STAT 633A - Generalized Linear Models I

Units: 4
Simple and multiple linear regression and logistic regression models. Inference about model parameters and predictions, diagnostic, assumption checking, remedial measures, and model building. Emphasis on real data. Introduction to model selection using optimization and machine learning techniques.

Prerequisites: STAT 630.
Possible Instructional Methods: On-ground, or Hybrid, or Online-Asynchronous.
Grading: A-F grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply regression methodologies, including a) descriptive statistics and graphical displays, b) hypothesis testing, confidence and prediction intervals as pertains to regression model.
Formulate and model practical problems for solutions using regression and machine learning.
Produce relevant computer output using standard statistical software and interpret the results appropriately.
Communicate regression concepts and analytical results clearly and appropriately to others.