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In probability theory and statistics, variance measures how far a set of numbers is spread out. (A variance of zero indicates that all the values are identical.) A nonzero variance is always positive: A small variance indicates that the data points tend to be very close to the mean (expected value) and hence to each other, while a high variance indicates that the data points are very spread out from the mean and from each other.
The square root of variance is called the standard deviation.
The variance is one of several descriptors of a probability distribution. In particular, the variance is one of the moments of a distribution. In that context, it forms part of a systematic approach to distinguishing between probability distributions. While other such approaches have been developed, those based on moments are advantageous in terms of mathematical and computational simplicity.
The variance is a parameter that describes, in part, either the actual probability distribution of an observed population of numbers, or the theoretical probability distribution of a sample (a notfullyobserved population) of numbers. In the latter case, a sample of data from such a distribution can be used to construct an estimate of its variance: in the simplest cases this estimate can be the sample variance.
The variance of a random variable X is its second central moment, the expected value of the squared deviation from the mean μ = E[X]:
This definition encompasses random variables that are discrete, continuous, neither, or mixed. The variance can also be thought of as the covariance of a random variable with itself:
The variance is also equivalent to the second cumulant of the probability distribution for X. The variance is typically designated as Var(X), , or simply σ^{2} (pronounced "sigma squared"). The expression for the variance can be expanded:
A mnemonic for the above expression is "mean of square minus square of mean".
If the random variable X is continuous with probability density function f(x), then the variance is given by
where is the expected value,
and where the integrals are definite integrals taken for x ranging over the range of X.
If a continuous distribution does not have an expected value, as is the case for the Cauchy distribution, it does not have a variance either. Many other distributions for which the expected value does exist also do not have a finite variance because the integral in the variance definition diverges. An example is a Pareto distribution whose index k satisfies 1 < k ≤ 2.
If the random variable X is discrete with probability mass function x_{1} ↦ p_{1}, ..., x_{n} ↦ p_{n}, then
where is the expected value, i.e.
(When such a discrete weighted variance is specified by weights whose sum is not 1, then one divides by the sum of the weights.)
The variance of a set of n equally likely values can be written as
The variance of a set of n equally likely values can be equivalently expressed, without directly referring to the mean, in terms of squared deviations of all points from each other:^{[1]}
The normal distribution with parameters μ and σ is a continuous distribution whose probability density function is given by:
It has mean μ and variance equal to:
The role of the normal distribution in the central limit theorem is in part responsible for the prevalence of the variance in probability and statistics.
The exponential distribution with parameter λ is a continuous distribution whose support is the semiinfinite interval [0,∞). Its probability density function is given by:
and it has expected value μ = λ^{−1}. The variance is equal to:
So for an exponentially distributed random variable σ^{2} = μ^{2}.
The Poisson distribution with parameter λ is a discrete distribution for k = 0, 1, 2, ... Its probability mass function is given by:
and it has expected value μ = λ. The variance is equal to:
So for a Poissondistributed random variable σ^{2} = μ.
The binomial distribution with parameters n and p is a discrete distribution for k = 0, 1, 2, ..., n. Its probability mass function is given by:
and it has expected value μ = np. The variance is equal to:
The binomial distribution with describes the probability of getting heads in tosses. Thus the expected value of the number of heads is , and the variance is .
A sixsided fair die can be modelled with a discrete random variable with outcomes 1 through 6, each with equal probability . The expected value is (1 + 2 + 3 + 4 + 5 + 6)/6 = 3.5. Therefore the variance can be computed to be:
The general formula for the variance of the outcome X of a die of n sides is:
Variance is nonnegative because the squares are positive or zero.
The variance of a constant random variable is zero, and if the variance of a variable in a data set is 0, then all the entries have the same value.
Variance is invariant with respect to changes in a location parameter. That is, if a constant is added to all values of the variable, the variance is unchanged.
If all values are scaled by a constant, the variance is scaled by the square of that constant.
