Raymond J. Johnson Jr. wrote:
> gn228169 wrote:
>
>> When analyzing a new method or process, we usually use a t-test to see
>> if the new method is equivalent. If it is not, we look at the variance
>> and target values to see if it failed because it was "better" Is there
>> a statistical test to check for "better" without checking first for
>> equivalence and then for variance and target?
>> Thanks, Jim
>
>
> You've got some 'splainin' to do. What's the difference between a
> "process" and a "method"? You put "better" in scare quotes, apparently
> because you understand that it's a relative concept, but you don't tell
> us about what makes "better" better. What are your criteria? Are you
> considering only output values? Are there fiscal considerations (i.e.,
> given equivalent output, is one process better because it's less
> costly)? What, exactly, are you trying to do?
A process and a method are the same thing, sorry. Better is less
variation and closer to target. A one sided t-test will not tell me if
the new method has less variation and is closer to the target as one
person suggested.
I think cpk,ppk,cpm etc. will work but I still have a question. If a
cpk, cpm score is greater using the new method, I would assume the new
method is "better". My question is, How much of an increase in Ppm is
significant? Example: If my Ppm value was a 0.90 and increased to a 0.99
using the new method, is this due to common noise or is this a truly
"better"?
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