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JMP Wish List

We want to hear your ideas for improving JMP software.

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We consider several factors when looking for what ideas to add to JMP. This includes what will have the greatest benefit to our customers based on scope, needs and current resources. Product ideas help us decide what features to work on next. Additionally, we often look to ideas for inspiration on how to add value to developments already in our pipeline or enhancements to new or existing features.

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Confidence Intervals for Overall and Within Sigma Estimates in Process Capability

Status: New Submitted by on ‎02-26-2024 02:09 PM
Comment

What inspired this wish list request? I am trying compare the Within Sigma estimate of variation between groups, and I am unable to do so since I cannot generate confidence intervals for those estimates.

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What is the improvement you would like to see?  The Capability platform now helpfully includes 95% Confidence Intervals for the various Cpx and Ppx estimates, but I can’t get the same type of information for the Within Sigma estimate.  Ultimately, I would like to be able to easily obtain confidence intervals for the Overall and Within Sigma estimates in Process Capability in all platforms where that is used.  (Distribution, Control Chart Builder, Process Capability, etc.)  It would also be beneficial to generate CIs for the Stability Index to provide a quick assessment of how this ratio compares to 1.

 

As an editorial comment, it is harder than I would like it to be to generate the confidence interval information for the Overall Sigma Std Deviation, but I can do it in the Distribution Platform using the Confidence Intervals option.  (Would like it to be an option for the summary statistics as well).  I can also get there in Fit Y by X under the Means and Std Deviations option in JMP 17, but you have to right click the table and add the columns to the report – and not a lot of people know you can even do that.

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Why is this idea important? Confidence intervals are important so that we can understand the uncertainty associated with any statistical estimate.  Without this information readily available, people will often make incorrect assumptions - especially when sample sizes are small.