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JMP® Education

ADVANCED TOPICS AND JMP PRO

JMP® Pro: Finding Important Predictors

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.

Duration: 4 half-day sessions

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.
Course Dates

This course is not available at this time. Please check back at a later date.

Don’t see a course you want listed on the dates that you need? Contact us at training@jmp.com.

 

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