SAS for Linear Models


Former title: SAS System for Linear Models

Authors: Ramon C. Littell, Walter W. Stroup, Rudolf J. Freund

Most of the work that statisticians do involves linear models, relatively simple mathematical models based on linear equations. Linear models form a broad category that includes techniques ranging from regression to analysis of variance. For statisticians who have at least a general familiarity with linear models, this book covers many of the practical considerations in applying linear models and implementing them using SAS/STAT. This book is comprehensive enough to serve as the starting point for most statisticians doing regressions and other linear models in SAS.


4 Paperback (2002–SAS 8)

4 Paperback

Year: 2002

ISBN: 1-59047-023-0

Pages: 466

Publisher’s list price: 66.95


  • 1. Introduction
  • 2. Regression
  • 3. Analysis of Variance for Balanced Data
  • 4. Analyzing Data With Random Effects
  • 5. Unbalanced Data Analysis: Basic Methods
  • 6. Understanding Linear Models Concepts
  • 7. Analysis of Covariance
  • 8. Repeated-Measures Analysis
  • 9. Multivariate Linear Models
  • 10. Generalized Linear Models
  • 11. Examples of Special Applications
  • Index

From the back cover

Delve into linear model theory

This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, the authors 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.

New for the Fourth Edition!

This edition has been substantially updated to reflect the evolution of contemporary software and statistical analysis methods. Recognizing their considerable impact on linear model analysis, this book covers MIXED and GENMOD procedures in detail. Also included in this edition are updated examples, new software-related features, and other new material. The book contains new chapters on generalized linear models, analysis of covariance, and repeated measures, plus new information about unbalanced mixed-model analyses.

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
  • Repeated measures


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, Professor of Statistics, North Carolina State University