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 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
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
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
Stata's new graph scheme
How to graph functions using Stata 18New
How to display text and calculations using Stata 18New
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
Tables Builder in Stata, part 1: IntroductionNew
Tables Builder in Stata, part 2: Specifying layoutNew
Tables Builder in Stata, part 3: How to transpose or split a tableNew
Tables Builder in Stata, part 4: Removing rows and columnsNew
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 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
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
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 differencesNew
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
Likelihood-ratio tests in StataNew
Wald tests in StataNew
One-sample t test
t test for two paired samples
t test for two independent samples
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
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
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, 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
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 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
Likelihood-ratio tests in StataNew
Wald tests in StataNew
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 in Stata
Pearson's correlation coefficient
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
Multilevel meta-analysis
Meta-analysis for prevalence
Meta-analysis in Stata
Leave-one-out meta-analysis
Multivariate meta-analysis
Galbraith plots
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
Setup, imputation, estimation—regression imputation
Setup, imputation, estimation—predictive mean matching
Setup, imputation, estimation—logistic regression
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
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
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
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
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
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
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
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
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 differencesNew
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
