Sample Correlation Coefficient Proof Help

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SUMMARY

The discussion centers on proving that the absolute value of the sample correlation coefficient, denoted as r, is less than or equal to 1. The user employs the Cauchy-Schwartz inequality to derive the relationship between covariance and variance, specifically using the formula r = Cov(x,y)/(sxsy). The user identifies a potential error in their application of the Cauchy-Schwartz inequality, which leads to the conclusion that abs(r) should be less than or equal to sxsy instead of 1. The discussion emphasizes the importance of correctly applying statistical inequalities in sample statistics.

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  • Understanding of sample statistics versus population statistics
  • Familiarity with the Cauchy-Schwartz inequality
  • Knowledge of covariance and variance calculations
  • Ability to interpret correlation coefficients
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Statisticians, data analysts, and students studying statistics who are looking to deepen their understanding of correlation coefficients and their proofs.

Seda
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I'm trying to prove that abs(r) <= 1.

(Ill apologize up front that I am not sure on how to write all equations properly in this forum, but Ill try to make it clear)

Note that this is all sample statistics, not population, which is why I'm using r and not rho.

I know that I have to use the Cauchy-Schwartz inequality, and I can use that without proving that.

I have:

r= Cov(x,y)/(sxsy)

Therefore by Cauchy-Schwartz:

abs(r) <= (var(x)var(y))/(sxsy)

And since variance is the deviation squared

abs(r) <= (sx2sy2)/(sxsy)

leaving me with

abs(r) <= (sxsy)

Instead of the "1" I want.

My guess my error is somewhere in utlizing the cauchy schwartz but I am not sure..
 
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Start this way.

[tex] cov(x,y) = \int \int (x - \overline x)(y - \overline y) h(x,y) \, dx dy[/tex]

or, if you prefer

[tex] cov(x,y) = E[(x-\overline x)(y - \overline y)][/tex]

Now apply cauchy-schwartz.
 
Last edited:

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