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. This course is designed to help scientists and engineers choose an appropriate technique for their particular situation.
assess 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. You will be able to
- Recognize situations that benefit from a screening experiment.
- Make a fractional factorial design or Plackett-Burman design.
- Make an orthogonal or near-orthogonal array or a definitive screening design.
- Make a Bayesian D-optimal design or split-plot design with custom design.
- Identify situations where each of the screening designs might be most useful.
- Identify likely effects in the response using the effect screening emphasis and tools in the Fit Least Squares platform or the Screening platform.
- Select a model using the Stepwise platform with forward selection or All Possible Models under the heredity restriction.
A qui s’adresse cette formation ?
Advanced JMP analysts who need to screen many factors through experimentation
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
Before attending this advanced course, you should complete the Plan d’expérience avec JMP® : les fondamentaux or JMP Software: Custom Design of Experiments courses or have equivalent experience.
This course is not an introduction to design of experiments.
Cette formation concerne JMP logiciel