| Tue 8/30 | 1. Linear models: Estimation | Intro to linear and generalized linear models | Lecture notes 1.1-1.2 | – |
| Thu 9/1 | 1. Linear models: Estimation | Least squares estimation | Lecture notes 1.3-1.5 | – |
| Tue 9/6 | 1. Linear models: Estimation | Correlation, multiple correlation, and R2 | Lecture notes 1.6 | – |
| Thu 9/8 | 1. Linear models: Estimation | Collinearity, adjustment, partial correlation | Lecture notes 1.7 | – |
| Tue 9/13 | 1. Linear models: Estimation | R Demo | Lecture notes 1.8, R4DS Ch. 1-8 | – |
| Thu 9/15 | 2. Linear models: Inference | Inferential preliminaries | Lecture notes 2.1, 2.2.1 | – |
| Tue 9/20 | 2. Linear models: Inference | Hypothesis testing | Lecture notes 2.2.2 | Homework 1 (PDF, GitHub, Solutions) due at 10am |
| Thu 9/22 | 2. Linear models: Inference | Power of hypothesis testing | Lecture notes 2.3 | – |
| Tue 9/27 | 2. Linear models: Inference | Confidence intervals, practical considerations | Lecture notes 2.4, 2.5 | – |
| Thu 9/29 | 2. Linear models: Inference | R Demo | Lecture notes 2.6 | – |
| Tue 10/4 | 3. Linear models: Misspecification | Finish R demo, misspecification overview, non-normality | Lecture notes 2.6, 3.1 | – |
| Thu 10/6 | (Fall break) | (Fall break) | (Fall break) | (Fall break) |
| Tue 10/11 | 3. Linear models: Misspecification | Heteroskedastic and correlated errors | Lecture notes 3.2 | Homework 2 (PDF, GitHub, Solutions) due at 10am |
| Thu 10/13 | 3. Linear models: Misspecification | Model bias | Lecture notes 3.3 | – |
| Tue 10/18 | 3. Linear models: Misspecification | Outliers | Lecture notes 3.4 | – |
| Thu 10/20 | 3. Linear models: Misspecification | R demo | Lecture notes 3.5 | – |
| Sun 10/23 | – | – | – | Take-home midterm exam (PDF, GitHub, Solutions) released at 9am (last year’s midterm PDF, GitHub, Solutions) |
| Mon 10/24 | – | – | – | Take-home midterm exam due at 9pm |
| Tue 10/25 | 4. GLMs: General theory | Exponential dispersion models | Lecture notes 4.1 | – |
| Thu 10/27 | 4. GLMs: General theory | Unit deviance, saddlepoint approximation, GLM definition and examples | Lecture notes 4.1, 4.2 | – |
| Sat 10/29 | – | – | – | Homework 3 (PDF, GitHub, Solutions) due at 9pm |
| Tue 11/1 | 4. GLMs: General theory | Estimation in GLMs | Lecture notes 4.3 | – |
| Thu 11/3 | 4. GLMs: General theory | Inference in GLMs | Lecture notes 4.4 | – |
| Tue 11/8 | 4. GLMs: General theory | Inference in GLMs | Lecture notes 4.4, 4.5 | – |
| Thu 11/10 | 5. GLMs: Special cases | Unit 4 R demo, logistic regression model | Lecture notes 4.5, 5.1.1 | – |
| Tue 11/15 | 5. GLMs: Special cases | Logistic regression inference | Lecture notes 5.1.2 | – |
| Thu 11/17 | 5. GLMs: Special cases | Poisson regression I | Lecture notes 5.2.1-5.2.3 | – |
| Sat 11/19 | – | – | – | Homework 4 (PDF, GitHub, Solutions) due at 9pm |
| Tue 11/22 | 5. GLMs: Special cases | Poisson regression II | Lecture notes 5.2.4-5.2.7 | – |
| Thu 11/24 | (Thanksgiving break) | (Thanksgiving break) | (Thanksgiving break) | (Thanksgiving break) |
| Tue 11/29 | 5. GLMs: Special cases | Negative binomial regression | Lecture notes 5.3 | – |
| Thu 12/1 | Further topics | R demo, Intro to multiple testing | Lecture notes 5.4 | – |
| Tue 12/6 | Further topics | FWER control | Lecture notes 6.1 | – |
| Thu 12/8 | Further topics | FDR control | Lecture notes 6.1 | – |
| Fri 12/9 | – | – | – | Homework 5 (PDF, GitHub, Solutions) due at 9pm |
| Thu 12/15 | – | – | – | Take-home final exam (GitHub) released at 9am |
| Sun 12/18 | – | – | – | Take-home final exam due at 9pm. Take-home final exam solutions |