Upgrading from:

Statistics`DescriptiveStatistics`

Functionality in this package has been added to the builtin Mathematica kernel.
Mode 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}]

Mode is replaced by Commonest:

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

CoefficientOfVariation can be computed using Mean and StandardDeviation:

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

ExpectedValue can be computed using Mean:

Version 5.2 << Statistics`DescriptiveStatistics`;
ExpectedValue[Sqrt, Range[1., 10.]]

SampleRange can be computed using Min and Max:

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

KurtosisExcess can be computed from Kurtosis:

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

ZeroMean can be computed by subtracting the mean from the data:

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

Standardize can be computed from Mean and StandardDeviation:

Version 5.2 << Statistics`DescriptiveStatistics`;
Standardize[Range[1., 10.], MLE -> True]

InterpolatedQuantile can be computed as a parameterized Quantile:

Version 5.2 << Statistics`DescriptiveStatistics`;
InterpolatedQuantile[{10, 50, 10, 15, 20}, 3/4]

LocationReport, DispersionReport, and ShapeReport can be constructed directly from the report elements:

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

Alternate standard deviations and variances can be computed from StandardDeviation and Variance:

Version 5.2 << Statistics`DescriptiveStatistics`;
StandardDeviationMLE[{10, 50, 10, 15, 20}]
Version 5.2 << Statistics`DescriptiveStatistics`;
VarianceMLE[{10, 50, 10, 15, 20}]
Version 5.2 << Statistics`DescriptiveStatistics`;
StandardErrorOfSampleMean[{10, 50, 10, 15, 20}]
Version 5.2 << Statistics`DescriptiveStatistics`;
VarianceOfSampleMean[{10, 50, 10, 15, 20}]

Pearson skewness measures can be computed from basic statistics:

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