that require switching between different sets of rules. However, this inflexibility might not necessarily hold with easier tasks. The current study investigated the developmental differences in cognitive flexibility using a task-switching procedure that compared reaction times and accuracy in 4- and 6-year-olds with those in adults. The experiment involved simple target detection tasks and was intentionally designed in a way that the stimulus and response conflicts were minimal together with a long preparation window. Global mixing costs (performance costs when multiple tasks are relevant in a context), and local switch costs (performance costs due to switching to an alternative task) are typically thought to engage endogenous control processes. If this is the case, we should observe developmental differences with both of these costs. Our results show, however, that when the accuracy was good, there were no age differences in cognitive flexibility (i.e., the ability to manage multiple tasks and to switch between tasks) between children and adults. Even though preschool children had slower reaction times and were less accurate, the mixing and switch costs associated with task switching were not reliably larger for preschool children. Preschool children did, however, show more commission errors and greater response repetition effects than adults, which may reflect differences in inhibitory control. Copyright © 2018 Elsevier Inc. All rights reserved.

  20. Physiological Aging Influence on Brain Hemodynamic Activity during Task-Switching: A fNIRS Study.

    PubMed

    Vasta, Roberta; Cutini, Simone; Cerasa, Antonio; Gramigna, Vera; Olivadese, Giuseppe; Arabia, Gennarina; Quattrone, Aldo

    2017-01-01

    Task-switching (TS) paradigm is a well-known validated tool useful for exploring the neural substrates of cognitive control, in particular the activity of the lateral and medial prefrontal cortex. This work is aimed at investigating how physiological aging influences hemodynamic response during the execution of a color-shape TS paradigm. A multi-channel near infrared spectroscopy (fNIRS) was used to measure hemodynamic activity in 27 young (30.00 ± 7.90 years) and 11 elderly participants (57.18 ± 9.29 years) healthy volunteers (55% male, age range: (19-69) years) during the execution of a TS paradigm. Two holders were placed symmetrically over the left/right hemispheres to record cortical activity [oxy-(HbO) and deoxy-hemoglobin (HbR) concentration] of the dorso-lateral prefrontal cortex (DLPFC), the dorsal premotor cortex (PMC), and the dorso-medial part of the superior frontal gyrus (sFG). TS paradigm requires participants to repeat the same task over a variable number of trials, and then to switch to a different task during the trial sequence. A two-sample t -test was carried out to detect differences in cortical responses between groups. Multiple linear regression analysis was used to evaluate the impact of age on the prefrontal neural activity. Elderly participants were significantly slower than young participants in both color- ( p < 0.01, t = -3.67) and shape-single tasks ( p = 0.026, t = -2.54) as well as switching ( p = 0.026, t = -2.41) and repetition trials ( p = 0.012, t = -2.80). Differences in cortical activation between groups were revealed for HbO mean concentration of switching task in the PMC ( p = 0.048, t = 2.94). In the whole group, significant increases of behavioral performance were detected in switching trials, which positively correlated with aging. Multivariate regression analysis revealed that the HbO mean concentration of switching task in the PMC ( p = 0.01, β = -0.321) and of shape single-task in the sFG ( p = 0.003, β = 0

  1. The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.

    ERIC Educational Resources Information Center

    Fanning, Fred; Newman, Isadore

    Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…

  2. Regression Analysis with Dummy Variables: Use and Interpretation.

    ERIC Educational Resources Information Center

    Hinkle, Dennis E.; Oliver, J. Dale

    1986-01-01

    Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

  3. Modified Regression Correlation Coefficient for Poisson Regression Model

    NASA Astrophysics Data System (ADS)

    Kaengthong, Nattacha; Domthong, Uthumporn

    2017-09-01

    This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

  4. Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

    NASA Astrophysics Data System (ADS)

    Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

    2015-04-01

    Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

  5. A task control architecture for autonomous robots

    NASA Technical Reports Server (NTRS)

    Simmons, Reid; Mitchell, Tom

    1990-01-01

    An architecture is presented for controlling robots that have multiple tasks, operate in dynamic domains, and require a fair degree of autonomy. The architecture is built on several layers of functionality, including a distributed communication layer, a behavior layer for querying sensors, expanding goals, and executing commands, and a task level for managing the temporal aspects of planning and achieving goals, coordinating tasks, allocating resources, monitoring, and recovering from errors. Application to a legged planetary rover and an indoor mobile manipulator is described.

