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 criteriaperform stepwise and all subsets regression select important predictors using decision trees, variable clustering, and predictive models perform penalized regression for Gaussian and non-Gaussian responsesuse 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
Before attending this course, you should have experience using JMP and performing data analysis. Completion of JMP Software: Statistical Decisions Using ANOVA and Regression is also recommended.
