Regression Analysis: Spring 2024

QTM 220 with David A. Hirshberg

Description

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.

Goals

By the end of this course, students will be able to

Meeting Times

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.

Work and Evaluation

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.

End of Semester Schedule

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)

Policies

Attendance
I would like to see you at the majority of our class meetings. That said, schedule conflicts and illness happen. Please do not come to class sick. I will record the lectures and post recordings soon after class. There is no need to explain your absences, but please try to inform me of them in advance of class meetings.

Accessibility and Accomodations
As the instructor of this course I endeavor to provide an inclusive learning environment. I want every student to succeed. The Department of Accessibility Services (DAS) works with students who have disabilities to provide reasonable accommodations. It is your responsibility to request accommodations. In order to receive consideration for reasonable accommodations, you must register with the DAS here. Accommodations cannot be retroactively applied so you need to contact DAS as early as possible and contact me as early as possible in the semester to discuss the plan for implementation of your accommodations. For additional information about accessibility and accommodations, please contact the Department of Accessibility Services at (404) 727-9877 or accessibility@emory.edu.

Writing Center
Tutors in the Emory Writing Center and the ESL Program are available to support Emory College students as they work on any type of writing assignment, at any stage of the composing process. Tutors can assist with a range of projects, from traditional papers and presentations to websites and other multimedia projects. Writing Center and ESL tutors take a similar approach as they work with students on concerns including idea development, structure, use of sources, grammar, and word choice. They do not proofread for students. Instead, they discuss strategies and resources students can use as they write, revise, and edit their own work. Students who are non-native speakers of English are welcome to visit either Writing Center tutors or ESL tutors. All other students in the college should see Writing Center tutors. Learn more, view hours, and make appointments by visiting the websites of the ESL Program and the Writing Center. Please review the Writing Center’s tutoring policies before your visit.

Honor Council
The Honor Code is in effect throughout the semester. By taking this course, you affirm that it is a violation of the code to cheat on exams, to plagiarize, to deviate from the teacher's instructions about collaboration on work that is submitted for grades, to give false information to a faculty member, and to undertake any other form of academic misconduct. You agree that the instructor is entitled to move you to another seat during examinations, without explanation. You also affirm that if you witness others violating the code you have a duty to report them to the honor council.