An Introduction to Mathematical Statistics and Its Applications (6th Edition)

Published by Pearson
ISBN 10: 0-13411-421-3
ISBN 13: 978-0-13411-421-7

Chapter 3 Random Variables - 3.6 The Variance - Questions - Page 157: 7

Answer

$\color{blue}{\frac{\sqrt{179}}{12} \approx 1.115}$

Work Step by Step

From the given graph of the pdf, we have $\begin{align*} f_Y(y) = \begin{cases} 1-y, & 0 \le y \le 1 \\ \frac{1}{2}, & 2 \le y \le 3 \\ 0, & \text{otherwise.} \end{cases} \quad\quad\color{blue}{\text{(*** Eqn. 1 ***)}} \end{align*}$ $\begin{align*} E(Y) &= \int_{\mathbb{R}} y\cdot f_Y(y)\ dy \qquad [\ \text{Definition of}\ E(Y)\ ] \\ &= \int_0^1 y\cdot (1-y)\ dy + \int_2^3 y\cdot \frac{1}{2}\ dy \qquad [\ \text{see Eqn. 1}\ ] \\ &= \int_0^1 (y-y^2)\ dy + \frac{1}{2}\int_2^3 y\ dy \\ &= \left( \frac{y^2}{2} - \frac{y^3}{3}\ \right\vert_0^1 + \frac{1}{2}\left( \frac{y^2}{2}\ \right\vert_2^3 \\ &= \left[\left( \frac{1^2}{2} - \frac{1^3}{3}\right) - \left( \frac{0^2}{2} - \frac{0^3}{3}\right)\right] + \frac{1}{2}\left( \frac{3^2}{2}- \frac{2^2}{2}\right) \\ &= \frac{1}{2} - \frac{1}{3} - 0 + \frac{1}{4}(3^2-2^2) \\ &= \frac{1}{6} + \frac{5}{4} \\ E(Y) &= \frac{17}{12} \\ \mu &= \frac{17}{12} \end{align*}$ $\begin{align*} E(Y^2) &= \int_{\mathbb{R}} y^2\cdot f_Y(y)\ dy \qquad [\ \text{Definition of}\ E(Y^2)\ ] \\ &= \int_0^1 y^2\cdot (1-y)\ dy + \int_2^3 y^2\cdot \frac{1}{2}\ dy \qquad [\ \text{see Eqn. 1}\ ] \\ &= \int_0^1 (y^2-y^3)\ dy + \frac{1}{2}\int_2^3 y^2\ dy \\ &= \left( \frac{y^3}{3} - \frac{y^4}{4}\ \right\vert_0^1 + \frac{1}{2}\left( \frac{y^3}{3}\ \right\vert_2^3 \\ &= \left[\left( \frac{1^3}{3} - \frac{1^4}{4}\right) - \left(\frac{0^3}{3} - \frac{0^4}{4}\right)\right] + \frac{1}{2}\left( \frac{3^3}{3}- \frac{2^3}{3}\right) \\ &= \frac{1}{3} - \frac{1}{4} - 0 + \frac{1}{6}(27-8) \\ &= \frac{1}{12} + \frac{19}{6} \\ E(Y^2) &= \frac{13}{4} \end{align*}$ By Theorem 3.6.1, since $\mu= \dfrac{17}{12}$ exists and $E(Y^2) = \dfrac{13}{4}$ is finite, $\begin{align*} \text{Var}(Y) &= E(Y^2) - \mu^2 \\ &= \frac{13}{4} - \left(\frac{17}{12}\right)^2 \\ &= \frac{468}{144} - \frac{289}{144} \\ \text{Var}(Y) &= \frac{179}{144} \\ \sqrt{\text{Var}(Y)} &= \frac{\sqrt{179}}{12} \\ \color{blue}{\sigma}\ &\color{blue}{= \frac{\sqrt{179}}{12} \;\approx\; 1.115} \\ \end{align*}$
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