DSC 531 - Generalized Linear Models

Course Description: Introduction to generalization of ordinary linear regression and applications with implementation in computer languages such as Python, R, SAS, and Matlab. Estimated parameters with maximum likelihood, maximum quasi-likelihood, or Bayesian techniques. Learn when to apply common distributions for typical uses and their canonical link functions. Six lecture hours and two laboratory hours per week for each week of the half-semester session.

Credit Hours: 3

Prerequisite Courses: None
Additional Restrictions/ Requirements: None
Course Repeatability: Course may not be repeated

ADDITIONAL COURSE INFORMATION

Equivalent Courses: None
Undergraduate Crosslisting: None
Additional Course Fees: None
Course Attribute: None

Click here for the Fall 2024 Class Schedule.