This course introduces students to widely-used procedures for regression analysis and provides intuitive, applied, and formal foundations for more advanced methods treated in later courses. The course covers descriptive, population and causal inference with regression from a modern perspective. While the course will emphasize the mathematical foundations of these concepts, each topic will also cover the implementation of the relevant methods in an application through the use of the statistical programming language R. This course represents the culmination of the required courses for QTM majors. It uses the coding skills learned in QTM 150 and the math skill learned in Math 210 and QTM 210 to conduct much of the analysis discussed in QTM 110. QTM 220 also provides an introduction and a foundation for future study of topics such as machine learning and causal inference.
Class will meet on Mondays and Wednesdays from 2:30-3:45 and Friday from 2:30-3:20 in PAIS 230. I will hold office hours Mondays from 5:00-7:00 in PAIS 583.
Problem sets will be assigned approximately weekly throughout the semester. The homework assignments will consist of analytical problems, computer work, and/or data analysis. Assignments will be posted on and submitted via Canvas. No late work will be accepted. We encourage students to confer with each other on the homework assignments, but you must write your own solutions. The use of Large Language Models, e.g. GPT4, to assist you in writing them is permitted. However, you are expected to be able to explain anything you turn in.
There will be two in-class midterm exams and a final exam at the assigned exam time and location. Collaboration is not permitted on the exams.
Grades will be based on homework (35 %), midterm exams (40 %), and the final exam (25 %). Incomplete grades will not be given unless there is an agreement between the instructor and the student prior to the end of the course. The instructor retains the right to determine legitimate reasons for an incomplete grade.
Week 13 | ||
M Apr 8 | No Class | Eclipse |
W Apr 10 | Lecture | Least Squares Regression |
F Apr 12 | Lab | Trees |
Week 14 | ||
M Apr 15 | Lecture | Model Choice and Bias |
W Apr 17 | Lecture | Bias and Interval Estimation |
F Apr 19 | Lab | Problem Sets 3 and 4 Review |
Week 15 | ||
M Apr 22 | Review Session | Review for Midterm 2 |
W Apr 24 | Exam | Midterm 2 |
F Apr 26 | Lab | Mental Health Case Study and Model Selection |
Week 16 | ||
M Apr 29 | Lecture | Inverse Probability Weighting |
Final | ||
? May TBD | Review Session | Review for Final Exam |
W May 8 | Exam | Final Exam (3-5:30 PM in PAIS 230) |