Custom DoE screening design

[#1]

I have two very basic questions about custom DoE screening design.

1. If I only wanted to know which main effects are significant to my responses and use the custom main effect screening design with the default run numbers given by JMP, how can I be sure whether it is the main effect or other confounding two-way interaction that is really significant? If the model analysis shows factor A is significant but A is partially correlated with B*C, what factor(s) should be included in the optimization design later? I suppose only the main effects that are significant such as factor A should be further investigated for the optimization design following the main effect screening design but will B and C be missing if B*C partially correlated with factor A but B and C are not significant main effects from the screening DoE?

2. when I include some uncontrolled factors in the model, JMP does not show the color map for correlation any more. How can I evaluate the design with uncontrolled factors?

Thanks

LL

[#2]

I thought about it again…are you suggesting manually separate the data rows in the model into two blocks by adding a column with value of “-1” and “1”? If I have 20 runs totally but can only run 10 runs in one day, should just manually add a column in the model as block factor “day” and assign “-1” to the first 10 runs and “1” to the 11-20 runs? Sorry if I misunderstood your point.

[#3]

Not exactly, but again this depends on whether you intend to do a BIB or RCDB.  

I must apologize, but I don't think it is very efficient to teach you how to handle the issues you are confronting in this community chat.  I suggest you read papers/books on the subject so you can expose yourself to the different methodologies and applications.

Here are some papers I suggest:

• Sanders, D., Leitnaker M., and McLean R. (2002) “Randomized Complete Block Designs in Industrial Studies” Quality Engineering, Vol. 14, Issue 1
• Box, G.E.P., Stephen Jones (1992), “Split-plot designs for robust product experimentation”, Journal of Applied Statistics, Vol. 19, No. 1
• Jones, Bradley, Christopher J. Nachtsheim (2009) “Split-Plot Designs: What, Why, and How”, Journal of Quality Technology, Vol. 41, No. 4, pp. 340-361
• Bisgaard, Søren, (2000), “The Design and Analysis of 2 ^k-p X 2 ^q-r Split Plot Experiments”, Journal of Quality Technology, Vol. 32, No. 1, January
• Bisgaard, Søren, Murat Kulahei, (2001), “Robust Product Design: Saving Trials with Split-Plot Confounding”, Quality Engineering, 13(3), 525-530

[#4]

I apologize for so many questions and thank you for the recommendation of books and journal papers.

[#5 · Accepted solution]

@JMP38401 , Here are my thoughts:

1. There are three principles we rely on for fractional factorials and screen designs:

Scarcity: There are relatively few significant effects (analogous to the Pareto Principle)

Hierarchy: 1st order > 2nd order >> 3rd order, etc.

Heredity: In order for an interaction to be significant at least one parent must be significant

Regarding your question, my advice is to predict the rank order model effects (at least through 2nd order). Your predictions as to which effects you believe would be reasonable and likely will impact design resolution selection.  If all of your 1st order effects rank above 2nd order effects, then lower resolution seems reasonable to begin the iterative process of investigation.  In fact, this is the hierarchy principle.  Expand the number of factors (1st order effects) by confounding higher order effects.

2. If you suspect interaction effects (≥2nd order), then you might want to bump resolution to IV+.

3.  I know I don't represent the bulk of the thinking on optimal designs. I am not a huge fan of "partial confounding" to create a more efficient design as if there are instances that do not make sense in the data analysis, the next iteration can be a difficult choice (e.g., fold over designs don't work).

4. I don't completely understand your second question.  If you have covariates in the data table, you can certainly see the correlation between the covariates and the design factors.  Multivariate Methods>Multivariate will provide scatterplots and selecting the options (red triangle) you can get color maps.  Of course if you have many, you can get VIFs by right-clicking in the parameter estimates out put table and adding >Columns>VIFs.

[#6]

Thank you for the quick response!

"2. If you suspect interaction effects (≥2nd order), then you might want to bump resolution to IV+."

I know how to find the design resolutions in Minitab but am not able to tell the design resolution from JMP Custom Design. Can you please let me know how to get the design resolution information in JMP?

"4. I don't completely understand your second question.  If you have covariates in the data table, you can certainly see the correlation between the covariates and the design factors.  Multivariate Methods>Multivariate will provide scatterplots and selecting the options (red triangle) you can get color maps.  Of course if you have many, you can get VIFs by right-clicking in the parameter estimates out put table and adding >Columns>VIFs."

Sorry for the confusion. When I don't have any Uncontrolled factors (under factor Role column, it says Uncontrolled instead of continuous etc), I can see the color map of correlation and Alias Matrix under Design Evaluation. However, once I add any Uncontrolled factors, the Design Evaluation is gone and I am not able to see the Alias Matrix, color map of correlation and Power Analysis etc.