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  2. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    Example distribution with positive skewness. These data are from experiments on wheat grass growth. In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined.

  3. L-moment - Wikipedia

    en.wikipedia.org/wiki/L-moment

    The most useful of these are , called the L-skewness, and , the L-kurtosis. L-moment ratios lie within the interval ( −1, 1 ) . Tighter bounds can be found for some specific L-moment ratios; in particular, the L-kurtosis τ 4 {\displaystyle \ \tau _{4}\ } lies in [ ⁠− + 1 / 4 ⁠ , 1 ) , and

  4. Shape of a probability distribution - Wikipedia

    en.wikipedia.org/wiki/Shape_of_a_probability...

    The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include: mounded (or unimodal), U-shaped, J-shaped, reverse-J shaped and multi-modal. [1]

  5. Box plot - Wikipedia

    en.wikipedia.org/wiki/Box_plot

    The spacings in each subsection of the box-plot indicate the degree of dispersion (spread) and skewness of the data, which are usually described using the five-number summary. In addition, the box-plot allows one to visually estimate various L-estimators, notably the interquartile range, midhinge, range, mid-range, and trimean. Box plots can be ...

  6. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    Alternative measures of kurtosis are: the L-kurtosis, which is a scaled version of the fourth L-moment; measures based on four population or sample quantiles. [3] These are analogous to the alternative measures of skewness that are not based on ordinary moments. [3]

  7. Multimodal distribution - Wikipedia

    en.wikipedia.org/wiki/Multimodal_distribution

    where b 2 is the kurtosis and b 1 is the square of the skewness. Equality holds only for the two point Bernoulli distribution or the sum of two different Dirac delta functions. These are the most extreme cases of bimodality possible. The kurtosis in both these cases is 1. Since they are both symmetrical their skewness is 0 and the difference is 1.

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

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

    In the following, { x i } denotes a sample of n observations, g 1 and g 2 are the sample skewness and kurtosis, m j ’s are the j-th sample central moments, and ¯ is the sample mean. Frequently in the literature related to normality testing, the skewness and kurtosis are denoted as √ β 1 and β 2 respectively.

  9. L-estimator - Wikipedia

    en.wikipedia.org/wiki/L-estimator

    For example, the midhinge minus the median is a 3-term L-estimator that measures the skewness, and other differences of midsummaries give measures of asymmetry at different points in the tail. [1] Sample L-moments are L-estimators for the population L-moment, and have rather complex expressions. L-moments are generally treated separately; see ...