Functions > Statistics > Descriptive Statistics > Kurtosis and Skewness
Kurtosis and Skewness
kurt(A, B, C, ...)—Returns the kurtosis of the elements A, B, C, .... The kurtosis is defined as follows:
The kurtosis of a set of values indicates how flat or peaked the distribution is when compared to the normal distribution:
Value
Distribution Shape
kurt = 0
Normal distribution
kurt > 0
Relatively peaked distribution
kurt < 0
Relatively flat distribution
skew(A, B, C, ...)—Returns the skewness of the elements A, B, C, .... The skewness is defined as follows:
The skewness of a set of values measures the asymmetry about the mean:
Value
Distribution Shape
skew = 0
The distribution is symmetric about its mean, as is the case for the normal distribution.
skew > 0
The distribution "tail" extends towards positive values.
skew < 0
The distribution "tail" extends towards negative values.
Arguments
A, B, C, ... are scalars or arrays. There must be at least four elements in the arguments for kurt, and at least three for skew.
M is an array created from the function arguments A, B, C, .... The standard deviation of M must not equal zero.