JMP 13 Preview: Compare Designs for picking the best design for an experiment
The design of experiments (DOE) capabilities of JMP are world-class. You can choose from many designs, such as custom, definitive screening, classical, space-filling, choice and covering arrays. But how do you decide which design to use?
It used to be a time-consuming process to compare two designs for an experiment, for example, a definitive screening design compared to a traditional fractional-factorial screening design. Or a one-factor-at-a-time experiment compared to an optimal design produced by Custom Design. But not anymore, with the new Compare Designs platform in JMP 13.
“Now it’s easy to compare two or three designs. The platform does side-by-side comparisons, and you can see which design is better for which diagnostic,” says Ryan Lekivetz, Sr. Research Statistician Developer who worked on the new platform.
Below is a Fraction of Design Space plot comparing three designs:
Many of the diagnostics in Compare Designs include a color dashboard (see below) that helps you quickly determine which design is better. In addition to comparing two or three different designs, you can also see the impact of varying the number of runs on the same type of design – allowing you to perform a trade-off analysis in terms of experimental budget and design diagnostics.
Like many new capabilities in JMP 13, Compare Designs was driven by customer requests. And the feedback has been very positive.
Christine Anderson-Cook, a research scientist and JMP user at Los Alamos National Laboratory (LANL), demonstrates the new platform in a recent Analytically Speaking episode and has high praise for it: “JMP has long been leading the way with its experiment design construction and assessment tools, but the new Compare Designs platform takes choosing the right design for the goals of your specific experiment to a new level.” Christine has also written an article on the new platform for the upcoming JMP Foreword magazine.
Ryan notes that Compare Designs could be useful as a tool to understand and show the value of modern experiment design. “Statisticians often get the results of an experiment that they didn’t design. With this platform, they can compare the design that was used to another design, and they can see how much was lost by not using the better design,” he explains.
And he loves the challenge of helping customers with designing experiments. “I like hearing about the tough problems where people don’t think they can create a design and then showing them how they can do it, thanks to the flexibility of custom designs,” Ryan says.
You can talk to Ryan about your own experiment design problems at Discovery Summit 2016 in a few weeks. He’ll be around at “Meet the Developers” and will present a breakout session on the Simulate Responses option in JMP 13.
To learn more about what’s coming in JMP 13, stop by the preview page on our website. There, you can sign up to watch a live stream of JMP chief architect John Sall’s tour of JMP 13 on Sept. 21, as well as watch short videos about JMP 13 and JMP Pro 13.