- ANOVA — Analysis of variance for balanced data
- CATMOD — Categorical data modeling
- GENMOD — Generalized linear models
- GLIMMIX — Generalized linear mixed models
- GLM — General linear models
- GLMMOD — Constructs the design matrix for a general linear model
- GLMSELECT — Performs effect selection in the framework of general linear models
- INBREED — Calculates the covariance or inbreeding coefficients for a pedigree
- LATTICE — Analysis of variance and simple covariance for data for lattice designs
- MIXED — General linear models with fixed and random effects
- NESTED — Random-effects analysis of variance for data from an experiment with a nested structure
- NPAR1WAY — Nonparametric tests for location and scale differences across a one-way classification
- PLAN — Designs and randomization plans for factorial experiments
- TRANSREG — Linear models with optimal nonlinear transformations of variables
- TTEST — t tests and confidence limits
- VARCOMP — General linear models with random effects

Analysis of variance in the contemporary sense of statistical modeling and analysis is the study of the influences on the variation of a phenomenon. This type of analysis may, for example, take the form of an analysis of variance table based on sums of squares, a deviance decomposition in a generalized linear model, or a series of Type III tests followed by comparisons of least squares means in a mixed model.

Below are highlights of the capabilities of the SAS/STAT procedures that perform analysis of variance:

- ANOVA for balanced data
- general linear models
- unbalanced data
- analysis of covariance, response-surface models, weighted regression, polynomial regression, MANOVA, repeated measurements analysis
- least squares means
- random effects
- estimate linear functions of the parameters
- test linear functions of the parameters
- multiple comparison of means
- homogeneity of variance testing

- linear mixed models
- fixed and random effects
- REML, maximum likelihood, and MIVQUE0 estimation methods
- least-squares means and differences
- sampling-based Bayesian analysis
- many covariance structures, some of which are compound symmetry, unstructured, AR(1), Toeplitz, heterogeneous AR(1), and Huynh-Feldt
- multiple comparison of least-squares means
- repeated measurements analysis

- nonlinear mixed models
- variance components
- nested models
- lattice designs
- construction of randomized designs from nested and crossed experiments