For students who have not taken PHC 6052, in order to enroll you will need to
- have taken a graduate level course in basic statistics covering one and two variable methods
- demonstrate SAS competency at the PHC 6052 level by completing an assigned analysis in SAS prior to the start of the semester.
- Contact Dr. Amy Cantrell directly, preferrably at least 2 months prior to the start of classes, to discuss the possibility of enrolling without the pre-requisite course.
Main Course Goal
This course introduces graduate students in fields other than statistics to a wide range of modern regression methods. Emphasis is on modeling driven by actual data from studies in a variety of areas, primarily from health, biology, and ecology.
The primary topics are multiple linear regression, logistic regression, and Poisson regression. A main goal is to learn what approach to use among the linear and nonlinear models, and how to determine if the fit is adequate.
By the end of the course, students will achieve competency in carrying out the analyses in SAS.
The following objectives will be addressed.
- Information about how to obtain SAS 9.4 at UF can be found at:: https://software.ufl.edu/student-agreements/
- Our main SAS information page: SAS: http://bolt.mph.ufl.edu/software/sas/
The following broad topics will be covered, those given in bold will be our primary focus for most of the semester.
- Unit 1: Exploratory Methods and Inference in Case CQ
- Unit 2: Inference in Case QQ – Simple Linear Regression
- Unit 3: Multiple Linear Regression
- Unit 4: Inference in Case CC and QC – Contingency Tables and Simple Logistic Regression
- Unit 5: Multiple Logistic Regression
- Unit 6: Model Selection
- Unit 7: GLM and Poisson Regression
References and Suggested Textbooks
Penn State has two courses with excellent sets of online materials:
The course materials were originally developed using the following book which is available freely via the UF library. The textbook is not required for reading in this course but is a good reference book for regression methods at the applied level. The mathematical detail is kept to a minimum. The links may only work properly when connected to the UF network either directly or via VPN.
- Regression Methods in Biostatistics – Linear, Logistic, Survival, and Repeated Measures Models.
Authors: Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
ISBN: 978-1-4614-1352-3 (Print) 978-1-4614-1353-0 (Online)
I appreciate suggestions for good textbook from students and urge you to spend some time in the libraries on campus browsing books that are available on applied regression which resonate with you.
The following are a few other applied regression textbooks which are freely available through the UF library system.
- Regression Methods for Medical Research (1)
- Wiley Handbooks in Applied Statistics : Handbook of Regression Analysis (1)
- Wiley Series in Probability and Statistics : Regression Analysis by Example (5)
- Regression Analysis : Statistical Modeling of a Response Variable (2)
- Wiley Series in Probability and Statistics : Applied Linear Regression (4)