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

Midterm: Take home. Posted on Wednesday 2/9 at noon. Due on Thurs 2/10 at noon on Gradescope.
- Statistics on the Midterm scores
Final Exam Date/Time.
- Statistics on the Final Exam scores

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

Homework, slides and notes are in the Box folder.
Current lectures -- Winter 2022
Previously recorded lectures -- Spring 2021. (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

For statistical computation, R is recommended.

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