A design has two predictor variables (both continuous). The response is pass/fail. I would like to know how to use Binary Logistic Regression to know what value of each variable will give me a failure rate of 0.000001 with 95% confidence.
With one predictor variable, it's clear - but not with two. I usually use Minitab, but I would be limited to holding one predictor variable constant while varying the other - this could be done, but is tedious (and isn't really what I want to do).
I also don't want to overdesign.
I don't know if this can be done in Excel or R, but that would be good to know.
In many ways, I'm looking for the creation of a surface, where the predictor variables are on the "x" and "y" axes, and the probability is on the "z" axis. I would want the lower 95th% confidence of this surface meeting a failure rate of 0.000001.
Any responses are much appreciated.