GS01 1053 Linear Regression and Statistical Computing
Shete, Sanjay. Three semester hours. Fall annually. Prerequisite: introductory statistics, or permission of instructor.
This course will cover basic linear regression analysis. Topics to be covered include simple and multiple regression, diagnostics, influence, and model construction. The emphasis will be on the practical aspects of the construction and validation of linear models. The course will include extensive samples of the use of computer software to perform such analyses. The statistical package R will be used primarily for these examples, although other packages will be illustrated as well. (Students will be permitted to use whatever software they prefer for class assignments.)
This course is intended as an applied introduction to regression analysis. Theoretical results will be developed and presented as necessary, but the emphasis will be on applications.