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### Two Random Variables, Each Correlated to a Third

```Date: 04/29/2003 at 22:00:49
From: Huaguang
Subject: How two random variables, each correlated to a third, are
correlated to each other

If X, Y, and Z are 3 random variables such that X and Y are 90%
correlated, and Y and Z are 80% correlated, what is the minimum
correlation that X and Z can have?
```

```
Date: 04/30/2003 at 07:22:53
From: Doctor Mitteldorf
Subject: Re: How two random variables, each correlated to a third, are
correlated to each other

Dear Hua-guang,

If X is correlated with Y and Y is correlated with Z, then X and Z
must be correlated with each other. If the two separate correlation
coefficients are 0.9 and 0.8 respectively, then the correlation of X
and Z can be as much as (0.9)(0.8) + sqrt((1-0.9^2)(1-0.8^2)) or as
little as (0.9)(0.8) - sqrt((1-0.9^2)(1-0.8^2)), but it cannot be
outside this range of 0.458 to 0.982.

Here's the outline of a proof.

To begin, let's assume that X has a mean of zero and a variance of 1.
You can always subtract its mean and then divide by its standard
deviation to achieve this, and the correlation coefficients are
unchanged. Do the same with Y and Z, so that each of them has mean 0
and variance 1.

Next, express Xi as a linear combination of two parts, one part
perfectly correlated with Yi, the other part completely independent of
Yi. So we write Xi = A Yi + X*i, where X*i is, by assumption,
uncorrelated to Yi. Then we have

<XY> = A <Y^2> + <YX*>

By our assumption <YX*>=0, and by definition <XY> is the correlation
coefficient of X and Y (since their means are 0 and variances are 1).
So we have identified A as equal to the correlation,

A = Rxy

Now let's compute the variance of X, which we have already normalized
to unity:

1 = <X^2> = A^2 <Y^2> + 2 Rxy <YX*> + <X*^2>

1         = (Rxy)^2  + 0 + <X*^2>

So we have discovered that the variance of X* is 1 - (Rxy)^2.

Working with Y and Z instead of Y and X, we can derive a similar
relation for the variance of Z*:

<X*^2> = 1 - (Rxy)^2
<Z*^2> = 1 - (Rzy)^2

Now we are ready to compute the correlation of X and Z, writing Xi as
Xi = (Rxy) Yi + X*i and Zi as Zi = (Ryz) Yi + Z*i.

Rxz = <XZ> = (Rxy)(Ryz) <Y^2> + (Rxy)<YZ*> + (Ryz)<YX*> + <X*Z*>

Remember that <Y^2>=1 and the middle two terms are zero.

(Rxz) = (Rxy)(Ryz) + <X*Z*>

(Remember that X* and Z* do have means of 0, but they do not have
unit variance.)

If X* and Z* happen to be perfectly correlated (R=1), then the
greatest value that <X*Z*> can have is the product of the two
individual standard deviations. If they are perfectly anti-correlated
(R=-1), then the smallest value that <X*Z*> can have is minus the same
product.

(Rxz) = (Rxy)(Ryz) +/- sqrt(<X*^2><Z*^2>)

(Rxz) = (Rxy)(Ryz) +/- sqrt((1-(Rxy)^2)(1-(Rzy)^2))

(where the +/- is intended to indicate the range of values Rxz can
take, not that it has to assume one extreme or the other.)

- Doctor Mitteldorf, The Math Forum
http://mathforum.org/dr.math/
```
Associated Topics:
College Probability
College Statistics

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