Continuing his in-depth exploration of Design of Experiments, as outlined in his blog series Why DOE?, Systems Engineer Phil Kay looks at how JMP’s visual tools can help remove some of the complexity in your designed experiment. After recapping the main points from past episodes, Phil dives back into the DOE example he introduced in the first episode to tackle this issue of complex behaviors – particularly how factors respond when interacting with each other. Phil shows us how design of experiments and JMP’s interactive visualizations, even simple graphs of the data, can help you understand complex behaviors.
Read the original blog post here.
And you can find links to all posts in the series, along with the case study data, here.
