Brain storm!

As usual, I can't let this problem go without digging down to bedrock.  I was inspired by the discussion on linkedin, especially an idea that Ina put into my head about the magnitude of the parameter estimates.  I couldn't shake the thought and then I came across this article from the folks at Genentech.  If you get the chance, read it!  Here's my attempt to apply my understanding of their approach.

In my previous discussion about the aggregate present in the CaptoAdhere eluate was driven by the statistically significant pairwise interaction of load*load conductivity.  Now, the magnitude of the effect was only 0.02% (rounding).  Now consider this: the root mean square error (RMSE) is 0.09%!  So, even though we have a statistically significant model the expected variation is lost within the experimental variation.  Based upon this, the practical significance to the quality attribute variation is lost within the expected variation due to the process and the analytics.  As a result, we could argue the load and load conductivity are non-critical process parameters.  This can be demonstrated graphically within JMP using the prediction profiler.  What do you think?

Summary of Fit to Pairwise Interaction of Load*Load Conductivity

RSquare	0.73365
RSquare Adj	0.700356
Root Mean Square Error	0.000893
Mean of Response	0.00765
Observations (or Sum Wgts)	10

Predicted Variation to Aggregate