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- 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.

Next: Autocorrelation, Previous: Absolute deviation, Up: Statistics [Index]