Computational formula for the variance

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In probability theory and statistics, the computational formula for the variance Var(X) of a random variable X is the formula

where E(X) is the expected value of X. The result is called the König-Huygens theorem in French language literature.

A closely related identity can be used to calculate the sample variance, which is often used as an unbiased estimate of the population variance:

The second result is sometimes, unwisely, used in practice to calculate the variance. The problem is that subtracting two values having a similar value can lead to catastrophic cancellation.[1]


The computational formula for the population variance follows in a straightforward manner from the linearity of expected values and the definition of variance:

Generalization to covariance

This formula can be generalized for covariance, with two random variables Xi and Xj:

as well as for the n by n covariance matrix of a random vector of length n:

and for the n by m cross-covariance matrix between two random vectors of length n and m:

where expectations are taken element-wise and and are random vectors of respective lengths n and m.


Its applications in systolic geometry include Loewner's torus inequality.

See also

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  1. Donald E. Knuth (1998). The Art of Computer Programming, volume 2: Seminumerical Algorithms, 3rd edn., p. 232. Boston: Addison-Wesley.

ca:Fórmula de càlcul per a la variància de:Verschiebungssatz (Statistik) fr:Théorème de König-Huyghens