Here's a bit of a refinement on Ted's first suggestion.
N <- 10000
graphics.off()
par(mfrow = c(1,2), pty = "s")
for(k in 1:20) {
m <- (rowMeans(matrix(runif(M*k), N, k)) - 0.5)*sqrt(12*k)
hist(m, breaks = "FD", xlim = c(-4,4), main = k,
prob = TRUE, ylim = c(0,0.5), col = "lemonchiffon")
pu <- par("usr")[1:2]
x <- seq(pu[1], pu[2], len = 500)
lines(x, dnorm(x), col = "red")
qqnorm(m, ylim = c(-4,4), xlim = c(-4,4), pch = ".", col = "blue")
abline(0, 1, col = "red")
Sys.sleep(1)
}
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
[EMAIL PROTECTED]
Sent: Friday, 22 April 2005 4:48 AM
To: Paul Smith
Cc: r-help@stat.math.ethz.ch
Subject: RE: [R] Using R to illustrate the Central Limit Theorem
On 21-Apr-05 Paul Smith wrote:
> Dear All
>
> I am totally new to R and I would like to know whether R is able and
> appropriate to illustrate to my students the Central Limit Theorem,
> using for instance 100 independent variables with uniform distribution
> and showing that their sum is a variable with an approximated normal
> distribution.
>
> Thanks in advance,
>
> Paul
Similar to Francisco's suggestion:
m<-numeric(10000);
for(k in (1:20)){
for(i in(1:10000)){m[i]<-(mean(runif(k))-0.5)*sqrt(12*k)}
hist(m,breaks=0.3*(-15:15),xlim=c(-4,4),main=sprintf("%d",k))
}
(On my slowish laptop, this ticks over at a satidfactory rate,
about 1 plot per second. If your mahine is much faster, then
simply increase 10000 to a larger number.)
The real problem with demos like this, starting with the
uniform distribution, is that the result is, to the eye,
already approximately normal when k=3, and it's only out
in the tails that the improvement shows for larger values
of k.
This was in fact the way we used to simulate a normal
distribution in the old days: look up 3 numbers in
Kendall & Babington-Smith's "Tables of Random Sampling
Numbers", which are in effect pages full of integers
uniform on 00-99, and take their mean.
It's the one book I ever encountered which contained
absolutely no information -- at least, none that I ever
spotted.
A more dramatic illustration of the CLT effect might be
obtained if, instead of runif(k), you used rbinom(k,1,p)
for p > 0.5, say:
m<-numeric(10000);
p<-0.75; for(j in (1:50)){ k<-j*j
for(i in(1:10000)){m[i]<-(mean(rbinom(k,1,p))-p)/sqrt(p*(1-p)/k)}
hist(m,breaks=41,xlim=c(-4,4),main=sprintf("%d",k))
}
Cheers,
Ted.
--------------------------------------------------------------------
E-Mail: (Ted Harding) <[EMAIL PROTECTED]>
Fax-to-email: +44 (0)870 094 0861
Date: 21-Apr-05 Time: 19:48:05
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