How do you compute the expected value of a random variable? The problem I found asks; $ W = rV^3$ where $r$ is a constant and $V$ is a normally distributed random variable with mean 6 and standard deviation 1. How can I compute $E[W]$?
Expected value of a normally distributed random variable
2 Answers
Write $V = \mu + \sigma Z$, where $Z$ is the standard normal random variable. Then $ \mathbb{E}(W) = r \mathbb{E}\left( \left(\mu + \sigma Z\right)^3 \right) = r \left( \mu^3 +3 \mu^2 \sigma \mathbb{E}(Z) + 3 \mu \sigma^2 \mathbb{E}(Z^2) + \sigma^3 \mathbb{E}(Z^3) \right) $ Since $Z$ is symmetric, i.e. $Z \stackrel{d}{=} -Z$, it follows that $\mathbb{E}(Z) = 0$ and $\mathbb{E}(Z^3)=0$. It only remains to evaluate $ \mathbb{E}(Z^2) = \int_{-\infty}^\infty z^2 \frac{1}{\sqrt{2\pi}} \mathrm{e}^{-z^2/2} \mathrm{d} z = \sqrt{\frac{2}{\pi}} \int_0^\infty z^2 \mathrm{e}^{-z^2/2} \mathrm{d} z \stackrel{z^2 = x}{=} \frac{1}{\sqrt{2\pi}} \underbrace{\int_0^\infty \sqrt{x} \mathrm{e}^{-x/2} \mathrm{d} x}_{2^{3/2} \Gamma(3/2) = \sqrt{2\pi}} = 1 $ Now combine these results to readily obtain $\mathbb{E}(W)$.
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1+1 To avoid the integral, note that $\mathbb{E}(Z^2)$ is the sum of its variance and the square of its mean, i.e. (as $Z$ has a *standard* normal distribution) $1+0^2=1$. – 2012-10-19
$E[W] = rE[V^{3}]$ since $r$ is a constant. You can obtain the information about higher moments of the normal distribution here http://en.wikipedia.org/wiki/Normal_distribution See "Raw moment"
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0Maybe you don't need to know how to compute it, but are you allowed to use that information? I can't see how you can obtain the third moment otherwise. – 2012-10-19