BST 5013 Statistical Methods II: Generalized Linear and Survival Models 3.0 Credits
The objective of this course is to introduce students to advanced generalized linear regression models (theoretical properties, model interpretation and application) as well as covers the basic techniques of survival analysis. Topics include review of categorical data and related sampling distributions and two- and three-way contingency tables, logistic regression and Poisson regression, loglinear models for contingency tables, generalized linear mixed models for categorical responses, principles of MLE in generalized linear model, and time to event (i.e. failure time, survival time, event time) methods and applications.
Repeat Status: Not repeatable for credit
Prerequisites: BST 5012 [Min Grade: C] or BST 569 [Min Grade: C] or BST 5003 [Min Grade: B] or BST 560 [Min Grade: B]