Hi,
I am new to DOE and Definitive Screening Design, that is why I am having some questions regarding the analysis of DSD. I am using JMP 13.
I want to find significant factors in the process of spherical crystallization. I did Definitive Screening Design with 6 factors on three levels, with four additional runs (2 fake factors). I am measuring several different responses. The design evaluation shows me, that I have good statistical power. However, when I try to analyse the results using Fit Definitive Screening, I get none or one at best significant main effects. As a consequence, I also get none significant second order effects (see the added photo below, right). I think the reason for that is very high RMSE. I was learning how to do Fit Defintive Screening on the example shown in the JMP tutorial (file Extraction 3, see added photo below, left). When I analysed the example I got the same results as shown in the tutorial. I also noticed, that in analysing extraction 3 RMSE was much lower, although responses in Extraction 3 and in my experiment were approximately the same size, since both of responses are Yield.
I don't understand how is it possible that the difference in the RMSE is so big. If you look at the distributon in main effect plot, you can see, that the distribution in my experiment is similar, if not a bit narrower that the distribution in Extraction 3. I also calculated RMSE using Fit Y by X tool, where I got bigger RMSE in Extraction 3 (around 20) than in my experiment (around 18). That's why it looks like the analysis with Fit Definitive Screening makes the difference.
Am I missing something?
Should I do some preanalysis processes with my data prior to Fit Definitive Screening?
Or do I really have an experiment with big RMSE. If so, is it possible to fix that?
Thank you.
Danijel