Quickly learn specific Stata topics with our 300+ short video tutorials. Topics covered include linear regression, time series, descriptive statistics, Excel imports, Bayesian analysis, *t* tests, instrumental variables, and tables. Explore our full topic list below, or visit our YouTube channel. New videos are added regularly.

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Tour of Stata 18^{New}

Tour of the Stata 18 interface

PDF documentation in Stata 18

Example datasets included with Stata 18

Overview of what's new in Stata 18

Bayesian model averaging

Causal model averaging

Creating and exporting tables of descriptive statistics

Heterogeneous difference in differences

Group sequential designs

Multilevel meta-analysis

Meta-analysis for prevalence

New features in robust inference for linear models

Wild cluster bootstrap for linear regression

Local projections for impulse–response functions

Flexible demand systems

Time-varying covariates in the interval-censored Cox model

Lasso for Cox proportional hazards models

Relative excess risk due to interaction (RERI)

Instrumental-variables quantile regression

Alias variables across frames

New features in the Data Editor

Stata's new graph scheme

Stata basics

Tour of the Stata 18 interface

PDF documentation in Stata 18

Example datasets included with Stata 18

What it's like—Getting started in Stata

Quick help

Installing community-contributed commands in Stata

Tour of Stata Project Manager

Postestimation Selector

Enhancements to the Do-file Editor

Do-file Editor enhancements in Stata

New features in the Data Editor

Data management

Loading, saving, importing, and exporting data

Input simple datasets into Stata

Import datasets from the internet into Stata

Import SAS datasets into Stata

Importing delimited data

Load a subset of data from a Stata dataset

Import data from SPSS and SAS

Import FRED (Import Federal Reserve Economic Data)

Copy/paste data from Excel into Stata

Import Excel data into Stata

Export results to Excel using Stata

Changing and renaming variables

Convert a string variable to a numeric variable

Convert categorical string variables to labeled numeric variables

Create a categorical variable from a continuous variable

Convert missing value codes to missing values

Frames

Combining data

Creating and dropping variables

Create a new variable that is calculated from other variables

Identify and replace unusual data values

Create a date variable from a date stored as a string

Optimize the storage of variables

Round a continuous variable

Stata's Expression Builder

Examining data

Labeling, display formats, and notes

Label variables

Label the values of categorical variables

Change the display format of a variable

Add notes to a variable

Reshaping datasets

Strings

Graphics

Stata's new graph scheme

How to graph functions using Stata 18^{New}

How to display text and calculations using Stata 18^{New}

Modifying graphs using the Graph Editor

Transparency in Stata graphs

Modifying sizes of elements in graphs

Bar graphs

Box plots

Contour plots

Histograms

Pie charts

Basic scatterplots

Galbraith plots

Automated document and report creation

Tables Builder in Stata, part 1: Introduction^{New}

Tables Builder in Stata, part 2: Specifying layout^{New}

Tables Builder in Stata, part 3: How to transpose or split a table^{New}

Tables Builder in Stata, part 4: Removing rows and columns^{New}

Creating and exporting tables of descriptive statistics

Customizable tables in Stata

Customizable tables: Cross-tabulations

Customizable tables: One-way tables of summary statistics

Customizable tables: Two-way tables of summary statistics

Customizable tables: How to create tables for a regression model

Customizable tables: How to create tables for multiple regression models

Create reproducible reports in Stata

Turning interactive use in Stata into reproducible results

Automatic production of webpages from dynamic Markdown documents

Create PDF reports from within Stata

Create Word documents from within Stata

Create customized Word documents with Stata results and graphs

Create documents with Markdown-formatted text and Stata output

Bayesian analysis

Bayesian model averaging

Bayesian econometrics

Bayesian vector autoregressive models

Bayesian dynamic forecasting

Bayesian impulse–response functions and forecast error-variance decompositions

Bayesian dynamic stochastic general equilibrium models

Bayesian panel-data models

Bayesian multilevel modeling

Bayesian analysis: Multiple chains

Bayesian analysis: Predictions

A prefix for Bayesian regression

Bayesian linear regression using the bayes prefix

Bayesian linear regression using the bayes prefix: How to specify custom priors

Bayesian linear regression using the bayes prefix: Checking convergence of the MCMC chain

