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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
The pdf and cdf of the function ... Contrary to the arithmetic standard deviation, the arithmetic coefficient of variation is independent of the arithmetic mean.
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]
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 ...
Download as PDF; Printable version; ... Coefficient of determination; Coefficient of variation; Concordance correlation coefficient;
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.
In this case efficiency can be defined as the square of the coefficient of variation, i.e., [13] e ≡ ( σ μ ) 2 {\displaystyle e\equiv \left({\frac {\sigma }{\mu }}\right)^{2}} Relative efficiency of two such estimators can thus be interpreted as the relative sample size of one required to achieve the certainty of the other.
Variation varies between 0 and 1. Variation is 0 if and only if all cases belong to a single category. Variation is 1 if and only if cases are evenly divided across all categories. [1] In particular, the value of these standardized indices does not depend on the number of categories or number of samples.