  6. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

    PubMed Central

    Hu, L.; Zhang, Z.G.; Mouraux, A.; Iannetti, G.D.

    2015-01-01

    Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

  7. Brain activation of semantic category-based grouping in multiple identity tracking task

    PubMed Central

    Wei, Liuqing; Lyu, Chuang; Hu, Siyuan; Li, Zhen

    2017-01-01

    Using Multiple Identity Tracking task and the functional magnetic resonance imaging (fMRI) technology, the present study aimed to isolate and visualize the functional anatomy of neural systems involved in the semantic category-based grouping process. Three experiment conditions were selected and compared: the category-based targets grouping (TG) condition, the targets-distractors grouping (TDG) condition and the homogenous condition. In the TG condition, observers could utilize the categorical distinction between targets and distractors, to construct a uniform presentation of targets, that is, to form a group of the targets to facilitate tracking. In the TDG condition, half the targets and half the distractors belonged to the same category. Observers had to inhibit the grouping of targets and distractors in one category to complete tracking. In the homogenous condition, where targets and distractors consisted of the same objects, no grouping could be formed. The “TG-Homogenous” contrast (p<0.01) revealed the activation of the left fusiform and the pars triangularis of inferior frontal gyrus (IFG). The “TG-TDG” contrast only revealed the activation of the left anterior cingulate gyrus (ACC). The fusiform and IFG pars triangularis might participate in the representation of semantic knowledge, IFG pars triangularis might relate intensely with the classification of semantic categories. The ACC might be responsible for the initiation and maintenance of grouping representation. PMID:28505166

  8. Inhibitory saccadic dysfunction is associated with cerebellar injury in multiple sclerosis.

    PubMed

    Kolbe, Scott C; Kilpatrick, Trevor J; Mitchell, Peter J; White, Owen; Egan, Gary F; Fielding, Joanne

    2014-05-01

    Cognitive dysfunction is common in patients with multiple sclerosis (MS). Saccadic eye movement paradigms such as antisaccades (AS) can sensitively interrogate cognitive function, in particular, the executive and attentional processes of response selection and inhibition. Although we have previously demonstrated significant deficits in the generation of AS in MS patients, the neuropathological changes underlying these deficits were not elucidated. In this study, 24 patients with relapsing-remitting MS underwent testing using an AS paradigm. Rank correlation and multiple regression analyses were subsequently used to determine whether AS errors in these patients were associated with: (i) neurological and radiological abnormalities, as measured by standard clinical techniques, (ii) cognitive dysfunction, and (iii) regionally specific cerebral white and gray-matter damage. Although AS error rates in MS patients did not correlate with clinical disability (using the Expanded Disability Status Score), T2 lesion load or brain parenchymal fraction, AS error rate did correlate with performance on the Paced Auditory Serial Addition Task and the Symbol Digit Modalities Test, neuropsychological tests commonly used in MS. Further, voxel-wise regression analyses revealed associations between AS errors and reduced fractional anisotropy throughout most of the cerebellum, and increased mean diffusivity in the cerebellar vermis. Region-wise regression analyses confirmed that AS errors also correlated with gray-matter atrophy in the cerebellum right VI subregion. These results support the use of the AS paradigm as a marker for cognitive dysfunction in MS and implicate structural and microstructural changes to the cerebellum as a contributing mechanism for AS deficits in these patients. Copyright © 2013 Wiley Periodicals, Inc.

