STA 471 - Regression Analysis

Prerequisites:   A grade of C or better in STA 270 and MAT 136.

Catalog Description:   Simple linear, multiple linear, curvilinear, and multiple curvilinear regression models.   Simple, multiple, and partial correlation.   Variable selection techniques.

Objectives:   On completion of this course, you will be able to:   (1) recognize situations appropriate for regression modeling; (2) use the language and techniques of statistics, and appropriate technology, in developing regression models; (3) assess the quality of these models by diagnostic and validation techniques; and (4) investigate and interpret the consequences, properties, and limitations of regression models.

Course Content:   Topics will include:   review of basic statistical concepts and matrix notation; simple linear regression and correlation; multiple regression and polynomial models; weighted least squares; model building, variable selection, and lack-of-fit tests; residual analysis and regression diagnostics; transformations; and possibly nonlinear models.   Content will be selected from chapters 1 through 16 (and possibly 24) of Draper & Smith.

Homework assignments	30%
Exams I & II	20% each
Comprehensive final examination	30%

Grade Policy:   Grade cutoffs will be:   A greater than or equal to 90%; B 80-89%; C 65-79%; D 50-64%.
Exam Dates:   Exams I & II are tentatively scheduled for September 30^th and October 30^th respectively.
Final Examination:   The final examination will be given 7:30-9:30am on Tuesday, December 9^th.