Speaker | Transcript |
| Hello, my name is Chris Gotwalt |
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| has been developed for variance |
| components models, we we think |
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| statistical process control |
| program, one has to understand |
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| ascertain how much measurement |
| error is attributable to testing |
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| there might be five or 10 units |
| or parts tested per operator, |
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| different measuring tools is |
| small enough that differences in |
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| measurement to measurement, |
| repeatability variation, or |
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| measurement systems analyses, as |
| well as a confidence interval on |
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| interval estimates in the report |
| and obtain a valid 95% interval |
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| calculate confidence intervals, |
| because we believed it would be |
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| and the sum of the variance |
| components. Unfortunately, the |
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| r&r study. So because variance |
| components explicitly violate |
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| you were to use the one click |
| bootstrap on variance components |
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| less. So when we were designing |
| fit mixed, and the REML |
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| independent. So back to the |
| drawing board. So it turns out |
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| in JMP. One approach is called |
| the parametric bootstrap that |
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| comparison of the two kind of |
| families of bootstrap. So the |
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| they're, they're not assuming |
| any underlying model. And it's |
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| the rows in the data table are |
| independent from one another. |
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| values, it has the advantage |
| that we don't have to make this |
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| bootstrap simulation. The |
| downside to this is that you |
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| do a quick introduction to what |
| the bootstrap...the parametric |
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| to identify or wanted to |
| estimate the crossing time of a |
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| 162.8. Now, we want to use a |
| parametric bootstrap to to go |
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| has the ability to save the |
| simulation formula back to the |
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| that uses the estimates in the |
| report as inputs into a random |
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| And we take our estimates and |
| pull them out into a separate |
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| And then what we have can be |
| seen as a random sample from the |
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| formula column for the crossing |
| time. And that is automatically |
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| those...on the crossing time, or |
| any quantity of interest. When |
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| simulation, create a formula |
| column of whatever function of |
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| derive quantity of interest and |
| obtain confidence intervals |
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| the add in so that you're able |
| to do this quite easily for |
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| we'll start by showing you how |
| to run the add in yourself once |
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| first version was presented at |
| the JMP 2020 Discovery Summit |
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| overview, but we'll show you the |
| references where you can dive in |
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| perfectly fine as well. So I'm |
| going to go ahead and start with |
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| makes use of the fit mixed |
| platform, right, created from |
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| the add in will only work with |
| JMP Pro. So someone might, |
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| want some measure like |
| reproducibility. So that would |
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| as we said, to calculate the |
| estimate for these, there's no |
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| columns here. The reality is |
| much, much, much more |
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| of the estimate without |
| considering the worst case |
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| production that the actual |
| variance is higher than they have |
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| don't risk being out of spec in |
| production. So to run the add in |
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| From here, I can select the |
| linear combination of confidence |
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| simulations, you get a better |
| estimate of the confidence |
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| 2500. I'm going to leave it as |
| 1000 here just for demonstration |
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| operator or the batch variable, |
| and then press perform analysis. |
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| purpose of this demonstration, I |
| think I will stop it early. |
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| calculated confidence limits, the |
| bootstrap quantiles, which are |
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| these two tabs. But if you'd |
| like to see how those compare, |
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| so what does enough mean, enough |
| for your confidence limits to |
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| stopped it before a thousand. So |
| that's how the add in works. And |
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| distributed around the original |
| estimate, they are in fact |
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| relaunch this analysis. So |
| you'll see that when the |
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| European Discovery, required |
| bounded variance confidence |
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| that, if that happens for some |
| of the bootstrap samples or for |
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| early, again, I'll just let it |
| run a little bit. Yeah, so I, as |
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| the samples are allowed, in some |
| cases, to be below zero. So in |
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| simulation column here, this |
| column of simulated |
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| see them both at the same time. |
| It's a bit... it's a bit tricky, |
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| right components, it's a good |
| idea to run the add in directly |
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| that column is then deleted. So |
| one thing to to mention, before |
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| accounts for the skewness of the |
| bootstrap distributions, right, |
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| that. And then the accelerated |
| takes that even further. So here |
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| thing to mention is that the |
| alpha in this represents the |
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| value for which it's been |
| calculated? And what can we do to |
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| up to investigate the four |
| different kinds of the variance |
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| method, the bias corrected |
| method and the BCa. We also |
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| study. So for all 16 |
| combinations of these three |
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| combinations of confidence |
| intervals, and kept track of how |
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| coverage as we're varying these |
| three variables, and we see here |
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| techniques. And the second best |
| is the bias corrected and |
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| the best one. Now, if you turn |
| no bounds on, which means that |
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| variance components with a |
| pretty close to 95% coverage. |
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| intervals are performing |
| similarly at about 93%. But |
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| to what a master's thesis paper's |
| research would have, would |
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| potentially more work to be |
| done. There's other interval |
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| things like generalized |
| confidence intervals. General |
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| intervals might also do the |
| trick for us as well. Hadley's |
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| so that you can now do |
| parametric bootstrap simulations |
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| 16. When you bring that up, you |
| can enter the linear combination |
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