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May 25, 2017 6:33 PM
(827 views)

Hi,

What is the best method to do a failure rate comparison between two process.

10 REPLIES

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May 26, 2017 7:13 AM
(810 views)

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May 28, 2017 10:54 PM
(773 views)

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May 26, 2017 7:44 AM
(807 views)

To add to Dale's reply, see **Help** > **Books** > **Reliability and Survival Methods** > **Life Distribution**. There is a feature of this platform to compare groups that is fully explained in this chapter along with examples.

This solution assumes that you have life data, censored or exact.

Learn it once, use it forever!

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May 28, 2017 10:57 PM
(772 views)

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May 29, 2017 3:51 AM
(765 views)

If the problem is as simple as you describe (compare two failure rates), then you can use a contingency table analysis. Try these steps:

- Enter your data as
**Chamber**(**A**,**B**),**Status**(**Defective**,**Non-defective**), and**Devices**(counts). - Select
**Analyze**>**Fit Y by X**. - Select
**Status**and click**Y**. - Select
**Chamber**and click**X**. - Select
**Devices**and click**Freq**.

Here is what my data table looks like, based on your example:

Here is the result of the analysis:

Does this approach help?

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May 29, 2017 5:34 AM
(760 views)

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Jun 2, 2017 1:48 AM
(667 views)

Dear dale,

Able to elaborate what do you mean by my data probably is censored?

Rgrds

Irfan

Able to elaborate what do you mean by my data probably is censored?

Rgrds

Irfan

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Jun 2, 2017 4:48 AM
(660 views)

From your response to Mark below, it sounds like you have observations that span a period of time during which some items fail and some do not. At the end of that time period, the question is what is that state of items that did not fail? Are they beyond the end of their useful life? Are you only interested in whether they fail within X months time? If the time period is arbitrarily chosen (that's when data collection ended), then all you know about items that have not failed is that they have not failed "yet." It is the "yet" that would make your data censored. This means that you can't claim they won't fail, only that they will not have failed at that particular time mark.

Expanding a bit further - your question to Mark below speaks of wanting to model the time to failure. This is a typical kind of survival analysis. I think there are 2 general approaches. If the items have all reached the end of their useful life, but some items have failed and they fail at different times, then your dependent variable would be the the time to failure and you would do a regression analysis (not the contingency analysis which only look at whether they fail or not, but an analysis that focuses on the time to failure). If the end of data collection is arbitrary (in the sense I describe above) then a survival analysis would be appropriate. The dependent variable is still the time to failure (it is a type of regression analysis), but your data is censored - so you would use the survival platform rather than the fit model platform.

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Jun 2, 2017 1:47 AM
(667 views)

yes this sure helps, another problem that i have lets assume that for a particular chamber the high failure rate is due to some degrading parts and it is not related to the baseline performance of the chambers, is there a way for statistical analysis to screen this out. Another problem that I have is if would like to analyze the Mean time to failure which means that maybe a certain chamber is able to run until fail for 20 days while the other chamber might run and fail after every other 4 days. Do I use the contigency table as well?