The course provides an introduction to statistical analysis using Stata. The course will introduce students to data cleaning, providing meaningful descriptive statistics, running ordinary least squares regressions and interpreting results. A particular focus is placed on best practices in coding and using precise language in statistics. If time permits, the students will learn about methods for causal inference.
Learning Outcomes
1. Interpret OLS regressions and tables
2. Discuss statistical and economic significance
3. Develop skills and intuition for pointing out statistical errors or misrepresentations
Professionnal Skills
Set up reproducible research projects in Stata and Github; Present results to various audiences.
- Online learning activities: 4 hours a semester
- Reading and Preparation for Class: 4 hours a week / 48 hours a semester
- Research and Preparation for Group Work: 26 hours a semester
- Other: Problem Sets / Coding: 4 hours a week / 48 hours a semester
- Project and presentation – Assigned around Week 4, Due around Week 10 (30%)
- 6-9 In-class quizzes based on readings (20%)
Students should prepare for each class having read and studied the assigned theoretical reading materials so that class time can be maximized for Stata tutorials. After discussing any questions on the reading materials, a short quiz will assess each student's understanding at the beginning of each class.
The remainder of each class will focus on Stata. Each week students will have to prepare and submit a Problem Set, generally a Stata do-file, based on the previous week's tutorial. Learning to code requires a lot of trial and error. Students are expected to be fairly autonomous and seek out answers on their own using resources discussed in class.
Problem set and quiz answers will be discussed weekly.Students will prepare a group project. They will receive initial feedback on their topic and plan, and a grading rubric before project submission.