Announcements: Have a wonderful spring break! Due to the ENAR conference, the lectures for March 20 and 21 will be recorded in advance. Moreover, I have decided to switch the units on longitudinal data analysis and time-to-event outcomes. This is now reflected on the syllabus, in the notes below, and on the homework assignments. Please be certain you have the most recent copies of these.
Syllabus: The Spring 2023 syllabus can be found here.
Lecture notes: The lecture notes for this course are posted here. They are not to be copied without permission of the instructor.
| # | Topic | Slides |
|---|---|---|
| 1 | Welcome and review | Link |
| 2 | Simple linear regression | Link |
| 3 | Multiple linear regression (fundamentals) | Link |
| 4 | Multiple linear regression (interactions, subgroup effects) | Link |
| 5 | Transformations and basis expansions | Link |
| 6 | Weighted least squares | Link |
| 7 | Regression with binary outcomes | Link |
| 8 | Regression with nominal, ordinal, and count outcomes | Link |
| 9 | Methods for longitudinal data | Link |
| 10 | Regression with time-to-event outcomes | Link |
| 11 | Prediction and penalized regression | Link |
| 12 | Nonlinear regression | Link |
| 13 | Methods for missing data | Link |
| 14 | The bootstrap | Link |
| 15 | Bayesian methods | Link |
Labs: Links to this year’s labs are provided below. Note that not all formal labs occur on Mondays, and not all Mondays are formal labs. For instance, sometimes lab periods are better utilized as time for exam review.
| # | Date | Topic | Lab |
|---|---|---|---|
| 1 | 01/12 | Introduction to Stata | Link |
| 2 | 01/30 | Prediction and diagnostics | Link |
| 3 | 02/06 | Subgroups and omnibus tests | Link |
| 4 | 02/13 | Transformations and nonlinearity | Link |
| 5 | 03/07 | Discrete outcome regression | Link |
| 6 | 03/27 | Longitudinal regression methods | Link |
| 7 | 04/03 | Survival analysis | Link |
| 8 | 04/10 | Penalized regression | Link |
| 9 | 04/17 | Missing data | Link |
Problem sets and keys: This year’s collection of problems can be found here. Problems will be due in batches (see schedule below).
| # | Due date | All students | Biostat students |
|---|---|---|---|
| 1 | 01/19 | A1 | B1 |
| 2 | 01/26 | A2, A3 | B2 |
| 3 | 02/02 | A4, A5 | B3 |
| 4 | 02/09 | A6, A7 | B4 |
| 5 | 02/21 | A8, A9 | B5 |
| 6 | 03/23 | A10, A11, A12 | — |
| 7 | 03/30 | A13, A14 | B6 |
| 8 | 04/06 | A15, A16 | B7 |
| 9 | 04/13 | A17 | B8 |
| 10 | 04/18 | A18 | B9 |
Additionally, the problems from a prior offering of this this course are provided here, along with the keys. You have access to all of last year’s data sets, and so you should view this as an additional resource to practice—particularly on material you find challenging.
Data: Data sets and documentation can be found here (you will need the password).
Lecture recordings: This year’s lecture recordings can be found here (you will need the password).
Exams: This year’s exams will be posted here, along with their keys. Exams and keys from prior years are provided below. Note that emphasis changes from year to year, as does the ordering of topics.
| Description | Exam | Key |
|---|---|---|
| Exam 1 (2020) | Link | Link |
| Exam 2 (2020) | Link | Link |
| Exam 1 (2021) | Link | Link |
| Exam 2 (2021) | Link | Link |
| Exam 1 (2022) | Link | Link |
| Exam 2 (2022) | Link | Link |
| Exam 1 (2023) | Link | Link |
| Exam 2 (2023) | Link | Link |