| Lecture | Slides | Chapter | Extras |
|---|---|---|---|
| 00 | Introduction | 1 | |
| 01 | No lecture. | ||
| 02 | Simple linear regression model; least squares; residuals | 2 | |
| 03 | Introduction to MATLAB | demo.m plot_gpa_fit.m my_regress.m CH01PR19.txt Getting started guide |
|
| 04 | Normal error regression model; maximum likelihood | ||
| 05 | Confidence intervals and hypothesis testing in the normal regression model. | ||
| 06 | Proof of Gauss Markov Theorem | ||
| 07 | Inference in Normal Regression Model | 2.7-2.10 | |
| 08 | ANOVA | Cochran’s theorem | |
| 09 | Diagnostics and Remedial Measures | 3 | |
| 10 | Remedial Measures and Transformations | 4 | |
| 11 | Joint estimation, Bonferroni joint confidence intervals | 5 | |
| 12 | Linear Algebra Review | Cheat sheets Matrix, Gaussian, Linear Algebra |
|
| 13 | cont. | ||
| 14 | cont. | ||
| 15 | Multivariate Normal Review | ||
| 16 | cont. | ||
| 17 | Matrix Linear Regression | 5 | |
| 18 | Multiple linear regression, Testing | 6 | |
| 19 | Quantitative and Qualitative Inputs, Interactions, and Interpretation | 8 | |
| 20 | ANOVA / Extra Sums of Squares | 7 | |
| 21 | Proof of Cochran’s Theorem, extra |