This is documentation for an old release of SciPy (version 0.7.). Read this page in the documentation of the latest stable release (version 1.16.2).
scipy.stats.kurtosis¶
- scipy.stats.kurtosis(a, axis=0, fisher=True, bias=True)¶
Computes the kurtosis (Fisher or Pearson) of a dataset.
Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.
If bias is False then the kurtosis is calculated using k statistics to eliminate bias comming from biased moment estimators
Use kurtosistest() to see if result is close enough to normal.
Parameters: a : array
axis : int or None
fisher : bool
If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0).
bias : bool
If False, then the calculations are corrected for statistical bias.
Returns: The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s :
definition and 0 for Pearson’s definition. :
References
[CRCProbStat2000] section 2.2.25
