Hi, I have a process that is log-normally distributed and has an upper spec limit, and I'd like to calculate a Cpk. I tried two approaches that gave me different answers:
Which of these approaches would you recommend? Or could you suggest a better approach?
I consider both of the techniques you specified as good vialble processes. However, the process that I would actually recommend is a very nice builtin capability of the Distribution Platform. If you run the Distribution Platform, you can then go to
It will then evaluate your data to find which distribution(s) it fits. Once done, and if a Log Normal distribution is found to be the fit, you can go under "GLot" distribution and
It creates a new column using the determined Generalized Log algorithm
Thanks Jim. It's a bit frustrating that the LogNormal transform can't be saved in the same way - the GLog transform adds additional complexity, which is hard to justify and would be (even) harder to explain to my colleagues.
Any idea why I got different answers between my initial two approaches?
I understand your frustration. The question on why log normal does not have a Save Transform would have to be answered by the JMP developer.
Concerning the difference between the Cp/Cpk for the Distribution lognormal and your own transformed to log(x) and then calculate.......it appears that there are differences in the calculation of the PDF. I am taking that from the description within
But a more complete answer should be available from JMP Support