Analysis of CCD results

[#1]

Hi everyone,

I did screening desing and steepest ascent which showed me a saddle point. Then, I conducted experiment (ordinary central composite design) on this area to capture the curvature. But I don't know how to analyse the results, my model isn't significant with quadratic terms and the lack of fit is significant (the model only become significant when I raise my model to the power of 4).

Could someone please help me to understand more?

[#2]

Okay. I'm not sure what level of E was used in the steepest ascent experiment - I have assumed SA. I have also made some assumptions about how the CCD factors map to factors A, B, C, D.

I pulled all the data into one data table (attached). I think it has been useful but probably does not give you an answer that you were hoping for.

It seems like something strange happened between the steepest ascent experiment and the CCD. The results from these two phases do not fit the same model. The factor ranges used in the CCD are very similar to the best runs in the steepest ascent but the response is much lower in the CCD (<100 compared with 240 to 260). Again, I am assuming that the setting of factor E is SA for the steepest ascent and the CCD experiments.

In any case, I am not sure that I would recommend the approach that you took. The screening results on their own suggest that only factors E and C are important, so I am not sure why you also carried forward the other factors for the steepest ascent experiment. I don't understand the steepest ascent method as I have not used that approach. Using very narrow factor ranges for the CCD is also not something that I would recommend unless your objective was to determine if the process is robust over these ranges.

I hope that this has been a useful experience for you, even if you did not get the result you were hoping for. You might want to explore some resources to learn more about DOE before your next project. The Statistical Thinking for Industrial Problem Solving course has a good module on DOE.

[#3]

I have tried to follow your thought process, but, of course, I don't understand the "science" of your investigation.  I will share some of my thoughts.

I see you ran a Res. V fractional factorial with 3 center points (although there is no CP for the one categorical factor).  There is no mention of practical significance of the response?  How much of a change in the response is of practical importance?  Statistical significance is a bit of a challenge because you have 3 replicates of the CP.  One of the DF's is used to estimate curvature and the others are unassignable and therefore may be the basis for estimating the MSE.  How does the MSE compare with the variation you see in the process normally?  E seems to be the most "active" with C second.  I don't see much (again I don't have any context with respect to the response variable) with the rest.  Why did you continue with A and B?  No evidence of curvature.  A model based on E and C looks like there are some issues (one unusual event and the residuals are certainly not normally distributed).  

What model did you use for steepest ascent?  Looks like you used insignificant factors in that model?  Not sure why you continued so far past the best results in your SA method?

The most significant factor was set to the least optimum level...huh?  I would want to investigate that factor more.  Could it be more quantitative?  

Not sure why the CCD?  How does it relate to the other work you did?  Also be careful here as statistical significance does not play as big a role in optimization.  By the point of optimization, you already know the factors matter, so what are you looking for?  Sweet spots (local maxima which are difficult to maintain) or flat spots that provide more robust results.

Lastly, I don't see any strategy to "handle" noise?  No repeats/nesting (short term noise like measurement error) or blocks or split-plots (for long term noise like ambient, raw material lots)?