Linear regression

Influence statistics and fit diagnostics

Ramsey regression specification-error test for omitted variables

Variance-inflation factors

Cook’s distance




Diagonal elements of hat matrix

Residuals, standardized residuals, studentized residuals

Standard errors of the forecast, prediction, and residuals

Welsch distance


Instrumental variables

Heteroskedastic regression

Truncated regression

Errors in variables

Tests for heteroskedasticity

Cook and Weisberg test

Szroetzer’s rank test

Information matrix test

Cameron and Trivedi’s decomposition

White’s test

Tests for autocorrelation


Durbin–Watson d statistic


Tests for structural breaks

Unknown break point

Known break points

Cumulative sum test for stability of coefficients

ARCH LM test

Moran’s test for spatial dependence

Diagnostic plots

Added variable (leverage) plot

Component plus residual plot

Leverage vs. squared residual plot

Residual vs. fitted plot

Residual vs. predictor

Effect sizes

Eta-squared—η 2


Confidence intervals

Fixed- and random-effects models for panel data

Traditional, robust (Huber/White/sandwich), cluster–robust, bootstrap, or jackknife standard errors

Robust regression

Graph predictions and confidence intervals

Newey–West estimator of variance

Variance-weighted least squares


GLS for cross-sectional time-series data

Multiple imputation

Finite mixture models

Bayesian estimation

Watch Heteroskedastic linear regression.

Watch Tests for multiple breaks in time series.

Watch Simple linear regression.

Watch Instrumental-variables regression.

Watch A tour of effect sizes.


Censored outcomes

Interval censored (such as income reported in ranges)

Tobit model

Correlated data corrections to standard errors

Heteroskedastic consistent standard errors

Model heteroskedasticity


Outcome in the absence of censoring

Outcome conditional on being in the censoring interval

Outcome with censoring imposed

Probability of censoring

Finite mixture models

Bayesian estimation

Interval regression with endogenous regressors, treatment effects, and sample selection


Sample-selection linear models

Maximum likelihood and Heckman’s two-step estimation

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Combine with endogenous regressors and treatment effects


Hurdle models

Linear and exponential

Lower or upper boundary values

Robust, cluster—robust, bootstrap, and jackknife standard errors


Stochastic frontier models

Production and cost frontiers

Half-normal, exponential, and truncated-normal distributions

Modelling of conditional heteroskedasticity


Quantile regression

Median regression

Least absolute deviations (LAD)

Regression of any quantile

Interquantile range regression

Standard errors

Koenker and Bassett

Robust — choose bandwidth and kernel


Extended regression models

Combine endogeneity, Heckman-style selection, and treatment effects

Linear regression

Exogenous or endogenous regressors

Exogenous or endogenous treatment assignment

Binary treatment–untreated/treated

Ordinal treatment levels–0 doses, 1 dose, 2 doses, etc.

Endogenous selection using probit or tobit

All standard postestimation command available, including predict and margins

Watch Extended regression models (ERMs)


Linear mixed models

Multilevel random effects

BLUP estimation

Residual-error structures for linear models

Standard errors of BLUPs

Bayesian estimation


Fractional polynomial regression

Support for a wide variety of models

Component-plus-residual plots

Support for zero-inflated regressors


Endogeneity and simultaneous systems

Two-stage least-squares regression

Poisson with endogenous regressors

LIML estimation

GMM estimation

Instrumental variables

Tests of instrumental relevance

Tests of overidentifying restrictions

Three-stage least-squares regression

Linear constraints within and across equations

Finite mixture models

Linear regression with endogenous regressors, treatment effects, and sample selection


Seemingly unrelated regressions

Linear constraints within and across equations


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

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

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 in odds-ratio metric

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