I am using JMP 14.3 to optimize a workflow with 6 input variables and 3 levels (attached is the file with the data table). My idea is to use JMP to obtain the best combination of the 6 variables that gives me the best output result but performing the least number of experiments.
It seems to me that the best option is to make a definitive screening design to see the main effects. I have defined my factor table with the extreme values (minimum and maximum for each variable) and I have created the table of experiments to be performed. The generated table proposes 17 experiments.
During several weeks I have carried out the 17 experiments and I have added the results (Y) in the attached table. With these data I have adjusted the screening and I have obtained that 4 of the variables are statistically significant. I have selected in the prediction profiler: Optimization and Desirability > Maximize Desirability, to obtain the most optimal combination of factors.
At this point I have several questions:
1. What values/levels should I choose for the non-significant values?. Could I choose any of them?.
2. I would like to create a model to predict or estimate the output (Y) based on the selected values of the factors. How could I do this?. Could I simulate a larger number of values?.
3. Could I get an equation (or formula from the model)?.
Thank you very much.
Best regards,
Wardiam
