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.
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
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 |