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where s x 2 and s y 2 are the variances of the x and y variates respectively, m x and m y are the means of the x and y variates respectively and s xy is the covariance of x and y. Although the approximate variance estimator of the ratio given below is biased, if the sample size is large, the bias in this estimator is negligible.
The variation ratio is a simple measure of statistical dispersion in nominal distributions; it is the simplest measure of qualitative variation. It is defined as the proportion of cases which are not in the mode category:
This follows from the fact that the variance and mean are independent of the ordering of x. Scale invariance: c v (x) = c v (αx) where α is a real number. [22] Population independence – If {x,x} is the list x appended to itself, then c v ({x,x}) = c v (x). This follows from the fact that the variance and mean both obey this principle.
Variance-to-mean ratio – mostly used for count data when the term coefficient of dispersion is used and when this ratio is dimensionless, as count data are themselves dimensionless, not otherwise. Some measures of dispersion have specialized purposes. The Allan variance can be used for applications where the noise disrupts convergence. [2]
The golden ratio φ and its negative reciprocal −φ −1 are the two roots of the quadratic polynomial x 2 − x − 1. The golden ratio's negative −φ and reciprocal φ −1 are the two roots of the quadratic polynomial x 2 + x − 1. The golden ratio is also an algebraic number and even an algebraic integer.
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 ...
The reason why there is no analog of mean value equality is the following: If f : U → R m is a differentiable function (where U ⊂ R n is open) and if x + th, x, h ∈ R n, t ∈ [0, 1] is the line segment in question (lying inside U), then one can apply the above parametrization procedure to each of the component functions f i (i = 1 ...
The arithmetic mean of a population, or population mean, is often denoted μ. [2] The sample mean ¯ (the arithmetic mean of a sample of values drawn from the population) makes a good estimator of the population mean, as its expected value is equal to the population mean (that is, it is an unbiased estimator).