Analysis of Variance and Designed Experiments
This section describes functions for analysis of variance models and for multiple comparison methods for means.
General Analysis of VarianceANOVA1 Function—Analyzes a one-way classification model.ANCOVAR Function—Analyzes a one-way classification model with covariates.ANOVAFACT Function—Analyzes a balanced factorial design with fixed effects. ANOVANESTED Function—Nested random model. ANOVABALANCED Function—Balanced fixed, random, or mixed model. Designed ExperimentsCRD_FACTORIAL Function—Analyzes data from balanced and unbalanced completely randomized experiments. RCBD_FACTORIAL Function—Analyzes data from balanced and unbalanced randomized complete-block experiments.LATIN_SQUARE Function—Analyzes data from latin-square experiments.LATTICE_DESIGN Function—Analyzes balanced and partially-balanced lattice experiments. SPLIT_PLOT Function—Analyzes a wide variety of split-plot experiments with fixed, mixed or random factors. SPLIT_SPLIT_PLOT Function—Analyzes data from split-split-plot experiments. STRIP_PLOT Function—Analyzes data from strip-plot experiments. STRIP_SPLIT_PLOT Function—Analyzes data from strip-split-plot experiments.UtilitiesHOMOGENEITY Function—Conducts Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance.MULTICOMP Function—Performs Student-Newman-Keuls multiple comparisons test.YATES Function—Estimates missing observations in designed experiments using Yate’s method.
