Jean Bréfort and Andreas J. Guelzowasked had an exchange about the
lognormal distribution on 22 Aug.
There seems to be little documentation on how the /randlognorm/ function
works. We see in the help file:
Name
|RANDLOGNORM|
Synopsis
|RANDLOGNORM|(/|zeta|/,/|sigma|/)
Description
|RANDLOGNORM| returns a lognormal-distributed random number.
Examples
|RANDLOGNORM|(1,2).
It is correct as stated there that /randlognorm/ returns a
lognormally-distributed random number. But what the help documentation
does not tell us is that the parameters zeta and sigma are for the
_normally_ distributed natural logarithm values. The function is
evidently constructed in such a way that it returns random lognormal
values using the mean and standard deviation (zeta and sigma
respectively) of the normally distributed log values. So
randlognorm(3.6586,0.2462) will return random lognormally distributed
values having a mean of 40 and standard deviation of 10.
Thus if I am running a montecarlo simulation of a process that is
assumed to have lognormally distributed process times with mean 40 and
standard deviation 10, I must first calculate the mean (zeta) and
standard deviation (sigma) of the normally distributed logarithms of the
process times. The conversion between the normal and related lognormal
parameters are clearly explained in most good statistics books, and at,
for example the Wikipedia:
http://en.wikipedia.org/wiki/Log-normal_distribution
The NIST online "Engineering Statistics Handbook," for which a link was
posted in the original post, is (to me) the most confusing description
possible on this topic, and I do not recommend it, even though I am an
engineer! The Dataplot software the NIST makes available is equally
opaque. Not sure if this is of any help to others, and am brand new to
the list, but it took me a while to figure it out and it may be
worthwhile adding something to the Gnumeric help file.
Regards,
Carl
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