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gaogao
Level I

DOE feasibility: Multiple factors vs multiple response (with different level of replicates)

Hello,

 

I am still new using JMP DOE. I have been trying one response with multiple factors when setting up full factorial DOE table, and it seems no issues to run the model.

 

However this time I am stuck because I was given with something as below. Due to preset parameters of the equipment, there is no freedom to adjust the no. of measurements for each response.

 

Response:
1) Brightness (20 measurement data)
2) Hardness (10 measurement data)
3) Conductivity (50 measurement data)
4) Friction force (1000 measurement data)

 

Factors:
1) Temperature (2 levels continuous)
2) Chemical concentration (2 levels continuous)
3) Time (2 levels continuous)

 

Is it possible to run full factorial DOE in this case even though each response has a different level of replicates?

 

Thank you.

1 REPLY 1
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statman
Level VII

Re: DOE feasibility: Multiple factors vs multiple response (with different level of replicates)

Welcome to the JMP community.  It would be helpful in the future that you let us know what version of JMP you are using and which OS.

 

The simple answer is YES.  I'm not sure what you mean by # of measurement data?  Are these from different locations on the experimental unit?  Why do you need 1000 measures of friction force???? Even though all of the data is taken (it seems automatically), can't you decide which values to use to evaluate the experiment?  I would always suggest you have hypotheses to explain why you are collecting the data you are collecting! (Because we always do it that way is not acceptable). If you can't reduce the # of data points for each of the 4 Y's, I would collect the data and create a column for every data point for each treatment combination (yes, 1080 columns: B1-B20, H1-H10, C1-C50, F1-F1000).  From there, you can remove columns that aren't informative (don't vary enough to be practically significant), you can run Multivariate Methods> Multivariate to reduce the columns that are strongly correlated. Then for each Y (B, H, C, F) stack those columns.  You can look at the variability within treatment (variability charts or S-charts). Then, if appropriate, summarize the stacked columns (e.g., mean and variation) by the treatment combinations.  Then proceed with analysis for each of those summarized Y's.

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