In Part 3 of JMP’s Phil Kay’s series, Why DOE?, we take a deeper dive into Design of Experiments. Learn more about how a smaller, designed experiment can enable you to learn about your process in the most efficient way. Using water contaminant data with eight key factors Phil shows you how, with more than 4,000 combinations you only need 26 runs to determine the best possible settings to achieve optimal water quality. Phil also shows us how the prediction profiler can quickly and easily take us through adjustments to all the linear and quadratic effects to determine the effect on the model.
See the blog post here.
And you can find links to all posts in the series, along with the case study data, here.