Dear Sir,
I am conducting a bioprocess experiment with four factors and one response variable. My goal is to optimize these factor concentrations for future lab studies. In JMP, only two factors were significant (P<0.05), though I also observed some quadratic interactions. When using the Prediction Profiler to maximize desirability, should I remove the non-significant factors first, or should I include both significant and non-significant effects in the final optimization.
also,
When performing model reduction for a bioprocess optimization in JMP, what is the best practice for handling non-significant terms in the Prediction Profiler? My model shows two significant main effects and some quadratic interactions. I am unsure if I should retain the non-interactive, non-significant factors when predicting the maximum desirability, or if keeping them might introduce unnecessary noise into the optimization.
Many thanks in advance for your response.
