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  • Balanced and unbalanced designs
  • Missing cells
  • Factorial, nested , Latin square , and mixed designs
  • Repeated measures
  • Box, Greenhouse–Geisser, and Huynh–Feldt corrections

Effect sizes

  • Eta-squared—η2
  • Omega-squared—ω2
  • Confidence intervals

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
    • 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


  • 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 New

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

Factor variables

  • Automatically create indicators based on categorical variables
  • Form interactions among discrete and continuous variables
  • Include polynomial terms
  • Perform contrasts of categories/levels
Watch Introduction to factor variables in Stata tutorials

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 New
  • Contrasts of margins
  • Pairwise comparisons of margins
  • Profile plots
  • Interaction plots
  • Graphs of margins and marginal effects
Watch Introduction to margins in Stata tutorials
Watch Profile plots and interaction plots in Stata tutorials


  • 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

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

See tests, predictions, and effects.

See New in Stata 14 for more about what was added in Stata 14.





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