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The covariance between two random variables $X$ and $Y$ is a measure of the degree of linear association between them. The covariance is defined as
$${\rm cov}(X,Y) = {\rm E} \left$\left( X - {\rm E}(X) \right) \left( Y - {\rm E}(Y) \right) \right$ = {\rm E} (XY) - {\rm E} (X) {\rm E} (Y) .$$
If $X$ and $Y$ are independent, we have ${\rm cov}(X,Y) = 0$. But the converse is in general not true.
The magnitude of the covariance depends on the variance of both $X$ and $Y$, and it is not easy to interpret. The correlation is often used instead to measure the degree of linear association between $X$ and $Y$ on a scale free basis.