@Jonas_Rinne - SVEM might be a method which uses some kind of validation without violating the design structure
@Victor_G - Or use statistical analysis, or Machine Learning algorithms robust to overfitting (random forests, SVM, ...) Replicates are more used for explainability (sort the random error/variation/statistical error from the factors influences), I wouldn't use them for predictive modeling, particularly for Machine Learning modelling
Video 2: Mixture design with constraints
Leily Javidfarad.mp4
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@Bill_Worley - Mixture models can be highly non-linear and it may be good to start off with a space-filling mixture doe to try and better understand the non-linearity.
@Victor_GOr do it sequentially : classical mixture design up to 2nd/3rd order, and then augment with Space-Filling to have points inside the experimental space.
Video 3: Number of trials : can we use the minimal advised by JMP in Optimal Design?
Thomas PEZÉ.mp4
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@Paul_J - This makes me wonder ... How does JMP decide the recommended number of runs? ... Could that give us an idea of why to use something more than the minimum?
@Victor_G - Very often, 4 extra runs than minimum number of runs
It also depends on your choices. If you have chosen some terms as "if possible" it will try to build these effects in with the added runs coming from the minimum. Otherwise it will add replicates for instance. JMP is trying to distribute the additional runs as best as possible based on your choices.
@martindemel - consider that you will get also information about the error estimate
