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A complete handout about the Lorenz curve including various applications, including an Excel spreadsheet graphing Lorenz curves and calculating Gini coefficients as well as coefficients of variation. LORENZ 3.0 is a Mathematica notebook which draw sample Lorenz curves and calculates Gini coefficients and Lorenz asymmetry coefficients from data ...
In statistics, McKay's approximation of the coefficient of variation is a statistic based on a sample from a normally distributed population. It was introduced in 1932 by A. T. McKay. [1] Statistical methods for the coefficient of variation often utilizes McKay's approximation. [2] [3] [4] [5]
Coefficient of variation (CV) used as a measure of income inequality is conducted by dividing the standard deviation of the income (square root of the variance of the incomes) by the mean of income. Coefficient of variation will be therefore lower in countries with smaller standard deviations implying more equal income distribution.
The data set [90, 100, 110] has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1; The data set [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18
A quantity analogous to the coefficient of variation, but based on L-moments, can also be defined: = / , which is called the "coefficient of L-variation", or "L-CV". For a non-negative random variable, this lies in the interval ( 0, 1 ) [1] and is identical to the Gini coefficient.
In probability theory and statistics, the index of dispersion, [1] dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized measure of the dispersion of a probability distribution: it is a measure used to quantify whether a set of observed occurrences are clustered or dispersed compared to a standard ...
Smith-Gill developed a statistic based on Morisita's index which is independent of both sample size and population density and bounded by −1 and +1. This statistic is calculated as follows [67] First determine Morisita's index ( I d) in the usual fashion. Then let k be the number of units the population was sampled from. Calculate the two ...
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.