ANOVA/ANCOVA 

Balanced and unbalanced designs

Missing cells

Factorial, nested, Latin square, and mixed designs

Repeated measures

Box, Greenhouse–Geisser, and Huynh–Feldt corrections

Watch One-way ANOVA in Stata.

Watch Two-way ANOVA in Stata.

Watch Analysis of covariance in Stata.

 

Effect sizes

Eta-squared—η2

Omega-squared—ω2

Confidence intervals

Watch A tour of effect sizes.

 

Postestimation after ANOVA

Tests for effects, including pooling and nonresidual error terms

Tests for expressions involving the coefficients of the underlying regression model

Bonferroni, Holm, and Šidák adjustments for multiple tests

Ability to display symbolic forms

Predictions and influence statistics

Expected values

Residuals, standardized residuals, studentized residuals

Standard error of the prediction or residuals

Leverage

Cook’s D

COVRATIO

DFBETAs

Diagonal of hat matrix

Welsch distance

Diagnostic plots

Residual versus fitted

Added-variable plot

Component plus residual

Augmented component plus residual

Residual versus predictor

Leverage versus squared residual

 

MANOVA

Multivariate test statistics

Wilks’ lambda

Pillai’s trace

Lawley–Hotelling trace

Roy’s largest root

Balanced and unbalanced designs

Missing cells

Factorial, nested, Latin square, and mixed designs

Repeated measures

 

Postestimation after MANOVA

Multivariate tests (Wilks’ lambda, Pillai’s trace, etc.) for

Terms from the model

Pooled terms

Terms (or pooled terms) tested using other terms (or pooled terms) as the error term

Linear combinations of the underlying design matrix

Wald tests of expressions involving the coefficients of the underlying regression model

Predictions

Point estimates

Standard error of point estimates

Residuals

Combinations of estimators

Linear and nonlinear

Confidence intervals

 

Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

Watch Postestimation Selector.

 

Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Perform contrasts of categories/levels

 

Marginal analysis

Estimated marginal means

Marginal and partial effects

Average marginal and partial effects

Least-squares means

Predictive margins

Adjusted predictions, means, and effects

Works with multiple outcomes simultaneously

Contrasts of margins

Pairwise comparisons of margins

Profile plots

Interaction plots

Graphs of margins and marginal effects

 

Contrasts

Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects

Comparisons against reference groups, of adjacent levels, or against the grand mean

Orthogonal polynomials

Helmert contrasts

Custom contrasts

ANOVA-style tests

Contrasts of nonlinear responses

Multiple-comparison adjustments

Balanced and unbalanced data

Contrasts of means, intercepts, and slopes

Graphs of contrasts

Watch Introduction to contrasts in Stata: Oneway ANOVA.

 

Pairwise comparisons

Compare estimated means, intercepts, and slopes

Compare marginal means, intercepts, and slopes

Balanced and unbalanced data

Nonlinear responses

Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student–Newman–Keuls adjustments

Group comparisons that are significant

Graphs of pairwise comparisons

 

Additional resource

In the spotlight: Factor variables