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.

Most of the commands above can accept one- and two-dimensional data sets. One-dimensional data sets can be supplied as a list, a Vector, a one-dimensional Array, or a DataSeries. Two-dimensional data sets can be supplied as a list of lists, a Matrix, a two-dimensional Array, or a DataFrame.

For more details on how two-dimensional data is handled, see the DataFrames in Statistics help page.

Missing values are represented by undefined or Float(undefined). Note that Float(undefined) propagates freely through most floating-point operations, which means that most statistics for a data set with missing values will yield undefined. The option ignore - which is available for most commands listed above - controls how missing data is handled. If ignore=true all missing items in a data set will be ignored. The default value of this option is false. For more details on a particular command, see the corresponding help page.

Weights can be added to data by supplying an optional argument weights=value, where value is a vector of numeric constants. The number of elements in the weights array must be equal to the number of elements in the original data set. By default all elements in a data set are assigned weight $1$. For more details on a particular command, see the corresponding help page.

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

$X≔\mathrm{RandomVariable}\left(\mathrm{NonCentralBeta}\left(3\,10\,2\right)\right)\:$

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

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

$\left[\begin{array}{c}\mathrm{minimum}=0.00284705659174078\\ \mathrm{lowerhinge}=0.188221218565660\\ \mathrm{median}=0.271179303591104\\ \mathrm{upperhinge}=0.364759629644148\\ \mathrm{maximum}=0.855336805054773\end{array}\right]$

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

$\left[\begin{array}{c}\mathrm{mean}=0.282220601028456\\ \mathrm{standarddeviation}=0.125176906233528\\ \mathrm{skewness}=0.440322599667426\\ \mathrm{kurtosis}=2.84590882372508\\ \mathrm{minimum}=0.00284705659174078\\ \mathrm{maximum}=0.855336805054773\\ \mathrm{cumulativeweight}=1.000000\times {10}^6\end{array}\right]$

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

$0.237418113813262$

$\mathrm{Moment}\left(A\,2\,\mathrm{origin}=0.3\right)$

$0.0159853492127288$

$\mathrm{Mean}\left(A\right),\mathrm{TrimmedMean}\left(A\,1\,99\right),\mathrm{WinsorizedMean}\left(A\,1\,99\right)$

$0.282220601028476,0.280991773683514,0.281923266592907$

$\mathrm{FrequencyTable}\left(A\,\mathrm{range}=0..1\,\mathrm{bins}=5\right)$

$\left[\begin{array}{ccccc}0...0.200000000000000& 283732.& 28.37320000& 283732.& 28.37320000\\ 0.200000000000000..0.400000000000000& 536939.& 53.69390000& 820671.& 82.06710000\\ 0.400000000000000..0.600000000000000& 168880.& 16.88800000& 989551.& 98.95510000\\ 0.600000000000000..0.800000000000000& 10420.& 1.042000000& 999971.& 99.99710000\\ 0.800000000000000..1.& 29.& 0.002900000000& 1.000000\times {10}^6& 100.\end{array}\right]$