Kaplon, Howard wrote:
What many authors do, I believe, is employ the Law of Large
Numbers, and say that for n sufficiently large, the probability
approaches 0 that | sigma - s | is different from 0. That is
sigma and s may be interchanged with minimal probability of any
change. And so
Ronny Richardson wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
enough and the population standard deviation is
At 12:49 PM 11/21/01 -0500, Ronny Richardson wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
enough and the population
Title: RE: When Can We Really Use CLT Student t
It has been a long time; so if I am wrong, please fan the flames gently.
The derivation of the t distribution is from the ratio of a Normal(0,1) over the square root of a ChiSquare divided by its degrees of freedom.
t = [(x-bar - mu
On 21 Nov 2001, Ronny Richardson wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
enough and the population standard
Ronny Richardson wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
enough and the population standard deviation is
Ronny Richardson wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
enough and the population standard deviation is
On 21 Nov 2001 10:18:01 -0800, [EMAIL PROTECTED] (Ronny
Richardson) wrote:
As I understand it, the Central Limit Theorem (CLT) guarantees that the
distribution of sample means is normally distributed regardless of the
distribution of the underlying data as long as the sample size is large
