STAT 230A: Linear Models

Course Syllabus

This is an abbreviated working draft of the syllabus and is subject to change. See bCourses for the full syllabus.

Topics

The following topics will be covered:

  • Ordinary least squares (OLS)
  • Gauss-Markov theorem
  • Finite-sample exact and asymptotic inference
  • Heteroskedasticity
  • Cluster-robust standard errors
  • Model checking
  • Model selection and shrinkage
  • Generalized linear models
  • Generalized estimating equations

Text and Equipment

  • Text: Linear Model and Extensions by Peng Ding. Available on Arxiv.

  • Equipment: Access to a computer with access to the Internet and R/Python installation will be necessary. If you do not have access to a computer, you can borrow one from the University library (see Hardware Lending).

Learning Goals

By the end of the semester you should be able to:

  1. Understand and interpret ordinary least squares regression models from a mathematical perspective.
  2. Adapt the basic regression model to common practical complications including violations of standard assumptions, high-dimensional regimes, and non-continuous outcomes.
  3. Evaluate the quality of a regression analysis in context and suggest improvements.
  4. Use R or Python to fit, report, and clearly communicate the process and results of a regression analysis.

Prerequisites

  • Master’s level probability at the level of STAT 201A.
  • Linear algebra, at the level of the appendix in the course notes.
  • R or Python programming.

Attendance

Regular attendance and participation in lecture is an important part of the learning experience in this course. As such, in-person attendance at lectures is expected and lecture recordings will not generally be released. Attendance is also encouraged at lab. Note that attendance in your assigned lab is required on days when quizzes are given.

Assessment

Homework

We anticipate giving 7 homework assignments during the semester. All homework assignments must be submitted as a single PDF file via Gradescope.

Quizzes

We will give 4 in-person quizzes in lab during the semester on Feb 9, Feb 23, April 13, and April 27. These will test concepts from the homework due the week before.

Exams

An in-person cumulative midterm exam will be given from 7:00 to 9:00 PM on Thursday March 12th, and an in-person cumulative final exam will be given from 7:00 to 10:00 PM on Monday May 11th. Please confirm now that you are available to attend both exams in person!

Final Project

Students will work in groups of one or two to carry out the final project. A written project proposal is due in April and the final report is due during finals week.

Overall Score

Your letter grade will be based on the following weights:

  • Homework: 14%
  • Quizzes (dropping lowest score): 18%
  • Midterm: 15%
  • Final: 30%
  • Group project: 23%

Policies

Late Assignments

All students will have 5 late days that they may use for homework. Late days cannot be used for the group project.

Academic Honesty and AI Policy

Homework must be done independently. This course allows limited use of generative AI tools (GenAI) on homework assignments and projects. If you use GenAI, you should include a statement acknowledging your use.