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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

    The median and the mode are the only measures of central tendency that can be used for ... For p = ∞ the largest number dominates, and thus the ∞-norm is the ...

  4. Median - Wikipedia

    en.wikipedia.org/wiki/Median

    Here, i is the number of points strictly less than the median and k the number strictly greater. Using these preliminaries, it is possible to investigate the effect of sample size on the standard errors of the mean and median. The observed mean is 3.16, the observed raw median is 3 and the observed interpolated median is 3.174.

  5. 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:

  6. Mean - Wikipedia

    en.wikipedia.org/wiki/Mean

    The mode income is the most likely income and favors the larger number of people with lower incomes. While the median and mode are often more intuitive measures for such skewed data, many skewed distributions are in fact best described by their mean, including the exponential and Poisson distributions.

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  8. Triangular distribution - Wikipedia

    en.wikipedia.org/wiki/Triangular_distribution

    This distribution for a = 0, b = 1 and c = 0.5—the mode (i.e., the peak) is exactly in the middle of the interval—corresponds to the distribution of the mean of two standard uniform variables, that is, the distribution of X = (X 1 + X 2) / 2, where X 1, X 2 are two independent random variables with standard uniform distribution in [0, 1]. [1]

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