On 24 Nov 1999 11:04:50 -0800, [EMAIL PROTECTED] (Robert
Dawson) wrote:
> Rich:
> Jennifer Collins did write:
>
> > > The problem is that the data I use, which is hurricane numbers, deals
> > > with small numbers. ^^^^^^^
>
> If one interprets this (as I did) as "numbers [=counts] of hurricanes"
> the Poisson distribution is not unreasonable. If, of course,it refers to
> some obscure measure of hurricane strength the Poisson distribution would
> not apply.
>
> "Small numbers" implies that a normal approximation to the Poisson model
> might not work.
Oh, yeah. You are right, and she does seem to be concerned with the
count. I apologize for missing that, and making something of it. I
think I identified "numbers" as the hurricane gale-force numbers,
without reading closely enough to be bothered by the other mentions --
partly because, "small numbers" is not a particular problem of
Poisson.
Which normal approximation? You can average several Poisson estimates
for a good estimate of the overall. You can use an add-on (say, 1/2)
to be even more robust for doing tests, using square-roots of the
numbers.
However, 5 vs. 5 is not a powerful design. And, as I said, she seems
to have in her hands a t-test between means, and not anything more.
Comparing ratios is what you do with log-normal -- which is not a good
model for counts.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
