Coefficient of Variation (CV)

Formula

Let $\mu$ = Population mean,

$\sigma$ = Population Standard devation

$$ CV = \frac{\sigma}{\mu} $$

Relation to distributions

• If CV = 1, it is an exponential distribution.
• The mean and the variance of the Poisson distribution are the same

Skewness

<aside> 💡 measures symmetry of distribution

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• If the skewness is between -0.5 & 0.5, the data are nearly symmetrical.
• If the skewness is between -1 & -0.5 (negative skewed) or between 0.5 & 1(positive skewed), the data are slightly skewed.
• If the skewness is lower than -1 (negative skewed) or greater than 1 (positive skewed), the data are extremely skewed.

Kurtosis

<aside> 💡 measures heaviness in the tails/ degree of presence of outliers

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The expected value of kurtosis is 3. This is observed in a symmetric distribution. A kurtosis greater than three will indicate Positive Kurtosis. In this case, the value of kurtosis will range from 1 to infinity. Further, a kurtosis less than three will mean a negative kurtosis. The range of values for a negative kurtosis is from -2 to infinity. The greater the value of kurtosis, the higher the peak.