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Analysis of Variance Procedures

• 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

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

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