At 02:04 PM 4/9/01 -0700, James Ankeny wrote:
Hello,
I have a question regarding the so-called normal approx. to the binomial
distribution. According to most textbooks I have looked at (these are
undergraduate stats books), there is some talk of how a binomial random
variable is
In article [EMAIL PROTECTED], Jay Warner [EMAIL PROTECTED] wrote:
one tech issue, one thinking issue, I believe.
1) Tech: if np _and_ n(1-p) are 5, the distribution of binomial
observations is considered 'close enough' to Normal. So 'large n' is
OK, but fails when p, the p(event), gets
I think you are confusing the idea of a sample with the source of a
binomial random variable. The binomial model applies when some action is
repeated a specified number of times, n; when we are interested in the
occurrence or not of some outcome; when the probability of that outcome
is the same
On Tue, 10 Apr 2001, Gary Carson wrote:
It's the proportion of success (x/n) which has approxiatmenly a normal
distribution for large n, not the number of success (x).
Both are approximately normal.
(If the r.v. W = (x/n) is (approximately) normally distributed, then
the r.v. V = x = n*W
[EMAIL PROTECTED] (James Ankeny) writes:
[snip]
Typically, a binomial rv is not thought of as a statistic, at least in these
books, but this is the only way that the approximation makes sense to me.
Actually, the binomial rv is the sufficient statistic for the data,
which are represented as
[EMAIL PROTECTED] (Jason Owen) writes:
[EMAIL PROTECTED] (James Ankeny) writes:
[snip]
Typically, a binomial rv is not thought of as a statistic, at least in these
books, but this is the only way that the approximation makes sense to me.
Actually, the binomial rv is the sufficient statistic
You may be interested in an applet I have on my website demonstrating the
normal approximation to the binomial.
http://www.ruf.rice.edu/~lane/stat_sim/normal_approx/index.html
--David
From: [EMAIL PROTECTED] (James Ankeny)
Organization: None
Newsgroups: sci.stat.edu
Date: 9 Apr 2001
James Ankeny [EMAIL PROTECTED] wrote:
: My question is, are they saying that the sampling
: distribution of a binomial rv is approximately normal for large n?
:
It's a special case of the CLT for a binary variable with probability p,
taking the sum of n observations
one tech issue, one thinking issue, I believe.
1) Tech: if np _and_ n(1-p) are 5, the distribution of binomial
observations is considered 'close enough' to Normal. So 'large n' is
OK, but fails when p, the p(event), gets very small.
Most examples you see in the books use p = .1 or .25
