Spring 2021

Generalized Linear Models and Dependent Data Analysis

Listed in: Mathematics and Statistics, as STAT-456


Brittney E. Bailey (Section 01)


Linear regression and logistic regression are powerful tools for statistical analysis, but they are only a subset of a broader class of generalized linear models. This course will explore the theory behind and practical application of generalized linear models for responses that do not have a normal distribution, including counts, categories, and proportions. We will also delve into extensions of these models for dependent responses such as repeated measures over time.

The course will use Moodle to foster asynchronous student engagement with peers and with course material, and will have synchronous activity-based sessions primarily using Zoom. Opportunities for in-person engagement for on-campus students (e.g., office hours, study sessions) are also expected.

Limited to 20 students. Spring semester. Professor Bailey. 

If Overenrolled: Priority for Statistics majors


Online Only, Quantitative Reasoning


2020-21: Offered in Spring 2021