Statistics 456 - Generalized Linear Models and Mixed Models

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.

Requisite:

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