  9. Schizotypy and Performance on an Insight Problem-Solving Task: The Contribution of Persecutory Ideation.

    PubMed

    Cosgrave, Jan; Haines, Ross; Golodetz, Stuart; Claridge, Gordon; Wulff, Katharina; van Heugten-van der Kloet, Dalena

    2018-01-01

    Insight problem solving is thought to underpin creative thought as it incorporates both divergent (generating multiple ideas and solutions) and convergent (arriving at the optimal solution) thinking approaches. The current literature on schizotypy and creativity is mixed and requires clarification. An alternate approach was employed by designing an exploratory web-based study using only correlates of schizotypal traits (paranoia, dissociation, cognitive failures, fantasy proneness, and unusual sleep experiences) and examining which (if any) predicted optimal performance on an insight problem-solving task. One hundred and twenty-one participants were recruited online from the general population and completed the number reduction task. The discovery of the hidden rule (HR) was used as a measure of insight. Multivariate logistic regression analyses highlighted persecutory ideation to best predict the discovery of the HR (OR = 1.05; 95% CI 1.01-1.10, p = 0.017), with a one-point increase in persecutory ideas corresponding to the participant being 5% more likely to discover the HR. This result suggests that persecutory ideation, above other schizotypy correlates, may be involved in insight problem solving.

  10. Research on air and missile defense task allocation based on extended contract net protocol

    NASA Astrophysics Data System (ADS)

    Zhang, Yunzhi; Wang, Gang

    2017-10-01

    Based on the background of air and missile defense distributed element corporative engagement, the interception task allocation problem of multiple weapon units with multiple targets under network condition is analyzed. Firstly, a mathematical model of task allocation is established by combat task decomposition. Secondly, the initialization assignment based on auction contract and the adjustment allocation scheme based on swap contract were introduced to the task allocation. Finally, through the simulation calculation of typical situation, the model can be used to solve the task allocation problem in complex combat environment.

  11. Strategies used by individuals with multiple sclerosis and with mild disability to maintain dynamic stability during a steering task.

    PubMed

    Denommé, Luke T; Mandalfino, Patricia; Cinelli, Michael E

    2014-06-01

    Changing direction during walking is a common task humans encounter every day. This destabilizing event requires the central nervous system (CNS) to quickly produce an appropriate response, maintain stability, and propel the body in the intended direction. Previous research has demonstrated that 'individuals with multiple sclerosis' (IwMS) with mild balance impairment display differences in gait characteristics during clinical tests compared with controls. The current study used dynamic stability margin [DSM, difference between COM (i.e. the weighted average of the central point of an individual's total body mass) and lateral BOS (i.e. the most lateral border of the foot that is in contact with the ground)] calculations in addition to gait kinematics to determine whether dynamic stability differences during a steering task were present between IwMS with mild balance impairment and 'healthy age-matched individuals' (HAMI) as well as between IwMS with mild balance impairment and 'community-dwelling older adults' (OA). All IwMS reported mild balance impairment with expanded disability status scale scores ranging between 1.0 and 3.0. The steering task required participants to walk 3 m towards a pressure sensitive trigger mat that would illuminate one of five lights to indicate the future direction of travel (i.e. straight, 45° or 60° to the left or right of the midline). Results revealed that IwMS displayed reduced walking speed and cadence during the approach phase in addition to a smaller DSM range (i.e. COM remained close to lateral BOS) during the entire steering task when compared with HAMI. However, when compared to OAs, IwMS did not display differences in any of the gait kinematics or DSM calculations. Findings suggest that the IwMS displayed a conservative gait strategy in order to maintain stability during the steering task. Lack of dynamic stability differences between IwMS and OAs indicate that both groups use similar strategies to adapt locomotion as a

  12. 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.

  13. A New Sample Size Formula for Regression.

    ERIC Educational Resources Information Center

    Brooks, Gordon P.; Barcikowski, Robert S.

    The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…

  14. Persistence of Multiple Tumor-Specific T-Cell Clones Is Associated with Complete Tumor Regression in a Melanoma Patient Receiving Adoptive Cell Transfer Therapy

    PubMed Central

    Zhou, Juhua; Dudley, Mark E.; Rosenberg, Steven A.; Robbins, Paul F.

