kurtosis#
- scipy.stats.mstats.kurtosis(a, axis=0, fisher=True, bias=True)[source]#
- 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 coming from biased moment estimators - Use - kurtosistestto see if result is close enough to normal.- Parameters:
- aarray
- data for which the kurtosis is calculated 
- axisint or None, optional
- Axis along which the kurtosis is calculated. Default is 0. If None, compute over the whole array a. 
- fisherbool, optional
- If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0). 
- biasbool, optional
- If False, then the calculations are corrected for statistical bias. 
 
- Returns:
- kurtosisarray
- The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s definition and 0 for Pearson’s definition. 
 
 - Notes - For more details about - kurtosis, see- scipy.stats.kurtosis.