STAT 327 - Regression Analysis

When Offered: F, S, SS

3 Credit(s)
3 Lecture Hour(s)
0 Lab Hour(s)

The course introduces principles and methods of linear regression models and generalized linear models that are used to solve data science problems. Linear models cover simple and multiple linear regression, including least squares estimation, inference, model building and variable selection. Generalized linear models cover logistic regression, Poisson regression, survival analysis and maximum likelihood estimation. The course utilizes statistical programming language such as R, SAS, and/or Python.

Prerequisite(s): STAT 324 or permission of instructor.