Individual Control Chart

[#1]

here you find 54 individual data, exponentially distributed.

JMP analyses the data and provides a STRANGE Control Chart

Why?

See the attached file

Fausto Galetto

[#2]

Dear  dlehman1 (Level V)

You say

• the process seems to be stable. 
• But the large differences in the control limits means someone with more knowledge will need to comment.

You should divide the values in the Control Chart by 1000 the factor that you used to get "almost integer values" needed for the T Chart.

Thank you

faustoG

[#3]

Yes, I realized that last night.  Since I had multiplied the values by 1000, that requires adjusting the control limits.  They still don't match, though they are the correct order of magnitude.  So, I don't know what accounts for the difference.  But it does seem that there are 2 ways to get the control chart you want from JMP - either using the T chart based on the Weibull distribution (after making the data into integer values), or by manually setting the control limits in an IR control chart.

[#4]

Unfortunately, I do not know

faustoG

P.S.

We learned that

1. my JMP Student Edition cannot deal with Rare Events
2. it computes wrong control limits
3. the distribution of the data matters
4. the usual formulae for CC are based on the Normal distribution, due to the Central Limit Theorem

[#5]

@faustoG 

You seem intent on complaining about the JMP Student Edition.  I'm not sure why, but I don't think it is productive.  No software claims to do everything you want.  In any case, your points 2-4 all pertain to control limits.  I agree with point 4 - the usual control limits are set on the basis of a normal distribution - but there is the opportunity to override these based on some other distribution or other considerations.  I still think it is important to distinguish between control limits (a property of the data) and specification limits (a property of the problem).  In the case of medical issues (such as you have here), I would put more emphasis on the specification limits.  A process could well be in control yet hazardous to your health.

By the way, if it JMP's difficulty dealing with your specific data that you have problems with, you can always try to find a Python or R program that suits your purpose and run that within JMP.  Specialized methods are generally more available in R, given the relatively larger user base among statisticians.  And, if you can specify the exact problem you are trying to solve that JMP does not have the built in capability for, you can often get someone willing to write a script to accomplish what you need - I have used the Community this way in the past.

I did find this recent article that you might be interested in:  https://www.tandfonline.com/doi/full/10.1080/08839514.2024.2322362#d1e4814.  I haven't examined it in detail, but it appears to be a kind of hybrid of control and specification limits - at least that is my high level attempt to interpret this.  Inclusion of economic parameters seems like a way to introduce meaning into the control limits rather than just relying on a statistical calculation based on the data.  If anybody can digest this paper, please confirm (or refute) this, and expand the interpretation if possible.

[#6]

dlehman1 Level V

IF my JMP Student Edition

• would have worked correctly with Rare Events
• I would not bother the JMP Community
• With the problem explained in my letter

Students must know how to analyse Rare Events

I will read the paper you suggested and I will come back

faustoG

[#7]

@ dlehman1 Level V

In the paper, you suggested there is …

• It is NONSENSE,
• unless the Researcher (Scholar) be enlightened by the Holy Spirit
• who tells him the TRUE values of the quantities Theta_0, LCL and UCL:
• these quantities MUST be estimated by the data and are numbers
• (determinations) of Random Variables

[#8]

dlehman1 Level V

In the paper you suggested there is …

I do not find this result

Can you?

faustoG

[#9]

dlehman1 Level V

2024-10-11

Dear colleagues,

Please find my analysis of the Car Seats data given in the paper suggested by dlehman1 Level V

I find it very strange that

• an Out Of Control (OOC) process is better than
• an In Control (IC) process

What do you think?

k1_A, k2_A,       k1_B, k2_B

are the Control Limits shown in the paper.

faustoG

[#10]

Control (as defined by Shewhart) has nothing to do with good or bad.  That is a function of specification. Specifications are typically (sic. always) independently derived from actual process data and variation.

My suggestion, you should try to understand what Shewhart meant by control as he is the creator of the control chart method, take it or leave it.  I'm out.

[#11]

@ statman, Super User with 769 Kudos

YOU WRITE:

• I'm out.
• "All models are wrong, some are useful" G.E.P. Box

It’s a real pity.

We will not see any case on the Control Charts you trust … (in spite that I asked you to find a case )

I think that, in spite of citing Shewhart and Box, you are unable to deal with “real” data and to make good Control Charts…

You did not analyse any data, you did not provide any data…

You made a lot of waffling

In my opinion, you should

1. Study the Shewhart and Deming books
2. Learn their “good” ideas
3. Study some “good” Statistical books
4. Learn that some ideas on models are TOTALLY WRONG

faustoG