Identify likely effects in the response using the effect screening emphasis and tools in the Fit Least Squares platform or the Screening platform. Identify situations where each of the screening designs might be most useful. Make a Bayesian D-optimal design or split-plot design with custom design. Make an orthogonal or near-orthogonal array or a definitive screening design. Make a fractional factorial design or Plackett-Burman design. Recognize situations that benefit from a screening experiment. This course is designed to help scientists and engineers choose an appropriate technique for their particular situation.Īssess the complexity of the situation in terms of the number of factors and the number and nature of terms in the model as a guide to selecting a method to design the screening experiment and analyze the empirical evidence. This advanced course presents strategies and methods for designing experiments to screen many factors in an optimal study, as well as several specialized analytical tools that respect the limited information available in such experiments. Location: Missouri Pacific, 2nd Floor, St. Completion of JMP Software: A Case Study Approach to Data Exploration and JMP Software: ANOVA and Regression is also recommended. Prerequisites: Before attending this course, you should have experience using JMP and performing data analysis. Who should attend: Analysts, researchers, technicians, or anyone filling similar roles, who want to determine which predictors in a large data set are important in predicting a response. Use the Generalized Regression platform to identify important predictors. Perform penalized regression for Gaussian and non-Gaussian responses. Select important predictors using decision trees, variable clustering and predictive models. Perform stepwise and all subsets regression. Validate statistical models using cross-validation, holdback validation and information-theoretic criteria. Identify a subset of predictors as important using a statistical model. 
Techniques will be illustrated using both JMP and JMP Pro. The demonstrations include modeling both designed and undesigned data.
Manual, graphical, and automated variable selection techniques will be presented, along with advanced modeling methods. This course teaches you techniques for fitting statistical models to identify important variables. Louis Union Station HotelĬonference Attendee Price: $975 (975 JMP Training Points/2.4 EPTO) Course Description: – 5 p.m.), and Tuesday, 17 October (9 a.m. JMP ® Software: Finding Important Predictorsĭates: Monday, 16 October (9 a.m.
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