STAT 632 - Linear and Logistic Regression

Units: 4
Simple linear regression, multiple linear regression models and logistic regression models. Inference about model parameters and predictions, diagnostic, assumption checking, remedial measures about the model, and model building. Emphasis on real data from science, engineering, and business. Computer-assisted analysis.

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.
Produce relevant computer output using standard statistical software and interpret the results appropriately.
Communicate regression concepts and analytical results clearly and appropriately to others.