Regression Expression! 24 Regression Methods in SAS
With so many regression procedures available for different situations, it can be difficult to know the breadth of available methods and how to select the ones to apply to a given problem. This course offers an overview of 24 regressionbased methods. A decision flowchart is provided to assist in selecting the most useful regression procedures for a given context. The course is practical and example driven, emphasizing which procedures to consider and how to apply them in real situations. A quick introduction to each method is followed by two worked examples, with discussion of use cases, options in the SAS procedures, and producing graphical output. The course begins with a basic overview of linear regression, progressing to more advanced techniques. Course modules include basic regression, procedures for specific data issues and needs (e.g., robust regression for outliers), special model types (e.g., quantile regression), logistic regression methods, and mixed, nonlinear, and nonparametric SAS procedures. This course will help discern which statistical methods should be considered in a given situation and provide details with source code and examples for using specific procedures.
Course Outline
Part 1: Regression Basics
Course Outline
Part 1: Regression Basics
 Overview of regression theory
 Simple Linear Regression
 General Linear Models
 Outliers and robust regression
 Illconditioned data and orthogonal regression
 Transformation of data before modeling
 Quantile regression
 Partial least squares
 Regression on survey data
 Proportional hazards with survey data
 Contingency table regression
 Response surface models
 Survival analysis
 Proportional hazards models
 Structural equation modeling
 Standard logistic regression
 General logistic, including Poisson regression
 Probit models
 Logistic regression on survey data
 Mixed models and metaanalysis
 General mixed models
 Standard nonlinear regression
 Nonlinear mixed models
 Local polynomial regression (LOESS)
 Additive models
About the Author
With a PhD in statistical astrophysics, David Corliss works as a data scientist in the automotive industry while continuing astrophysics research on the side. As an instructor, his focus in on analytic methods and best practices applied to emerging technology. He serves on the steering committee for the Conference on Statistical Practice, President of the Detroit ASA Chapter, and is the founder of PeaceWork, a volunteer cooperative of statisticians and data scientists applying statistical methods to issuedriven advocacy.
