Applied Linear Regression

This course is designed to equip students with essential skills and knowledge needed to understand, analyze, and interpret data through linear regression modeling. Topics include reviewing statistical estimation and uncertainty assessment, single and multiple regression models, least square fitting, probabilistic prediction, understanding assumptions of models, transformations, experimental designs, and model selection approaches. This course also includes practical exercises in which students learn to implement and interpret models using the statistical software R.

Offer as needed

030893