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
Grad Cert in Applied Statistics and Data Mgmt
SPRG 2022
Instructors
Classes Start:
January 10, 2022
Classes End:
April 25, 2022
Distance Education:
Yes
Class Type:
Lecture
Credits:
3.00
Delivery Method:
Internet
Restrictions:
P: Graduate Standing
