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It’s World Statistics Day! To honor the theme of the day, the JMP User Community is having conversations about the importance of trust in statistics and data. And we want to hear from you! Tell us the steps you take to ensure that your data is trustworthy.
I have tried going through various posts regarding how to deal with data censoring situations but i havent been able to figure out the best way to deal with my situation. So here it is...
We conducted a DoE with three variables: a type of surfactant (Surf), amount of water (Water) and amount of salt (Salt) and the response is the 'minimum amount of surfactant which is effective in the process (Surf_min). Ultimately we are looking to develop a model which calculates Surf_min for a given Surf, Water and Salt.
For some of the conditions in the DoE, we couldnt go to high enough concentrations where the surfactant is effective. This number could be different under different conditions. To me this is classic case of right censoring. Now, the question is how do i analyze this data set?
I have looked at the 2017 discovery summit talks and the related posting here. It seems that i could use the 'fit model' platform with 'generalized regression' option with JMP pro. I only have the usual JMP (13.1).
Will appreciate any inputs here. I would also appreciate if someone can point me to a good reference to have on this subject so that i can learn a bit more.
This Discovery presentation is a good resource for understanding how to use Generalized Regression in JMP Pro for censored data. Skip to 19:27 for a demonstration. In this case the response is left-censored because some observations are below limit of detection.