Business Knowledge Series course
Presented by Mark Bailey, Ph.D., Analytical Training Consultant, Education Division, SAS
This course introduces how to use JMP software and a state-of-the-art approach to designing industrial laboratory experiments that is based on the latest statistical theory and numerical methods. Advances in computer algorithms and hardware make this approach, once considered exotic and the domain of a few experts, available to everyone for all experiments.
Learn how to
- use new ways to design experiments that fit the purpose and circumstance of any test
- use custom designs to extend the underlying design principles to any problem
- apply the principles behind such choices as model terms and sample size.
Who should attend
Anyone involved in laboratory experimentation and who wants to learn about powerful new ways to design experiments that exactly fit the purpose and circumstances of any test
No experience with design of experiments is required, but students should be comfortable using JMP and should understand ANOVA and regression.
This course addresses JMP software.
Introducing Custom Design
Accommodating the Real World
- familiar results (reproducing classic designs)
- motivation for unfamiliar solution (everyday situations are not handled by classic designs)
- optimal design background
- advantages and benefits
Not Uncommon Complications (Optional)
- combinations of different kinds of factors (any kind of factor may be included)
- constrained combinations of factor levels (disallowed regions or combinations)
- reusing existing runs (augment existing data with new, complimentary runs)
- constrained randomization (when some factors are hard to change every run)
- analyzing multiple responses (ways to find best settings to satisfy more than one goal)
- covariate factors (design with immunity to effect of covariate)
- design for nonlinear models