(NOTE: This is part three of three-part series on stochastic optimization.) Over the last two weeks, I introduced robust process engineering and stochastic optimization – the effort to achieve good product in the face of variation among the factors. Last week, I gave a cooking example. This week, I present
Tag: JMP 7
(NOTE: This is part two of a three-part series on stochastic optimization.) In my previous post, I introduced stochastic optimization. In this post, I show a real example. This example was reported in the classic text by George Box and Norman Draper: Empirical Model-Building and Response Surfaces (page 32), and
Last week I showed how to make tornado charts in JMP and asked for input on the utility of these types of visualization. Here are thumbnails of the two alternative views of US population by age and sex. One commenter pointed out that the back-to-back bars of the tornado style
(NOTE: This is part one of a three-part series on stochastic optimization.) To get to the top of a hill, you just keep going up. However, hills can have subpeaks, so sometimes you have to hunt around to keep going up. But going up is still the basic idea. This
“Discovery,” the new name for the annual event formerly known as the JMP User Conference, seems like an appropriate moniker. At yesterday’s opening session, I ran across conference attendees who are using interactive JMP software from SAS in some pretty amazing ways. There’s the greenhouse gas specialist from the University
If you ever called or e-mailed a problem to JMP Technical Support, you may have been in contact with Duane Hayes. Duane manages JMP Technical Support for SAS. We recently discussed a tip that also may be helpful when sharing JMP reports with colleagues. Duane: People often send us JMP
Check out the article on JMP in the new issue of sascom magazine. It’s lean on words and big on visuals. You’ll see a variety of graphs created in JMP showing how the software helps answer and explore questions from a range of industries and organizations: pharmaceutical, marketing, public policy,
Randomizing an experiment completely is often either impossible or prohibitively expensive. That's where split-plot designs can be valuable. Split-plot designs allow you to fix certain factors for several runs in a row. Within each block of runs (or whole plot), the factors that are hard to change remain fixed while
Marie Gaudard, Phil Ramsey and Mia Stephens have taught JMP and used it in their North Haven Group Six Sigma consulting practice since the release of JMP Version 4 in the 1990s. All three are strong believers in the value of the JMP Partition platform for novice to expert users.
The inspiration for our latest data story – on using JMP for fantasy baseball – came from Lou Valente, one of our product managers and an ardent New York Yankees fan. Lou is a synthetic organic chemist, a Six Sigma Black Belt and a passionate practitioner of design of experiments.