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Consider our previous example 5.4.4. To compute the mean 1 and variance 2 (and hence the standard deviation) for this distribution,
\begin{equation*} \mu = \int_{-1}^2 x \cdot x^2/3 dx = \int_{-1}^2 x^3/3 dx = \frac{2^4}{12} - \frac{(-1)^4}{12} = \frac{15}{12} = \frac{5}{4} \end{equation*}
and by using the alternate formulas 5.4.6
\begin{align*} \sigma^2 & = E[X^2] - \mu^2\\ & = \int_{-1}^2 x^2 \cdot x^2/3 \; dx - \mu^2\\ & = \int_{-1}^2 x^4/3 \; dx - \left ( \frac{5}{4} \right )^2\\ & = \frac{2^5}{15} - \frac{(-1)^5}{15} - \frac{25}{16}\\ & = \frac{33}{15} - \frac{25}{16} = \frac{51}{80} \end{align*}
which gives
\begin{equation*} \sigma = \sqrt{\frac{51}{80}} \approx 0.7984. \end{equation*}
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