Variance

BRIEF DESCRIPTION: The dependent variable and any covariate(s) such as in ANCOVA and MANCOVA, should have the same slopes (b-coefficient) across all levels of the categorical grouping variable (factors). In other words, the covariate(s) must be linearly related to the dependent variable. On the other hand, covariate(s) and factors should not be significantly correlated.   Who cares ANCOVA MANCOVA Ordinal regression Probit response models   Why is it important The fact is: when groups differ significantly on the covariate (thus an interaction) then placing the covariate into the [READ MORE]

BRIEF DESCRIPTION: Homoscedasticity is the bivariate version of the univariate assumption of Homogeneity of variance, and the multivariate assumption of Homogeneity of variance-covariance matrices.  Refer to the post “Homogeneity of variance” for a discussion of equality of variances. In short, homoscedasticity suggests that the metric dependent variable(s) have equal levels of variability across a range of either continuous or categorical independent variables.  More specifically, in bivariate analysis such as regression, homoscedasticity means that the variance of errors [READ MORE]

Very brief description: When comparing groups, their dispersion (variances) on the dependent variable should be relatively equal at each level of the independent (factor or grouping) variable (and neither should their sample sizes vary greatly across the groups). In other words, the dependent variable should exhibit equal levels of variance across the range of groups. Homogeneity of variance is the univariate version of the bivariate test of homoscedasticity, and the multivariate assumption of homogeneity of variance-covariance matrices.   Who cares Both t-test and ANOVA are sensitive to a [READ MORE]