Higher moments (skewness and kurtosis) - GNU Scientific Library -- Reference Manual
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### 21.3 Higher moments (skewness and kurtosis)

— Function: 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,

>               skew = (1/N) \sum ((x_i - \Hat\mu)/\Hat\sigma)^3

> where 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`.

— Function: 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,

>               skew = (1/N) \sum ((x_i - mean)/sd)^3

> These functions are useful if you have already computed the mean and standard deviation of data and want to avoid recomputing them.

— Function: 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,

>               kurtosis = ((1/N) \sum ((x_i - \Hat\mu)/\Hat\sigma)^4)  - 3

> The kurtosis measures how sharply peaked a distribution is, relative to its width. The kurtosis is normalized to zero for a gaussian distribution.

— Function: 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,

>               kurtosis = ((1/N) \sum ((x_i - mean)/sd)^4) - 3

> 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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