WOLFRAM LANGUAGE COMPATIBILITY INFORMATION

Upgrading from:

Statistics`DescriptiveStatistics`

Functionality in this package has been added to the builtin Mathematica kernel.
is replaced by the kernel function Commonest.

CentralMoment, GeometricMean, HarmonicMean, InterquartileRange, Kurtosis, MeanDeviation, MedianDeviation, QuartileDeviation, Quartiles, QuartileSkewness, RootMeanSquare, Skewness, and TrimmedMean are now part of the built-in Mathematica kernel:

Version 5.2 << Statistics`DescriptiveStatistics`;
GeometricMean[{10, 50, 10, 15, 20}]
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is replaced by Commonest:

Version 5.2 << Statistics`DescriptiveStatistics`;
Mode[{10, 50, 10, 15, 20}]
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can be computed using Mean and StandardDeviation:

Version 5.2 << Statistics`DescriptiveStatistics`;
CoefficientOfVariation[{10, 50, 10, 15, 20}]
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can be computed using Mean:

Version 5.2 << Statistics`DescriptiveStatistics`;
ExpectedValue[Sqrt, Range[1., 10.]]
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can be computed using Min and Max:

Version 5.2 << Statistics`DescriptiveStatistics`;
SampleRange[{10, 50, 10, 15, 20}]
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can be computed from Kurtosis:

Version 5.2 << Statistics`DescriptiveStatistics`;
KurtosisExcess[{10, 50, 10, 15, 20}]
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can be computed by subtracting the mean from the data:

Version 5.2 << Statistics`DescriptiveStatistics`;
ZeroMean[{10, 50, 10, 15, 20}]
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can be computed from Mean and StandardDeviation:

Version 5.2 << Statistics`DescriptiveStatistics`;
Standardize[Range[1., 10.], MLE -> True]
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can be computed as a parameterized Quantile:

Version 5.2 << Statistics`DescriptiveStatistics`;
InterpolatedQuantile[{10, 50, 10, 15, 20}, 3/4]
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, , and can be constructed directly from the report elements:

Version 5.2 << Statistics`DescriptiveStatistics`;
LocationReport[{10, 50, 10, 15, 20}]
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Alternate standard deviations and variances can be computed from StandardDeviation and Variance:

Version 5.2 << Statistics`DescriptiveStatistics`;
StandardDeviationMLE[{10, 50, 10, 15, 20}]
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Version 5.2 << Statistics`DescriptiveStatistics`;
VarianceMLE[{10, 50, 10, 15, 20}]
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Version 5.2 << Statistics`DescriptiveStatistics`;
StandardErrorOfSampleMean[{10, 50, 10, 15, 20}]
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Version 5.2 << Statistics`DescriptiveStatistics`;
VarianceOfSampleMean[{10, 50, 10, 15, 20}]
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Pearson skewness measures can be computed from basic statistics:

Version 5.2 << Statistics`DescriptiveStatistics`;
PearsonSkewness1[{10, 50, 10, 15, 20}]
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Version 5.2 << Statistics`DescriptiveStatistics`;
PearsonSkewness2[{10, 50, 10, 15, 20}]
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