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A statistical measure of degree in asymmetry of distribution in a random variable and can be valued aspositive or negative on data average.


If the left tail is more enunciate than the right tail it is said to have negative skewness while the reverse is true then it is positive skewness, but when it is equal it is said to have zero skewness.

The qualitative analysis of the skewness is complex like for example in a unimodal distribution negative skew point out that the tail on the left side of the probability is wider than the right side. On the contrary positive skew indicates that the tail on the right side is wider than the left side. When the tail of both side of the mean isbalance, a zero value occurs in which symmetric andasymmetric distributions happen, however in multimodal and discrete distributions skewness is very difficult to construe. But most importantly skewness does not determined the relationship of mean and median.

Skewness is very essential to finance andinvestment wherein sets of data including stock prices and assets returns have either positive or negative skew and by knowing which data are skewed, one can better estimate whether a given data peak will be more or less than the mean.

See also:


random variable

statistical analysis