Spring 2025

Generalized Linear Models and Mixed Models

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.


Student has completed or is in the process of completing: STAT 230 and STAT/MATH 360. Limited to 20 students. Spring semester. Professor Bailey. 

How to handle overenrollment: Priority for Statistics majors

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, reading research articles, group work, use of computational software, projects

Course Materials


Other years: Offered in Spring 2023