Statistical Modeling (OCEANS 140/240)
This course schedule is an adaptation of Richard McElreath’s 2024 course, using his Statistical Rethinking text.
Schedule
| Week | Date | Topic | Pre-class | SR2 | Due |
|---|---|---|---|---|---|
| 1 | Tue., Jan. 7 | Intro to course; intro stats and coding review | Science Before Statistics | 1 | |
| 1 | Thu., Jan. 9 | Components of a Bayesian model | Garden of Forking Data (to 0:45) | 2 | |
| 2 | Tue., Jan. 14 | Sampling the imaginary | Garden of Forking Data (0:45-1:16) | 3 | HW1 |
| 2 | Thu., Jan. 16 | Intro to linear models | Geocentric Models | 4.1-4.4 | |
| 3 | Tue., Jan. 21 | Categorical effects, polynomial regression, and splines | Categories and Curves (to 1:14) | 4.5-4.6; 5.3 | HW2 |
| 3 | Thu., Jan. 23 | Multiple regression and intro to causal inference | Elemental Confounds | 5.1, SR 6 | |
| 4 | Tue., Jan. 28 | (More) causal inference | Good and Bad Controls | 6 | HW3 |
| 4 | Thu., Jan. 30 | Overfitting, regularization, model comparison | Overfitting | 7.1, 7.3-7.6 | |
| 5 | Tue., Feb. 4 | Markov chain Monte Carlo | MCMC | 8.1, 9 | HW4 |
| 5 | Thu., Feb. 6 | Generalized linear models; Binomial regression | Modeling Events (to 1:16) | 10.2, 11.1 | |
| 6 | Tue., Feb. 11 | Confounds; Poisson regression | Counts and Confounds | 11.2 | HW5 |
| 6 | Thu., Feb. 13 | Ordered categorical outcomes | Ordered Categories | 12 | |
| 7 | Tue., Feb. 18 | Multilevel models | Multilevel Models | 13 | HW6 |
| 7 | Thu., Feb. 20 | Multilevel models | Multilevel Adventures | 13 | |
| 8 | Tue., Feb. 25 | Multilevel models | Correlated Features | 14 | HW7 |
| 8 | Thu., Feb. 27 | Continuous categories | Gaussian Processes | 14.5 | |
| 9 | Tue., Mar. 4 | Measurement error | Measurement | 15.1 | HW8 |
| 9 | Thu., Mar. 6 | Missing data | Missing Data | 15.2 | |
| 10 | Tue., Mar. 11 | Scientific models | Generalized Linear Madness | 16 | HW9 |
| 10 | Thu., Mar. 13 | Course wrap-up | Horoscopes | 17 |