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# normal (Gaussian) distribution
_ln_dnorm(x, m, s) = -0.5 * log(2*pi) - log(s) - 0.5*((x-m)*1.0/s)**2
dnorm(x, mean, sd) = exp(_ln_dnorm(x, mean, sd))
pnorm(x, mean, sd) = norm((x-mean) * 1.0/sd)
# binomial distribution
_ln_binom(x, n, p) =\
lgamma(n+1) - lgamma(x+1) - lgamma(n-x+1) + x*log(p) + (n-x)*log(1-p)
dbinom(x, size, prob) = (x==int(x))? exp(_ln_binom(floor(x), size, prob)) : 0
pbinom(x, size, prob) =\
(x<0)? 0 : (x<size)? ibeta(size-floor(x), floor(x)+1, 1-prob) : 1
set terminal svg
set output "Normal approximation to binomial.svg"
set key 22,0.13
n = 48
p = 0.25
xmax = 25
set samples 50*xmax+1
plot [0:xmax] \
dbinom(x, n, p) with impulses title "Binom(48, 0.25)", \
dnorm(x, n*p, sqrt(n*p*(1-p))) linetype 3 title "Norm(12, 3)"
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to share – to copy, distribute and transmit the work
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Under the following conditions:
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share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
This licensing tag was added to this file as part of the GFDL licensing update.http://creativecommons.org/licenses/by-sa/3.0/CC BY-SA 3.0Creative Commons Attribution-Share Alike 3.0truetrue
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