Skip to content

Q3

← Back

Basic Info

Probability
└── Homework
    └── W7
        └── Q3.tex

Preview

\documentclass[12pt]{article}

\usepackage{graphicx}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage[margin=1in]{geometry}
\usepackage{fancyhdr}
\usepackage{enumerate}
\usepackage[shortlabels]{enumitem}

\pagestyle{fancy}
\fancyhead[l]{Li Yifeng}
\fancyhead[c]{Homework \#7}
\fancyhead[r]{\today}
\fancyfoot[c]{\thepage}
\renewcommand{\headrulewidth}{0.2pt}
\setlength{\headheight}{15pt}

\newcommand{\bE}{\mathbb{E}}
\newcommand{\bP}{\mathbb{P}}

\begin{document}

    \section*{Question 3}

    \noindent Let $X$ denote the number of heads when tossing two fair coins.

    \bigskip

    \begin{enumerate}[start=1,label={\bfseries Part \arabic*:},leftmargin=0in]
        \bigskip\item Construct the probability distribution of $X$. Compute the average and the standard deviation of $X$.

        \subsection*{Solution}

            By applying the principle of symmetry, easy to define the probability space as

            \[
                \begin{aligned}
                    \Omega &= \{\text{head}, \text{tail}\}\\
                    \mathcal{F} &= \mathcal{P}(\Omega)\\
                    \bP &:\enspace \bP(\{(\text{head}, \text{head})\}) = \bP(\{(\text{head}, \text{tail})\}) = \bP(\{(\text{tail}, \text{head})\}) = \bP(\{(\text{tail}, \text{tail})\}) = \frac{1}{4}
                \end{aligned}
            \]

            Then we have the $pdf$ of $X$

            \[
                p_X(x) =
                \begin{cases}
                    \begin{aligned}
                        \frac{1}{4},&\quad x = 0\\
                        \frac{1}{2},&\quad x = 1\\
                        \frac{1}{4},&\quad x = 2
                    \end{aligned}
                \end{cases}
            \]

            Then we have the average of $X$

            \[
                \begin{aligned}
                    \mu_X &= 0p_X(0) + 1p_X(1) + 2p_X(2)\\
                    &= 0 + \frac{1}{2} + \frac{1}{2}\\
                    &= 1
                \end{aligned}
            \]

            As well as the expectation of $X^2$

            \[
                \begin{aligned}
                    \bE\left[X^2\right] &= 0^2p_X(0) + 1^2p_X(1) + 2^2p_X(2)\\
                    &= 0 + \frac{1}{2} + 1\\
                    &= \frac{3}{2}
                \end{aligned}
            \]

            Then we have the standard deviation of $X$

            \[
            \begin{aligned}
                \sigma_X &= \sqrt{\bE\left[X^2\right] - {\mu_X}^2}\\
                &= \sqrt{\frac{1}{2}}\\
                &\approx 0.707
            \end{aligned}
            \]

        \subsection*{Answer}

            \[\boxed{
                \begin{aligned}
                    p_X(x) &=
                    \begin{cases}
                        \begin{aligned}
                            \frac{1}{4},&\quad x = 0\\
                            \frac{1}{2},&\quad x = 1\\
                            \frac{1}{4},&\quad x = 2
                        \end{aligned}
                    \end{cases}\\
                    \mu_X &= 1\\
                    \sigma_X &= \sqrt{\frac{1}{2}} \approx 0.707
                \end{aligned}
            }\]

        \bigskip\item Now suppose you toss a fair coin repeatedly until you get a tail. Let $Y$ be the number of heads obtained before the first tail. Construct the probability distribution of $Y$. Compute the average and the standard deviation of $Y$ and compare them with those of $X$.

        \subsection*{Solution}

            Easy to notice that $Y$ follows a geometric distribution with parameter $\frac{1}{2}$ (by applying the principle of symmetry), then we have the $pdf$ of $Y$

            \[
                p_Y(y) = \left(\frac{1}{2}\right)^y\left(\frac{1}{2}\right) = \frac{1}{2^{y+1}},\quad y\in\{0,1,\dots\}
            \]

            Then we have the average of $Y$

            \[
                \begin{aligned}
                    \mu_Y &= \sum_{y = 0}^{\infty}\frac{y}{2^{y+1}}\\
                    &= \frac{1}{2}\sum_{y=0}^{\infty}y\left(\frac{1}{2}\right)^y\\
                    &= \frac{1}{2}\left(\frac{\frac{1}{2}}{\left(1-\frac{1}{2}\right)^2}\right)\\
                    &= 1
                \end{aligned}
            \]

            As well as the expectation of $Y^2$

            \[
                \begin{aligned}
                    \bE\left[Y^2\right] &= \sum_{y=0}^{\infty}\frac{y^2}{2^{y+1}}\\
                    &= \frac{1}{2}\sum_{y=0}^{\infty}y^2\left(\frac{1}{2}\right)^y\\
                    &= \frac{1}{2}\left(\frac{\frac{1}{2}\left(\frac{1}{2}+1\right)}{\left(1-\frac{1}{2}\right)^3}\right)\\
                    &= 3
                \end{aligned}
            \]

            Then we have the standard deviation of $Y$

            \[
                \begin{aligned}
                    \sigma_Y &= \sqrt{\bE\left[Y^2\right] - {\mu_Y}^2}\\
                    &= \sqrt{2}\\
                    &\approx 1.414
                \end{aligned}
            \]

            Comparison as

            \[\mu_X = 1 = \mu_Y\]

            \[\sigma_X = \sqrt{\frac{1}{2}} \approx 0.707 < 1.414 \approx \sqrt{2} = \sigma_Y\]

        \subsection*{Answer}

            \[\boxed{
                \begin{aligned}
                    p_Y(y) &= \frac{1}{2^{y+1}},\quad y\in\{0,1,\dots\}\\
                    \mu_Y &= 1\\
                    \sigma_Y &= \sqrt{2} \approx 1.414\\
                    \mu_Y &= \mu_X\\
                    \sigma_Y &> \sigma_X
                \end{aligned}
            }\]
    \end{enumerate}

\end{document}