The measures of dispersion summarize how spread out (or scattered)
the data values are on the number line. MuPAD^{®} provides the following
functions for calculating the measures of dispersion. These functions
describe the deviation from the arithmetic average (mean) of a data
sample:

The

`stats::variance`

function calculates the variance, where is the arithmetic mean of the data sample

*x*_{1},*x*_{2},*...*,*x*_{n}.The

`stats::stdev`

function calculates the standard deviation, where is the arithmetic average of the data sample

*x*_{1},*x*_{2},*...*,*x*_{n}.The

`stats::meandev`

function calculates the mean deviation, where is the arithmetic average of the data sample

*x*_{1},*x*_{2},*...*,*x*_{n}.

The standard deviation and the variance are popular measures
of dispersion. The standard deviation is the square root of the variance
and has the desirable property of being in the same units as the data.
For example, if the data is in meters, the standard deviation is also
in meters. Both the standard deviation and the variance are sensitive
to outliers. A data value that is separate from the body of the data
can increase the value of the statistics by an arbitrarily large amount.
For example, compute the variance and the standard deviation of the
list `x`

that contains one outlier:

L := [1, 1, 1, 1, 1, 1, 1, 1, 100.0]: variance = stats::variance(L); stdev = stats::stdev(L)

The mean deviation is also sensitive to outliers. Nevertheless,
the large outlier in the list `x`

affects the mean
deviation less than it affects the variance and the standard deviation:

meandev = stats::meandev(L)

Now, compute the variance, the standard deviation, and the mean
deviation of the list `y`

that contains one small
outlier. Again, the mean deviation is less sensitive to the outlier
than the other two measures:

S := [100, 100, 100, 100, 100, 100, 100, 100, 1.0]: variance = stats::variance(S); stdev = stats::stdev(S); meandev = stats::meandev(S)

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