\documentclass[12pt]{article}
\oddsidemargin 0in \evensidemargin 0in \topmargin 0in
\columnsep 10pt \columnseprule 0pt 
\marginparwidth 90pt \marginparsep 11pt \marginparpush 5pt 
\headheight 0pt \headsep 0pt 
%\footheight 0pt \footskip 0pt 
\textheight 9in \textwidth 6.5in
\pagestyle{plain}
\usepackage{graphicx,latexsym}
%% graphicx package allows for easy inclusion of PDF graphics.
%% latexsym package gives you 11 symbols not found in default math mode

\newcommand \noi {\noindent}


\begin{document}

%%%%%%%%%%%%%
%   Title   %
%%%%%%%%%%%%%
\begin{center}
{\LARGE \bf 	 Emotion in patients with rare brain damage}\\
\vspace{.25in}
{\bf Written report for STAT:6220 Statistical Consulting}\\
\vspace{.25in}
{\bf by Consultant 1 and Consultant 2}\\
\end{center}


\vspace{0.5in}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%			Introduction				%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}
doot, doot, doot...\\

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%			Data Collection			%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Data Collection}
doot, doot, doot...\\

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%		Statistical Analysis			%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Statistical Analysis}
\subsection{Data Exploration}
doot, doot, doot...

% Put your graphics into the same folder as your LaTex script.
\includegraphics[width=4in]{figure1.pdf}



\pagebreak

\subsection{Modeling Fitting}
doot, doot, doot...\\

\noi Our model...
\begin{equation}
ln \left( \frac{P(Y_{i}=1)}{1-P(Y_{i}=1)} \right) = \beta_{0} + \beta_{1}X_{1i}+\cdots + \beta_{k}X_{ki}
\end{equation}

\vspace{.25in}

\noi Our SAS output...
\begin{verbatim}
proc glm data=eyes;
  class treatment subject;
  model reduction=subject treatment/solution;
  estimate `trt 1 vs. 3' treatment 1 0 -1;
  lsmeans treatment/adjust=tukey pdiff stderr;
  output out=diag r=resid p=yhat;
run;


*Only selected output follows*

The GLM Procedure
Dependent Variable: reduction
                                    Sum of
Source                    DF       Squares   Mean Square  F Value   Pr > F
Model                      7   6746.000000    963.714286    3.71    0.1114
Error                      4   1039.000000    259.750000
Corrected Total           11   7785.000000

The GLM Procedure
Dependent Variable: reduction

Source                    DF   Type III SS   Mean Square  F Value   Pr > F
subject                    5   5881.500000   1176.300000     4.53   0.0842
treatment                  2    186.000000     93.000000     0.36   0.7194


The GLM Procedure
Dependent Variable: reduction
                                        Standard
Parameter                 Estimate         Error  t Value  Pr > |t|

trt 1 vs. 3            -4.00000000    13.1592806    -0.30    0.7763


        Dependent Variable: reduction
i/j              1             2             3

   1                      0.7035        0.9510
   2        0.7035                      0.8606
   3        0.9510        0.8606
   
   
\end{verbatim}

\subsection{Results}
doot, doot, doot...\\

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%			Conclusion	                 	%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusion}
doot, doot, doot...


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%    References                 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{center}
REFERENCES\\
\end{center}
\begin{description}

\item Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977).  Maximum likelihood from incomplete data via the EM algorithm.  {\it J. R. Statist. Soc.} B {\bf 39}, 1-22.

\end{description}



\end{document}