Rong Fu (Waseda University)


From 15:00
to 16:30
From 15:00
to 16:30

Course description
The goal of this course is to offer a fundamental overview of how to conduct empirical analyses with STATA. The course is incorporated with various topics related to the challenges in to the post-COVID world. StataCorp's STATA is a general-purpose statistical software tool for data management, visualization, statistics, and automated reporting. STATA is frequently used in a variety of disciplines, including economics, sociology, epidemiology, and science. This course begins with fundamental knowledge of probability and statistics before moving on to basic concepts of how to perform regressions. Students will learn basic coding language using STATA in the second half of the course work. Then, students learn how to find open data for research topics of interest and how to compose an essay for empirical analyses after gaining theoretical and practical understanding of empirical analyses. The course is designed to cover the following main topics:

1. Review of Probability and Statistics
2. Simple Regression
3. Multiple Regression
4. Dummy Variable and Difference-in-Difference
5. STATA Coding for Basic Statistics
6. STATA Coding for Regressions
7. Data Selection


Teaching methods
The course will be held with lectures and with the aid of multimedia materials, together with STATA practice sessions. Students should accomplish weekly assignments during the first half of the course and present their proposals during the second half of the course for the final reports. A written report will be assigned at the end of the course work. The topic of report should be related to challenges in the post-COVID world. The report should utilize STATA for estimations and use the methodologies taught in the course for empirical analyses. Along with the report, a summary of STATA codes should be included.

Evaluation methods
- Weekly assignment: 30%
- In-class presentation: 20%
- Final report: 50%


Learning outcomes
Students should:
(1) understand the concept of health in the scope of the classical economic theory;
(2) able to implement empirical estimations on real-world issues;
(3) able to interpret the findings with policy implications.



Week 1: Orientation Week

Week 2:  Introduction to STATA and Data Analysis - Data Import and Cleaning
- Overview of the course objectives, structure, and expectations
- Importing different types of data into STATA
- Cleaning and preparing data for analysis in STATA

Week 3: Descriptive Statistics and Data Visualization
- Generating descriptive statistics using STATA
- Creating charts, graphs, and visualizations to explore data

Week 4: Hypothesis Testing and Inferential Statistics
- Introduction to hypothesis testing and statistical inference
- Conducting t-tests, chi-square tests, and other statistical tests in STATA

Week 5: Regression Analysis: Simple Linear Regression
- Understanding the principles of regression analysis
- Performing simple linear regression in STATA

Week 6: Regression Analysis: Multiple Linear Regression
- Extending regression analysis to multiple independent variables
- Conducting multiple linear regression in STATA

Week 7: Categorical and Binary Outcomes: Logistic Regression
- Introduction to logistic regression for categorical and binary outcomes
- Performing logistic regression analysis in STATA

Week 8: Midterm break

Week 9: Panel Data
- Introduction to panel data and its analysis
- Running panel data models in STATA

Week 10: Time Series Analysis
- Exploring time series data and its unique characteristics
- Performing time series analysis in STATA

Week 11: Data Visualization and Reporting in STATA
- Advanced data visualization techniques in STATA
- Creating publication-quality graphs and visualizations

Week 12: Advanced Topics in STATA
- Exploring advanced STATA functionalities and commands
- Handling complex data structures and advanced data manipulation
- Addressing specific challenges and questions in data analysis

Week 13-14: Post-COVID Data Analysis Projects
- Working on individual or group projects investigating the post-COVID world
- Applying STATA data analysis techniques to specific research questions
- Analyzing and interpreting data to draw insights related to the post-COVID era

Week 15: Exam Week - Final Reports and Course Wrap-up


Baum, C. F. (2009). An introduction to Stata programming (Vol. 2). College Station: Stata Press.
Global Aging Data




Last updated: July 18, 2023


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