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Using multivariate statistics

Research in neuroscience, whether at the level of genes, proteins, neurons or behavior, almost always involves the interaction of multiple variables, and yet many areas of neuroscience employ univariate statistical analyses almost exclusively. Since multiple variables often work together to produce a neuronal or behavioral effect, the use of univariate statistical procedures, analyzing one variable at a time, limits the ability of studies to reveal how interactions between different variables may determine a particular outcome. Multivariate statistical and data mining methods afford the opportunity to analyze many variables together, in order to understand how they function as a system, and how this system may change as a result of a disease or a drug. The aim of this review is to provide a succinct guide to methods such as linear discriminant analysis, support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression and classification, which have been used in circumscribed areas of neuroscience research, but which could be used more widely. Experimental phenomena in neuroscience usually involve the complex interaction of multiple variables. Nonetheless, historically, statistical analysis has been dominated by the comparison of one variable at a time between treatment groups. In many areas of neuroscience, univariate statistical analyses have been used almost exclusively.

Using Multivariate Statistics , 6th edition provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. She has published over 70 articles and technical reports and participated in over 50 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and consults in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and performance, educational computer games, effects of noise on annoyance and sleep, and fetal alcohol syndrome. Convert currency. Add to Basket.

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On the Application of Multivariate Statistical and Data Mining Analyses to Data in Neuroscience

Scientific Research An Academic Publisher. Tabachnick, B. Using Multivariate Statistics 6th ed. Boston, MA: Pearson. Kira , Linda Lewandowski , Jeffery S. Ashby , Andrea Z.


Barbara G. Tabachnick, California State University - Northridge. Linda S. Fidell, California State University - Northridge. © |Pearson | Out of print. Share this​.


Using Multivariate Statistics, 7th Edition

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Barbara G. Tabachnick and Linda S. Fidell

ISBN 13: 9780205849574

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  1. Sienna D.

    SIXTH EDITION. Barbara G. Tabachnick. California State University, Northridge. Linda S. Fidell. California State University, Northridge. Boston □. Columbus □.

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