From: Gabor Grothendieck <ggrothendieck_at_gmail.com>

Date: Sun 17 Jul 2005 - 04:45:37 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sun Jul 17 04:50:28 2005

Date: Sun 17 Jul 2005 - 04:45:37 EST

On 7/16/05, Jeff Newmiller <jdnewmil@dcn.davis.ca.us> wrote:

> I have a rather basic background in statistics, and am looking for

*> assistance in solving what I expect is a common type of problem.
**>
**> I have measurements of physical processes, and mathematical models of
**> those processes that I want to feed the measurements into. A simple case
**> is using measurements of electric power entering and leaving a
**> power conversion device, sampled at regular intervals, and summed to
**> estimate energy in and out, and dividing the energy out by the energy in
**> to get an estimate of efficiency. I know that power efficiency varies
**> with power level, but for this calculation I am interested in the
**> quantifying the "overall" efficiency rather than the instantaneous
**> efficiency.
**>
**> If the energy quantities are treated as a normally-distributed random
**> variable (per measurement uncertainty), is there a package that simplifies
**> the determination of the probability distribution function for the
**> quotient of these values? Or, in the general sense, if I have a function
**> that computes a measure of interest, are such tools general enough to
**> handle this? (The goal being to determine a confidence interval for the
**> computed quantity.)
**>
**> As an attempt to understand the issues, I have used SQL to generate
**> discrete sampled normal distributions, and then computed new abscissa
**> values using a function such as division and computing the joint
**> probability as the ordinate, and then re-partitioned the result into new
**> bins using GROUP BY. This is general enough to handle non-normal
**> distributions as well, though I don't know how to quantify the numerical
**> stability/accuracy of this computational procedure. However, this is
**> pretty tedious... it seems like R ought to have some straightforward
**> solution to this problem, but I don't seem to know what search terms to
**> use.
**>
*

There is some discussion about the ratio of normals at:

http://www.pitt.edu/~wpilib/statfaq.html

but you may just want to use bootstrapping:

library(boot)

library(simpleboot)

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sun Jul 17 04:50:28 2005

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