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
> "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
In article <[EMAIL PROTECTED]>,
Kaplon, Howard <[EMAIL PROTECTED]> wrote:
>This is a multi-part message in MIME format.
>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 C
In article <[EMAIL PROTECTED]>,
Ronny Richardson <[EMAIL PROTECTED]> 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
>eno
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
>
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 kn
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 kn
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
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
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 s
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 known.
It seems to me that most st
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