STA 108 Applied Statistical Methods: Regression Analysis

Units: 4

Format:
Lecture: 3 hours
Discussion: 1 hour

Catalog Description:
Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages.

Prerequisite: STA 013 or STA 032 or STA 100

Goals:
Course goals are to develop facility in the construction of multiple regression models and their application to the analysis of experimental data.

Summary of course contents: 

  • Standard regression model 
  • General linear model
    • Review of matrix arithmetic
    • Estimates of β and σ2
    • Confidence and prediction intervals
    • Decomposition of sum of squares, and lack of fit tests
  • Analysis of residuals to check on validity of assumptions
  • Multiple and polynomial regression-selecting correct variables
    • Criteria based on R2
    • Mallows's Cp
    • Stepwise Regression
    • Use of dummy variables
  • Regression diagnostics
  • Analysis of covariance
  • Use of computer packages to analyze real data sets 

Restrictions:
None

Illustrative reading:
Kutner, M.H., C.J. Nachtsheim, J. Neter and W. Li (2005). Applied Linear Statistical Models, 5th ed. McGraw-Hill, New York. 

GE3:
SE, QL, SL

Potential Overlap:
None

History:
None