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
.
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.
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.
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.