A sample of normal RVs - the distribution of Xi-Xbar?

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Discussion Overview

The discussion centers on finding the distribution of the difference between a normal random variable and the sample mean, specifically the expression X1 - Xbar, where Xbar is the mean of n independent normal random variables. The scope includes theoretical exploration of statistical properties and independence of random variables.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • One participant states that X1 - Xbar can be expressed as a combination of X1 and the average of the other random variables, suggesting a method to find the distribution of the difference.
  • Another participant proposes that the distribution of X1 - Xbar is normal with a variance of (1 + 1/n)sigma², but this claim is met with skepticism.
  • A later reply questions the correctness of the proposed variance, indicating uncertainty about the calculation.

Areas of Agreement / Disagreement

Participants do not appear to reach consensus on the variance of the distribution of X1 - Xbar, with at least one participant challenging the proposed value.

Contextual Notes

There are unresolved aspects regarding the assumptions made in deriving the distribution, particularly concerning the independence of the random variables and the calculations leading to the proposed variance.

Phillips101
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We have X1,...,Xn~N(mu, sigma2)

The crux of my problem is finding out the distribution of, say, X1-Xbar (where Xbar is the mean of the n RVs). This is going to end up proving the independence of Xbar and Sxx, btw.

I know Xbar~N(mu, sigma2/n), but I don't know how to find the distribution of a difference of normal RVs with different arguments?

Thanks for any help.
 
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Notice that

[tex] X_1 - \bar X = (1-\frac 1 n) X_1 - \frac 1 n \sum_{i\ge2}X_i[/tex]

and all of [itex]X_1[/itex] and [itex]X_2, \dots, X_n[/itex] are independent.

* Get the distribution of

[tex](1 - \frac 1 n) X_1[/tex]

as well as that of

[tex] \frac 1 n \sum_{n\ge2} X_i[/tex]

These are independent as well, so find the distribution of their difference.
 
I have Xi-Xbar ~ N(0, (1+1/n)sigma2) ?
 
Are you sure the variance is

[tex] \left(1 + \frac 1 n \right) \sigma^2[/tex]
 

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