수시로 업데이트 될 예정입니다.

여기 250개가 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.

PDF documentation in Stata 15

Automatic production of web pages from dynamic Markdown documents

Create PDF reports from within Stata

Create Word documents from within Stata

Extended regression models (ERMs)

Finite mixture models (FMMs)

Heteroskedastic linear regression

Import FRED (Import Federal Reserve Economic Data)

Interval-censored survival models

Latent class analysis (LCA)

Linearized DSGEs

Mixed logit models

Multilevel tobit and interval regression

Multiple-group generalized SEM

Nonlinear mixed-effects models

Nonparametric regression

Panel-data cointegration tests

Poisson with sample selection

Power analysis for cluster randomized designs and linear regression

A prefix for Bayesian regression

Spatial autoregressive models

Tests for multiple breaks in time series

Threshold regression

Transparency in Stata graphs

Zero-inflated ordered probit

Import FRED (Import Federal Reserve Economic Data)

Copy/paste data from Excel into Stata

Import Excel data into Stata

Saving estimation results to Excel

Importing delimited data

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

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

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

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

Mixed logit models

Poisson with sample selection

Zero-inflated ordered probit

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 New

Probit regression with continuous covariates New

Probit regression with categorical and continuous covariates New

Nonparametric regression

Spatial autoregressive models

Heteroskedastic linear regression

Mixed logit models

Multilevel tobit and interval regression

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 New

Probit regression with continuous covariates New

Probit regression with categorical and continuous covariates New

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

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

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

Power analysis for cluster randomized designs and linear regression

Tour of power and sample size

A conceptual introduction to power and sample size

New power and sample-size features 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

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

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

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

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

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 for survival models

Endogenous treatment effects