Linear regression 
- Influence statistics and fit diagnostics
- Ramsey regression specification-error test for omitted variables
- Variance-inflation factors
- Cook’s distance
- COVRATIO
- DFBETAs
- DFITs
- Diagonal elements of hat matrix
- Residuals, standardized residuals, studentized residuals
- Standard errors of the forecast, prediction, and residuals
- Welsch distance
- Constraints
- Instrumental variables
- Heteroskedastic regression
- Truncated regression

- Errors in variables
- Multivariate regression
- 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
- Durbin–Watson d statistic
- Breusch–Godfrey
- Tests for structural breaks
- 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
- Omega-squared—ω2
- 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

- GLM

- GLS for cross-sectional time-series data

- Multiple imputation

- Finite mixture models
- Bayesian estimation
Censored outcomes
- Interval censored (such as income reported in ranges)

- Tobit model

- Correlated data corrections to standard errors
- Heteroskedastic consistent standard errors
- Model heteroskedasticity
- Predictions

- 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
- Modeling 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
- Bootstrap
- Multiple imputation
Fractional polynomial regression
- Support for a wide variety of models
- Component-plus-residual plots
- Support for zero-inflated regressors
Extended regression models
- Combine endogeneity, Heckman-style selection, and treatment effects
- Linear regression
- Random effects in one or all equations
New
- 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
Linear mixed models

- Multilevel random effects
- BLUP estimation
- Residual-error structures for linear models
- Standard errors of BLUPs
- Multiple imputation
- Bayesian estimation
Endogeneity and simultaneous systems
- Two-stage least-squares regression

- 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
Factor variables
- Automatically create indicators based on categorical variables
- Form interactions among discrete and continuous variables Updated
- 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
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 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

Additional resources
See
tests, predictions, and effects.
See
New in Stata 16
for more about what was added in Stata 16.