Regression Modeling and Analysis ((4))

Catalog description

Advanced course in applied regression analysis. Students develop a sense of the applicability of regression as a tool for analysis of data. Topics include: linear regression, randomization tests for linear regression, correlation analysis, model diagnostics and remedial measures, matrix algebra notation and literacy for developing concepts of linear regression, variable and model selection techniques. Additional topics may include Poisson regression, logistic regression, path analysis, forecasting, and special topics selected by instructor. Statistics software R or SAS is used throughout the course.