\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 \oddsidemargin=0in % Good for US Letter paper \evensidemargin=0in \textwidth=6.3in \topmargin=-0.5in \headheight=0.1in \headsep=0.1in \textheight=9.4in \pagestyle{empty} % No page numbers \begin{document} \enlargethispage*{1000 pt} \begin{center} {\Large \textbf{STA 441s18 Formulas}}\\ \vspace{10 mm} \end{center} \noindent \renewcommand{\arraystretch}{2.0} \begin{tabular}{lcc} $y = \beta_0 + \beta_1 x_1 + \cdots + \beta_{p-1} x_{p-1} + \epsilon$ & $SST=SSR+SSE$ & $R^2 = \frac{SSR}{SST}$ \\ $a = \frac{R^2_F - R^2_R}{1-R^2_R}$ & $a = \frac{sF}{n-p+sF}$ & $F = \left( \frac{n-p}{s} \right) \left( \frac{a}{1-a} \right)$ \\ \multicolumn{3}{l}{\parbox{6.5in}{If an overall test has $s$ numerator degrees of freedom and critical value $c$, the critical value of a Scheff\'e follow-up test with $r$ degrees of freedom is $\left(\frac{s}{r}\right) \cdot c$.}} \\ && \\ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% $\ln\left(\frac{\pi}{1-\pi}\right) = \beta_0 + \beta_1 x_1 + \cdots + \beta_{p-1} x_{p-1} = L$ && $\pi = \frac{e^L} {1+e^L}$ \\ && \\ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% $\ln\left(\frac{\pi_1}{\pi_3} \right ) = \beta_{0,1} + \beta_{1,1} x_1 + \ldots + \beta_{p-1,1} x_{p-1} = L_1$ && $\pi_1 = \frac{e^{L_1}}{1+e^{L_1}+e^{L_2}}$ \\ $\ln\left(\frac{\pi_2}{\pi_3} \right ) = \beta_{0,2} + \beta_{1,2} x_1 + \ldots + \beta_{p-1,2} x_{p-1} = L_2$ && $\pi_2 = \frac{e^{L_2}}{1+e^{L_1}+e^{L_2}} $ \\ && $\pi_3 = \frac{1}{1+e^{L_1}+e^{L_2}}$ \\ \end{tabular} \renewcommand{\arraystretch}{1.0} \vspace{5mm} \begin{displaymath} \boldsymbol{\mu} = \left( \begin{array}{c} \mu_1 \\ \mu_2 \\ \vdots \\ \mu_k \\ \end{array} \right) = \left( \begin{array}{c} E[y_1|\textbf{X=x}] \\ E[y_2|\textbf{X=x}] \\ \vdots \\ E[y_k|\textbf{X=x}] \\ \end{array} \right) = \left( \begin{array}{c c c} \beta_{0,1} + \beta_{1,1}x_1 + & \cdots & + \beta_{p-1,1}x_{p-1} \\ \beta_{0,2} + \beta_{1,2}x_1 + & \cdots & + \beta_{p-1,2}x_{p-1} \\ \vdots & \vdots & \vdots \\ \beta_{0,k} + \beta_{1,k}x_1 + & \cdots & + \beta_{p-1,k}x_{p-1} \\ \end{array} \right) \end{displaymath} \vspace{3mm} \begin{center} \begin{tabular}{cccc} Unknown & Compound Symmetry & Autoregressive \\ (\texttt{type=un}) & (\texttt{type=cs}) & (\texttt{type=ar(1)}) \\ && \\ $\left( \begin{array}{c c c c} \sigma^2_1 & \sigma_{1,2} & \sigma_{1,3} & \sigma_{1,4} \\ \sigma_{1,2} & \sigma^2_2 & \sigma_{2,3} & \sigma_{2,4} \\ \sigma_{1,3} & \sigma_{2,3} & \sigma^2_3 & \sigma_{3,4} \\ \sigma_{1,4} & \sigma_{2,4} & \sigma_{3,4} & \sigma^2_4 \end{array} \right)$ & $\left( \begin{array}{c c c c} \sigma^2+\sigma^2_1 & \sigma^2_1 & \sigma^2_1 & \sigma^2_1 \\ \sigma^2_1 & \sigma^2+\sigma^2_1 & \sigma^2_1 & \sigma^2_1 \\ \sigma^2_1 & \sigma^2_1 & \sigma^2+\sigma^2_1 & \sigma^2_1 \\ \sigma^2_1 & \sigma^2_1 & \sigma^2_1 & \sigma^2+\sigma^2_1 \end{array} \right)$ & $\sigma^2 \left( \begin{array}{c c c c} 1 & \rho & \rho^2 & \rho^3 \\ \rho & 1 & \rho & \rho^2 \\ \rho^2 & \rho & 1 & \rho \\ \rho^3 & \rho^2 & \rho & 1 \end{array} \right)$ \\ \end{tabular} \end{center} \end{document}