# What is the expectation of one random variable divided over another (both independent)?

Suppose I have $X,Y$, which are independent random variables.

Why is it that $E(\frac{X}{Y}) = E(X)E(\frac{1}{Y})$?

Also, why is it that $E(X^2Y^2)=E(X^2)E(Y^2)$? How is it that the square of an independent random variable is also independent in relation to $Y$ or $Y^2$? Thanks!

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Let $W=\frac{1}{Y}$. Is $W$ independent of $X$? –  Scortchi Feb 5 '14 at 12:08
Assuming that the expectations exist (cf. Paul Staab's comment on his answer), the law of the unconscious statistician gives \begin{align}E\left[\frac{X}{Y}\right]&=\int\int\frac{x}{y}f_{X,Y}(x,y)\,dx\,d‌​y\\&= \int\int\frac{x}{y}f_X(x)f_Y(y)\,dx\,dy\\&=\int xf_X(x)\,dx\int \frac{1}{y}f_Y(y)\,dy\\&=E[X]E\left[\frac{1}{Y}\right]\end{align} –  Dilip Sarwate Feb 5 '14 at 14:41

## 2 Answers

Basically, if $X$ and $Y$ are independent, then also $f(X)$ and $g(Y)$ are independent if $f$ and $g$ are measurable functions:

\eqalign{ P(f(X) \in A,\ g(Y) \in B) &= P\left(X \in f^{-1}(A),\ Y \in g^{-1}(B)\right) \\ & = P\left(X \in f^{-1}(A)\right) \ P\left(Y \in g^{-1}(B)\right) \\ & = P\left(f(X) \in A\right) \ P\left(g(Y) \in B\right). }

In particular all continuous functions (like the $f(x)=1/x$ and $f(x)=x^2$ in your examples) are Borel-measurable, and hence also $X$ and $1/Y$ as well as $X^2$ and $Y^2$ are independent.

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i have assumed your answer in my proof for the independence between $X$ and $Z$. Thanks! –  omidi Feb 5 '14 at 12:38
Of course, this only holds when 'independence' and $1/Y$ are well defined, e.g. $X$ and $Y$ as well as $f(X)$ and $g(Y)$ live in the same space and $Y > 0$. –  Paul Staab Feb 5 '14 at 12:45
g(X) in the first paragraph of the answer should read g(Y) –  RossXV Feb 7 '14 at 16:51
@RossXV right, I corrected it. Thanks for spotting it. –  Paul Staab Feb 7 '14 at 17:46

let $Z=\frac{1}{Y}$, then we have:

$$E(XZ) = \int \int XZ p(X,Z) \mathrm{d}x \mathrm{d}z$$

but $X$ and $Z$ are independent, so $p(X,Z) = p(X)p(Z)$ we have

$$E(XZ) = \int \int x .z . p(X=x).p(Z=z) \mathrm{d}x \mathrm{d}z$$

which can be arranged as: $$E(XZ) = \int x . p(X=x) \mathrm{d}x \int z.p(Z=z) \mathrm{d}z = E(X)E(Z)$$

using the same argument you can show that $E(X^2Y^2) = E(X^2).E(Y^2)$

does that help?

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I believe that "but $X$ and $Z$ are independent" is the statement the O.P. is asking about. The answer by Paul Staab directly addresses that issue in full generality. –  whuber Feb 5 '14 at 22:39