Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

STATA Mastery: Practical Data Analysis & Interpretation

Name: STATA Mastery: Practical Data Analysis & Interpretation
Rating: 4.3 (34 reviews)

Hands-On STATA: Practical Application & Clear Interpretation

Created byDr. Nasir Munir

Last updated 2/2026

English

What you'll learn

Students will be able to navigate the STATA interface and use its basic commands.
Students will demonstrate the ability to import and manage datasets within STATA.
Students will perform descriptive and inferential statistical analyses using STATA.
Students will interpret statistical results and understand their implications.
Students will create and customize graphs and charts to present data effectively.

Course content

13 sections • 58 lectures • 8h 41m total length

Introduction2:06
Explore Stata's practical applications, covering data management, descriptive and inferential statistics, data visualization, and practical case studies to prepare reports and final projects.
Introduction about all the tabs of data10:26
Explore stata's interface, including the file, graphics, statistics, and do-file editor tabs, and learn to import data from Excel via data editor or browse, and label variables.
How to download Data from World Development Indicator (WDI)4:21
Data downloading from International monetary Fund (IMF)7:14
How to make Panel data7:09
Importing data into STATA9:20
Learn how to import data into Stata from Excel, including first-row variable names and sheet selection. Use data editor or browse mode to view, edit, and verify imports.
Data issues2:42

Multicollinearity definition and its Causes6:23
Identification of Multicollinearity8:48
Effects of Multicollinearity6:58
Explore how multicollinearity causes unstable regression coefficients and inflated standard errors, lowering t-statistics and statistical power, and producing misleading R-squared that jeopardizes forecasting.
Detection of Multicollinearity5:16
Detect multicollinearity using correlation matrix, VIF, tolerance, and condition index, with 80% thresholds and practical analysis to follow in software.
Solutions to Multicollinearity4:02
Multicollinearity in STATA Practical Sessions12:12

Checking the stationary of time series data6:26
Converting Data into First Difference and seeing its stationarity14:08
Converting non-stationary Series into Stationary Series6:16
Converting Time series into Panel data7:02
Master stata techniques to differentiate time series and panel data and convert time series data into panel data using multi-country examples.
Checking the Stationarity of the Panel Data12:40
Check the stationarity of the panel data by running unit root tests, including the living Q and Hendry M tests, after declaring the data as panel in Stata.

Introduction of Heteroscedasticity and Homoscedasticity9:57
Causes of Heteroskedasticity14:40
Identify and explain the seven main causes of heteroscedasticity, including changing relationships, outliers, incorrect models, omitted variables, measurement error, time-related variability, and data aggregation, with practical examples.
Consequences of Heteroscedasticity7:46
Detection of Heteroscedasticity7:57
Detect heteroscedasticity with a graphical method using OLS residuals and a residuals vs predicted values plot, and with a park test on ln e^2 and ln x at 0.05.
Breusch–Pagan/Cook–Weisberg test for heteroskedasticity4:30
Remedial measure of Hetero4:54

Descriptive Statistics, interpretations.13:12
Apply descriptive statistics in Stata to raw data to reveal mean, median, mode, variance, standard deviation, skewness, kurtosis, and identify outliers.
Linear Regression Model11:51
Quantile Regression7:56
How to apply 2SLS and 3SLS5:14
Understanding and Interpreting P values, T-values, R-square and F-stat18:15
Obtaining the Residuals and checking their Normality8:21
Checking that Residuals are Constant or Heteroscedastic7:49
Learn to check residuals for constant variance in Stata by importing Excel data, running a regression, generating residuals, and applying a heteroskedasticity test with p-values guiding the decision.
Checking that Residuals are serially correlated or not9:31
Durbin–Watson d-statistic, Testing for serial correlation3:30
Removal of serial Correlation (Auto correlation)17:06
Jarque-Bera and Sk normality test5:55

Johansen Test of Cointegration11:30
Granger Causality Test11:32
Apply the granger causality test in stata to assess whether gdp and population causally influence each other, interpret p-values, select lags, and report one-way or two-way effects.
Westerlund Test for cointegration7:19
Apply the Westerlund co-integration test to panel data to determine if some panels are co-integrated or all panels are co-integrated, and learn to run it in Stata and interpret p-values.
Pedroni test for cointegration3:39
Kao test for cointegration4:31

