Exam 13: Analysis of Variance Anova

The term "homogeneous subsets" means that groups that have statistically identical means.

An interaction effect between two variables describes the way that variables "work together" to have an effect on the dependent variable.

Two-way analysis of variance, for example, allows for testing of the effect of two different independent variables on the dependent variable.

Tukey, Bonferroni, and Scheffe are the names of three popular t-tests.

Our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups.

Analysis of variance is used to test the equality of means across three or more categories.

Analysis of variance appears to be less robust than the t-test for deviations from normality.

The Levene's test is a test for testing whether changes in means are linear.

The global F-test tests whether all groups have significantly different means.

The Levene's test is a test for testing whether variances across groups are equal.

Analysis of variance is not appropriate for testing the statistical significance of continuous ordinal variable relationships.

A post hoc test tests all possible pairs of mean differences.

Multiple analysis of variance involves the analysis of more than one independent variable on a single dependent variable.

A nonparametric alternative to one-way analysis of variance (ANOVA) is Kruskal-Wallis' H test of one-way ANOVA.

Kruskal-Wallis' H test reports mean values of the dependent variable and identifies heterogeneous subsets.