Regression Expression: 26 Regression Methods in SAS
Thursday, May 22
Course Description:
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 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: Fundamentals of Regression - 8:00 AM - Noon
Section 1-A: Regression Basics
Section 1-B: Special Data Needs
Section 1-C: Special Model Types
Part 2: Advanced Regression Methods - 1:00 - 5:00 PM
Section 2-A: Advanced Linear Regression
Section 2-B: Beyond Linear Models
Section 2-C: Alternative Modeling Methods
Section 2-D: Generalized Models
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 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: Fundamentals of Regression - 8:00 AM - Noon
Section 1-A: Regression Basics
- Overview of regression theory
- Simple linear regression
- Ridge regression
- LASSO Regression
Section 1-B: Special Data Needs
- Outliers and robust regression
- Quantile regression
- Transformation of data before modeling
- Ill-conditioned data and orthogonal regression
Section 1-C: Special Model Types
- Survival analysis
- Proportional hazards models
- Regression on survey data
- Proportional hazards with survey data
- Standard logistic regression
- Probit models
- Logistic regression on survey data
Part 2: Advanced Regression Methods - 1:00 - 5:00 PM
Section 2-A: Advanced Linear Regression
- Partial least squares and PCA regression
- Contingency table regression
- Response surface models
Section 2-B: Beyond Linear Models
- Standard non-linear regression
- Mixed models and repeated measures
- Non-linear mixed models
Section 2-C: Alternative Modeling Methods
- Local polynomial regression (LOESS)
- Additive models
- Structural equation modeling
Section 2-D: Generalized Models
- General linear models
- General logistic, including Poisson regression
- General mixed models and meta-analysis
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About the Instructor
With a PhD in statistical astrophysics, David Corliss is a Senior Data Scientist at DTE Energy where he develops machine learning and AI applications to drive business value. He is active in the American Statistical Association, serving on their board as the representative for local chapters in the Midwest region. His research focuses on industry applications in time series analysis and physics-informed AI, building bridges between academic and industry, and ethical best practices in data and statistical practice. Dr. Corliss is the founder of Peace-Work, a volunteer cooperative of statisticians, data scientists and other researchers applying analytics in issue-driven advocacy.
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