For two random variates and
, the correlation is defined bY
(1)
|
where
denotes standard deviation and
is the covariance of
these two variables. For the general case of variables
and
, where
, 2, ...,
,
(2)
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where
are elements of the covariance matrix. In general,
a correlation gives the strength of the relationship between variables. For
,
(3)
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The variance of any quantity is always nonnegative by definition, so
(4)
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From a property of variances, the sum can be expanded
(5)
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(6)
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(7)
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Therefore,
(8)
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Similarly,
(9)
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(10)
|
(11)
|
(12)
|
Therefore,
(13)
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so .
For a linear combination of two variables,
(14)
| |||
(15)
| |||
(16)
| |||
(17)
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Examine the cases where ,
(18)
|
(19)
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The variance will be zero if , which requires that the argument of the
variance is a constant. Therefore,
, so
. If
,
is either perfectly correlated (
) or perfectly anticorrelated (
) with
.