Design of experiments (DOE) is a data-driven strategy that helps practitioners to better understand the underlying behavior of a system by identifying those factors that most affect desired outcomes. By optimizing your experimental design, you can more effectively take covariate information into account when setting up an experiment.
In this recorded seminar, DOE experts Malcolm Moore and Phil Kay will show how you can accelerate learning cycles with a general, flexible method for experimentation. Together, they will demonstrate how to use optimal designs which reduce the cost of experimentation, accommodate multiple
types of factors and can be optimized when the design space is constrained. Optimal DOE is available in JMP® and is appropriate in virtually any situation that suggests the possible use of DOE.
You will learn how to:
- Drive optimal decisions by reducing the number of experimentation cycles, experimentation time and cost of new data collection.
- Ensure you find the best solution to your factor-constrained problem using the Custom Design platform in JMP.
- Use JMP Split Plot Design capabilities to increase the uptake of DOE methods when the nature of the experiment requires constraints on experimental run order.
- Mine existing data for correlated predictors, empty cells and outliers to better inform your next DOE.
The presenters will illustrate the above concepts using case studies of pigment milling, constrained factor space, wind tunnel design and power calculation via simulation.