The variance of a sum of two random variables is given by:
where Cov(., .) is the covariance. In general we have for the sum of random variables :
These results lead to the variance of a linear combination as:
If the random variables are such that
they are said to be uncorrelated. It follows immediately from the expression given earlier that if the random variables are uncorrelated, then the variance of their sum is equal to the sum of their variances, or, expressed symbolically:
Since independent random variables are always uncorrelated, the equation above holds in particular when the random variables are independent. Thus independence is sufficient but not necessary for the variance of the sum to equal the sum of the variances.
One reason for the use of the variance in preference to other measures of dispersion is that the variance of the sum (or the difference) of uncorrelated random variables is the sum of their variances:
This statement is called the Bienaymé formula^{[2]} and was discovered in 1853.^{[citation needed]} It is often made with the stronger condition that the variables are independent, but uncorrelatedness suffices. So if all the variables have the same variance σ^{2}, then, since division by n is a linear transformation, this formula immediately implies that the variance of their mean is
That is, the variance of the mean decreases when n increases. This formula for the variance of the mean is used in the definition of the standard error of the sample mean, which is used in the central limit theorem.
If two variables X and Y are independent, the variance of their product is given by^{[3]}^{[4]}
In general, if the variables are correlated, then the variance of their sum is the sum of their covariances:
(Note: The second equality comes from the fact that Cov(X_{i},X_{i}) = Var(X_{i}).)
Here Cov is the covariance, which is zero for independent random variables (if it exists). The formula states that the variance of a sum is equal to the sum of all elements in the covariance matrix of the components. This formula is used in the theory of Cronbach's alpha in classical test theory.
So if the variables have equal variance σ^{2} and the average correlation of distinct variables is ρ, then the variance of their mean is
This implies that the variance of the mean increases with the average of the correlations. Moreover, if the variables have unit variance, for example if they are standardized, then this simplifies to
This formula is used in the Spearman–Brown prediction formula of classical test theory. This converges to ρ if n goes to infinity, provided that the average correlation remains constant or converges too. So for the variance of the mean of standardized variables with equal correlations or converging average correlation we have
Therefore, the variance of the mean of a large number of standardized variables is approximately equal to their average correlation. This makes clear that the sample mean of correlated variables does generally not converge to the population mean, even though the Law of large numbers states that the sample mean will converge for independent variables.
The scaling property and the Bienaymé formula, along with this property from the covariance page: Cov(aX, bY) = ab Cov(X, Y) jointly imply that
This implies that in a weighted sum of variables, the variable with the largest weight will have a disproportionally large weight in the variance of the total. For example, if X and Y are uncorrelated and the weight of X is two times the weight of Y, then the weight of the variance of X will be four times the weight of the variance of Y.
The expression above can be extended to a weighted sum of multiple variables:
The general formula for variance decomposition or the law of total variance is: If and are two random variables, and the variance of exists, then
Here, is the conditional expectation of given , and is the conditional variance of given . (A more intuitive explanation is that given a particular value of , then follows a distribution with mean and variance . The above formula tells how to find based on the distributions of these two quantities when is allowed to vary.) This formula is often applied in analysis of variance, where the corresponding formula is
here refers to the Mean of the Squares. It is also used in linear regression analysis, where the corresponding formula is
This can also be derived from the additivity of variances, since the total (observed) score is the sum of the predicted score and the error score, where the latter two are uncorrelated.
Similar decompositions are possible for the sum of squared deviations (sum of squares, ):
A formula often used for deriving the variance of a theoretical distribution is as follows:
This will be useful when it is possible to derive formulae for the expected value and for the expected value of the square.
This formula is also sometimes used in connection with the sample variance. While useful for hand calculations, it is not advised for computer calculations as it suffers from catastrophic cancellation if the two components of the equation are similar in magnitude and floating point arithmetic is used. This is discussed in the article Algorithms for calculating variance.
The population variance for a nonnegative random variable can be expressed in terms of the cumulative distribution function F using
where H(u) = 1 − F(u) is the right tail function. This expression can be used to calculate the variance in situations where the CDF, but not the density, can be conveniently expressed.
