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Oct 26, 2015 11:57 PM
(1438 views)

So I did a blocking(X1), two levels(-1,+1) three factors(A,B,C). 5 runs in one block, and 5 runs in the next block. One control in each block. The Alias was interaction A*B*C. I couldn't control one of my parameters for two of the runs. For C, two of the run was actually control at +0.70 instead of +1. So, I input that into the software.

1 analysis) Using the Fit model platform, A and B was not significant. C was significant. The intercept was lower than where it should be. This was informative but not as useful.

2 analysis) Using the Screening Platform, significant coefficients are C and C*C. I run the model using C and C*C, the result make sense. Biologically, this also expected because I was not surprise that the responding result is a curvature. Also, the intercept is where its should be. The resulting model is logical and more useful to me.

So, my co-worker argue that my initial design was not a surface respond design. It is basically a blocking full factorial DoE, therefore, I can not have a model that is a square(curvature). Since I don't fully understand the screening platform to fully explain the C*C in a report, I should take a more conservative approach and use the first analysis which is classical but a less informative model.

If my co-worker is correct, why would the Screening Platform give me a C^2 when my initial design was not mean to detect it. Is there a way to justify the resulting square coefficient?

7 REPLIES

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Oct 27, 2015 7:09 AM
(1123 views)

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Oct 27, 2015 11:10 AM
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Oct 28, 2015 2:42 PM
(1123 views)

You are both right....

If you are being a detective then perhaps if C is significant and no other terms are significant and there is lack of fit (since you mentioned that you repeated the center point in each block) then perhaps C*C is possible but realize that it is confounded with other possible terms (B*B and C*C).

To truly elucidate the active terms one could augment the experiment with 5 additional runs to fit the RSM.

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Oct 27, 2015 11:34 AM
(1123 views)

I think you haven't given us quite enough information about the design.

Did you create the original design in JMP? If so, which design generator did you use (e.g. Custom, Screening, etc.)?

5 runs in one block, and 5 runs in the next block. One control in each block.

How many runs total does this add up to? 10 or 12? What A, B and C settings did you use for the "Control" run?

-Jeff

-Jeff

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Oct 27, 2015 12:12 PM
(1123 views)

Yes it was in JMP Custom. total of 10 runs. The control was the mid point(0)

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Oct 29, 2015 7:27 PM
(1123 views)

The Screening platform is a very simple platform. It assumes that you are in a screening situation so that the screening principles hold. effect sparsity, effect hierarchy, effect heredity, and projection). It is not clear that this experiment is, in fact, screening, no matter which design platform in JMP was used. So the results from Screening might be suspect.

The Screening platform creates *contrasts* based on these principles until the model is *saturated*. So it enters the largest additive effect first, then the second largest and so on based on the effect hierarchy principle. After all the main effects are in the model, it starts to add the second order effects (e.g., interactions and quadratic terms) using the main effects in order of significance based on the effect heredity principle. It continues until it has used all of the degrees of freedom (i.e., 10 terms in your case). The idea is that saturating the model increases the chance that effect sparsity holds and increases the validity of the *pseudo-standard error* used in the *t*-tests. Since C entered as the most significant main effect, then it would enter early in a second order term, such as A*C or C^2, based on effect heredity principle. Because you have only a center point, the assignment of curvature is going to be to C, the most significant main effect based on the effect heredity principle. There is, in fact, no data in your experiment to actually determine which factors are responsible for curvature in the response.

Learn it once, use it forever!

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Oct 30, 2015 10:04 AM
(1123 views)