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  2. Mode (statistics) - Wikipedia

    en.wikipedia.org/wiki/Mode_(statistics)

    The mode of a sample is the element that occurs most often in the collection. For example, the mode of the sample [1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17] is 6. Given the list of data [1, 1, 2, 4, 4] its mode is not unique. A dataset, in such a case, is said to be bimodal, while a set with more than two modes may be described as multimodal.

  3. Central tendency - Wikipedia

    en.wikipedia.org/wiki/Central_tendency

    A simple example of this is for the center of nominal data: instead of using the mode (the only single-valued "center"), one often uses the empirical measure (the frequency distribution divided by the sample size) as a "center".

  4. Unimodality - Wikipedia

    en.wikipedia.org/wiki/Unimodality

    The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. If there is a single mode, the distribution function is called "unimodal". If it has more modes it is "bimodal" (2), "trimodal" (3), etc., or in general, "multimodal". [2]

  5. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...

  6. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    Comparison of mean, median and mode of two log-normal distributions with different skewness. The mode is the point of global maximum of the probability density function. In particular, by solving the equation (⁡) ′ =, we get that:

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  8. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    [18] [19] Geary has shown, assuming that the mean and variance are finite, that the normal distribution is the only distribution where the mean and variance calculated from a set of independent draws are independent of each other. [20] [21] The normal distribution is a subclass of the elliptical distributions.

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