STAT 100C: Linear Models

Winter 2022

Theory of linear models, with emphasis on matrix approach to linear regression and connections to multivariate normal distribution. Topics include simple and multiple linear regression, model fitting, inference about parameters, testing general linear hypotheses, specification issues, model checking and model selection.

General info

  • Lectures: TR 3:30pm-4:45pm.
    • First two weeks: Delivered via Zoom.
    • Afterwards: In person @ DODD 146
  • Instructor: Arash A. Amini
  • Office Hours: No office hours.
  • TA: Kaiwen Jiang
    • TA Office hours: Mon & Thurs 9-10am (First 2 weeks via Zoom, afterwards @ MS 8141)
    • TA Discussion Section: W 8-8:50am & 9-9:50am (First 2 weeks via Zoom, afterwards @ BOELTER 9436)
  • Grader: Yifei Xu

Please read!

  • Notice: Please do not email me your late homework. Instead, post a note on Campuswire (it can have attachments) and set the visibility to TAs and Instructors only. We will address the issue there. Only requests through Campuswire are considered.

Exams

Gradescope and Campuswire

  • Announcements: Will be posted on Campuswire (Code: 9535)
  • Use Gradescope for homework submission. (Code V82EBW)
  • Grading: Attendance 5%, Homework 23%, Midterm 27%, Final 50%.
  • Prerequisites: STAT 100B and linear algebra such as Math 33A.

Resources

Textbook

  • B. Abraham and J. Ledolter, Introduction to Regression Modeling, 2006. ISBN: 978-0534420758 Corrections, courtesy of Prof. Ledolter.

Data

Supplementary texts

Syllabus

  • Review of linear algebra
  • Random vectors and matrices
  • Multivariate normal distribution
  • Multiple linear regression
    • Simple linear regression
  • Inference
  • Quadratic forms
  • Specification issues
  • Model checking
  • Model selection

Miscellaneous

p-value controversies