Hi Lingo, I took a quick peek at your design. What you want to do (subset of 30) will require a more simplified model than simply sacrificing the 3rd order terms (3-way interactions and cubic terms). Just to estimate a main effects (1st order) and 2-way interaction model would consume all available degrees of freedom (ie the design would be saturated) in a 30 run subset. In such a case, you would not have any left over df to estimate error. You received great advice from Ryan. It is possible to bring in your existing design identifying all your factors as covariates. In this case your existing 90 run design will be the candidate set from which algorithms can select the most balanced 30 run subset for a much more simplified model of your choice. The question is whether any 30 run subset is "rich enough" in information to estimate your chosen model. One of the complications is that your candidate set has excess levels for most factors (5 levels for first 5 factors) than really will be needed for an estimable simplified model. So although one might normally think of a 90 run candidate set as "rich enough" to select a 1/3 subset, your 90 runs may not cover the 7 dimensional space as thoroughly as compared to a full factorial candidate set. Although I think you can likely come up with a reasonable approach for those 30 runs, I think you should be realistic in expecting some colinearity for most any simplified model. Of course the real beauty of JMP's DOE capability is that you can easily see that in the "color on correlation map" in the "evaluate design" or when you do algorithmically select the subset. You can also see the "correlation of estimates" in the "fit model" report by doing a dummy analysis on a Y with random data before actually committing to measure the subset. best of luck
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