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 regression-based 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, non-linear, and non-parametric 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

• Overview of regression theory
• Simple Linear Regression
• General Linear Models

Part 2: Special Data Needs

• Outliers and robust regression
• Ill-conditioned data and orthogonal regression
• Transformation of data before modeling

Part 3: Special Model Types

• 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

Part 4: Logistic Regression

• Standard logistic regression
• General logistic, including Poisson regression
• Probit models
• Logistic regression on survey data

Part 5: Linear Mixed Regression

• Mixed models and meta-analysis
• General mixed models

Part 6: Non-Linear Methods

• Standard non-linear regression
• Non-linear mixed models

Part 7: Non-Parametric Methods

• 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 Peace-Work, a volunteer cooperative of statisticians and data scientists applying statistical methods to issue-driven advocacy.