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User Guidelines for JMP User Community

The JMP User Community is a service hosted by the JMP Division of SAS Institute Inc. to facilitate the exchange of technical information pertaining to JMP products, services and best practices. Share your questions and JMP experiences with other JMP customers; pitch in and help someone solve a problem; or just read and learn.
While our community is aimed at JMP customers and users, we welcome everyone with an interest in programming, analytical, data management or data visualization software, as long as you adhere to these community rules:
  • Understand that this is a public site. Do not include any data or information that is confidential or that contains the personal information of other individuals. Be aware that any information you include in your Community profile will be accessible to all registered users of the Community. JMP has the right to post, not to post, to modify, and to remove any of your content and/or to terminate your access to and use of the Community in its sole discretion.
  • Be yourself. In your posts or in your profile, you should identify yourself as well as any relevant company affiliation you may have. If you choose to upload a profile image, it should be one that helps identify you to other users, and in which you are recognizable.
  • Respect each other. Please respect the intellectual property rights of others and do not use images, logos, or other materials that belong to someone else without their express permission. Respect members by posting positive and constructive comments. Spam and abusive posts will not be tolerated.
  • Appreciate everyone’s time. Search the community for your question before you ask. If still unanswered, ask well-thought-out questions that explain prior research and experimentation. Supply sample data if possible, making sure it’s not confidential (see #1).
  • Tell us what worked. Mark answers as “accepted solutions” or “like” them so that other community members can benefit from your experience and opinion.
  • Be relevant. Please do not post duplicate messages in different communities – this is confusing and fragments discussion. If a discussion sparks a new question, start a new thread rather than interrupting the original train of thought.
We are glad you’ve found a home on the JMP User Community and hope you find this site valuable. Now that you know the ground rules, start exploring!

Hello All,

I have a lot of Non-normal Data so i read how to use Continuous fit and Fit All,please see below Eg. Figure 1.

Now I have to enforce our Specs of 8.4-9.2 , so first i transformed the data by picking Log Normal (Using"Save Fitted Quantile") Figure-2 because Johnson-Si & Johnson Su showed a very weird peak.







Now i am wondering :

1) Did i pick the correct transformation

2) If yes, do i need to transform the Spec values of 8.4 & 9.2

3) Is it correct to use the capability analysis on transformed data as the data is still not normal (shown by Goodness of Fit)

Please advise 

Thanks in advance  


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