msd - mean squared deviation

### Calling Sequence

  `y=msd(x)`
  `y=msd(x,'r') or m=msd(x,1)`
  `y=msd(x,'c') or m=msd(x,2)`

### Parameters

- `x` real or complex vector or matrix

### Description

This function computes the mean squared deviation of the values of a vector or matrix `x` .

For a vector or a matrix `x` , `y=msd(x)` returns in the scalar `y` the mean squared deviation of all the entries of `V x` .

`y=msd(x,'r')` (or, equivalently, `y=msd(x,1)` ) is the rowwise mean squared deviation. It returns in each entry of the row vector `y` the mean squared deviation of each column of `x` .

`y=msd(x,'c')` (or, equivalently, `m=msd(x,2)` ) is the columnwise mean squared deviation. It returns in each entry of the column vector `y` the mean squared deviation of each row of `x` .

### Examples

    x=[0.2113249 0.0002211 0.6653811;0.7560439 0.3303271 0.6283918]
    m=msd(x)
    m=msd(x,'r')
    m=msd(x,'c')

### Author

Carlos Klimann

### Bibliography

Wonacott, T.H. & Wonacott, R.J.; Introductory Statistics, fifth edition, J.Wiley & Sons, 1990.