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In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution.
With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.
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, covariance is a measure of the joint variability of two random variables. [ 1 ] The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables.
Coefficient of variation: Normalizing dispersion, using the mean as a measure of scale, particularly for positive distribution such as the exponential distribution and Poisson distribution. Min-max feature scaling
In fluid dynamics, normalized root mean square deviation (NRMSD), coefficient of variation (CV), and percent RMS are used to quantify the uniformity of flow behavior such as velocity profile, temperature distribution, or gas species concentration. The value is compared to industry standards to optimize the design of flow and thermal equipment ...
In probability theory, ... A closed-form formula for the characteristic function ... the arithmetic coefficient of variation is independent of the arithmetic mean.
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.