Univariate and Multivariate General Linear Models (2nd Ed.) Theory and Applications with SAS, Second Edition
Auteurs : Kim Kevin, Timm Neil
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
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.
Date de parution : 12-2019
15.2x22.9 cm
Date de parution : 10-2006
Ouvrage de 500 p.
15.2x22.9 cm
Thèmes d’Univariate and Multivariate General Linear Models :
Mots-clés :
MANCOVA Model; Multiple Linear Regression; applied statistics; SAS IML; linear regression model; SAS Code; hypothesis testing; Proc IML; experimental design; Simultaneous Confidence Sets; univariate normality; SUR Model; REML Estimate; Full Rank Model; FGLS Estimate; Multivariate Normal; Unweighted Test; Compound Symmetry; Proc GLM; ANOVA Table; Random Coefficient Model; Random Independent Variables; Multivariate Normal Random Variables; Growth Curve Model; MMM Analysis; Missing Data; Multivariate Normality Tests; PROC CATMOD; Split Plot Design; Box Cox Power Transformation