Course Description
This course provides an in-depth study of building, validating, and predicting using regression models. Topics include multiple linear regression models with both continuous and categorical predictors, model selection techniques, and residual diagnostics. Bayesian regression models are also explored. Categorical data analysis is covered including contingency table analysis and logistic regression models. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses.
DE Program
Statistics Online Masters Program
SUM1 5W 2019
Instructors
Classes Start:
May 15, 2019
Classes End:
June 18, 2019
Distance Education:
Yes
Class Type:
Lecture
Credits:
3.00
Delivery Method:
Internet
Restrictions:
Requirement: NDS or Grad Students Only
