STAT 100C: Linear Models

Spring 2021

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: MW 2:00pm-3:15pm. Delivered online via Zoom. (First lecture 3/29/21)
Links to Zoom meetings will be posted on Campuswire.
To install Zoom follow this link.
Please also see UCLA policy regarding protection of privacy and data when using Zoom.
You might also find UCLA resources for remote learning useful.
Instructor: Arash A. Amini
Office Hours: No office hours.
TA: Ian McGovern
Grader: Wenbin Guo

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.

Gradescope and Campuswire

Announcements: Will be posted on Campuswire (Code: 5620) Please sign-up as soon as possible by following the link.

Use Gradescope for homework submission. (Code 3YDED6)

Midterm: Posted May 10 in the evening. Due May 11 at 23 pm on Gradescope.
- Statistics on the Midterm Grades
Final: Posted on Tuesday, June 8 (roughly in 5-6pm). Will be due on Gradescope June 9 at 23:59 pm on Gradescope.
- Statistics on the Final Exam
Grading: Homework 20%, Midterm 30%, Final 50%.
Prerequisites: STAT 100B and linear algebra such as Math 33A.

Resources

Homework, slides and notes are in the Box folder.
Current lecture videos. (Youtube playlist)
Previously recorded lectures -- Winter 2021 (Youtube playlist)
Previously recorded lectures -- Spring 2020 (Youtube playlist)

Textbook

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

Data

Datasets for the book.

Supplementary texts

J. J. Faraway, Practical Regression and Anova using R: Introduction to doing regression in R.
G. Strang, The Four Fundamental Subspaces: 4 Lines: Short overview of linear algebra.

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

Class attendance and participation is highly recommended.
For statistical computation, R is recommended.
Lecture notes, scribed by you, might be posted on the website.