Linear and Logistic Regression Models
This workshop aims to increase participants understanding of the principles, methods, and interpretation of regression models, with particular application to health research.
Dates and venues
The course will be held over two half-days, and comprises of approximately half instructor-led teaching and discussion, and, half practical sessions getting hands-on experience with regression analysis of health data.
Wednesday, 9 April 2014
11:00 - 14:50
Burwood campus, Room B 2.20 (Blue Room)
Friday, 11 April 2014
10:00 - 13:50
Burwood campus, Room B 4.06
The scope of the course runs from basic principles of regression methods to deciphering the output of statistical analyses, and also covers the practical aspects of running these regression methods in the SPSS / Stata software environment.
By the end of the course, participants will be able to:
- understand the principles of regression methods and modelling strategies
- report and present the output from such analyses
- have experience in applying regression methods to health data in SPSS / Stata
- Principles of multiple linear regression and logistic regression (including diagnostic plots)
- Modelling strategies for assessing confounding and interaction
- Running regression models using SPSS / Stata
- Interpreting the results
The course is aimed at researchers in the health sciences who are considering using regression approaches in their research. The course would suit people currently involved in, or about to start, postgraduate study and other researchers who wish to expand their quantitative research skills into regression methods.
Exercises will be based on SPSS / Stata and some prior experience with this software would be an advantage. We will provide details of a web-based introductory session in SPSS / Stata which participants can follow prior to the course. Participants are encouraged to bring their own laptop with SPSS / Stata installed. The SPSS / Stata code required to complete the exercises will be provided. Participants are expected to have a basic familiarity with the concepts of analytic epidemiology, descriptive statistics and elementary statistical hypothesis testing.