Log-normal process capability

Question

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:

* Save spec limit as column property for process. Plot distribution of process, then Continuous Fit --> LogNormal to get a Cpk (checking goodness-of-fit of course)
* Add new column. Set column formula as log(process), save column upper spec limit as log(upper spec limit). Plot distribution, get Cpk from capability analysis. Validate via Continuous Fit --> Normal and checking goodness-of-fit.

Which of these approaches would you recommend? Or could you suggest a better approach?

Many thanks,

Alex

txnelson · Accepted Answer

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

Continuous Fit==>All

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

Save Transform

It creates a new column using the determined Generalized Log algorithm

txnelson · Accepted Answer

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

Help==>Books==>Basic Analysis

But a more complete answer should be available from JMP Support