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Scilab help >> Statistics > msd

# 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)```

### Arguments

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 `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')```

Carlos Klimann

### Bibliography

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