수시로 업데이트 될 예정입니다.

여기 300개가 넘는 튜토리얼 비디오가 Stata를 어떻게 사용하고 문제를 어떻게 해결해야 하는지 보여줍니다.

선형회귀분석부터,시계열/패널데이터분석,베이지안분석, t검정,도구변수 그리고 엑셀파일을 불러오는 방법까지 다양한 비디오들이 준비되어 있습니다.

또,테이블출력은 언제나 인기 많은 주제 중에 하나입니다. Stata에 대한 모든 비디오 튜토리얼은 아래의 목록에서 각 주제별로 확인할 수 있습니다.

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 mediation analysis

Creating and exporting tables of descriptive statistics

Heterogeneous difference in differences

Group sequential designs

Multilevel meta-analysis

Meta-anaysis 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 New

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

Saving estimation results to Excel

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

Alias variables across datasets New

Working with multiple datasets in memory

Combining data

How to merge files into a single dataset

How to append files into a single dataset

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

New features in the Data Editor New

Identify and remove duplicate observations

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

Reshape data from wide format to long format

Reshape data from long format to wide format

Strings

Unicode

Tour of long strings and BLOBs

Creating and exporting tables of descriptive statistics New

Customizable tables in Stata

Customizable tables: Crosstabulations

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 web pages 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 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 New

Causal mediation analysis New

Introduction to treatment effects in Stata: Part 1

Introduction to treatment effects in Stata: Part 2

Treatment effects: Regression adjustment

Treatment effects: Inverse-probability weighting

Treatment effects: Inverse-probability weighted regression adjustment

Treatment effects: Augmented inverse-probability weighting

Treatment effects: Nearest-neighbor matching

Treatment effects: Propensity-score matching

Treatment-effects estimation using lasso

Difference in differences

Treatment effects for survival models

Endogenous treatment effects

Heterogeneous difference in differences New

Flexible demand systems New

Instrumental-variables quantile regression New

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 New

Relative excess risk due to interaction (RERI) New

Time-varying covariates in the interval-censored Cox model New

Lasso for Cox proportional hazards models New

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

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

New features in robust inference for linear models New

Wild cluster bootstrap for linear regression New

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

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

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

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

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 New

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

Panel-data survival models

Survival models for SEM

Treatment effects for survival models

Local projections for impulse–response functions New

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

Heterogeneous difference in differences New

Causal mediation analysis New

Introduction to treatment effects in Stata: Part 1

Introduction to treatment effects in Stata: Part 2

Treatment effects: Regression adjustment

Treatment effects: Inverse-probability weighting

Treatment effects: Inverse-probability weighted regression adjustment

Treatment effects: Augmented inverse-probability weighting

Treatment effects: Nearest-neighbor matching

Treatment effects: Propensity-score matching

Treatment-effects estimation using lasso

Difference in differences

Treatment effects for survival models

Endogenous treatment effects