    2007-01-01

    Summary The authors recently reported that adoptive immunotherapy with autologous tumor-reactive tumor infiltrating lymphocytes (TILs) immediately following a conditioning nonmyeloablative chemotherapy regimen resulted in an enhanced clinical response rate in patients with metastatic melanoma. These observations led to the current studies, which are focused on a detailed analysis of the T-cell antigen reactivity as well as the in vivo persistence of T cells in melanoma patient 2098, who experienced a complete regression of all metastatic lesions in lungs and soft tissues following therapy. Screening of an autologous tumor cell cDNA library using transferred TILs resulted in the identification of novel mutated growth arrest-specific gene 7 (GAS7) and glyceral-dehyde-3-phosphate dehydrogenase (GAPDH) gene transcripts. Direct sequence analysis of the expressed T-cell receptor beta chain variable regions showed that the transferred TILs contained multiple T-cell clonotypes, at least six of which persisted in peripheral blood for a month or more following transfer. The persistent T cells recognized both the mutated GAS7 and GAPDH. These persistent tumor-reactive T-cell clones were detected in tumor cell samples obtained from the patient following adoptive cell transfer and appeared to be represented at higher levels in the tumor sample obtained 1 month following transfer than in the peripheral blood obtained at the same time. Overall, these results indicate that multiple tumor-reactive T cells can persist in the peripheral blood and at the tumor site for prolonged times following adoptive transfer and thus may be responsible for the complete tumor regression in this patient. PMID:15614045

  15. Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

    PubMed

    Enders, Felicity

    2013-12-01

    Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P < 0.001). Postcourse students had similar results to practicing statisticians (mean (SD) of 20.1(3.5); P = 0.21). Students also showed significant improvement pre/postcourse in each of six domain areas (P < 0.001). The REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

  16. Children With ADHD Show Impairments in Multiple Stages of Information Processing in a Stroop Task: An ERP Study.

    PubMed

    Kóbor, Andrea; Takács, Ádám; Bryce, Donna; Szűcs, Dénes; Honbolygó, Ferenc; Nagy, Péter; Csépe, Valéria

    2015-01-01

    This study investigated the role of impaired inhibitory control as a factor underlying attention deficit hyperactivity disorder (ADHD). Children with ADHD and typically developing children completed an animal Stroop task while electroencephalogram (EEG) was recorded. The lateralized readiness potential and event-related brain potentials associated with perceptual and conflict processing were analyzed. Children with ADHD were slower to give correct responses irrespective of congruency, and slower to prepare correct responses in the incongruent condition. This delay could result from enhanced effort allocation at earlier processing stages, indicated by differences in P1, N1, and conflict sustained potential. Results suggest multiple deficits in information processing rather than a specific response inhibition impairment.

  17. Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

    USDA-ARS?s Scientific Manuscript database

    A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

  18. Moderation analysis using a two-level regression model.

    PubMed

    Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

    2014-10-01

    Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

  19. Multi-task feature learning by using trace norm regularization

    NASA Astrophysics Data System (ADS)

    Jiangmei, Zhang; Binfeng, Yu; Haibo, Ji; Wang, Kunpeng

    2017-11-01

    Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

  20. The Multivariate Regression Statistics Strategy to Investigate Content-Effect Correlation of Multiple Components in Traditional Chinese Medicine Based on a Partial Least Squares Method.

    PubMed

    Peng, Ying; Li, Su-Ning; Pei, Xuexue; Hao, Kun

    2018-03-01

    Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.

  1. Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

    PubMed

    Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

    2015-01-01

    Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse. Published by Elsevier B.V.

  2. Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

    PubMed Central

    Zainudin, Suhaila; Arif, Shereena M.

    2017-01-01

    Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

  3. Clifford support vector machines for classification, regression, and recurrence.

    PubMed

    Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

    2010-11-01

    This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

  4. Perceptual and Conceptual Priming of Cue Encoding in Task Switching

    ERIC Educational Resources Information Center

    Schneider, Darryl W.

