\documentclass[10pt]{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} % Good for US Letter paper \topmargin=-0.75in \textheight=9.5in \usepackage{fancyhdr} \renewcommand{\headrulewidth}{0pt} % Otherwise there's a rule under the header \setlength{\headheight}{15.2pt} \fancyhf{} \pagestyle{fancy} \cfoot{Page \thepage {} of 2} % \pagestyle{empty} % No page numbers \begin{document} %\enlargethispage*{1000 pt} \begin{flushright} Name \underline{\hspace{60mm}} \\ $\,$ \\ Student Number \underline{\hspace{60mm}} \end{flushright} \vspace{2mm} \begin{center} {\Large \textbf{STA 431 Quiz 2}}\\ \vspace{1 mm} \end{center} \noindent % \emph{Calculators are allowed.} %\vspace{3mm} \begin{enumerate} \item (3 points) Let $\mathbf{A}$ be a real, symmetric, positive definite matrix. Show that the eigenvalues of $\mathbf{A}$ are all strictly positive. Start with the definition $\mathbf{Ax}=\lambda\mathbf{x}$. \vspace{100mm} \item (3 points) Although eigen\emph{vectors} are always non-zero, it is possible for an eigen\emph{value} to equal zero. Let $\mathbf{A}$ be a square matrix, not necessarily symmetric, and let $(\lambda,\mathbf{x})$ be an (eigenvalue, eigenvector) pair with $\lambda=0$. Show that $\mathbf{A}$ does not have an inverse. \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \item (4 points) Let the $p \times 1$ random vector $\mathbf{x}$ have expected value $\boldsymbol{\mu}$ and variance-covariance matrix $\mathbf{\Sigma}$, and let $\mathbf{A}$ be an $m \times p$ matrix of constants. Using the definition of a variance-covariance matrix on the formula sheet and familiar properties of expected value, derive the variance-covariance matrix of $\mathbf{Ax}$. % \end{enumerate} \end{document}