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.
Apprendre à
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.
A qui s’adresse cette formation ?
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 de formation disponibles | Durée standard (peut varier selon les sessions. Se référer au calendrier) | | |
Formations à distance: |
4 sessions d'une demi-journée chacune |
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Before attending this course, you should have experience using JMP and performing data analysis. Completion of JMP® : ANOVA et Régression is also recommended.
