Statistics 6238 – Spring, 2007

Logistic Regression

 

 

Department of Statistics University of Central Florida

 

Announcement: 

SAS Global Forum Scholarship is currently accepting applications. Hurry up if you are interested in receiving a scholarship and giving a presentation during the meeting of SAS Global Forum 2007. 

Description of the Course: 

The logistic regression model is one of most popular models in statistical data analysis for binary outcome. In this course, I will cover from the fundamental of the logistic regression for categorical data to the cutting-edge statistical analyses associated with special study designs. SAS programming language will be the primary statistical analytical tool for the course. Throughout the semester, we will emphasize the application of logistic regression on biomedical studies in two major angles: modeling and interpreting data. This course will provide students an adequate training in using their statistical knowledge and SAS programming skill to solve real problem in practice and to be able to communicate with others based on statistical results. The following topics are tentatively included for the lectures: simple logistic regression model, multiple logistic regression model, model building strategies, model assessment, logistic regression for response with multiple levels, logistic regression for case-control studies, generalized estimating equation (GEE) for data with repeated measurements, an introduction to survival analysis.

Statistical Computing:  SAS Enterprise Miner 9.1 is available. In order to reinstall the new version of SAS and SAS Enterprise Miner on your home computer, please bring 7 CDs and one floppy disk to the data mining lab, which is located at Room 350, MAP Building. Before you go, please check their time schedule. 

Syllabus and the grading policy:

Class Notes: The Acrobat Reader, which is free, is needed in order to view pdf files appropriately.  

 

 Homework assignments and Class Handouts:

 

 

 Reading Articles and Materials

• Lim, T.S.,  Loh, W.Y., and Shin, Y.S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40: 203-229.
• Lemeshow, S., Teres, D., Avrunin, J. S., and Pastides, H. (1988). Predicting the Outcome of Intensive Care Unit Patients. Journal of the American Statistical Association, 83: 348-356.
• Pregibon, D. (1981). Logistic Regression Diagnostics. The Annals of Statistics, 9: 705-724.
• Lesaffre, E. and Albert, A. (1989). Multiple-Group Logistic Regression Diagnostics. Applied Statistics, 38: 425-440.

 

 

 Other Resources: 

 

• Reference Textbook: McCullagh, P. and Nelder, J.A. (1990). Generalized Linear Models. 2nd Edition. Chapman and Hall: London, UK.
• SAS -V9 Documentation is free on the web, both HTML and PDF http://support.sas.com/v9doc/, or within your software: HELP -> SAS Help and Documentation. 
• To have offline SAS help info, you might want to install this HotFix – only applicable to SAS V9.
• I also recommend you to install SAS System Viewer.  
• Another place to download SAS documents. In particular, here is a copy of PROC LOGISTIC for SAS V8.