Ready. Set. Go Stata.

Explore the fundamentals of data management, graphics, and basic statistics using the Stata interface. Learn to navigate the menu system, Command window, and Data Editor efficiently. Discover resources to find help for your own analytical projects.

Regression modeling for continuous, binary, and count outcomes

This session covers the theoretical concepts necessary to understand regression models and how to fit them using Stata. You will learn how to implement ordinary least-squares regression; maximum likelihood estimation for probit, logistic, and Poisson models; and marginal analysis.

Bayesian analysis using Stata

Learn how easily you can fit Bayesian regression models in Stata via the bayes prefix with an overview of Bayesian concepts. Also learn how to perform Bayesian regressions and interpret their results and how to specify priors, compare models, check convergence, and perform interval hypothesis testing.

Machine learning using Stata via H2O

See how to fit two of the most-used models in machine learning via H2O (gradient boosting machine and random forest) to predict continuous, binary, and multinomial outcomes; how to evaluate model performance; and how to optimally select model hyperparameters. We will also explore the tools available in Stata to interpret and explain the predictions of our machine learning models.

Reproducible reporting: Creating customized reports in Word, Excel, and PDF

Find out how to interact Stata's features with Word's features and how to automate your work when you want to regularly update reports. We demonstrate how to create Word documents with Stata summary statistics, regression tables, and graphs and how easy it is to use putpdf and putexcel to similarly create reproducible reports in PDF and Excel.

Creating graphs with Stata

With Stata, you can create univariate and bivariate graphs, graphs of summary statistics, diagnostic graphs based on the results of your model, and much more. Here we demonstrate how to create graphs for continuous and categorical variables, diagnostic graphs, and graphs that help you understand the results of your model, as well as different ways to customize a graph.

Multilevel/longitudinal modeling using Stata

Join us as we introduce the concepts and jargon of multilevel modeling for nested and longitudinal data. We will demonstrate how to fit multilevel and longitudinal models and how to visualize the results using Stata's predict, twoway, margins, and marginsplot commands. We also include a brief introduction to other commands that can be used to fit multilevel models.

Introduction to causal inference and treatment effects

View an introduction to the basic concepts of causal inference, including counterfactuals and potential outcomes. We demonstrate how to use Stata's teffects suite of commands to fit causal models using propensity-score matching, inverse-probability weighting, regression adjustment, “doubly robust” estimators, and nearest-neighbor matching.

Tables of descriptive statistics

Learn how to use the dtable command to create and export tables of descriptive statistics in both regular data and svy data, including those that display statistics by group and test for differences across groups. We also demonstrate how to customize the statistics, formatting, and style using dtable's own options and the collect suite of commands, and more.