gn228169 wrote:
> 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"?
It's only "better" if it saves money. Nitpicking the numbers *wastes*
money. It's not clear whether you're trying to solve a particular
problem or just dealing with theory, but if you're talking about a
difference of .09 PPM, it's hard to believe it's worth worrying about.
|
