Covariance

returns
the covariance. `C`

= cov(`A`

)

If

`A`

is a vector of observations,`C`

is the scalar-valued variance.If

`A`

is a matrix whose columns represent random variables and whose rows represent observations,`C`

is the covariance matrix with the corresponding column variances along the diagonal.`C`

is normalized by the number of observations`-1`

. If there is only one observation, it is normalized by 1.If

`A`

is a scalar,`cov(A)`

returns`0`

. If`A`

is an empty array,`cov(A)`

returns`NaN`

.

returns
the covariance between two random variables `C`

= cov(`A`

,`B`

)`A`

and `B`

.

If

`A`

and`B`

are vectors of observations with equal length,`cov(A,B)`

is the`2`

-by-`2`

covariance matrix.If

`A`

and`B`

are matrices of observations,`cov(A,B)`

treats`A`

and`B`

as vectors and is equivalent to`cov(A(:),B(:))`

.`A`

and`B`

must have equal size.If

`A`

and`B`

are scalars,`cov(A,B)`

returns a`2`

-by-`2`

block of zeros. If`A`

and`B`

are empty arrays,`cov(A,B)`

returns a`2`

-by-`2`

block of`NaN`

.

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