2012/12/11 terry mcintyre
>
>
>
> > From: Darren Cook
> >
> >> How much [effort] to determine whether there are multiple peaks?
>
>
> >> Now the tough question: How can this information be used to improve
> move
> > selection?
> >
> > One approach, not at all sophisticated, is better time ma
> From: Darren Cook
>
>> How much [effort] to determine whether there are multiple peaks?
>> Now the tough question: How can this information be used to improve move
> selection?
>
> One approach, not at all sophisticated, is better time management: spend
> less time on normal distri
There are many tests of normality that might be well suited. The
Kolmogorov-Smirnov test (
http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) for instance
should be easy to compute in terms of the function erf().
On Mon, Dec 10, 2012 at 7:07 PM, Darren Cook wrote:
> > How much [eff
> How much [effort] to determine whether there are multiple peaks?
The Shapiro-Wilk test can give you a probability of how non-normal the
distribution is:
http://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test
As an R example, here is some test data:
set.seed(7);
data <- c(rnorm(2000,0,40)
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> From: ""Ingo Althöfer"" <3-hirn-ver...@gmx.de>
>David Fotland was so kind to point on an inaccuracy in my description
>on "Crazy Shadows":
>
>http://www.althofer.de/crazy-shadows.html
>
>The x-axis gives the outcome of the random games played. Typically
>it ranges from
Hello,
David Fotland was so kind to point on an inaccuracy in my description
on "Crazy Shadows":
http://www.althofer.de/crazy-shadows.html
The x-axis gives the outcome of the random games played. Typically
it ranges from about +150 to -150 points. The y-axis gives the
frequencies of the outcome
