Spring 2023

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:

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

STAT 456 - LEC

Section 01
M 8:30 AM - 9:50 AM SMUD 006
W 8:30 AM - 9:50 AM SMUD 006

This is preliminary information about books for this course. Please contact your instructor or the Academic Coordinator for the department, before attempting to purchase these books.

ISBN Title Publisher Author(s) Comment Book Store Price
Generalized Linear Models with Examples in R Springer, 2018 Peter K. Dunn, Gordon K. Smyth Online/pdf version available for free online for Amherst students at https://link-springer-com.amherst.idm.oclc.org/book/10.1007/978-1-4419-0118-7 TBD

Offerings

2023-24: Not offered
Other years: Offered in Spring 2023