Statistics 151A: Linear Models
UC Berkeley, Spring 2024
Instructors
Instructor: Ryan Giordano
Office: 389 Evans Hall
Office hours: 12-2pm Mondays
rgiordano@berkeley.edu
pronouns: He / him
GSI: Dohyeong Ki
Office: TBD Evans Hall
Office hours: 3:30pm-5:30pm Thursdays (subject to change)
dohyeong_ki@berkeley.edu
pronouns: He / him
Important
Day-to-day announcements can be found in BCourses. Discussions can be found in ED. (See links above.)
Schedule
Lectures will be held Jan 16 2024 – May 03 2024 on Tuesday and Thursday, 9:30 am – 10:59 am, in Etcheverry 3108.
Labs will be held on Wednesdays from 9:00 am – 11:00 am and 1:00pm – 3:00 pm in Evans 344.
(Link to official course calendar.)
The following subject schedule is tentative and subject to change.
Week 1
| Jan 16: | Lecture 1 Class goals and organization. | |
|---|---|---|
| Jan 18: | Lecture 2 Simple regression as EDA, prediction, and inference |
Week 2
| Jan 23: | Lecture 3 Multilinear regression as loss minimization | |
|---|---|---|
| Jan 25: | Lecture 4 Multilinear regression as projection |
Week 3
| Jan 30: | Lecture 5 Different ways to draw lines through points | |
|---|---|---|
| Feb 1: | Lecture 6 Transformations of regressors: Some payoffs from the linear algebra perspective |
Week 4
| Feb 6: | Lecture 7 Univariate statistics and limit theorems | |
|---|---|---|
| Feb 8: | Lecture 8 Vector and matrix-valued statistics and limit theorems |
Week 5
| Feb 13: | Lecture 9 The Gaussian assumption | |
|---|---|---|
| Feb 15: | Lecture 10 Simulations and the law of large numbers |
Week 6
| Feb 20: | Lecture 11 Consistency of OLS and the residual variance under the Gaussian assumptions | |
|---|---|---|
| Feb 22: | Lecture 12 Linear algebra review and Quiz 2 retake |
Week 7
| Feb 27: | Lecture 13 Unbiased estimates of the residual variance | |
|---|---|---|
| Feb 29: | Lecture 14 Sampling variability of the coefficients |
Week 8
| Mar 5: | Lecture 15 Implications of Gaussianity (and deviations from it) | |
|---|---|---|
| Mar 7: | Lecture 16 Heteroskedasticity and the sandwich covariance matrix |
Week 9
| Mar 12: | Lecture 17 Interpreting the coefficients | |
|---|---|---|
| Mar 14: | Lecture 18 Inference on the coefficients: Gaussian versus asymptotic results |
Week 10
| Mar 19: | Lecture 19 The role of the regressor covariance in uncertainty estimates | |
|---|---|---|
| Mar 21: | Lecture 20 Hypothesis testing and confidence intervals |
Week 11
| Mar 25: | Holiday Spring break |
|---|
Week 12
| Apr 2: | Lecture 21 Variable selection and the F-test | |
|---|---|---|
| Apr 4: | Lecture 22 Cross validation and information criteria |
Week 13
| Apr 9: | Lecture 23 Ridge regression | |
|---|---|---|
| Apr 11: | Lecture 24 LASSO and variable selection |
Week 14
| Apr 16: | Lecture 25 Conformal prediction | |
|---|---|---|
| Apr 18: | Lecture 26 Influence and outliers |
Week 15
| Apr 23: | Lecture 27 Project consultation | |
|---|---|---|
| Apr 25: | Lecture 28 Project consultation |
No matching items