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Summary statistics

In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations ina measure of location, or central tendency, such as the arithmetic mean a measure of statistical dispersion like the standard mean absolute deviation a measure of the shape of the distribution like skewness or kurtosis if more than one variable is measured, a measure of statistical dependence such as a correlation coefficient

Examples
Location Common measures of location, or central tendency, are the arithmetic mean, median, mode, and interquartile mean. Spread Common measures of statistical dispersion are the standard deviation, variance, range, interquartile range, absolute deviation, mean absolute difference and the distance standard deviation. Measures that assess spread in comparison to the typical size of data values include the coefficient of variation. The Gini coefficient was originally developed to measure income inequality and is equivalent to one of the L-moments. A simple summary of a dataset is sometimes given by quoting particular order statistics as approximations to selected percentiles of a distribution. Shape Common measures of the shape of a distribution are skewness or kurtosis, while alternatives can be based on L-moments. A different measure is the distance skewness, for which a value of zero implies central symmetry. Dependence The common measure of dependence between paired random variables is the Pearson product-moment correlation coefficient, while a common alternative summary statistic is Spearman's rank correlation coefficient. A value of zero for the distance correlation implies independence. ==Human perception of summary statistics==
Human perception of summary statistics
Humans efficiently use summary statistics to quickly perceive the gist of auditory and visual information. ==See also==
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