Intro to Regression Models

Catalog description

This course covers mathematical foundation and practical implementations of linear and generalized linear regression models. Topics include estimation and diagnostics of linear model, transformations of variables, model selection, polynomial regression and logistic regression. The emphasis of the course will be on applications. The materials will partially cover the syllabus of SOA Exam Statistics for Risk Modeling and that of Exam Predictive Analytics. Prerequisites: C or better in MATH 448 and in one of the following: MATH 329, MATH 445, MATH 446, DIDA 325, or a similar computing course approved by the Director of Undergraduate Studies (DUS); or consent of instructor. Offered regularly.