Re: scalling of poisson distribution

"Nick" <tulse04-news1@xxxxxxxxxxx> wrote in
news:kuadnfVNabw4R7LbnZ2dnUVZ8qminZ2d@xxxxxx:


"Nick" <tulse04-news1@xxxxxxxxxxx> wrote in message
news:T5WdnVX1qNFPU7LbRVnytwA@xxxxxxxxx

snip

It is a long time since I did this, but if the mean is lambda for
period t, then the mean is lambda for period 2t

The probability (x,2*lambda) = (exp(-lambda)*lambda^x)/x factorial

and Sum of the probability (x,2*lambda) (x=0,1,...) is 1

I think that the fact that the distribution is discrete rather than
continuous comes into it. After all, if we take it the other way
round, if we have x=0,1,2,... in unit time t and we then halve the
unit to t/2, we can't divide the x values by 2. We are still looking
at discrete values of x. If it was a continuous variable then we
could truly scale the distribution.

In fact, see the contribution in the other thread where it is stated
that doctor's visits per day are Poisson, whereas doctor's visits per
year are binomial. You could say that that is just a matter of scale.

Did you think "z" was correct in the other thread regarding the
distribution of doctor and hospital visits per year? I have never seen a
dataset where a binomial distribution fit the data for healthcare visits
particularly well. Most "real data" regarding annual visits will have a
large peak at zero visits and then the rest spread out with a right skewed
density. Don't believe everything you read on the Internet.

--
David Winsemius
.

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