Course Detail

Probability and Statistics (41901)

Course Description by Faculty

  • Kaji, Tetsuya
  • Content
    This is a PhD course that introduces fundamental statistical concepts for academic research in business and economics. It covers basic topics in probability and statistics, including limit theorems, principles of estimation and inference, linear and logistic regression, and causal inference. Much emphasis is put on large-sample (asymptotic) theory.
  • Prerequisites
    Real analysis and linear algebra. BUSN 41901=STAT 32400

  • Materials
    There is no required textbook.
  • Grades
    Grades are based on a midterm exam and a final exam.
  • Syllabus
  • Autumn 2022Section: 41901-50TH 4:00PM-7:00PMHarper Center3B - Seminar RoomRemote-Only
Description and/or course criteria last updated: August 25 2022