The second moment of a random variable attains the minimum value when taken around the first moment (i.e., mean) of the random variable, i.e. . Conversely, if a continuous function satisfies for all random variables X, then it is necessarily of the form , where a > 0. This also holds in the multidimensional case.^{[5]}
Let's define as a column vector of n random variables , and c as a column vector of N scalars . Therefore is a linear combination of these random variables, where denotes the transpose of vector . Let also be the variancecovariance matrix of the vector X. The variance of is given by:^{[6]}
Unlike expected absolute deviation, the variance of a variable has units that are the square of the units of the variable itself. For example, a variable measured in inches will have a variance measured in square inches. For this reason, describing data sets via their standard deviation or root mean square deviation is often preferred over using the variance. In the dice example the standard deviation is √2.9 ≈ 1.7, slightly larger than the expected absolute deviation of 1.5.
The standard deviation and the expected absolute deviation can both be used as an indicator of the "spread" of a distribution. The standard deviation is more amenable to algebraic manipulation than the expected absolute deviation, and, together with variance and its generalization covariance, is used frequently in theoretical statistics; however the expected absolute deviation tends to be more robust as it is less sensitive to outliers arising from measurement anomalies or an unduly heavytailed distribution.
The delta method uses secondorder Taylor expansions to approximate the variance of a function of one or more random variables: see Taylor expansions for the moments of functions of random variables. For example, the approximate variance of a function of one variable is given by
provided that f is twice differentiable and that the mean and variance of X are finite.
It has been suggested that portions of this section be moved into Unbiased estimation of standard deviation. (Discuss) 
Realworld distributions such as the distribution of yesterday's rain throughout the day are typically not fully known, unlike the behavior of perfect dice or an ideal distribution such as the normal distribution, because it is impractical to account for every raindrop. Instead one estimates the mean and variance of the whole distribution as the computed mean and variance of a sample of n observations drawn suitably randomly from the whole sample space, in this example the set of all measurements of yesterday's rainfall in all available rain gauges.
This method of estimation is close to optimal, with the caveat that it underestimates the variance by a factor of (n − 1) / n. (For example, when n = 1 the variance of a single observation is obviously zero regardless of the true variance). This gives a bias which should be corrected for when n is small by multiplying by n / (n − 1). If the mean is determined in some other way than from the same samples used to estimate the variance then this bias does not arise and the variance can safely be estimated as that of the samples.
In general, the population variance of a finite population of size N with values x_{i} is given by
where
is the population mean. The population variance therefore is the variance of the underlying probability distribution. In this sense, the concept of population can be extended to continuous random variables with infinite populations.
In many practical situations, the true variance of a population is not known a priori and must be computed somehow. When dealing with extremely large populations, it is not possible to count every object in the population, so the computation must be performed on a sample of the population.^{[7]} Sample variance can also be applied to the estimation of the variance of a continuous distribution from a sample of that distribution.
We take a sample with replacement of n values y_{1}, ..., y_{n} from the population, where n < N, and estimate the variance on the basis of this sample.^{[8]} Directly taking the variance of the sample gives:
Here, denotes the sample mean:
Since the y_{i} are selected randomly, both and are random variables. Their expected values can be evaluated by summing over the ensemble of all possible samples {y_{i}} from the population. For this gives:
Hence gives an estimate of the population variance that is biased by a factor of (n1)/n. For this reason, is referred to as the biased sample variance. Correcting for this bias yields the unbiased sample variance:
Either estimator may be simply referred to as the sample variance when the version can be determined by context. The same proof is also applicable for samples taken from a continuous probability distribution.
The use of the term n − 1 is called Bessel's correction, and it is also used in sample covariance and the sample standard deviation (the square root of variance). The square root is a concave function and thus introduces negative bias (by Jensen's inequality), which depends on the distribution, and thus the corrected sample standard deviation (using Bessel's correction) is biased. The unbiased estimation of standard deviation is a technically involved problem, though for the normal distribution using the term n − 1.5 yields an almost unbiased estimator.
