Find expected value using CDF I'm going to start out by saying this is a homework problem straight out of the book. I have spent a couple hours looking up how to find expected values, and have determined I understand nothing.

Let $X$ have the CDF $F(x) = 1 - x^{-\alpha}, x\ge1$.
  Find $E(X)$ for those values of $\alpha$ for which $E(X)$ exists.

I have no idea how to even start this. How can I determine which values of $\alpha$ exist? I also don't know what to do with the CDF (I'm assuming this means Cumulative Distribution Function). There are formulas for finding the expected value when you have a frequency function or density function. Wikipedia says the CDF of $X$ can be defined in terms of the probability density function $f$ as follows:
$F(x) = \int_{-\infty}^x f(t)\,dt$
This is as far as I got. Where do I go from here?
EDIT: I meant to put $x\ge1$.
 A: The Answer requiring change of order is unnecessarily ugly. Here's a more elegant 2 line proof.
$\int udv = uv - \int vdu$ 
Now take $du = dx$ and $v = 1- F(x)$
$\int_{0}^{\infty} [ 1- F(x)] dx = [x(1-F(x)) ]_{0}^{\infty} + \int_{0}^{\infty} x f(x)dx$
$= 0 + \int_{0}^{\infty} x f(x)dx$
$= \mathbb{E}[X] \qquad \blacksquare$
A: The result extends to the $k$th moment of $X$ as well. Here is a graphical representation:

A: Edited for the comment from probabilityislogic
Note that $F(1)=0$ in this case so the distribution has probability $0$ of being less than $1$, so $x \ge 1$, and you will also need $\alpha > 0$ for an increasing cdf. 
If you have the cdf then you want the anti-integral or derivative which with a continuous distribution like this 
$$f(x) = \frac{dF(x)}{dx}$$ 
and in reverse $F(x) = \int_{1}^x  f(t)\,dt$ for $x \ge 1$.
Then to find the expectation you need to find 
$$E[X] = \int_{1}^{\infty}  x f(x)\,dx$$ 
providing that this exists.  I will leave the calculus to you.
A: Usage of the density function is not necessary
Integrate 1 minus the CDF
When you have a random variable $X$ that has a support that is non-negative (that is, the variable has nonzero density/probability for only positive values), you can use the following property:
$$
E(X) = \int_0^\infty \left( 1 - F_X(x) \right) \,\mathrm{d}x
$$
A similar property applies in the case of a discrete random variable.
Proof
Since $1 - F_X(x) = P(X\geq x) = \int_x^\infty f_X(t) \,\mathrm{d}t$,
$$
\int_0^\infty \left( 1 - F_X(x) \right) \,\mathrm{d}x = \int_0^\infty P(X\geq x) \,\mathrm{d}x = \int_0^\infty \int_x^\infty f_X(t) \,\mathrm{d}t \mathrm{d}x
$$
Then change the order of integration:
$$
= \int_0^\infty \int_0^t f_X(t) \,\mathrm{d}x \mathrm{d}t = \int_0^\infty \left[xf_X(t)\right]_0^t \,\mathrm{d}t = \int_0^\infty t f_X(t) \,\mathrm{d}t
$$
Recognizing that $t$ is a dummy variable, or taking the simple substitution $t=x$ and $\mathrm{d}t = \mathrm{d}x$,
$$
= \int_0^\infty x f_X(x) \,\mathrm{d}x = \mathrm{E}(X)
$$
Attribution
I used the Formulas for special cases section of the Expected value article on Wikipedia to refresh my memory on the proof. That section also contains proofs for the discrete random variable case and also for the case that no density function exists.
A: I think you actually mean $x\geq 1$, otherwise the CDF is vacuous, as $F(1)=1-1^{-\alpha}=1-1=0$.
What you "know" about CDFs is that they eventually approach zero as the argument $x$ decreases without bound and eventually approach one as $x \to \infty$.  They are also non-decreasing, so this means $0\leq F(y)\leq F(x)\leq 1$ for all $y\leq x$.
So if we plug in the CDF we get:
$$0\leq 1-x^{-\alpha}\leq 1\implies 1\geq \frac{1}{x^{\alpha}}\geq 0\implies x^{\alpha}\geq 1 > 0\implies x\geq 1 \>.$$
From this we conclude that the support for $x$ is $x\geq 1$.  Now we also require $\lim_{x\to\infty} F(x)=1$ which implies that $\alpha>0$
To work out what values the expectation exists, we require:
$$\newcommand{\rd}{\mathrm{d}}E(X)=\int_{1}^{\infty}x\frac{\rd F(x)}{\rd x}\rd x=\alpha\int_{1}^{\infty}x^{-\alpha} \rd x$$
And this last expression shows that for $E(X)$ to exist, we must have $-\alpha<-1$, which in turn implies $\alpha>1$.  This can easily be extended to determine the values of $\alpha$ for which the $r$'th raw moment $E(X^{r})$ exists.
A: In case when a conditional expectation using only CDF is needed, we can formulate two cases,
$\mathbb{E}\left(x|x\geq y\right)=y+\frac{\int_{y}^{\infty}\left(1-F(x)\right)dx}{\left(1-F(y)\right)}$
$\mathbb{E}\left(x|x\leq y\right)=y-\frac{\int_{-\infty}^{y}F(x)dx}{F(y)}$
The derivation leverages on previous post such that we first define following integral,
$\int_{y}^{\infty} [ 1- F(x)] dx = [x(1-F(x)) ]_{y}^{\infty} + \int_{y}^{\infty} x f(x)dx$
$\int_{y}^{\infty} x f(x)dx=\mathbb{E}\left(x|x\geq y\right)(1-F(y))$
Then using this definition and some algebra we arrive at first result. The second result can be obtained in the same way.
