Lectures and Labs

Stat 151A: Linear Models

Lecture 1: Jan 16th:
- Class organization
Lab Jan 17th:
- Style problem
Lecture 2: Jan 18th:
- Different kinds of questions
- Note on linear and affine functions

Lecture 3: Jan 23rd:
- Multilinear regression as loss minimization
Lab Jan 17th:
- Linear algebra in R
Lecture 4: Jan 25th:
- Multilinear regression as projection

Lecture 5: Jan 29th:
- Quiz 1
- Alternative ways to draw lines through points
Lab Jan 30th:
- Homework 1 review
Lecture 6: Feb 1st:
- Transformations of regressors: Some payoffs from the linear algebra perspective

Lecture 7: Feb 6th:
- Univariate statistics and limit theorems
Lab Feb 7th:
- Homework consultation
Lecture 8: Feb 8th:
- Vector and matrix-valued statistics and limit theorems

Lecture 9: Feb 13th:
- The Gaussian assumption
Lab Feb 14th:
- Homework 2 review
Lecture 10: Feb 15th:
- Simulations and the law of large numbers

Lecture 11: Feb 20th:
- Consistency of OLS and the residual variance under the Gaussian assumptions
Lab Feb 21st:
- Homework 3 consultation
Special session: Feb 22nd:
- Linear algebra review and Quiz 2 retake

Lecture 12: Feb 27th:
- Residual distribution under normality
Lab Feb 28th:
- Homework 3 solutions
Lecture 13: Feb 29th:
- Sampling variability of the coefficients
- Sampling variability of the coefficients (notebook)

Lecture 14: Mar 5th:
- Implications of Gaussianity (and deviations from it)
Lab Mar 6th:
- Homework 4 consultation
Lecture 15: Mar 7th:
- Heteroskedasticity and the sandwich covariance matrix

Lecture 16: Mar 12th:
- Sampling variability of the coefficients under normality
Lab Mar 13th:
- Homework 4 solutions
Lecture 17: Mar 14th:
- Interpreting the regression coefficients
- Interpreting the regression coefficients (notebook)

Lecture 18: Mar 19th:
- The role of regressors in the coefficient variance
- The role of regressors in the coefficient variance (notebook)
Lab Mar 20th:
- Homework 5 consultation
Lecture 19: Mar 21st:
- Confidence intervals and hypothesis testing

Lecture 20: April 2nd:
- Testing groups of coefficients with the F-test
Lab April 3rd:
- Project consultation
Lecture 21: April 4th
- Cross-validation and information criteria
- Cross-validation and information criteria (notebook)

Lecture 22: April 9th:
- Ridge regression and complexity penalties
Lab April 10th:
- Project consultation
Lecture 23: April 11th
- The Lasso and sparsity
- The Lasso and sparsity (notebook)

Lecture 24: April 16th:
- Influence and outliers
- Influence and outliers (notebook)
Lab April 17th:
- Project and HW consultation
April 18th: Course review (no lecture notes)

April 23rd:
- Project office hours
Lab April 24th:
- Project consultation
Lecture 25: April 25th:
- Omitted variables in inference and prediction