Fit one- and two-way models. Or fit models with three, four, or even more factors. Analyze data with nested factors, with fixed and random factors, or with repeated measures. Use ANCOVA models when you have continuous covariates and MANOVA models when you have multiple outcome variables. Further explore the relationships between your outcome and predictors by estimating effect sizes and computing least-squares and marginal means. Perform contrasts and pairwise comparisons. Analyze and plot interactions. And much more.


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



  • Eta-squared—η2
  • Epsilon-squared—ε2
  • Omega-squared—ω2
  • Confidence intervals



  • 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


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



  • 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


  • 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
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



  • 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