Uncertain which predictors to use in your regression?

Use Bayesian model averaging to account for this uncertainty in your analysis. Explore influential models and predictors, obtain better predictions, and more.

Causal analysis quantifies causal effects. Causal mediation analysis disentangles them.

Are these effects mediated through another variable? Estimate direct and indirect effects. Calculate the proportion mediated.

Estimate treatment effects that vary over groups and time. Fit models for repeated cross-sectional or panel data.

Visualize effects. Aggregate effects within group, time, or exposure to treatment.

White background • Horizontal y-axis labels • Bright color palette • Side legend • And more

You can also graph colors by variable.

Create tables of descriptive statistics more easily with the new dtable command!

Export to Word, Excel, PDF, LaTeX, HTML, Markdown, and more.

Use variables from multiple datasets as if they exist in one.

And you can now work with frame sets.

Calculate efficacy and futility stopping bounds for clinical trials. Find required sample sizes for interim and final analyses when testing proportions, means, or survivor functions.

Do your studies have effect sizes nested within multiple grouping levels? Use multilevel meta-analysis to account for possible dependence among the effect sizes when combining results.

You asked, we delivered! Perform meta-analysis for proportion or prevalence. Produce forest plots. Explore heterogeneity. Perform subgroup analysis. And more.

Stata's robust features for linear models became even more robust. Learn how.

Small number of clusters? Unequal observations per cluster? No problem! Wild cluster bootstrap handles them all.

How do exposures interact to increase risk?

Use reri to find out.

Incorporate time-varying covariates in your interval-censored Cox analysis, including prediction and plots of survivor and other functions!

Select variables in a Cox model using lasso and elastic net.

Compute predictions. Graph survivor, failure, and other functions.

Want to know whether your survival model fits your data well? estat gofplot makes this easy. Use it with right-censored and interval-censored data, parametric and semiparametric models, and more.

Estimate impulse–response functions (IRFs) via local projections. Test hypotheses of multiple IRF coefficients. Graph IRFs, orthogonalized IRFs, and dynamic multipliers.

Compare potential ARIMA or ARFIMA models using AIC, BIC, and HQIC. Select the best number of autoregressive and moving-average terms.

Estimate demand for a basket of goods. Evaluate sensitivity to price and expenditure changes. Choose from eight demand systems, including Cobb–Douglas, translog, AIDS, and QUAIDS.

Estimate effects of covariates on quantiles of the outcome's conditional distribution. Account for endogeneity. Plot coefficients across quantiles.

Modeling a proportion or rate?

Have endogenous covariates?

Fit your model with ivfprobit.

Interface enhancements

Data Editor—Pinnable rows and columns, tooltips for truncated text, variable labels in headers, much more.

Do-file Editor—Automatic backups and syntax highlighting for user-defined keywords.