Skewness
In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking, a distribution has positive skew if the positive tail is longer and negative skew if the negative tail is longer.
Skewness, the third standardized moment, is defined as μ3 / σ3, where μ3 is the third moment about the mean and σ is the standard deviation. The skewness of a random variable X is sometimes denoted Skew[X].
For a sample of N values the sample skewness is Σi(xi − μ)3 / Nσ3, where xi is the ith value and μ is the mean.
If Y is the sum of n independent random variables, all with the same distribution as X, then it can be shown that Skew[Y] = Skew[X] / √n.
Given samples from a population, the equation for population skewness above is a biased estimator of the population skewness. An unbiased estimator of skewness is
- <math> \mbox{Skew} = \frac{n}{(n-1)(n-2)}
\sum_{i=1}^N \left( \frac{x_i - \bar{x}}{\sigma} \right)^3
<math>
where σ is the sample standard deviation and μ is the sample mean.
See also: mean, variance, kurtosis, cumulant.
