The DataSummary function computes six summary statistics for the input data set. These include the mean, standard deviation, coefficient of skewness, coefficient of kurtosis, minimum and maximum of the sample. By default the DataSummary command returns a list of equations of the form quantity=value where quantity is one of mean, standarddeviation, skewness, kurtosis, minimum, or maximum.

The first parameter can be a data sample (e.g., a Vector), or a Matrix data sample.

If the option quantity is not included, then data summary is a list of equations defining the mean, standard deviation, coefficient of skewness, coefficient of kurtosis, minimum and maximum. If quantity is one of the names mean, standarddeviation, skewness, kurtosis, minimum, or maximum, then the data summary is just that value. Finally, if quantity is a list of these names, then the data summary is a list of equations defining these quantities.

If the option output is not included or is specified to be output=value, then the function will return the value of the data summary. If output=plot is specified, then the function will return a plot of the input data set and all of its statistics. If output=both is specified, then both the value and the plot of the data summary will be returned.

If there are floating point values or the option numeric is included, then the computation is done in floating point. Otherwise the computation is exact.

If the option output is not included or is specified to be output= value, then the function will return the data summary: those statistics specified by the expect option, or all of them if the expect option is not present. If output=plot is specified, then the function will return a plot of the input data set and all selected statistics. If output=both is specified, then both the value and the plot of the data summary will be returned.

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

$A≔⟨-4\,\mathrm{\pi }\,\exp \left(3\right)⟩$

$A≔\left[\right]$

$\mathrm{DataSummary}\left(A\,\mathrm{quantity}=\mathrm{mean}\right)$

$-\frac{4}{3}+\frac{\mathrm{\pi }}{3}+\frac{{\ⅇ}^3}{3}$

$\mathrm{DataSummary}\left(A\,\mathrm{numeric}\right)$

$\left[\mathrm{mean}=6.40904319225915\,\mathrm{standarddeviation}=12.3707496533778\,\mathrm{skewness}=0.368551181161869\,\mathrm{kurtosis}=0.999999999227709\,\mathrm{minimum}=\mathrm{-4.}\,\mathrm{maximum}=20.08553692\right]$

$\mathrm{DataSummary}\left(A\,\mathrm{quantity}=\left[\mathrm{mean}\,\mathrm{maximum}\right]\right)$

$\left[-\frac{4}{3}+\frac{\mathrm{\pi }}{3}+\frac{{\ⅇ}^3}{3}\,{\ⅇ}^3\right]$

$\mathrm{DataSummary}\left(A\,\mathrm{output}=\mathrm{plot}\right)$

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

$M≔\left[\right]$

$\mathrm{DataSummary}\left(M\,\mathrm{numeric}\right)$

$\mathrm{datasummary},\mathrm{graph}≔\mathrm{DataSummary}\left(M\,\mathrm{numeric}\,\mathrm{output}=\mathrm{both}\right)$

$\mathrm{datasummary},\mathrm{graph}≔\left[?\right],$

$\mathrm{datasummary}$

$\mathrm{graph}$

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

The Student[Statistics][DataSummary] command was introduced in Maple 18.

For more information on Maple 18 changes, see Updates in Maple 18.