The MedianDeviation function computes the median absolute deviation from the median of the specified random variable or data set.

The first parameter can be a data set (given as e.g. a Vector), a Matrix data set, a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).

By default, all computations involving random variables are performed symbolically (see option numeric below).

All computations involving data are performed in floating-point; therefore, all data provided must have type realcons and all returned solutions are floating-point, even if the problem is specified with exact values.

For more information about computation in the Statistics package, see the Statistics[Computation] help page.

The ds_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[DescriptiveStatistics] help page.

ignore=truefalse -- This option controls how missing data is handled by the MedianDeviation command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the MedianDeviation command will return undefined. If ignore=true all missing items in A will be ignored. The default value is false.

weights=Vector -- Data weights. The number of elements in the weights array must be equal to the number of elements in the original data sample. By default all elements in A are assigned weight $1$.

The rv_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[RandomVariables] help page.

numeric=truefalse -- By default, the median absolute deviation is computed symbolically. To compute the median absolute deviation numerically, specify the numeric or numeric = true option.

$\mathrm{with}\left(\mathrm{Statistics}\right)\:$

$\mathrm{MedianDeviation}\left(\mathrm{Normal}\left(3\,1\right)\right)$

$\mathrm{RootOf}\left(2\mathrm{erf}\left(\mathrm{\_Z}\right)-1\right)\sqrt{2}$

$\mathrm{MedianDeviation}\left(\mathrm{Normal}\left(3\,1\right)\,\mathrm{numeric}\right)$

$0.674489750196106$

$A≔\mathrm{Sample}\left(\mathrm{Normal}\left(3\,1\right)\,{10}^6\right)\:$

$\mathrm{MedianDeviation}\left(A\right)$

$0.674845782314400$

$X≔\mathrm{RandomVariable}\left(\mathrm{Normal}\left(5\,2\right)\right)\:$

$B≔\mathrm{Sample}\left(X\,{10}^6\right)\:$

$\mathrm{MedianDeviation}\left(X\right)$

$2\mathrm{RootOf}\left(2\mathrm{erf}\left(\mathrm{\_Z}\right)-1\right)\sqrt{2}$

$M≔\mathrm{Median}\left(X\right)$

$M≔5$

$\mathrm{Median}\left(\mathrm{abs}\left(X-M\right)\right)$

$2\mathrm{RootOf}\left(2\mathrm{erf}\left(\mathrm{\_Z}\right)-1\right)\sqrt{2}$

$\mathrm{MedianDeviation}\left(X\,\mathrm{numeric}\right)$

$1.34897950039221$

$\mathrm{MedianDeviation}\left(B\right)$

$1.34821184853011$

$V≔⟨\mathrm{seq}\left(i\,i=57..77\right)\,\mathrm{undefined}⟩\:$

$W≔⟨2\,4\,14\,41\,83\,169\,394\,669\,990\,1223\,1329\,1230\,1063\,646\,392\,202\,79\,32\,16\,5\,2\,5⟩\:$

$\mathrm{MedianDeviation}\left(V\,\mathrm{weights}=W\right)$

$2.$

$\mathrm{MedianDeviation}\left(V\,\mathrm{weights}=W\,\mathrm{ignore}=\mathrm{true}\right)$

$2.$

$M≔\mathrm{Matrix}\left(\left[\left[3\,1130\,114694\right]\,\left[4\,1527\,127368\right]\,\left[3\,907\,88464\right]\,\left[2\,878\,96484\right]\,\left[4\,995\,128007\right]\right]\right)$

$M≔\left[\begin{array}{ccc}3& 1130& 114694\\ 4& 1527& 127368\\ 3& 907& 88464\\ 2& 878& 96484\\ 4& 995& 128007\end{array}\right]$

$\mathrm{MedianDeviation}\left(M\right)$

$\left[\begin{array}{ccc}1.& 117.& 13313.\end{array}\right]$

Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.

The A parameter was introduced in Maple 16.

For more information on Maple 16 changes, see Updates in Maple 16.