Spring 2022

Generalized Linear Models and Mixed Models

Listed in: Mathematics and Statistics, as STAT-456

Faculty

Brittney E. Bailey (Section 01)

Description

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: STAT 230 and STAT 360. Limited to 20 students. Spring semester. Professor Bailey. 

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 Students with documented disabilities who will require accommodations in this course should be in consultation with Accessibility Services and reach out to the faculty member as soon as possible to ensure that accommodations can be made in a timely manner.

Offerings

2023-24: Not offered
Other years: Offered in Spring 2021, Spring 2022