This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material.

Review:

"The authors aim to write a book that offers a broad coverage of regression and ANOVA models. They have completed the mission. The first chapter, 'Introduction, ' is clear and should be read first to get a sense of the road map to the linear models. Because of the breadth rather than depth of its content, it fits intermediate users; however, advanced users may use it for quick reference. That is, this book is good for an overview as well as a reference. The whole book is user friendly, and it is easy to follow the content. Its special feature is the comparison of current advancements in selecting methods (such as PROC ANOVA and PROC GLM) for analyzing linear models. I highly recommend this book for ANOVA/SAS courses."

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