Department of Statistics

Current Sections   Recent Instructors White, G Denogean, L	ST430/ST514	Introduction to Regression Analysis
	Course Description Regression analysis as a flexible statistical problem solving methodology. Matrix review; variable selection; prediction; multicolinearity; model diagnostics; dummy variables; logistic and non-linear regression. Emphasizes use of computer. Course Syllabus Matrix theory Multiple linear regression model Multiple linear regression model Model specification Selection procedures Prediction Cross validation PRESS procedure Multicollinearity Effects of multiple collinearity Principle components Ridge regression Influential observations Detection of high leverage points Detection of highly influential observations Plots Residual plots Partial residual plots Non-linear regression Mathematical motivation Techniques (Gauss-Newton, Marquardt's, et. al., ) Use of dummy variables Logistic regression Course Prerequisites ST 302 MA 305 or MA 405 Course Corequisites None Recent Textbooks A Second Course in Statistics: Regression Analysis, 6 ed., Mendenhall III, Sincich (2003).