% Sample Question document for STA256 \documentclass[12pt]{article} %\usepackage{amsbsy} % for \boldsymbol and \pmb %\usepackage{graphicx} % To include pdf files! \usepackage{amsmath} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage[colorlinks=true, pdfstartview=FitV, linkcolor=blue, citecolor=blue, urlcolor=blue]{hyperref} % For links \usepackage{fullpage} %\pagestyle{empty} % No page numbers \begin{document} %\enlargethispage*{1000 pt} \begin{center} {\Large \textbf{Sample Questions: Moment-generating functions}} STA256 Fall 2019. Copyright information is at the end of the last page. %\rule{6in}{.01in} % Width and height \rule{6in}{.005in} % Horizontal line (Width and height) % \vspace{3 mm} \end{center} \begin{enumerate} \item {\Large Let $X$ have a moment-generating function $M_{_X}(t)$ and let $a$ be a constant. Show $M_{_{aX}}(t) = M_{_X}(at)$. } \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X$ have a moment-generating function $M_{_X}(t)$ and let $a$ be a constant. Show $M_{_{a+X}}(t) = e^{at}M_{_X}(t)$. } \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X$ and $Y$ be independent, (continuous) random variables. Show $M_{_{X+Y}}(t) = M_{_X}(t) \, M_{_Y}(t)$. } \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X \sim N(0,1)$. Calculate $M_{_X}(t)$. } \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X \sim N(\mu,\sigma^2)$. Calculate $M_{_X}(t)$. } \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X \sim N(\mu,\sigma^2)$. Show $Y = \frac{X-\mu}{\sigma} \sim N(0,1)$ using moment-generating functions.} \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X \sim N(\mu,\sigma^2)$. Find the distribution of $Y = a + bX$ using moment-generating functions.} \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $X_1 \sim N(\mu_1,\sigma^2_1)$ and $X_2 \sim N(\mu_2,\sigma^2_2)$ be independent. Find the distribution of $Y = aX_1+bX_2 + c$}. \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Let $Z \sim N(0,1)$ and let $Y = Z^2$. Find the distribution of $Y$. Note that the MGF of a chi-squared random variable is $M(t)=(1-2t)^{-\frac{\nu}{2}}$.} \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Independently for $i=1, \ldots, n$, let $Y_i \sim \chi^2(\nu_i)$. Find the distribution of $W=\sum_{i=1}^n Y_i$.} \pagebreak %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item {\Large Independently for $i=1, \ldots, n$, let $X_i \sim N(\mu_i,\sigma_i)$. What is the distribution of $Y=\sum_{i=1}^n \left(\frac{X_i-\mu_i}{\sigma_i}\right)^2$? Justify your answer.} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{enumerate} % End of all the questions \vspace{160mm} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \noindent \begin{center}\begin{tabular}{l} \hspace{6in} \\ \hline \end{tabular}\end{center} This handout was prepared by \href{http://www.utstat.toronto.edu/~brunner}{Jerry Brunner}, Department of Mathematical and Computational Sciences, University of Toronto. It is licensed under a \href{http://creativecommons.org/licenses/by-sa/3.0/deed.en_US} {Creative Commons Attribution - ShareAlike 3.0 Unported License}. Use any part of it as you like and share the result freely. The \LaTeX~source code is available from the course website: \begin{center} \href{http://www.utstat.toronto.edu/~brunner/oldclass/256f19} {\small\texttt{http://www.utstat.toronto.edu/$^\sim$brunner/oldclass/256f19}} \end{center} \end{document} % The answer is a number. Circle your answer.