The tendency of two elements (variables) to exhibit similar variances, while not necessarily establishing a cause and effect relationship.

Multiplies the deviation of each variable from its mean, adds those products and then divides by the number of observations.

A measure of the degree to which two variables move in relation to each other. A positive covariance means that both variables tend to be above or below their mean at the same time.

Between two variables X and Y, this is defined as Cov(X,Y)=. If X and Y are independent the covariance between them is 0, but a covariance of 0 does not necessarily imply independence. Covariance arises naturally from considering Var(X+Y) and Var(X-Y). correlation = Cov(X,Y)

A measure of the relationship between two variables whose values are observed at the same time.

In statistics, the correlation between two variables times the standard deviation of each. see also variance.

A statistical measure of the correlation between two variables. In geostatistics, covariance is usually treated as the simple inverse of the variogram, computed as the overall sample variance minus the variogram value. These covariance values, rather than variogram values, are actually used in the Geo-EAS kriging matrix equations for greater computational efficiency.

The covariance is similar to the correlation coefficient in that it measures the relationship between a pair of variables. However, unlike the correaltion coefficient it is understandardised (in a correlation coefficient the covariance is divided by the standard deviations of x and y). Because the covariance is unstandardised there is no limit to possible values and it is difficult to compare covariances.

A measure of association between pairs of variables, similar to the correlation coefficient. Covariance values can be very large or small because they are not standardized like the correlation coefficient is. Most typically, covariance calculations are referred to in the literature as steps on the way to more sophisticated analyses or it is said in a loose fashion that two variables “co-vary” (deviations from the mean are systematic across pairs) without imputing precision to the expression. [See correlation coefficient

statistical measure of the variance of two random variables measured in the same mean time period

A statistical term for the correlation between two variables multiplied by the standard deviation for each of the variables.

A statistical measure used in computing the correlation coefficient between two variables; the covariance is the mean of ( )( ) overall pairs of values for the variables and , where is the mean of the values and is the mean of the values.

as opposed to contravariance; we say that a language applies the covariance rule if, when an inherited method is redefined, the argument types or the result type can be replaced by a more specific type. For example, if the class APPLE inherits from FRUIT, the arguments (or the result) of the redefined methods can pass from FRUIT to APPLE. This type change is valid according to the covariance rule. So the scope for redefinition varies in the same direction as the inheritance relationship, hence the name co variance. Eiffel applies the covariance rule because that rule corresponds to the most useful way of using inheritance. [ edit

A statistical measure of the degree to which two variables move in the same direction. A positive covariance means the variables move together in the same direction. A negative covariance means they move in opposite directions. See also variance.

The variation in common between two related variables. See also Analysis of covariance.

Covariance measures the degree to which two variables move together over time relative to their individual mean returns. It is calculated by multiplying the correlation between two variables by the standard deviation for each of the variables. = ρ (σx) (σy)

( Stat.). The sum of the products of two or more correlated variables from their means, divided by the number of degrees of freedom. ( USFT.).

The average value of the quantity x(r1) * x(r2); where is a randomly varying function of the variable and r1 and r2 are two given values of. Covariance describes the interdependence between variables and is typically expressed in a variance-covariance matrix where the diagonal elements are the variances of the corresponding variables, and those off the main diagonal are the covariance values.

How one quantity varies with another. A covariance error matrix is a systematic means of storing the statistical information of how errors in each quantity represented depend on the errors in others. The covariance between and is found from: where mean.

The impact of one variable upon others in the same group.

is the expected value of the product of differences between random variables and their expected values. If the random variables are truly independent their covariance is zero [pg 146, 2

A measure of the extent to which two variables tend to vary together linearly. The covariance between two variables is their linear correlation times the product of each of their standard deviations.

Measures the variance between two random variables

measures the tendencies of data file values for the same pixel, but in different bands, to vary with each other in relation to the means of their respective bands. These bands must be linear. Covariance is defined as the average product of the differences between the data file values in each band and the mean of each band.

A parameter, related to correlation, that indicates the tendency for two random variables to "move together" of "co-vary."

A measure of how strongly correlated a set of variables is.

The expected value of the product ( − ζ), where ξ denotes the mean of and ζ the mean of . See correlation.

The tendency for either i) many households to be affected by a risk at the same time or ii) several risks to consistently occur together (at the same time or under the same circumstances).

A measure of the strength of the relationship between two numbers... more on Covariance

The measure of how two variables change in relation to each other (covariability). If larger values of one variable tend to be associated with larger values of the other, the covariance will be positive. If larger values of one variable are associated with smaller values of the other, the covariance will be negative. When there is no particular association between the two variables, the covariance value will approach zero.

A measure of the comovement between 2 variables.

The degree of movement between two variables.

A measure of the comovement between two variables.

In probability theory and statistics, the covariance between two real-valued random variables X and Y, with expected values E(X)=\mu and E(Y)=\nu is defined as