    2016-01-01

    Transition effects in task-cuing experiments can be partitioned into task switching and cue repetition effects by using multiple cues per task. In the present study, the author shows that cue repetition effects can be partitioned into perceptual and conceptual priming effects. In 2 experiments, letters or numbers in their uppercase/lowercase or…

  5. What cognitive strategies do orangutans (Pongo pygmaeus) use to solve a trial-unique puzzle-tube task incorporating multiple obstacles?

    PubMed

    Tecwyn, Emma C; Thorpe, Susannah K S; Chappell, Jackie

    2012-01-01

    Apparently sophisticated behaviour during problem-solving is often the product of simple underlying mechanisms, such as associative learning or the use of procedural rules. These and other more parsimonious explanations need to be eliminated before higher-level cognitive processes such as causal reasoning or planning can be inferred. We presented three Bornean orangutans with 64 trial-unique configurations of a puzzle-tube to investigate whether they were able to consider multiple obstacles in two alternative paths, and subsequently choose the correct direction in which to move a reward in order to retrieve it. We were particularly interested in how subjects attempted to solve the task, namely which behavioural strategies they could have been using, as this is how we may begin to elucidate the cognitive mechanisms underpinning their choices. To explore this, we simulated performance outcomes across the 64 trials for various procedural rules and rule combinations that subjects may have been using based on the configuration of different obstacles. Two of the three subjects solved the task, suggesting that they were able to consider at least some of the obstacles in the puzzle-tube before executing action to retrieve the reward. This is impressive compared with the past performances of great apes on similar, arguably less complex tasks. Successful subjects may have been using a heuristic rule combination based on what they deemed to be the most relevant cue (the configuration of the puzzle-tube ends), which may be a cognitively economical strategy.

  6. Assessing risk factors for periodontitis using regression

    NASA Astrophysics Data System (ADS)

    Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

    2013-10-01

    Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

  7. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

    NASA Astrophysics Data System (ADS)

    Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

    2017-07-01

    This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

  8. Relationships of physical performance tests to military-relevant tasks in women.

    PubMed

    Szivak, Tunde K; Kraemer, William J; Nindl, Bradley C; Gotshalk, Lincoln A; Volek, Jeff S; Gomez, Ana L; Dunn-Lewis, Courtenay; Looney, David P; Comstock, Brett A; Hooper, David R; Flanagan, Shawn D; Maresh, Carl M

    2014-01-01

    This investigation sought to determine the most predictive measures of performance on a repetitive box lifting task (RBLT) and load bearing task (LBT) among 123 women (aged ±4 years, height 165±7 cm, body mass 64±10 kg). To determine the relationship of various predictors to performance on the RBLT and LBT, multiple regression analysis was conducted on body mass, height, leg cross-sectional area, upper and lower body muscular strength, lower body explosive power, upper and lower body local muscular endurance, and aerobic capacity. The mean±SD (range) number of repetitions for the RBLT was 86±23 (20-159). The mean±SD (range) time to complete the LBT was 2,054±340 seconds (1,307-3,447). The following equations were generated: RBLT (number of repetitions)=57.4+0.2(peak jump power)+0.4(number of pushups in 2 minutes)+0.15(number of repetitions during the squat endurance test)+1.39(one repetition maximal strength boxlift (kg))-0.04(2-mile run time (2MR) in seconds), R=0.81; standard error of the estimate (SEE)=14; LBT (in seconds)=1,831-4.28(number of repetitions during the squat endurance test)+0.95(2MR in seconds)-13.4(body mass), R=0.73; SEE=232. We found that the 2MR and squat endurance test were significant predictive factors for performance on both load carriage tasks. These data also imply that women's performance in combat-related tasks can be improved with training that targets muscular strength, power, and local muscular endurance in addition to aerobic capacity.

  9. Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks.

    PubMed

    Devi, D Chitra; Uthariaraj, V Rhymend

    2016-01-01

    Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.

