__Statistics:__double**gsl_stats_skew***(const double*`data`[], size_t`stride`, size_t`n`)-
This function computes the skewness of
`data`, a dataset of length`n`with stride`stride`. The skewness is defined as,where @math{x_i} are the elements of the dataset

`data`. The skewness measures the asymmetry of the tails of a distribution.The function computes the mean and estimated standard deviation of

`data`via calls to`gsl_stats_mean`

and`gsl_stats_sd`

.

__Statistics:__double**gsl_stats_skew_m_sd***(const double*`data`[], size_t`stride`, size_t`n`, double`mean`, double`sd`)-
This function computes the skewness of the dataset
`data`using the given values of the mean`mean`and standard deviation`sd`,These functions are useful if you have already computed the mean and standard deviation of

`data`and want to avoid recomputing them.

__Statistics:__double**gsl_stats_kurtosis***(const double*`data`[], size_t`stride`, size_t`n`)-
This function computes the kurtosis of
`data`, a dataset of length`n`with stride`stride`. The kurtosis is defined as,The kurtosis measures how sharply peaked a distribution is, relative to its width. The kurtosis is normalized to zero for a gaussian distribution.

__Statistics:__double**gsl_stats_kurtosis_m_sd***(const double*`data`[], size_t`stride`, size_t`n`, double`mean`, double`sd`)-
This function computes the kurtosis of the dataset
`data`using the given values of the mean`mean`and standard deviation`sd`,This function is useful if you have already computed the mean and standard deviation of

`data`and want to avoid recomputing them.

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