a statistical measure of the relationship of two variables, formed by multiplying the difference of each variable from its mean, both variables being measured at the same time, and averaging all such products.
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 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 â€œco-varyâ€ (deviations from the mean are systematic across pairs) without imputing precision to the expression. [See correlation coefficient
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.
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. = Ï (Ïƒx) (Ïƒy)
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.
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.
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.