Recordings DOE Club Q1 2025

[#1]

Question 1: DOE model analysis

Comments:

@Phil_Kay 

@Victor_G : Stepwise selection might not be the default approach to recommend. 

It is helpful as a guide and comparison, but I would prefer to compare the outcomes with GenReg and other platforms

 Raster plots can also help see the frequencies of terms inclusion on many models, to see how they compare : https://community.jmp.com/t5/JMP-Wish-List/Raster-plots-or-other-visualization-tools-to-help-model/i...

@bdclark23 : Multiple model approaches with a preponderance of evidence that consistently show the same effects across different models, each with their own biases and assumptions.

Question 2: “Covariate Approach” of applying constraints

Comments

@Victor_G : About constraints : https://community.jmp.com/t5/JMP-Blog/Demystifying-Factor-Constraints/ba-p/619898#U619898

@Ryan_Lekivetz : https://community.jmp.com/t5/Abstracts/Candidate-Set-Designs-Tailoring-DOE-Constraints-to-the-Proble...

@Phil_Kay : I posted about this on LinkedIn recently as well: https://www.linkedin.com/posts/drphilkay_candidate-set-approach-for-complex-experiments-activity-729...

Love this discussion. It's relatively easy to jump (JMP!) to the technical answer. But important to challenge the "why" as well. 

@Chandramouli_R : 

Video 3: Full factorial design: How many center points you recommend?

@Phil_Kay : I've posted about centre points before as well: https://www.linkedin.com/posts/drphilkay_doebyphilkay-doebyphilkay-designofexperiments-activity-7056...

@Victor_G : Centre points can help detecting curvature, but doesn't allow estimation of several quadratic effects

Q: How to add center points in a custom design?

A: 

@Victor_G : About scoping designs and their use as preliminary runs to check design space : Scoping Designs | Prism

Video 4: How can I reset / delete factors in the easy DoEs without changing the experiment design?

Comments: 

@Chandramouli_R : https://community.jmp.com/t5/Discussions/Hard-to-change-factors-center-points-and-replicates/td-p/22... 

@Victor_G : 

• In "analysieren" you can select the effects in and out of the model
• Tab "analysieren" --> Try "Bestes Model" 
• Or create a custom model with "Fit Model" using the JMP datatable with your results

@Jonas_Rinne : I think you can also do this by going into the flexible mode in Easy DOE

@Victor_G : Fit your responses separately.  But I think there are many ressources to help you create and compare models.

OFAT has no model behind, it's only a comparative approach, so not very helpful to guide decision-making

Video 5: Analytic Culture for Using DOE

Comments: 

@Uwe_Weber : Use the Profiler and they will understand

@anchildress : We have the most success at Eastman when we focus on relationship building with customers/clients, starting small to build trust with you and your techniques. Once they see some wins or successful DOEs with colleagues perhaps they are more trusting of DOE.

Results and cost savings are the most convincing technique

@DB1 : Changing a culture: Patience. Work around the edges. When you can convince one scientist or engineer, they can be more convincing of DOE use to their teammates than us. As Bea says, one really good DOE dataset in a company can convince a lot of people if used diplomatically and audience-aimed.

[#2]

Thanks for the recording @maria_astals !

Some comments to elaborate on points discussed yesterday :

1. Stepwise selection
   
   

   Stepwise regression can be problematic in designed experiments, as it :

   • ignores the experimental structure of the DoE : hierarchy principle, sparsity principle, and sometimes heredity principle, but this last principle can be forced in Generalized Regression platform,
   • creates biased/inflated parameters estimates, as these terms are primarily selected because of their magnitude/effect size,
   • increases the risk of false positives (probability of detecting an active effect that is not active) due to multiple testing without adjustment,
   • provides unreliable p-values, as they can be both used for terms selection and estimates statistical testing,
   • doesn't adress multicollinearity, which can be a problem with strong aliases/correlations between terms in the design, and/or in the case of Mixture designs,
   • completely ignores the assumed model related to the generated design.

   There may be other reasons, some litterature and discussions on this topic can be found easily :

   SP0016 Stepwise or not to stepwise? the do’s and dont’s of multivariable modelling | Annals of the R...

   Using stepwise regression to address multicollinearity is not appropriate - PMC

   Why we hate stepwise regression | Statistical Modeling, Causal Inference, and Social Science

   Stepwise selection of variables in regression is Evil.

   Stepwise selection can still be helpful as a benchmark model, and if the number of factors is not too high, the The All Possible Models Option can be interesting to see how different models agree and disagree about the terms selection and estimation. But the model comparison and selection should be guided by the assumed model behind the design, and by domain expertise.

   Some discussions and posts in the Community related to Stepwise approaches on DoE data :

   HOW to fit two model with different terms at the same time 

   Backward regression for Mixture DOE analysis with regular (non pro) JMP? 

   Analysis of a Mixture DOE with stepwise regression 

   DoE Model issues Augmentation 

   Fit Definitive Screening vs. Stepwise (min. AICC) for model selection 

2.  Constraints
   
   You can add constraints in two ways :
   - By adding constraints and limits through Define Factor Constraints panel, that will restrict design points generation according to constraints,
   - By directly restricting the design points that can be used for design generation using a Candidate Set approach : Candidate Set Designs: Tailoring DOE Constraints to the Problem (2021-EU-30MP-784) 
   
   The blog post from @Jed_Campbell about Demystifying Factor Constraints is a must-read on this topic.
   
   
3. Centre points
   
   The choice of adding centre points is often more related to the objectives and practical reasons : detecting curvature, ensuring measurement stability process during experiments, having an estimate of pure error/variability, having an estimate in the centre of the design space (in the context of robustness analysis with regards to factors space/Quality by Design), ... Depending on the assumed model and the type of design, centre points may not be required ; classical ones like Central Composite Designs and Definitive Screening Designs have centre points, whereas computed optimal designs may not require them. 
   
   Some litterature on this topic :
   5.3.3.7. Adding centerpoints
   Center Points: Finding the Mathematical Center of Your Data - isixsigma.com
   https://www.statease.com/blog/importance-center-points-central-composite-designs/ 
   
   And some discussions and posts in the Community :
   effect of centre points 
   Re: Forcing center points to be equally distributed across blocks custom design 
   How many center points to add in a design? 

@maria_astals As a suggestion for next meetings, it would be great if some of the topics could be voted and prepared in advance : as the DoE club is a quaterly meeting, we would have enough time to create a poll with different topics, and vote for them before the next club meeting. This would ensure more in-depth discussions as well as more variety in the topics (and perhaps more participation/engagement). A mix between prepared/voted topics and spontaneous ones/questions could be a good idea for future meetings ?

It was great to have you in the meeting Bill @statman. Really enjoy learning from you. 

[#3]

Thanks for the clarifications @Victor_G !

There is a topic for discussion secction in this community: https://community.jmp.com/t5/Design-of-Experiments-Topic/idb-p/doe-grouphubidea-board

Let´s gather some ideas from the group and send the poll in advance.