Proof of Covariance I am dealing extremely often with the covariance during my statistics classes. However, the only proof I have found so far is  that. My question is, how to deal with the double integral of the covariance or is there another proof for the covariance?
\begin{align}\text{Cov}(X,Y)& = \mathbb{E}(X- \mathbb{E}(X))(Y- \mathbb{E}(Y))\\ &= \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\;(x- \mathbb{E}(X))(y- \mathbb{E}(Y))f(x,y)dx\;dy\\&= \mathbb{E}(XY)- \mathbb{E}(X)\mathbb{E}(Y)\; .\end{align}
UPDATE
Could that also be true?
\begin{align}\text{Cov}(X,Y)&=\mathbb{E}(XY -X\mathbb{E}Y -Y\mathbb{E}X + \mathbb{E}X\mathbb{E}Y)\\&=\mathbb{E}(XY)- \mathbb{E}X\mathbb{E}Y -\mathbb{E}Y\mathbb{E}X + \mathbb{E}X \mathbb{E}Y\\&= \mathbb{E}(XY)- \mathbb{E}X \mathbb{E}Y \; . \end{align}
 A: I wish you had asked this question on stats.SE where you essentially have a very similar question to which @cardinal's comment provides the answer. But, to cross the $i$'s and dot the $t$'s, the notion of covariance applies to random variables $X$ and $Y$ that enjoy the property that $E[X^2]$ and $E[Y^2]$ are finite. This property implies that the random variables have finite means that we can denote by $\mu_X$ and $\mu_Y$ respectively, and also finite variances that we denote by $\sigma_X^2$
and $\sigma_Y^2$. For such random variables, the covariance $\operatorname{cov}(X,Y)$
of$X$ and $Y$ is defined as
$$\operatorname{cov}(X,Y) = E[(X-\mu_X)(Y-\mu_Y)]$$
where the expectation shown exists and has finite value because of our assumption 
that $E[X^2]$ and $E[Y^2]$ are finite. In fact,
$$|\operatorname{cov}(X,Y)| \leq \sigma_X\sigma_Y.$$  Now, if we multiply out the right hand
side in the definition and use the linearity of expectation 
$$E\left[a + \sum_i b_iX_i\right] = a + \sum_i b_iE[X_i],$$
we have that
$$\begin{align}
\operatorname{cov}(X,Y) &= E[(X-\mu_X)(Y-\mu_Y)]\\
&= E[XY - \mu_XY - \mu_YX + \mu_x\mu_Y]\\
&= E[XY] - \mu_XE[Y] - \mu_YE[X] + \mu_x\mu_Y\\
&= E[XY] - \mu_x\mu_Y - \mu_x\mu_Y + \mu_x\mu_Y\\
&= E[XY] - \mu_x\mu_Y\\
&= E[XY] - E[X]E[Y]
\end{align}$$
without worrying about discrete or continuous random variables and whether
we have integrals or sums, etc.
A: Hint: Use $\mathbb{E}(X+Y)=\mathbb{E}(X)+\mathbb{E}(Y)$, and expand the right hand side of your original equation.
The integral form only works for the continuous case. What if you have a discrete case? say $X\sim Bernouli(1/2)$.
