- 1. Real-Life (score: 1)
- Author: Jim Stapleton <stapleton@stt.msu.edu>
- Date: Wed, 25 Jun 2003 17:27:15 -0400
- My earlier answer wasn't "real-life", but there are certainly examples for which the distr. is a mixture of a normal and, with small probability, a distribution with prob. mass far to the right. I re
- /archives/html/s-news/2003-06/msg00189.html (7,665 bytes)
- 2. Re: Real-Life (score: 1)
- Author: Spencer Graves <spencer.graves@PDF.COM>
- Date: Wed, 25 Jun 2003 14:59:18 -0700
- Wouldn't the problems you mention be caught by reasonable plots like normal probability plots of data and residuals? Spencer Graves Jim Stapleton wrote: My earlier answer wasn't "real-life", but ther
- /archives/html/s-news/2003-06/msg00190.html (8,829 bytes)
- 3. Re: Real-Life (score: 1)
- Author: Spencer Graves <spencer.graves@PDF.COM>
- Date: Wed, 25 Jun 2003 19:48:23 -0700
- I've gotten a couple of responses to my earlier reply so I will elaborate here. Consider: qqnorm(c(rnorm(99), 1e6), datax=T) I just did this 10 times, and in every case, the image was a vertical line
- /archives/html/s-news/2003-06/msg00192.html (9,840 bytes)
- 4. Re: Real-Life (score: 1)
- Author: "Brian S Cade" <brian_cade@usgs.gov>
- Date: Fri, 27 Jun 2003 08:15:15 -0600
- Perhaps the important thing to keep in mind here is that our data can often deviate alot from normality yet inferences about means, changes in means, etc. will not be too far astray. But start asking
- /archives/html/s-news/2003-06/msg00201.html (7,643 bytes)
- 5. Re: Real-Life (score: 1)
- Author: Spencer Graves <spencer.graves@PDF.COM>
- Date: Fri, 27 Jun 2003 07:38:53 -0700
- Yes, definitely for tolerance intervals, you need a deeper understanding of the distribution. Nonparametric tolerance invervals require much larger samples to produce tight and stable results than do
- /archives/html/s-news/2003-06/msg00202.html (8,751 bytes)
This search system is powered by
Namazu