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Analysis of Variance & General Linear Models in NCSS

Introduction

Analysis of variance is the statistical technique for testing differences among group means. NCSS contains several analysis of variance procedures, including general linear models (GLM), one-way analysis of variance, unweighted means analysis of variance, and MANOVA. You can perform a simple one-way analysis of variance, a complex repeated-measures analysis of variance, a factorial analysis of variance, an analysis of covariance, and a host of multiple-comparison tests. All procedures work with balanced and unbalanced experimental designs.

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Features

> Analysis of covariance
> ANOVA table
> Area under curve
> Assumptions testing
> Automatic F-ratios
> Bonferroni tests
> Box plots
> Box’s M test
> Comparisons
> Contrasts
> Covariance analysis
> Cross-Over design analysis
> Duncan’s test
> Expected mean squares
> F-ratios
> Factorial designs
> Fishers LSD test
> Fixed terms
> Fractional factorial designs
> Geisser-Greenhouse
> GLM solution
> Hotelling’s trace
> Huynh-Feldt correction
> Interation plots
> Kruskal Wallis z tests
> Latin square designs		 > Least-squares means
> Levene variance test
> MANOVA (with up to 10 factors)
> Mauchley’s test
> Mean squares
> Means plots
> Multiple comparisons of factors
> Multiple comparisons of interactions
> Nested terms
> Newman-Keuls test
> Normality tests
> Pillai’s trace
> Planned comparisons
> Post-hoc tests
> Power calculations
> Random terms
> Randomized complete block
> Repeated measures
> R and R study
> Roy’s root
> Scheffe’s test
> Split-plot designs
> Tukey-Kramer HSD tests
> Unbalanced data
> Wilks’ lambda
> Within-subject analysis

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