Type 1 Gage R&R - Analyzing Run Chart and I-mR

M

Mr.Agi

#1
Hi All, I'm new in this forum (first post actually), even if I read the forum I don't have an answer for this question.

I'm analyzing an appraiser to measure the airtight capability
I measured 20 times the same pumps.
Before starting the test I analyzed the values, not normal distributed.

The Run Chart tells me that there is an oscillation, (p-value 0,048) so I can suppose the process is instable.

The I-mR chart tells me that the process is under control, no problems.

How is it possible? I mean, is it possible a process to be unstable but under statistical control?

Thanks all for your reply
 
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bobdoering

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#2
Re: Type 1 R&R - analyzing run chart and I-mR

I'm analyzing an appraiser to measure the airtight capability
I measured 20 times the same pumps.
Before starting the test I analyzed the values, not normal distributed.

The Run Chart tells me that there is an oscillation, (p-value 0,048) so I can suppose the process is instable.

The I-mR chart tells me that the process is under control, no problems.

How is it possible? I mean, is it possible a process to be unstable but under statistical control?

For a process to be stable in traditional SPC using Western Electric rules, the data will randomly jump about the mean. So, the calculations may analyze the data and may not detect an actual oscillation, and calculate control limits no problem. In essence, it will appear to be in statistical control.

The issue is that the data does not tell the story, it illustrates the story. Or, in other words, it supports the theory not generates the theory. So, the question is: what is the theory? Should there be oscillation? Can it be explained? Is it truly oscillation, or some kind of measurement error? Bottom line, what are the variations of the process?
 
M

Mr.Agi

#6
I use software MINITAB to perform my analysis. The software gives me also a P-Value, so that I can accept or refuse an hypotesis. In the Oscillation case the P-Value is 0,048, so very close to 0,05. Maybe a lack of experience is the problem, the data actually seem random distributed. Thanks
 

bobdoering

Stop X-bar/R Madness!!
Trusted Information Resource
#7
I use software MINITAB to perform my analysis. The software gives me also a P-Value, so that I can accept or refuse an hypotesis. In the Oscillation case the P-Value is 0,048, so very close to 0,05. Maybe a lack of experience is the problem, the data actually seem random distributed. Thanks
Be careful with the 0.05 conclusion.
 
B

Barbara B

#9
mbar is imho the abbreviation of millibar :2cents:

The Run Chart tells me that there is an oscillation, (p-value 0,048) so I can suppose the process is instable.

The I-mR chart tells me that the process is under control, no problems.
There isn't much information about your process in the data, simply because n=20 is a very small sample to evaluate a process. The test for oszillation providede by Minitab is an approximate test (s. attachment), so every result will come with a built-in uncertainty. In combination with a small sample size you should be very careful in relying on the characteristics of this test.

E.g. for your data the approx. p-value is p=0.048 <0.05 (see picture "Run Chart of Value [mbar].png"), meaning: You have a chance of 4.8% to get this value for the test statistic or a more extreme value, if there isn't any oszillation present in your data. If you drop the last reading you get a p-value of p=0.064 >5% (see picture "Run Chart of Value [mbar]_1.png") and if you add another reading with value=1.1 the approx. p-value is calculated as p=0.103 >5% (see picture "Run Chart of Value [mbar]_2.png")

All in all I would be more concerned about the non-normality than the p-value of an approximate test like the test for oszillation :bigwave:

Regards,

Barbara
 

Attachments

M

Mr.Agi

#10
I performed the run chart test because I expected a trend on the measures, but not find it. Anyway, another question on the probability plot, if I perform this kind of analysis (type 1 Gage Study) on the same sample 50 times measured, then it is possible to have some stack data. In this case what I can conclude, I mean the stack data mean the appraiser is good (I measure the same value many times) or the stack data mean my appraiser do not have an adeguate resolution?
 
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