Given that $\operatorname{E}[Y\mid X] = 1$, show that $\operatorname{Var}(XY)\geqslant\operatorname{Var}(X)$ 
Given that $E[Y\mid X] = 1$, show that $\operatorname{Var}(XY)\geqslant\operatorname{Var}(X)$.

So I tried to expand $\operatorname{Var}(XY) = \operatorname{E}(X^2 Y^2) - 1$ and was stuck here.
 A: We have $$\operatorname{Var}[XY] = \operatorname{E}[X^2 Y^2] - \operatorname{E}[XY]^2.$$
Try to tackle each of these two terms by first conditioning on $X$ and then taking expectation.  For a full proof, hover below.

 Write $\operatorname{E}[XY] = \operatorname{E}[\operatorname{E}[XY \mid X]] = \operatorname{E}[X \operatorname{E}[Y \mid X] ] = \operatorname{E}[X]$ since $\operatorname{E}[Y \mid X] = 1$.  Lastly, we have $$\operatorname{E}[X^2Y^2] = \operatorname{E}[ X^2 \operatorname{E}[Y^2 \mid X]] \geq \operatorname{E}[ X^2 \operatorname{E}[Y\mid X]^2 ] = \operatorname{E}[X^2]$$ where we use Jensen's inequality for conditional expectation.  Thus $$\operatorname{Var}[XY] \geq \operatorname{E}[X^2] - \operatorname{E}[X]^2.$$ 

A: $\newcommand{\v}{\operatorname{var}}\newcommand{\e}{\operatorname{E}}$By the law of total variance, you have
\begin{align}
\v(XY) & = \v(\e(XY\mid X)) + \e(\v(XY\mid X)) \\[10pt]
& = \v(X\e(Y\mid X)) + \e(X^2\v(Y\mid X)) \\[10pt]
& = \v(X) + \e(X^2\v(Y\mid X)) \\[10pt]
& \ge \v(X).
\end{align}