The unbiased sample variance is a Ustatistic for the function ƒ(y_{1}, y_{2}) = (y_{1} − y_{2})^{2}/2, meaning that it is obtained by averaging a 2sample statistic over 2element subsets of the population.
Being a function of random variables, the sample variance is itself a random variable, and it is natural to study its distribution. In the case that y_{i} are independent observations from a normal distribution, Cochran's theorem shows that s^{2} follows a scaled chisquared distribution:^{[9]}
As a direct consequence, it follows that
and^{[10]}
If the y_{i} are independent and identically distributed, but not necessarily normally distributed, then^{[11]}
where κ is the excess kurtosis of the distribution and μ_{4} is the fourth moment about the mean.
If the conditions of the law of large numbers hold for the squared observations, s^{2} is a consistent estimator of σ^{2}.^{[citation needed]}. One can see indeed that the variance of the estimator tends asymptotically to zero.
Samuelson's inequality is a result that states bounds on the values that individual observations in a sample can take, given that the sample mean and (biased) variance have been calculated.^{[12]} Values must lie within the limits
It has been shown^{[13]} that for a sample {y_{i}} of real numbers,
where y_{max} is the maximum of the sample, A is the arithmetic mean, H is the harmonic mean of the sample and is the (biased) variance of the sample.
This bound has been improved on, and it is known that variance is bounded by
where y_{min} is the minimum of the sample.^{[14]}
If is a vectorvalued random variable, with values in , and thought of as a column vector, then the natural generalization of variance is , where and is the transpose of , and so is a row vector. This variance is a positive semidefinite square matrix, commonly referred to as the covariance matrix.
If is a complexvalued random variable, with values in , then its variance is , where is the conjugate transpose of . This variance is also a positive semidefinite square matrix.
Testing for the equality of two or more variances is difficult. The F test and chi square tests are both adversely affected by nonnormality and are not recommended for this purpose.
Several non parametric tests have been proposed: these include the BartonDavidAnsariFruendSiegelTukey test, the Capon test, Mood test, the Klotz test and the Sukhatme test. The Sukhatme test applies to two variances and requires that both medians be known and equal to zero. The Mood, Klotz, Capon and BartonDavidAnsariFruendSiegelTukey tests also apply to two variances. They allow the median to be unknown but do require that the two medians are equal.
The Lehman test is a parametric test of two variances. Of this test there are several variants known. Other tests of the equality of variances include the Box test, the BoxAnderson test and the Moses test.
Resampling methods, which include the bootstrap and the jackknife, may be used to test the equality of variances.
The term variance was first introduced by Ronald Fisher in his 1918 paper The Correlation Between Relatives on the Supposition of Mendelian Inheritance:^{[15]}
The great body of available statistics show us that the deviations of a human measurement from its mean follow very closely the Normal Law of Errors, and, therefore, that the variability may be uniformly measured by the standard deviation corresponding to the square root of the mean square error. When there are two independent causes of variability capable of producing in an otherwise uniform population distributions with standard deviations and , it is found that the distribution, when both causes act together, has a standard deviation . It is therefore desirable in analysing the causes of variability to deal with the square of the standard deviation as the measure of variability. We shall term this quantity the Variance...
The variance of a probability distribution is analogous to the moment of inertia in classical mechanics of a corresponding mass distribution along a line, with respect to rotation about its center of mass.^{[citation needed]} It is because of this analogy that such things as the variance are called moments of probability distributions.^{[citation needed]} The covariance matrix is related to the moment of inertia tensor for multivariate distributions. The moment of inertia of a cloud of n points with a covariance matrix of is given by^{[citation needed]}
This difference between moment of inertia in physics and in statistics is clear for points that are gathered along a line. Suppose many points are close to the x axis and distributed along it. The covariance matrix might look like
That is, there is the most variance in the x direction. However, physicists would consider this to have a low moment about the x axis so the momentofinertia tensor is
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