Statistics 16:960:563 Regression Analysis


The course is normally offered during the Fall, Spring, and Summer semesters.

  • Class meeting dates: Please visit the University's academic calendar.
  • Schedule and Instructor: Please visit the University's schedule of classes for the instructor, time, and room.
  • Instructor and Teaching Assistant Office Hours: Please visit the Mathematical Finance program's office hour schedule.
Course AbstractReview of basic statistical theory and matrix algebra; general regression models, computer application to regression techniques, residual analysis, selection of regression models, response surface methodology, nonlinear regression models, experimental design models, analysis of covariance. Emphasis on applications and many illustrative examples.


Please contact the instructor.

Primary Textbooks

Required : J. Neter et al Applied Linear Regression Models. 4th Edition, Irwin publishing company.
Suggested: Montgomery and Peck Introduction to Regression Analysis, Wiley 1992.
Suggested: Box, Hunter and Hunter Statistics for Experimenters, Wiley 1978


Please contact the instructor.

Class Policies

Please contact the instructor.

Previous Course Instructor Websites

Sara Lopez-Pintado

Weekly Lecturing Agenda and Readings

LectureTopicsReading Assignments
1 Introduction , Basic Stats, Linear Models  
2 Linear Models: Diagnostics and matrix formulation  
3 Multiple regression  
4 Multiple regression: diagnostics and testing  
6 Model selection I: testing, subset selection  
7 Model selection II: selection criteria  
8 Model selection III: bootstrap, cross-validation  
9 Regularized regression: PCreg, Ridge and Lasso. Bayesian methods  
10 Nonlinear regression, Weighted LS  
11 Looking ahead: generalized linear models  
12 CART, non-parametric methods  
13 Review  
14 Student Presentation  

Library Reserves

All textbooks referenced on this page should be on reserve in the Hill Center Mathematical Sciences Library (1st floor). Please contact the instructor if reserve copies are insufficient or unavailable.