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## Linear models

 Regression Ordinary, constrained, instrumental variables, censored, and errors in variables 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 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 ARCH LM test Diagnostic plots Added variable (leverage) plot Component plus residual plot Leverage vs. squared residual plot Residual vs. fitted plot Residual vs. predictor Nested logit models Fixed- and random-effects models for panel data Traditional, robust (Huber/White/sandwich), cluster–robust, bootstrap, or jackknife standard errors Robust regression Graph estimates and confidence intervals Newey–West estimator of variance Variance-weighted least squares GLM GLS for cross-sectional time-series data List estimates and confidence intervals Watch Simple Linear Regression in Stata tutorial. 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 Koenker and Bassett or bootstrapped standard errors Linear mixed models Multilevel random effects BLUP estimation Residual-error structures for linear models Standard errors of BLUPs Fractional polynomial regression Mean adjustment to variables Component-plus-residual plots Simultaneous systems Three-stage least-squares regression Two-stage least-squares regression LIML estimation GMM estimation Tests of instrumental relevance Tests of overidentifying restrictions Linear constraints within and across equations Models with selection Seemingly unrelated regressions Linear constraints within and across equations 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 Estimated marginal means Marginal and partial effects Average marginal and partial effects Least-squares means Predictive margins Adjusted predictions, means, and effects Contrasts of margins Pairwise comparisons of margins Profile plots Graphs of margins and marginal effects Watch Introduction to Margins in Stata 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 Interaction plots 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
See tests, predictions, and effects.

See New in Stata 12 for more about what was added in Stata Release 12.

Stata 12
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