Course Detail

Applied Regression Analysis (41100)

Course Description by Faculty

  • Smetanina, Ekaterina (Katja)
  • Content
    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.
  • Prerequisites
    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. 
    • No non-Booth Students

  • Materials
    All of the instructor’s notes will be available on the course website.
  • Grades
    Based on homework assignments (groups allowed), a midterm exam, and a final exam. Cannot be taken pass/fail. No auditors. No non-Booth students, unless permission to enroll is granted by the instructor directly.
    • No auditors

    • No pass/fail grades

  • Syllabus
  • Winter 2023Section: 41100-01M 8:30AM-11:30AMHarper CenterC02In-Person Only
  • Winter 2023Section: 41100-02TH 8:30AM-11:30AMHarper CenterC02In-Person Only
  • Winter 2023Section: 41100-81F 6:00PM-9:00PMGleacher Center208In-Person Only
Description and/or course criteria last updated: November 3 2022