On Friday, 23 March 2012 at 05:51:40 UTC, Manfred Nowak wrote:
| For samples, if it is known that they are drawn from a
symmetric
| distribution, the sample mean can be used as an estimate of
the
| population mode.
I'm not printing the population mode, I'm printing the 'sample
mode'.
It has a very clear meaning: most frequent value. To have
frequency,
I group into 'bins' by precision: 12.345 and 12.3111 will both
go to the 12.3 bin.
and the program computes the variance as if the values of the
sample
follow a normal distribution, which is symmetric.
This program doesn't compute the variance. Maybe you are talking
about another program. This program computes the standard
deviation
of the sample. The sample doesn't need to of any distribution
to have a standard deviation. It is not a distribution parameter,
it is a statistic.
Therefore the mode of the sample is of interest only, when the
variance
is calculated wrongly.
???
The 'sample mode', 'median' and 'average' can quickly tell you
something about the shape of the histogram, without
looking at it.
If the three coincide, then maybe you are in normal distribution
land.
The only place where I assume normal distribution is for the
confidence intervals. And it's in the usage help.
If you want to support estimating weird probability
distributions parameters, forking and pull requests are
welcome. Rewrites too. Good luck detecting distribution
shapes!!!! ;-)
-manfred
PS: I should use the t student to make the confidence intervals,
and for computing that I should use the sample standard
deviation (/n-1), but that is a completely different story.
The z normal with n>30 aproximation is quite good.
(I would have to embed a table for the t student tail factors,
pull reqs velcome).
PS2: I now fixed the confusion with the confidence interval
of the variable and the confidence interval of the mu average,
I simply now show both. (release 0.4).
PS3: Statistics estimate distribution parameters.
--jm
