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  2. Central tendency - Wikipedia

    en.wikipedia.org/wiki/Central_tendency

    The most common measures of central tendency are the arithmetic mean, the median, and the mode. A middle tendency can be calculated for either a finite set of values or for a theoretical distribution, such as the normal distribution .

  3. 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: ⁡ [] =.

  4. Median - Wikipedia

    en.wikipedia.org/wiki/Median

    The median of a symmetric unimodal distribution coincides with the mode. The median of a symmetric distribution which possesses a mean μ also takes the value μ. The median of a normal distribution with mean μ and variance σ 2 is μ. In fact, for a normal distribution, mean = median = mode.

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is the variance. The standard deviation of the distribution is σ {\textstyle \sigma } (sigma).

  6. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    The skewness is not directly related to the relationship between the mean and median: a distribution with negative skew can have its mean greater than or less than the median, and likewise for positive skew. [2] A general relationship of mean and median under differently skewed unimodal distribution.

  7. Mode (statistics) - Wikipedia

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

    Like the statistical mean and median, the mode is a way of expressing, in a (usually) single number, important information about a random variable or a population. The numerical value of the mode is the same as that of the mean and median in a normal distribution, and it may be very different in highly skewed distributions.

  8. Univariate (statistics) - Wikipedia

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

    It is used to estimate the central location of the univariate data by the calculation of mean, median and mode. [7] Each of these calculations has its own advantages and limitations. The mean has the advantage that its calculation includes each value of the data set, but it is particularly susceptible to the influence of outliers. The median is ...

  9. Mean - Wikipedia

    en.wikipedia.org/wiki/Mean

    The mean of a set of observations is the arithmetic average of the values; however, for skewed distributions, the mean is not necessarily the same as the middle value (median), or the most likely value (mode). For example, mean income is typically skewed upwards by a small number of people with very large incomes, so that the majority have an ...