"The major enhancement in the fourth edition involves the addition of substantial information and detailed examples on mixed models using PROC MIXED as well as thorough comparisons of PROC MIXED and PROC GLM analyses. It serves as an excellent introduction to PROC MIXED for the most common mixed-models situations (nested, two-way cross-classifications, split-plots, models with mixtures of crossed and nested effects and repeated measures), using classical random-effects assumptions. There are good discussions about random versus fixed effects, problems with unbalanced date or missing cells, techniques or analysis."
Leigh W. Murray
University Statistics Center
"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."
Mayling M. Chu, Ph.D.
California State University, Stanislaus
"This is a book for the statistically sophisticated SAS software user. The coverage is quite broad, starting with a brief review of basic regression ideas and extending through mixed models and generalized linear models, including Poisson models, logistic models, models that use quasi-likelihood and generalized estimating equations. Advanced concepts are presented in a user-friendly way and interesting relevant examples are presented. "
David A. Dickey
North Carolina State University