# corr2cov

Convert standard deviation and correlation to covariance

## Syntax

``ExpCovariance = corr2cov(ExpSigma)``
``ExpCovariance = corr2cov(___,ExpCorrC)``

## Description

example

````ExpCovariance = corr2cov(ExpSigma)` converts standard deviation and correlation to covariance.```

example

````ExpCovariance = corr2cov(___,ExpCorrC)` specifies options using one or more optional arguments in addition to the input arguments in the previous syntax.```

## Examples

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This example shows how to convert standard deviation and correlation to covariance.

```ExpSigma = [0.5 2.0]; ExpCorrC = [1.0 -0.5 -0.5 1.0]; ExpCovariance = corr2cov(ExpSigma, ExpCorrC)```
```ExpCovariance = 2×2 0.2500 -0.5000 -0.5000 4.0000 ```

## Input Arguments

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Standard deviations of each process, specified as a vector of length `n` with the standard deviations of each process. `n` is the number of random processes.

Data Types: `double`

(Optional) Correlation matrix, specified as an `n`-by-`n` correlation coefficient matrix. A correlation coefficient is a statistic in which the covariance is scaled to a value between minus one (perfect negative correlation) and plus one (perfect positive correlation).

If `ExpCorrC` is not specified, the processes are assumed to be uncorrelated, and the identity matrix is used.

Data Types: `double`

## Output Arguments

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Covariance matrix, returned as an `n`-by-`n` covariance matrix, where `n` is the number of processes.

The (i,j) entry is the expectation of the i'th fluctuation from the mean times the j'th fluctuation from the mean.

```ExpCov(i,j) = ExpCorrC(i,j)*ExpSigma(i)*ExpSigma(j) ```

## Version History

Introduced before R2006a