Why You Are Using PROC GLM Too Much (and What You Should Be Using Instead)
Presented: Tuesday September 3, 2019, 8:00am-5:00pm
Deanna is a Lead Research Statistician and Data Manager on permanent contract through the Henry M Jackson Foundation to USUHS and Walter Reed National Military Medical Center in Bethesda, MD. She is also an Independent Consultant for Statistics, Research Methods, and Data Management in the private sector through Juxdapoze, LLC. Deanna has an MS in Health and Life Science Analytics, a BS in Statistics, and a BS in Psychology. Deanna has presented as a contributed and invited speaker at over 50 local, regional, national, and global SAS user group conferences since 2011.
It is common knowledge that the general linear model (linear regression and ANOVA) is one of the most commonly used statistical methods. However, the analytical problems that we encounter often violate the assumptions of this model type, leading to its inappropriate implementation. Lucky for us, modern modeling techniques have been created to overcome these violations and provide better results, which has resulted in the development of specialized SAS PROCs to assist with their implementation. These include: Quantile regression, Robust regression, Cubic splines and other forms of splines, Multivariate adaptive regression splines (MARS), Regression trees, Multilevel models, Ridge Regression, LASSO, and Elastic Nets, among other methods. Covered PROCs include QUANTREG, ROBUSTREG, ADAPTIVEREG and MIXED.
This workshop will begin with a brief refresher on regression, including a discussion of the assumptions of the GLM and ways of diagnosing violations. It is designed with the assumption that attendees have a working knowledge of linear regression with PROC GLM.