  10. Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

    PubMed Central

    Devi, D. Chitra; Uthariaraj, V. Rhymend

    2016-01-01

    Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods. PMID:26955656

  11. Examination of Parameters Affecting the House Prices by Multiple Regression Analysis and its Contributions to Earthquake-Based Urban Transformation

    NASA Astrophysics Data System (ADS)

    Denli, H. H.; Durmus, B.

    2016-12-01

    The purpose of this study is to examine the factors which may affect the apartment prices with multiple linear regression analysis models and visualize the results by value maps. The study is focused on a county of Istanbul - Turkey. Totally 390 apartments around the county Umraniye are evaluated due to their physical and locational conditions. The identification of factors affecting the price of apartments in the county with a population of approximately 600k is expected to provide a significant contribution to the apartment market.Physical factors are selected as the age, number of rooms, size, floor numbers of the building and the floor that the apartment is positioned in. Positional factors are selected as the distances to the nearest hospital, school, park and police station. Totally ten physical and locational parameters are examined by regression analysis.After the regression analysis has been performed, value maps are composed from the parameters age, price and price per square meters. The most significant of the composed maps is the price per square meters map. Results show that the location of the apartment has the most influence to the square meter price information of the apartment. A different practice is developed from the composed maps by searching the ability of using price per square meters map in urban transformation practices. By marking the buildings older than 15 years in the price per square meters map, a different and new interpretation has been made to determine the buildings, to which should be given priority during an urban transformation in the county.This county is very close to the North Anatolian Fault zone and is under the threat of earthquakes. By marking the apartments older than 15 years on the price per square meters map, both older and expensive square meters apartments list can be gathered. By the help of this list, the priority could be given to the selected higher valued old apartments to support the economy of the country

  12. A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

    PubMed Central

    Tighe, Elizabeth L.; Schatschneider, Christopher

    2015-01-01

    The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

  13. A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

    PubMed

    Tighe, Elizabeth L; Schatschneider, Christopher

    2016-07-01

    The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

  14. Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study

    PubMed Central

    Rosso, Luigi; Riatti, Riccardo

    2016-01-01

    The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6–16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases. PMID:27995136

  15. Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study.

    PubMed

    Perinetti, Giuseppe; Rosso, Luigi; Riatti, Riccardo; Contardo, Luca

    2016-01-01

    The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6-16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, -0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases.

  16. Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

    PubMed

    Dai, Huanping; Micheyl, Christophe

    2012-11-01

    Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

  17. Task demand influences relationships among sex, clustering strategy, and recall: 16-word versus 9-word list learning tests.

    PubMed

    Sunderaraman, Preeti; Blumen, Helena M; DeMatteo, David; Apa, Zoltan L; Cosentino, Stephanie

    2013-06-01

    We compared the relationships among sex, clustering strategy, and recall across different task demands using the 16-word California Verbal Learning Test-Second Edition (CVLT-II) and the 9-word Philadelphia (repeatable) Verbal Learning Test (PrVLT). Women generally score higher than men on verbal memory tasks, possibly because women tend to use semantic clustering. This sex difference has been established via word-list learning tests such as the CVLT-II. In a retrospective between-group study, we compared how 2 separate groups of cognitively healthy older adults performed on a longer and a shorter verbal learning test. The group completing the CVLT-II had 36 women and 26 men; the group completing the PrVLT had 27 women and 21 men. Overall, multiple regression analyses revealed that semantic clustering was significantly associated with total recall on both tests' lists (P<0.001). Sex differences in recall and semantic clustering diminished with the shorter PrVLT word list. Semantic clustering uniquely influenced recall on both the longer and shorter word lists. However, serial clustering and sex influenced recall depending on the length of the word list (ie, the task demand). These findings suggest a complex nonlinear relationship among verbal memory, clustering strategies, and task demand.

