Skewness and Kurtosis
Skewness
In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
The skewness value can be positive, zero, negative, or undefined.
Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data.
If the curve is shifted to the left or to the right, it is said to be skewed.
Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution
Kurtosis
In probability theory and statistics, kurtosis is a measure of the “tailedness” of the probability distribution of a real-valued random variable.
The sharpness of the peak of a frequency-distribution curve.
Kurtosis is only useful when used in conjunction with standard deviation.
It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good).
Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).
The normal curve is called Mesokurtic curve.
If the curve of a distribution is peaked than a normal or mesokurtic curve then it is referred to as a Leptokurtic curve.
If a curve is less peaked than a normal curve, it is called as a Platykurtic curve.
SciPy Stats Describe
Result(
nobs=5000,
minmax=(0.0, 1.0),
mean=0.3454,
variance=0.22614406881376278,
skewness=0.6502649087627079,
kurtosis=-1.577155548431827)