Bayesian linear regression using the bayes prefix: How to customize the MCMC chain

Bayesian analysis

Graphical user interface for Bayesian analysis

Introduction to Bayesian statistics, part 1: The basic concepts

Introduction to Bayesian statistics, part 2: MCMC and the Metropolis–Hastings algorithm

Binary, ordinal, count, and fractional outcomes

Heteroskedastic ordered probit models

Mixed logit models

Poisson with sample selection

Zero-inflated ordered probit

Zero-inflated ordered logit model

Fitting and interpreting regression models: Probit regression with categorical predictors

Fitting and interpreting regression models: Probit regression with continuous predictors

Fitting and interpreting regression models: Probit regression with continuous and categorical predictors

Fitting and interpreting regression models: Multinomial probit regression with categorical predictors

Fitting and interpreting regression models: Multinomial probit regression with continuous predictors

Fitting and interpreting regression models: Multinomial probit regression with continuous and categorical predictors

Fitting and interpreting regression models: Logistic regression with categorical predictors

Fitting and interpreting regression models: Logistic regression with continuous predictors

Fitting and interpreting regression models: Logistic regression with continuous and categorical predictors

Fitting and interpreting regression models: Multinomial logistic regression with categorical predictors

Fitting and interpreting regression models: Multinomial logistic regression with continuous predictors

Fitting and interpreting regression models: Multinomial logistic regression with continuous and categorical predictors

Fitting and interpreting regression models: Poisson regression with categorical predictors

Fitting and interpreting regression models: Poisson regression with continuous predictors

Fitting and interpreting regression models: Poisson regression with continuous and categorical predictors

Logistic regression in Stata, part 1: Binary predictors

Logistic regression in Stata, part 2: Continuous predictors

Logistic regression in Stata, part 3: Factor variables

Regression models for fractional data

Probit regression with categorical covariates

Probit regression with continuous covariates

Probit regression with categorical and continuous covariates

Case–control studies

Causal inference

Heterogeneous difference in differences

Causal mediation analysis

Tour of treatment-effects estimators in Stata

Introduction to treatment effects in Stata: Part 1

Introduction to treatment effects in Stata: Part 2

Treatment effects in Stata: Difference in differences (DID)^{New}

Treatment effects in Stata: Heterogeneous difference in differences^{New}

Treatment effects in Stata: Regression adjustment

Treatment effects in Stata: Inverse-probability weighting

Treatment effects in Stata: Matching estimators

Treatment effects in Stata: AIPW and IPWRA

Treatment-effects estimation using lasso

Difference in differences

Treatment effects for survival models

Endogenous treatment effects

Choice models

Classical hypothesis tests

Likelihood-ratio tests in Stata^{New}

Wald tests in Stata^{New}

One-sample *t* test

*t* test for two paired samples

*t* test for two independent samples

Descriptive statistics, tables, and cross-tabulations

Creating and exporting tables of descriptive statistics

Descriptive statistics

Tables and cross-tabulations

Combining cross-tabulations and descriptives

Pearson's chi-squared and Fisher's exact test

Dynamic stochastic general equilibrium models (DSGEs)

Econometrics

Heterogeneous difference in differences

Flexible demand systems

Instrumental-variables quantile regression

Fixed-effects and random-effects multinomial logit models

Difference in differences

Nonparametric tests for trends

Linearized DSGEs

Nonlinear DSGE models

Heteroskedastic linear regression

Instrumental-variables regression

Mixed logit models

Multilevel tobit and interval regression

Nonparametric regression

Spatial autoregressive models

Extended regression models (ERMs)

Extended regression models, part 1: Endogenous covariates

Extended regression models, part 2: Nonrandom treatment assignment

Extended regression models, part 3: Endogenous sample selection

Extended regression models, part 4: Interpreting the model

Probit regression with categorical covariates

Probit regression with continuous covariates

Probit regression with categorical and continuous covariates

Fitting and interpreting regression models: Multinomial probit regression with categorical predictors

