Regression analysis is a commonly used statistical methodology. As a broad topic it includes analysis of variance (ANOVA), logistic regression, linear mixed models, and generalized linear models. This course aims to teach the general concepts of regression methods and provide the framework of these methods with the aim improving statistical literacy on this important topic. Specific methods covered will be simple and multiple linear regression, logistic regression, and generalized linear models (GLM). Connections will be made to other topics including the important ANOVA regression connection. A basic approach to statistical model building via regression analysis will be demonstrated. In laboratory sessions participants will learn to implement lecture materials with SPSS software.
Target Audience: Designed for Faculty, Fellows, and Clinical Research Investigators. Participants should have an understanding of basic statistics. A self-assessment is included on the application.
Upon completion of this activity, participants will be able to:
Indicate basic concepts of linear regression
Interpret biostatisitcal results obtained through regression modeling
Identify and describe limitations and extensions of various regression models
Course Dates, Time and Locations:
LECTURES—Tanya Logvinenko, PhD
Tuesdays, March 1 - 22, 2016 (3-4:30pm)
Location: Byers B, Enders Building
SAS LAB SESSIONS—Peter Forbes, MA, Senior Biostatistician
Thursdays, March 3 - 24, 2016 (3:30-5pm)
Location: Training Room 6, One Autumn St. Basement