[PDF] Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition (Statistics: A Series of Textbooks and Monographs)

ISBN: 158488634X

Category: Tutorial


Posted on 2017-11-25, by luongquocchinh.

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Author: Kevin Kim | Publisher: Chapman and Hall/CRC | Category: Mathematics | Language: English | Page: 549 | ISBN: 158488634X | ISBN13: 9781584886341 |

Description: Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.

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