Hi @schlesin,

To clarify the information from FDS plot :

It is generated by calculating relative prediction variance for different design space, and shows the proportion of the design space over which the relative prediction variance lies below a given value : Fraction of Design Space Plot (jmp.com)

It is only linked to the design created and the factor settings, not to the known standard deviation of the response(s) or the confidence level chosen.

You can estimate the actual variance of prediction at any setting by multiplying the relative variance of prediction at that setting with the error variance (mean squared error (MSE) of the model fit for example). You can read the part "Relative Prediction Variance" to get more details : Prediction Variance Profile (jmp.com)

It seems that you may be interested also in Power analysis if you're mentioning confidence level and known standard deviation of your response (and want to check if your design is properly sized, perhaps to be able to screen efficiently significant effects) ?

I hope this answer will help you,

Victor GUILLER

L'Oréal Data & Analytics

"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)