Statistics 151a: Linear models

UC Berkeley, Spring 2026

Calendar (tentative)
Date Day Note Unit Topic Assignment
Jan 21 Wednesday Lecture Introduction Class policies Lab 0
Jan 23 Friday Lecture Real-world questions
Jan 26 Monday Lecture Inference and probability
Jan 28 Wednesday Lecture Prediction and uncertainty Lab 1
Jan 30 Friday Lecture Linear algebra review HW 0
Feb 2 Monday Lecture Unit 1: Multilinear regression Simple linear regression
Feb 4 Wednesday Lecture Multilinear regression Lab (review)
Feb 6 Friday Lecture ——– Quiz 0
Feb 9 Monday Lecture Paths to the least squares objective
Feb 11 Wednesday Lecture Transforming the regressors Lab 2
Feb 13 Friday Lecture Transforming the response
Feb 16 Monday Administrative holiday
Feb 18 Wednesday Lecture Unit 2: Testing and inference Confidence intervals review Lab 3
Feb 20 Friday Lecture The multivariate normal HW 1
Feb 23 Monday Lecture Z and T tests
Feb 25 Wednesday Lecture Model selection and F-tests Lab (review)
Feb 27 Friday Lecture ——– Quiz 1
Mar 2 Monday Lecture Asymptotics and heteroskedasticity
Mar 4 Wednesday Lecture Misspecification Lab 4
Mar 6 Friday Lecture Statistical and practical significance
Mar 9 Monday Lecture Unit 3: Regularization and machine learning Canceled class
Mar 11 Wednesday Lecture Statistical and practical significance Lab 5
Mar 13 Friday Lecture Splines and basis expansions HW 2
Mar 16 Monday Lecture Bayesian estimators
Mar 18 Wednesday Lecture Ridge regression Lab (review)
Mar 20 Friday Lecture ——– Quiz 2
Mar 23 Monday Spring break ——–
Mar 24 Tuesday Spring break ——–
Mar 25 Wednesday Spring break ——–
Mar 26 Thursday Spring break ——–
Mar 27 Friday Spring break ——–
Mar 30 Monday Lecture Unit 4: How regression can go wrong Outliers and leverage
Apr 1 Wednesday Lecture The influence function Lab 6
Apr 3 Friday Lecture Regression to the mean HW 3
Apr 6 Monday Lecture The FWL theorem
Apr 8 Wednesday Lecture Omitted variable bias Lab (review)
Apr 10 Friday Lecture ——– Quiz 3
Apr 13 Monday Lecture Unit 5: Generalizations Canceled class
Apr 15 Wednesday Lecture Logistic regression Lab 7
Apr 17 Friday Lecture Poisson regression HW 4
Apr 20 Monday Lecture Nonlinear least squares
Apr 22 Wednesday Lecture Random effects models Lab (review)
Apr 24 Friday Lecture Hierarchial modeling Quiz 4
Apr 27 Monday Lecture Unit 6: Miscellaneous topics Bootstrap
Apr 29 Wednesday Lecture Lasso and sparsity Lab (projects)
May 1 Friday Lecture TBD HW 5
May 4 Monday Lecture Final projects
May 6 Wednesday Lecture Lab (projects)
May 8 Friday Lecture

Unelss otherwise noted, the primary materials for the course are the lecture notes, which will be posted to the course website in advance of class. The following textbooks are useful supplementary texts and are all either freely available online or available electronically through the UC Berkeley library:

Other books of interest to the class are