Attribute , for example C chart

Achim

Registered
#1
How can I eliminate the lower control limit in Jump?
sometimes I just want to control defect count and want my lower control limit to be zero.
 
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Achim

Registered
#3
Hello Jim,
Jump or Minitab are statistical programs.
You chose a chart type, upload your data and the programs plot your charts. In this case I want to track particles and use a C chart for it.
I am not interested to show any auditor the lower control limit! Iam only interested tracking the upper.
But the programs always shows also the lower control limit.
Therefore, does anyone know how to turn off the function to calculate the LCL in the program JUMP?
Or better, is there any Attribute chart not showing the LCL.
Or what chart types do you use for Particle charts?
Or how do you guys explain out of control points under LCL to auditors, and what actions should that drive, when just simply lower particles are better!
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#4
It’s JMP, pronounced jump. JMP actually stands for Just another Mac Program...it is like MINITAB.
I thought JMP automatically set the lower limit to zero if it was negative for ‘attribute’ charts. I’m not at work until Tuesday so I can’t check right now. You can set the lower limit to zero manually.

Out of control low for count charts is good. it means something good happened. That is waht you explain to auditors. If hte auditor leant’ understand this absolutely basic chart concept then they are not very competent.
 

Achim

Registered
#5
It’s JMP, pronounced jump. JMP actually stands for Just another Mac Program...it is like MINITAB.
I thought JMP automatically set the lower limit to zero if it was negative for ‘attribute’ charts. I’m not at work until Tuesday so I can’t check right now. You can set the lower limit to zero manually.

Out of control low for count charts is good. it means something good happened. That is waht you explain to auditors. If hte auditor leant’ understand this absolutely basic chart concept then they are not very competent.
 

Achim

Registered
#6
Thank you,
Iam new in JMP, and have version 5.0.1 to play with right now.
I have a ticket in to get the new version.
I did not see the functionality to manually set LCL to zero.
And the data is all positive.
Let me know once you are at work, I would greatly appreciate the help!!
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#7
JMP 5 is ancient. You will probably see what you are looking fo when you get the most recent version (I am on 15).

However a second comment is warranted. Particle counts can be quite ‘high’ and there is probably a legitimate lower control limit. If JMP calculates a lower control limit (greater than zero - as it should if the counts are high enough) then you should leave the limit. To set it a zero would be wrong and any knowledgable auditor would question this and your competence/knowledge of SPC.

Again a properly calculated LCL > 0 for counts of things is appropriate and useful.
 

Achim

Registered
#8
Consider the process, if you are looking at an EPI reactor, on every run you are creating more particles.
You are depositing dopant, so it is part of the process.
This particle data may be ‘dependent’ data.
This is why I am looking for a chart just having an upper limit.
Granted with MY SPC knowledge and in Minitab I would do the following:
Prove independence first. I have not found this functionality in JMP.
I believe dependent data should not have any LCL and UCL, therefore again my question, what is the right way to chart with just an upper limit and out of control action plan to react to too much particles.

Any EPI grower out there, what chart are you using?

Second I would transform the data ( if independence is proven) then I am fine with the C chart and lower LCL.
Do you agree Bev?

I also struggle to find the BOX COX transformation in my old JMP version. Maybe you can also point me to Independence and normality assumptions when you are back in front of your version 15.
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#9
Are you looking for ‘particles’ or stacking faults?

NEVER EVER transform your data. EVER. Transformations require that you know what the actual distribution is os that you select right transformation formula AND that the data are homogenous. Count data is usually Poisson but not always. and depending on what you are actually counting it is rarely homogenous.
 
#10
Particles!
But in a process which creates particles, the EPI process, the dopants waste product are creating particles every process run.
So, I more and more think this data is for sure dependent data, my books tell me not to make statistical conclusions from dependent data....
 
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