This course is about regression, a powerful and widely used data analysis technique which is used to understand how different random quantities relate to one another. Students will learn how to use regression to analyze a variety of complex real-world problems, with the aim of gaining insights from the data and also to potentially predict future events. Focus is placed on the understanding of fundamental concepts and its implementation in a programming language (R, or alternative). Real-world examples are used throughout the course to illustrate the application of techniques. Topics covered include: (i) short review of simple linear regression; (ii) multiple regression and model checking and diagnostics; (iii) generalized linear models (e.g. logistic regression); (iv) time series models and forecasting. We will also discuss the use of machine learning in the context of regression.
All Non-Booth students require instructor permission. Business 41000 or familiarity with the topics covered in Business 41000. This course is only for students with a basic background in statistics, and preferably some prior exposure to linear regression. Note: There is a homework due on the first day of class.
All of the instructor’s notes will be available on the course website.
Based on homework assignments (groups allowed), class participation, midterm exam, and a final exam. Cannot be taken pass/fail. No auditors. Non-Booth students cannot enroll unless permission to enroll is granted by the instructor directly.
- Mandatory attendance week 1
- No auditors
- No pass/fail grades
Description and/or course criteria last updated: September 15 2025
