Description
A tutorial approach and many examples lead readers through methods related to analysis of variance with fixed and random effects and show how to choose the most appropriate SAS procedures for most experiment designs, such as completely random, randomized blocks, and split plot designs, as well as factorial treatment designs and repeated measures. Also covered are analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. This fourth edition is updated to reflect the evolution of contemporary software and statistical analysis methods, covering MIXED and GENMOD procedures in detail. Littell teaches statistics at the University of Florida. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related features, and new material, including a chapter on generalised linear models. Version 8 of the SAS System was used to run the SAS code examples in the book. * Provides clear explanations of how to use SAS to analyse linear models * Includes numerous SAS outputs * Includes new chapter on generalised linear models * Uses version 8 of the SAS system This book assists data analysts who use SAS/STAT software to analyse data using regression analysis and analysis of variance. It assumes familiarity with basic SAS concepts such as creating SAS data sets with the DATA step and manipulating SAS data sets with the procedures in base SAS software.
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