PV-WAVE Advantage > IMSL Statistics Reference Guide > Analysis of Variance and Designed Experiments
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 Variance
*ANOVA1 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 Experiments
*CRD_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.
*Utilities
*HOMOGENEITY 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.