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
A single continuous variable
Interactions of categorical variables
Interactions of categorical and continuous variables
Interactions of two continuous variables
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