Variance

For a single variate having a distribution with known population mean , the population variance , commonly also written , is defined as

(1)

where is the population mean and denotes the expectation value of . For a discrete distribution with possible values of , the population variance is therefore

(2)

whereas for a continuous distribution, it is given by

(3)

The variance is therefore equal to the second central moment .

Note that some care is needed in interpreting as a variance, since the symbol is also commonly used as a parameter related to but not equivalent to the square root of the variance, for example in the log normal distribution, Maxwell distribution, and Rayleigh distribution.

If the underlying distribution is not known, then the sample variance may be computed as

(4)

where is the sample mean.

Note that the sample variance defined above is not an unbiased estimator for the population variance . In order to obtain an unbiased estimator for , it is necessary to instead define a "bias-corrected sample variance"

(5)

The distinction between and is a common source of confusion, and extreme care should be exercised when consulting the literature to determine which convention is in use, especially since the uninformative notation is commonly used for both. The bias-corrected sample variance for a list of data is implemented as Variance[list].

The square root of the variance is known as the standard deviation.

The reason that gives a biased estimator of the population variance is that two free parameters and are actually being estimated from the data itself. In such cases, it is appropriate to use a Student's t-distribution instead of a normal distribution as a model since, very loosely speaking, Student's t-distribution is the "best" that can be done without knowing .

Formally, in order to estimate the population variance from a sample of elements with a priori unknown mean (i.e., the mean is estimated from the sample itself), we need an unbiased estimator for . This is given by the k-statistic , where

(6)

and is the sample variance uncorrected for bias.

It turns out that the quantity has a chi-squared distribution.

For set of data , the variance of the data obtained by a linear transformation is given by

			(7)
			(8)
			(9)
			(10)
			(11)
			(12)

For multiple variables, the variance is given using the definition of covariance,

			(13)
			(14)
			(15)
			(16)
			(17)

A linear sum has a similar form:

			(18)
			(19)
			(20)

These equations can be expressed using the covariance matrix.