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.
Potential Overlap:
None
History:
None