Fitting and interpreting regression models: Multinomial probit regression with continuous predictors

Fitting and interpreting regression models: Multinomial probit regression with continuous and categorical predictors

Effect sizes

Epidemiology

Causal mediation analysis

Relative excess risk due to interaction (RERI)

Time-varying covariates in the interval-censored Cox model

Lasso for Cox proportional hazards models

Logistic regression in Stata, part 1: Binary predictors

Logistic regression in Stata, part 2: Continuous predictors

Logistic regression in Stata, part 3: Factor variables

Fitting and interpreting regression models: Logistic regression with categorical predictors

Fitting and interpreting regression models: Logistic regression with continuous predictors

Fitting and interpreting regression models: Logistic regression with continuous and categorical predictors

Odds ratios for case–control data

Stratified analysis of case–control data

Cox proportional hazards model for interval-censored data

Interval-censored survival models

Learn how to set up your data for survival analysis

How to describe and summarize survival data

How to construct life tables

How to calculate incidence rates and incidence-rate ratios for survival data

How to calculate the Kaplan–Meier survivor and Nelson–Aalen cumulative hazard functions

How to graph survival curves

How to test the equality of survivor functions using nonparametric tests

How to fit a Cox proportional hazards model and check proportional-hazards assumption

Multilevel survival analysis

Survival models for SEM

A conceptual introduction to power and sample size

Extended regression models (ERMs)

Extended regression models (ERMs)

Extended regression models, part 1: Endogenous covariates

Extended regression models, part 2: Nonrandom treatment assignment

Extended regression models, part 3: Endogenous sample selection

Extended regression models, part 4: Interpreting the model

Extended regression models for panel data

Factor variables

IRT (item response theory)

IRT (item response theory) models

Item response theory using Stata: One-parameter logistic (1PL) models

Item response theory using Stata: Two-parameter logistic (2PL) models

Item response theory using Stata: Three-parameter logistic (3PL) models

Item response theory using Stata: Nominal response (NRM) models

Item response theory using Stata: Rating scale (RSM) models

Item response theory using Stata: Graded response (GRM) models

IRT models for multiple groups

Lasso

Lasso for Cox proportional hazards models

Using BIC in lasso

Treatment-effects estimation using lasso

Using lasso with clustered data for prediction and inference

Lasso for inference

Lasso for prediction and model selection

Latent class analysis and finite mixture models

Linear models

Likelihood-ratio tests in Stata^{New}

Wald tests in Stata^{New}

New features in robust inference for linear models

Wild cluster bootstrap for linear regression

Fitting and interpreting regression models: Linear regression with categorical predictors

Fitting and interpreting regression models: Linear regression with continuous predictors

Fitting and interpreting regression models: Linear regression with continuous and categorical predictors

Heteroskedastic linear regression

One-way ANOVA

Two-way ANOVA

Analysis of covariance

Simple linear regression

Pearson's correlation coefficient

Marginal means, predictive margins, and contrasts

Introduction to margins in Stata, part 1: Categorical variables

Introduction to margins in Stata, part 2: Continuous variables

Introduction to margins in Stata, part 3: Interactions

Profile plots and interaction plots in Stata, part 1: A single categorical variable

Profile plots and interaction plots in Stata, part 2: A single continuous variable

Profile plots and interaction plots in Stata, part 3: Interactions of categorical variables

Profile plots and interaction plots in Stata, part 4: Interactions of continuous and categorical variables

Profile plots and interaction plots in Stata, part 5: Interactions of two continuous variables

