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
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