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  • JMP will suspend normal business operations for our Winter Holiday beginning on Wednesday, Dec. 24, 2025, at 5:00 p.m. ET (2:00 p.m. ET for JMP Accounts Receivable).
    Regular business hours will resume at 9:00 a.m. EST on Friday, Jan. 2, 2026.
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martin_snogdahl
Level III

Computing statistical tolerance intervals in JMP

Hi,

I am looking into the calculation of one-sided tolerance intervals. Searching the documentation, I found the following paragraph:

 

 Capture.PNG

 

Now, please correct me if I am wrong, but as I understand it:

  • s is the standard deviation of the data samples
  • is the sample mean
  • n is the number of samples
  • 1-α is the confidence
  • t is the noncentral t inverse cumulative distribution function
  • and finally  is the inverse cumulative normal distribution

The reason I am asking is that I am having difficulties recreating the tolerance interval calculated by this method in matlab. Please let me know if there is something here that I got wtong. Thank you.

 

1 ACCEPTED SOLUTION

Accepted Solutions
MRB3855
Super User

Re: Computing statistical tolerance intervals in JMP

It looks like you have it correct; for the one-sided case, g' can be calculated exactly (as shown in the documentation). For the two sided case, however, a variety of approximations are available and some are better than others. But, be warned, it is not the case that two one-sided tolerance intervals (lower and upper, respectively, each at 1-alpha/2 confidence) gives the same interval as a two-sided tolerance interval at 1-alpha confidence.  

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1 REPLY 1
MRB3855
Super User

Re: Computing statistical tolerance intervals in JMP

It looks like you have it correct; for the one-sided case, g' can be calculated exactly (as shown in the documentation). For the two sided case, however, a variety of approximations are available and some are better than others. But, be warned, it is not the case that two one-sided tolerance intervals (lower and upper, respectively, each at 1-alpha/2 confidence) gives the same interval as a two-sided tolerance interval at 1-alpha confidence.  

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