Note: this schedule is subject to change. Please check for updates frequently!
Links under each lecture and lab go to its notes, WOD (warm-up exercise), and lab handouts. Readings are posted on WebCampus.
| Week | Lecture 1 | Lab | Lecture 2 | Readings |
|---|---|---|---|---|
| Aug. 24 | Course introduction Notes WOD: Self-diagnostic |
Lab 1: Data visualizations, math/stats prerequisites Act 1 Act 2 Act 3 |
Algorithms in statistics Notes WOD: Permutation pseudocode |
Bolker Ch 1-3, Bolker appendix 1 |
| Aug. 31 | Working with functions Notes WOD: R syntax and notation |
Lab 2: Working with functions Notes Act 1 Act 2 Act 3 |
Bestiary of functions Notes WOD: Limits and a derivative |
Bolker Ch. 3, Bolker appendix 2-4 |
| Sept. 7 | No class (Labor Day) | Lab 3: Working with probabilities Notes Act 1 Act 2 Act 3 |
Probability Notes WOD: A probability puzzle |
Bolker Ch. 4 |
| Sept. 14 | Combinatorics, random variables, and expectation Notes WOD: A probability puzzle |
Final project proposals- getting into groups | Probability distribution functions Notes WOD: Expectation and the sample mean |
Bolker Ch. 4 |
| Sept. 21 | Synthetic data simulation Notes WOD: Choosing a data-generating model |
Lab 4: Data-generating models and power analysis Notes Act 1 |
Intro to likelihood Notes WOD: Writing the likelihood |
Bolker Ch. 5, Bolker Ch. 6 |
| Sept. 28 | Intro to maximum likelihood Notes |
Lab 5: Maximum likelihood estimation Notes Act 1 Act 2 |
Live coding: power analysis (Lab 4 review) Notes WOD: Find the bugs |
Bolker Ch. 6 (6.1-6.3) |
| Oct. 5 | Likelihood and parameter uncertainty Notes WOD: Reading the interval |
Lab 6: More likelihood! Notes Act 1 Act 2 |
Logistic regression; Bayesian inference Notes |
Bolker Ch. 6 (6.1-6.5) |
| Oct. 12 | Bayesian inference (cont.) Notes WOD: How much did the prior matter? WOD: Newton's method by hand (take-home) |
Group meetings: final project proposals | Optimization Notes WOD: Curvature by hand (optional take-home) |
Bolker Ch. 6 (6.2.2, 6.4.2); Ch. 7 (7.1-7.2, 7.4.4) |
| Oct. 19 | Optimization (cont.): Nelder-Mead, simulated annealing; intro to MCMC (Metropolis) Notes |
Lab 7: Bayesian inference with MCMC Notes Act 1 Act 2 |
Hamiltonian Monte Carlo and Stan Notes WOD: Getting Stan running (take-home) cmdstanr quick start |
Bolker Ch. 7.2-7.3; Monnahan et al. 2017 (Methods Ecol. Evol.) |
| Oct. 26 | Model comparison: likelihood ratio tests, AIC; regularization (ridge, lasso) Notes WOD: Connect the dots |
Lab 8: Model comparison and regularization Notes Act 1 Act 2 |
Causal diagrams (DAGs): which variables belong in the model? Notes |
Bolker Ch. 6.6; optional: Tredennick et al. 2021 (Ecology); Hooten & Hobbs 2015 (Ecol. Monogr.) |
| Nov. 2 | Standard statistics revisited: linear models, ANOVA, GLMs as likelihood models Notes WOD: Linear or not? |
Lab 9: Linear algebra for statistics Notes Lab 9 |
Modeling variance: random effects and autocorrelation Notes WOD: Two cores from one tree |
Bolker Ch. 9, Appendix A.8, Ch. 10 (10.1-10.3) |
| Nov. 9 | Bayesian multilevel models | Lab 10: Bayesian multilevel models | No class (Veterans Day) | Bolker Ch. 10 |
| Nov. 16 | Nonlinear regression and GAMs | Group meetings: final project results | Nonlinear regression and GAMs | |
| Nov. 23 | Spatial regression models | Lab 11: Spatial regression models | Spatial regression models | Bolker Ch. 11 |
| Nov. 30 | Dynamic models | Lab 12: Dynamic models | Dynamic models | Bolker Ch. 11 |
| Dec. 7 | Class wrap-up- where to go from here | Student project presentations! | No class (prep day) | |
| Dec. 14 | Project write-ups due by midnight Dec. 14 |