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  2. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    The Pearson median skewness, or second skewness coefficient, [12] [13] is defined as ⁠ 3 ( mean − median ) / standard deviation ⁠ . Which is a simple multiple of the nonparametric skew .

  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. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    "Excess coefficient", Encyclopedia of Mathematics, EMS Press, 2001 [1994] Kurtosis calculator; Free Online Software (Calculator) computes various types of skewness and kurtosis statistics for any dataset (includes small and large sample tests).. Kurtosis on the Earliest known uses of some of the words of mathematics

  5. 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 ...

  6. Nonparametric skew - Wikipedia

    en.wikipedia.org/wiki/Nonparametric_skew

    The nonparametric skew is one third of the Pearson 2 skewness coefficient and lies between −1 and +1 for any distribution. [5] [6] This range is implied by the fact that the mean lies within one standard deviation of any median. [7] Under an affine transformation of the variable (X), the value of S does not change except for a possible change ...

  7. Coefficient of variation - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_variation

    The data set [90, 100, 110] has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1; The data set [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18

  8. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    This term was intended to be analogous to the coefficient of variation, for describing multiplicative variation in log-normal data, but this definition of GCV has no theoretical basis as an estimate of itself (see also Coefficient of variation). Note that the geometric mean is smaller than the arithmetic mean.

  9. Geometric distribution - Wikipedia

    en.wikipedia.org/wiki/Geometric_distribution

    Skewness Excess kurtosis + + ... The index of dispersion of the geometric distribution is and its coefficient of variation is . The distribution is ...