  18. Task Demand Influences Relationships Among Sex, Clustering Strategy, and Recall: 16-Word Versus 9-Word List Learning Tests

    PubMed Central

    Sunderaraman, Preeti; Blumen, Helena M.; DeMatteo, David; Apa, Zoltan; Cosentino, Stephanie

    2013-01-01

    Objective We compared the relationships among sex, clustering strategy, and recall across different task demands using the 16-word California Verbal Learning Test–Second Edition (CVLT-II) and the 9-word Philadelphia (repeatable) Verbal Learning Test (PrVLT). Background Women generally score higher than men on verbal memory tasks, possibly because women tend to use semantic clustering. This sex difference has been established via word-list learning tests such as the CVLT-II. Methods In a retrospective between-group study, we compared how 2 separate groups of cognitively healthy older adults performed on a longer and a shorter verbal learning test. The group completing the CVLT-II had 36 women and 26 men; the group completing the PrVLT had 27 women and 21 men. Results Overall, multiple regression analyses revealed that semantic clustering was significantly associated with total recall on both tests’ lists (P < 0.001). Sex differences in recall and semantic clustering diminished with the shorter PrVLT word list. Conclusions Semantic clustering uniquely influenced recall on both the longer and shorter word lists. However, serial clustering and sex influenced recall depending on the length of the word list (ie, the task demand). These findings suggest a complex nonlinear relationship among verbal memory, clustering strategies, and task demand. PMID:23812171

  19. Multiple Language Use Influences Oculomotor Task Performance: Neurophysiological Evidence of a Shared Substrate between Language and Motor Control

    PubMed Central

    Heidlmayr, Karin; Doré-Mazars, Karine; Aparicio, Xavier; Isel, Frédéric

    2016-01-01

    In the present electroencephalographical study, we asked to which extent executive control processes are shared by both the language and motor domain. The rationale was to examine whether executive control processes whose efficiency is reinforced by the frequent use of a second language can lead to a benefit in the control of eye movements, i.e. a non-linguistic activity. For this purpose, we administrated to 19 highly proficient late French-German bilingual participants and to a control group of 20 French monolingual participants an antisaccade task, i.e. a specific motor task involving control. In this task, an automatic saccade has to be suppressed while a voluntary eye movement in the opposite direction has to be carried out. Here, our main hypothesis is that an advantage in the antisaccade task should be observed in the bilinguals if some properties of the control processes are shared between linguistic and motor domains. ERP data revealed clear differences between bilinguals and monolinguals. Critically, we showed an increased N2 effect size in bilinguals, thought to reflect better efficiency to monitor conflict, combined with reduced effect sizes on markers reflecting inhibitory control, i.e. cue-locked positivity, the target-locked P3 and the saccade-locked presaccadic positivity (PSP). Moreover, effective connectivity analyses (dynamic causal modelling; DCM) on the neuronal source level indicated that bilinguals rely more strongly on ACC-driven control while monolinguals rely on PFC-driven control. Taken together, our combined ERP and effective connectivity findings may reflect a dynamic interplay between strengthened conflict monitoring, associated with subsequently more efficient inhibition in bilinguals. Finally, L2 proficiency and immersion experience constitute relevant factors of the language background that predict efficiency of inhibition. To conclude, the present study provided ERP and effective connectivity evidence for domain-general executive

  20. Multi-tasking arbitration and behaviour design for human-interactive robots

    NASA Astrophysics Data System (ADS)

    Kobayashi, Yuichi; Onishi, Masaki; Hosoe, Shigeyuki; Luo, Zhiwei

    2013-05-01

    Robots that interact with humans in household environments are required to handle multiple real-time tasks simultaneously, such as carrying objects, collision avoidance and conversation with human. This article presents a design framework for the control and recognition processes to meet these requirements taking into account stochastic human behaviour. The proposed design method first introduces a Petri net for synchronisation of multiple tasks. The Petri net formulation is converted to Markov decision processes and processed in an optimal control framework. Three tasks (safety confirmation, object conveyance and conversation) interact and are expressed by the Petri net. Using the proposed framework, tasks that normally tend to be designed by integrating many if-then rules can be designed in a systematic manner in a state estimation and optimisation framework from the viewpoint of the shortest time optimal control. The proposed arbitration method was verified by simulations and experiments using RI-MAN, which was developed for interactive tasks with humans.

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