University student. I am analyzing data from experiments not designed using JMP, and would like to find out which parameters have significance in affecting the outcome.
The experiment stacks 2 materials, and then applies an anneal, so those are my three categorical variables. For one material I have 2 levels, for the other I have three. There are 3 possible anneals.
So I guess it's a full-factorial experiment because I have 5 sample results (except one has only 4 due to a sample being dropped) for every possible combination (material A 2 levels, material B 3 levels, Anneal 3 levels = 18 combinations of 5 samples each).
The output figure of merit is a contiguous continuous variable.
There are several uncontrolled parameters where I'd like to determine if they correlate with the output variable. For example, date of process. date of measurement. position within the process chamber. etc.
I recall having a course in which we did a JMP correlation of some type that can reveal which factors are likely significant, and then you re-run it dropping out the ones that are not significant or that are mutually confounding, progressively narrowing the number of possible factors until you are left with only the parameters that actually matter. I don't recall now if it was just an ANOVA; my understanding is that ANOVA only works for linear correlations so I'm not sure if that would be the correct analysis to use on this data.
Unfortunately the site-license for JMP at my University has expired so I'm left to finish my capstone project analysis with the demo version and no longer have access to the JMP training videos available in my class those years ago to refresh my memory how to do this (they were not free at the time I took the class). I'm trying to graduate this month so I don't think it's appropriate to buy a student license again at this point in time.
Can someone explain, or point me to the right help documents or videos so I can quickly move on with this analysis?
Is the feature I'm thinking of in the DOE Module? Worried it might be in JMP pro and not available in the demo version.
Including a random jsl so that I can post. This one-way analysis showed that 2 of the anneals were not statistically different from each other but the third was different from the other two.
Oneway( Y( :"SENSITIVITY*"n ), X( :Anneal ), Means( 1 ), Mean Diamonds( 1 ) )