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  2. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    The function γ(u) satisfies −1 ≤ γ(u) ≤ 1 and is well defined without requiring the existence of any moments of the distribution. [22] Bowley's measure of skewness is γ(u) evaluated at u = 3/4 while Kelly's measure of skewness is γ(u) evaluated at u = 9/10.

  3. Skew normal distribution - Wikipedia

    en.wikipedia.org/wiki/Skew_normal_distribution

    The exponentially modified normal distribution is another 3-parameter distribution that is a generalization of the normal distribution to skewed cases. The skew normal still has a normal-like tail in the direction of the skew, with a shorter tail in the other direction; that is, its density is asymptotically proportional to for some positive .

  4. Beta distribution - Wikipedia

    en.wikipedia.org/wiki/Beta_distribution

    The accompanying plot of skewness as a function of variance and mean shows that maximum variance (1/4) is coupled with zero skewness and the symmetry condition (μ = 1/2), and that maximum skewness (positive or negative infinity) occurs when the mean is located at one end or the other, so that the "mass" of the probability distribution is ...

  5. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    This formula has the simpler ... is the version found in Excel and several statistical packages ... computes various types of skewness and kurtosis statistics ...

  6. D'Agostino's K-squared test - Wikipedia

    en.wikipedia.org/wiki/D'Agostino's_K-squared_test

    The sample skewness g 1 and kurtosis g 2 are both asymptotically normal. However, the rate of their convergence to the distribution limit is frustratingly slow, especially for g 2 . For example even with n = 5000 observations the sample kurtosis g 2 has both the skewness and the kurtosis of approximately 0.3, which is not negligible.

  7. Exponentially modified Gaussian distribution - Wikipedia

    en.wikipedia.org/wiki/Exponentially_modified...

    This can also be seen as a three-parameter generalization of a normal distribution to add skew; another distribution like that is the skew normal distribution, which has thinner tails. The distribution is a compound probability distribution in which the mean of a normal distribution varies randomly as a shifted exponential distribution .