Introduction to contrasts in Stata: One-way ANOVA

Meta-analysis

Multilevel meta-analysis

Meta-analysis for prevalence

Meta-analysis in Stata

Leave-one-out meta-analysis

Multivariate meta-analysis

Galbraith plots

Multilevel mixed-effects models

Nonlinear mixed-effects models with lags and differences

Multilevel tobit and interval regression

Nonlinear mixed-effects models

Introduction to multilevel linear models, part 1

Introduction to multilevel linear models, part 2

Tour of multilevel GLMs

Multilevel models for survey data

Multilevel survival analysis

Small-sample inference for mixed-effects models

Multiple imputation

Setup, imputation, estimation—regression imputation

Setup, imputation, estimation—predictive mean matching

Setup, imputation, estimation—logistic regression

Nonparametric statistics

Panel data

Fixed-effects and random-effects multinomial logit models

Extended regression models for panel data

Random-effects regression with endogenous sample selection

Panel-data cointegration tests

Ordered logistic and probit for panel data

Panel-data survival models

Power, precision, and sample size

Precision and sample-size analysis

Tour of power and sample size

A conceptual introduction to power and sample size

Power and sample-size features added in Stata 14

Sample-size calculation for comparing a sample mean to a reference value

Power calculation for comparing a sample mean to a reference value

Find the minimum detectable effect size for comparing a sample mean to a reference value

Sample-size calculation for comparing a sample proportion to a reference value

Power calculation for comparing a sample proportion to a reference value

Minimum detectable effect size for comparing a sample proportion to a reference value

How to calculate sample size for two independent proportions

How to calculate power for two independent proportions

How to calculate minimum detectable effect size for two independent proportions

Sample-size calculation for comparing sample means from two paired samples

Power calculation for comparing sample means from two paired samples

How to calculate the minimum detectable effect size for comparing the means from two paired samples

Sample-size calculation for one-way analysis of variance

Power calculation for one-way analysis of variance

Minimum detectable effect size for one-way analysis of variance

Power analysis for cluster randomized designs and linear regression

Programming

Python integration with Stata

How to install Python

How to install Python packages with PIP

How to install Anaconda/Python

Jupyter Notebook with Stata

PyStata - Python and Stata

SEM (structural equation modeling)

Latent class analysis (LCA)

Finite mixture models (FMMs)

Multiple-group generalized SEM

Tour of multilevel generalized SEM

SEM Builder

Satorra–Bentler adjustments for SEM

Survey data support for SEM

Survival models for SEM

Spatial autoregressive models

Statistical calculators

Cross-tabulation and chi-squared tests calculator

One-sample *t*-tests calculator

Two-sample *t*-tests calculator

Incidence-rate ratios calculator

Odds-ratio calculator

Risk-ratios calculator

Survey data analysis

Basic introduction to the analysis of complex survey data

Specifying the design of your survey data

How to download, import, and merge multiple datasets from the NHANES website

How to download, import, and prepare data from the NHANES website

Multilevel models for survey data

Survey data support for SEM

Survival analysis

Time-varying covariates in the interval-censored Cox model

Cox proportional hazards models for interval-censored data

Interval-censored survival models

Learn how to set up your data for survival analysis

How to describe and summarize survival data

How to construct life tables

How to calculate incidence rates and incidence-rate ratios for survival

How to calculate the Kaplan–Meier survivor and Nelson–Aalen cumulative hazard functions

How to graph survival curves

How to test the equality of survivor functions using nonparametric tests

How to fit a Cox proportional hazards model and check proportional-hazards assumption

Multilevel survival analysis

Panel-data survival models

Survival models for SEM

Treatment effects for survival models

Time series

Local projections for impulse–response functions

Import FRED (Import Federal Reserve Economic Data)

Threshold regression

Tests for multiple breaks in time series

Tour of forecasting

Formatting and managing dates

Time-series operators

Correlograms and partial correlograms

Line graphs and tin()

Introduction to ARMA/ARIMA models

Markov-switching models

Moving-average smoothers

Treatment effects

Introduction to treatment effects in Stata: Part 1

Introduction to treatment effects in Stata: Part 2

Tour of treatment-effects estimators in Stata

Treatment effects in Stata: Difference in differences (DID)^{New}

Treatment effects in Stata: Heterogeneous difference in differences^{New}

Treatment effects in Stata: Regression adjustment

Treatment effects in Stata: Inverse-probability weighting

Treatment effects in Stata: Matching estimators

Treatment effects in Stata: AIPW and IPWRA

Treatment-effects estimation using lasso

Difference in differences

Treatment effects for survival models

Endogenous treatment effects