covariance

A statistical measure defined as scriptstyle operatorname Cov(X,Y)= operatorname E((X-μ)(Y-ν)) given two real-valued random variables X and Y, with expected values scriptstyle E(X),=,μ and scriptstyle E(Y),=,ν.

The elements of such a correlation matrix do not have asymptotic variances and covariances of the form (1.2), even if S has a Wishart distribution.

Consequently, it can be shown that a covariance of two binary variables measures the extent to which the observed joint distribution of these variables differs from their expected joint distribution under the assumption that they are statistically independent.

The conversion of data types from wider to narrower in certain situations.

As we will see in Chapter 8, we see both covariance and contravariance throughout the Java Collections. They largely exist to ensure that the generics just “do the right thing” and behave in a manner that should not surprise the developer.