Analysis of Variance and Designed Experiments

IMSL Statistics Reference Guide > 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 (PV-WAVE Advantage)—Analyzes a one-way classification model.

ANCOVAR Function (PV-WAVE Advantage)—Analyzes a one-way classification model with covariates.

ANOVAFACT Function (PV-WAVE Advantage)—Analyzes a balanced factorial design with fixed effects.

ANOVABALANCED Function (PV-WAVE Advantage)—Balanced fixed, random, or mixed model.

Designed Experiments

CRD_FACTORIAL Function (PV-WAVE Advantage)—Analyzes data from balanced and unbalanced completely randomized experiments.

RCBD_FACTORIAL Function (PV-WAVE Advantage)—Analyzes data from balanced and unbalanced randomized complete-block experiments.

LATIN_SQUARE Function (PV-WAVE Advantage)—Analyzes data from latin-square experiments.

LATTICE_DESIGN Function (PV-WAVE Advantage)—Analyzes balanced and partially-balanced lattice experiments.

SPLIT_PLOT Function (PV-WAVE Advantage)—Analyzes a wide variety of split-plot experiments with fixed, mixed or random factors.

SPLIT_SPLIT_PLOT Function (PV-WAVE Advantage)—Analyzes data from split-split-plot experiments.

STRIP_PLOT Function (PV-WAVE Advantage)—Analyzes data from strip-plot experiments.

STRIP_SPLIT_PLOT Function (PV-WAVE Advantage)—Analyzes data from strip-split-plot experiments.

Utilities

HOMOGENEITY Function (PV-WAVE Advantage)—Conducts Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance.

MULTICOMP Function (PV-WAVE Advantage)—Performs Student-Newman-Keuls multiple comparisons test.

YATES Function (PV-WAVE Advantage)—Estimates missing observations in designed experiments using Yate’s method.

Version 2017.0