Many procedures in SAS/STAT enable you to incorporate classification effects into your model and to perform statistical inferences for experimental factors and their interactions. These procedures do not necessarily rely on sums of squares decompositions to perform these inferences.

CATMOD

fits linear models and performs analysis of variance and repeated measures analysis of variance for categorical responses. See Chapter 8, Introduction to Categorical Data Analysis Procedures, and Chapter 28, The CATMOD Procedure, for more information.

GENMOD

fits generalized linear models. PROC GENMOD is especially suited for responses with discrete outcomes, and it performs logistic regression and Poisson regression as well as fitting generalized estimating equations for repeated measures data. Bayesian analysis capabilities for generalized linear models are also available with the GENMOD procedure. See Chapter 8, Introduction to Categorical Data Analysis Procedures, and Chapter 37, The GENMOD Procedure, for more information.

GLIMMIX

fits generalized linear mixed models by likelihood-based methods. PROC GLIMMIX offers many facilities for analyzing and comparing classification effects and their levels, including multiplicity-adjusted linear estimates. See Chapter 38, The GLIMMIX Procedure, for more information.

LOGISTIC

fits logistic models for binomial and ordinal outcomes. PROC LOGISTIC provides a wide variety of model-building methods and computes numerous regression diagnostics. See Chapter 8, Introduction to Categorical Data Analysis Procedures, and Chapter 51, The LOGISTIC Procedure, for more information.

NPAR1WAY

performs nonparametric one-way analysis of rank scores.

TTEST

compares the means of two groups of observations.

The following section discusses procedures in SAS/STAT that compute analysis of variance in models with classification factors in the narrow sense—that is, they produce analysis of variance tables and form F tests based on sums of squares, mean squares, and expected mean squares.

The subsequent sections discuss procedures that perform statistical inference in models with classification effects in the broader sense.

The following section also presents an overview of some of the fundamental features of analysis of variance. Subsequent sections describe how this analysis is performed with procedures in SAS/STAT software. For more detail, see the chapters for the individual procedures. Additional sources are described in the section References.