JMP^® Pro: Finding Important Predictors

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 graphical methods and decision trees.
• 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

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.

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.