This course teaches you techniques for fitting statistical models to identify important variables. Manual, graphical, and automated variable selection techniques are presented, along with advanced modeling methods. The demonstrations include modeling both designed and undesigned data. Techniques are illustrated using both JMP software and JMP Pro software. Note that JMP Pro software is needed for the advanced techniques covered in the second half of this course.

Learn how to * identify a subset of predictors as important using a statistical model

• validate statistical models using cross-validation, holdback validation, and information-theoretic criteria
• perform stepwise and all subsets regression
• select important predictors using decision trees, variable clustering, and predictive models
• perform penalized regression for Gaussian and non-Gaussian responses
• use the Generalized Regression platform to identify important predictors.

Who should attend Analysts, researchers, technicians, or anyone filling similar roles, who want to determine which predictors in a large set are important in predicting a response Formats available Duration
: 4 half day sessions

Before attending this course, you should have experience using JMP and performing data analysis. Completion of is also recommended.

Introduction to Variable Selection

• introduction to models
• model validation methods
• variable importance
• OLS regression and backward selection

Classic Methods of Variable Selection

• graph-based methods
• stepwise regression
• all subsets regression

Alternative Methods of Variable Selection

• decision tree models
• variable clustering
• partial least squares models

Generalized Regression (JMP Pro Required)

• introduction
• lasso/ridge/elastic net
• adaptive lasso/adaptive elastic net
• methods with multiple passes

Special Situations for Generalized Regression (JMP Pro Required)

• binomial response
• Poisson response
• multiple responses

JFIP13.