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Skewness is a measure of the asymmetry of a distribution. A distribution has positive skew if the assymetry is towards the positive direction, and negative skew if it is towards the negative direction. (i.e. positive skew if the positive tail is longer and negative skew if the negative tail is longer.) |
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Skewness is a measure of the asymmetry of the distribution of a real-valued random variable. Roughly speaking, a distribution has positive skew if the asymmetry is towards the positive direction, and negative skew if it is towards the negative direction (i.e., positive skew if the positive tail is longer and negative skew if the negative tail is longer). |
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Formula for skewness: (Σi(xi-xmean)3)/Nσ3 |
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Skewness 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]. |
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where N is the number of data points, xi the ith data point, xmean the mean, and σ the standard deviation. |
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For a set of N values the skewness can be calculated as Σi(xi - μ)3 / Nσ3, where xi is the ith value and μ is the mean. |
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Skewness is the third moment about the mean. |
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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. |
Skewness 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 set of N values the skewness can be calculated as 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.
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