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)

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