| Tue 8/29 | 1. Linear models: Estimation | Geometry of least squares | Lectures notes 1.1-1.4 | – |
| Thu 8/31 | 1. Linear models: Estimation | Variance decompositions; regression to the mean | Lectures notes 1.5-1.6 | – |
| Tue 9/5 | 1. Linear models: Estimation | Collinearity; adjustment; connections to causal inference | Lecture notes 1.7 | – |
| Thu 9/7 | 1. Linear models: Estimation | tidyverse; programming best practices | tidyverse; programming best practices; R4DS Ch. 1-9 | – |
| Tue 9/12 | 1. Linear models: Estimation | Creating high-quality figures; Git and GitHub; Linux command line; Unit 1 R demo | Lecture notes 1.8 | – |
| Thu 9/14 | 2. Linear models: Inference | Inferential preliminaries | Lecture notes 2.1, 2.2.1 | – |
| Tue 9/19 | 2. Linear models: Inference | Hypothesis testing | Lecture notes 2.2.2 | Homework 1 due at 10am (PDF; GitHub; Solutions) |
| Thu 9/21 | 2. Linear models: Inference | Power of hypothesis testing | Lecture notes 2.3 | – |
| Tue 9/26 | 2. Linear models: Inference | Confidence intervals, practical considerations | Lecture notes 2.4, 2.5 | – |
| Thu 9/28 | 2. Linear models: Inference | Unit 2 R demo; high-performance computing | Lecture notes 2.6; HPC basics | – |
| Tue 10/3 | 3. Linear models: Misspecification | Overview of misspecification I | Lecture notes 3.1 | – |
| Thu 10/5 | 3. Linear models: Misspecification | Overview of misspecification II | Lecture notes 3.1 | – |
| Tue 10/10 | 3. Linear models: Misspecification | Sandwich covariances, random effects models, feasible GLS | Lecture notes 3.2 | Homework 2 due at 10am (PDF; GitHub; Solutions) |
| Thu 10/12 | (Fall break) | (Fall break) | (Fall break) | (Fall break) |
| Tue 10/17 | 3. Linear models: Misspecification | The bootstrap and the permutation test | Lectures notes 3.3, 3.4 | – |
| Thu 10/19 | 3. Linear models: Misspecification | Robust estimation, R demo | Lecture notes 3.5, 3.6 | – |
| Tue 10/24 | 4. GLMs: General theory | Exponential dispersion models | Lecture notes 4.1 | – |
| Thu 10/26 | 4. GLMs: General theory | Saddlepoint approximation, GLM definition | Lecture notes 4.1-4.2 | Homework 3 due at 10am (PDF; GitHub; Solutions) |
| Tue 10/31 | 4. GLMs: General theory | Estimation in GLMs | Lecture notes 4.3 | – |
| Thu 11/2 | 4. GLMs: General theory | Inference in GLMs | Lecture notes 4.4 | – |
| Tue 11/7 | 4. GLMs: General theory | Inference in GLMs and R demo | Lecture notes 4.4-4.5 | – |
| Thu 11/9 | 5. GLMs: Special cases | Logistic regression I | Lecture notes 5.1 | – |
| Tue 11/14 | 5. GLMs: Special cases | Logistic regression II | Lecture notes 5.1 | |
| Thu 11/16 | 5. GLMs: Special cases | Poisson regression I | Lecture notes 5.2 | Homework 4 due at 9pm (PDF; GitHub) |
| Tue 11/21 | 5. GLMs: Special cases | Poisson regression II and negative binomial regression I | Lecture notes 5.3 | – |
| Thu 11/23 | (Thanksgiving break) | (Thanksgiving break) | (Thanksgiving break) | (Thanksgiving break) |
| Tue 11/28 | 5. GLMs: Special cases | Negative binomial regression II and R demo | Lecture notes 5.3 and 5.4 | – |
| Thu 11/30 | 6. Multiple testing | Intro to multiple testing and global testing | Lecture notes 6.1 and 6.2 | – |
| Tue 12/5 | 6. Multiple testing | Multiple testing I | Lectures notes 6.3 | Homework 5 due at 10am (PDF, GitHub) |
| Thu 12/7 | 6. Multiple testing | Multiple testing II | Lectures notes 6.3 | – |
| Thu 12/14 | – | – | – | Take-home final exam released at 9am |
| Mon 12/18 | – | – | – | Take-home final exam due at 9pm. |