Lag selection Criteria 1st Method6:06
Explore the first method to determine the lag length for a VAR model using LR, FPE, AIC, HQC, and SBIC, with data imported from Excel to Stata.
Lag Selection Criteria 2nd Method3:48
Conditions for Applying Vector Auto Regressive (VAR)7:28
Vector Auto Regressive (VAR) Estimation and Interpretation16:01
Diagnostic Testing of VAR5:50
Vector Error Correction Model (VECM)22:32
Forecasting in VAR Model12:28

Requirements

Basic knowledge of Using computer.

Description

Course Description

This course provides a comprehensive introduction to STATA, a powerful statistical software widely used in various fields such as economics, sociology, political science, and public health. The course focuses on the practical application of STATA, covering data management, statistical analysis, and interpretation of results. Students will learn how to perform a range of statistical tests, create meaningful visualizations, and draw valid conclusions from their data. Through hands-on exercises and real-world examples, students will develop the skills necessary to effectively use STATA for their research and professional needs.

Course Objectives

To provide students with a thorough understanding of the STATA interface and its functionalities.
To teach students how to import, manage, and manipulate datasets in STATA.
To equip students with the knowledge to perform a variety of statistical tests and analyses using STATA.
To enable students to interpret and communicate the results of their statistical analyses effectively.
To develop students’ ability to create and customize data visualizations in STATA.
Introduction to STATA
Overview of STATA
Installation and setup
Navigating the STATA interface
Basic commands and syntax
Data types and structures in STATA

Data Management in STATA

Importing data from various sources (Excel, CSV, etc.)
Data cleaning and preparation
Data transformation (sorting, merging, reshaping)
Handling missing data

Descriptive Statistics

Summary statistics (mean, median, mode, etc.)
Frequency distributions and cross-tabulations
Measures of dispersion (variance, standard deviation, etc.)
Data visualization (histograms, bar charts, pie charts)

Inferential Statistics I

Hypothesis testing
t-tests (one-sample, independent, paired)
Chi-square tests
Analysis of variance (ANOVA)

Inferential Statistics II

Correlation analysis
Simple linear regression
Multiple regression analysis
Assumptions and diagnostics for regression models

Advanced Statistical Tests

Logistic regression
Factor analysis
Time series analysis
Panel data analysis

Estimating Models in STATA

Detecting Multicollinearity
Solution to Multicollinearity
Stationarity of Time Series
Stationarity of Panel data
Converting non-stationary series to Stationary
R-Sqaure
Auto-Correlation
Removal of Auto-Correlation
Johansen-cointegration Test
Granger Causality Test
VAR model
Forecasting in VAR model
VECM model
Fixed Effect
Random Effect
Hausman Test

Preparing Reports and Documentation

Creating tables and reports in STATA
Documenting and annotating your work
Best practices for reproducible research
Sharing and collaborating on STATA projects

Special Topics in STATA

Survey data analysis
Handling complex survey designs
Advanced econometric techniques
Customizing STATA with user-written commands

Review and Final Project

Review of key concepts and techniques
Q&A and troubleshooting
Final project presentations
Course wrap-up and feedback

Who this course is for:

For all who is affiliated with the field of teaching, and doing research.

STATA Mastery: Practical Data Analysis & Interpretation

What you'll learn

Explore related topics

Course content

Introduction7 lectures • 43min

How to make Different Charts in Stata3 lectures • 26min

Multicollinearity6 lectures • 44min

Stationarity of the data5 lectures • 47min

Heteroscedasticity6 lectures • 50min

Linear Regression model and Diagnostic Testing11 lectures • 1hr 49min

Cointegration tests5 lectures • 39min

VAR (Vector Auto regressive Model) and Vector Error Correction Model (VECM)7 lectures • 1hr 14min

Fixed effect and Random effect2 lectures • 27min

Auto Regressive Distributed Lagged model (ARDL)2 lectures • 29min

Requirements

Description

Who this course is for: