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%FIRST DRAFT: July 01, 2004
\title{Department of Statistics\\
University of Wisconsin, Madison\\
Fall Semester, 2009\\
\vspace{0.5cm}
Stat 849: \\
Theory and Application of Regression and Analysis of Variance-I }
\author{}
\date{}
\usepackage{natbib}


\begin{document}
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\maketitle

\vspace{-2cm}

\begin{table}[h]
\begin{center}
\begin{tabular}{|ll|}
\hline
\textbf{Class hours:} &  8:25-9:40am MW \\
\textbf{Classroom:} &  133 SMI   \\
\textbf{Instructor:} & S\"und\"uz Kele\c{s}\\
\textbf{Office:} &1245B MSC\\
\textbf{Phone:}& (608) 263 4533\\
\textbf{Email:}& \texttt{keles@stat.wisc.edu}.\\
\textbf{Course website:} & \url{http://www.stat.wisc.edu/~st849-1}\\
\textbf{Office hours:} & M: 4-4:40pm; R: 12:00-1:00pm. (subject to change)\\
\textbf{TA:}& TBA\\
\textbf{TA office:}& TBA\\
\textbf{TA office hours:}& TBA\\
\textbf{Discussion:} &   1:00-2:15pm (F); 8:25-9:40am (F).\\
\textbf{Course email list:}& stat849-1-f09@lists.wisc.edu \\
\hline
\end{tabular}
\end{center}
\end{table}

\noindent \textbf{Description.} The aim of this course is to provide
an introduction to theory and application of regression and analysis
of variance. In particular, we will cover estimation and testing
theory for least squares fits under general, Gauss-Markov, and
related assumptions; diagnostic tools; prediction and model
selection in linear regression; and generalized linear models. The
lectures will mainly focus on the theory and provide examples of
applications. The homework assignments will provide the opportunity
to implement many of the methods on real data and case studies.

\noindent \textbf{Prerequisites.} Linear algebra, introductory
statistics \& probability.

\noindent \textbf{Reading List.} All of the four books below are
on reserve at Wendt library. As our main text book, we will
use
\begin{itemize}
\item \textit{Linear regression analysis} by G.A.F. Seber and A.J.
Lee (2nd edition).
\end{itemize}

For some of the topics that will be covered, you
might also find the following book useful.
\begin{itemize}
\item \textit{Linear models: least squares and alternatives} by C.
R. Rao and H. Toutenburg.
\end{itemize}
In the last few weeks of the course, we will be using
\begin{itemize}
\item \textit{Generalized linear models} by P. McCullagh and J.A.
Nelder (2nd edition).
\end{itemize}
A good source on R/S is
\begin{itemize}
\item \textit{Modern Applied Statistics with S} by W. N. Venables
and B.D. Ripley (4th edition).
\end{itemize}


\noindent \textbf{Grading.} Grading will be composed of following components:
\begin{itemize}
\item \textbf{Homework assignments: 25\%.} Bi-weekly homework
assignments will include a lab component, where you will be
typically required to analyze a data set, and a theory component.
Homework assignments will be given on a Monday and will be collected
on  Friday after two weeks. The main computing environment will be
\texttt{R} (see below). Homework assignments including a lab
component should be typed in latex, word, or any other text
processing language. Analytical parts can be handwritten. Submission
of raw computer output is not allowed. You should incorporate your
results in your text as relevant Tables and Figures with informative
captions  and provide the source code as appendix. We will provide a
basic introduction to latex and a lab report template. There will be
a total of 5-6 homework assignments.

\item \textbf{Midterm exam I: 25\%.} An in class midterm exam is
scheduled on October 12, 2009. This is subject to change, in
particular to after 6pm of the same day.

\item \textbf{Midterm exam II: 25\%.} An in class exam is
tentatively scheduled on November 16, 2009.  This is subject to
change, in particular to after 6pm of the same day.

\item \textbf{Final project: 25\%.} The final project will include
analyzing a data set of your choice (everyone should be analyzing a
different data set) which will be subject to my approval. You are
also welcome to review a paper of your choice (should be approved by
me) or a paper from the list I will provide. You are expected to
evaluate and criticize the paper and repeat some of the data
analysis or simulations presented. Your final output will be a
detailed report of the data analysis/paper review that includes all
the analysis that you will perform.  We will have a poster session
at the end of the semester where you will have a chance to present
posters of your projects.

In order to avoid leaving things to the last minute, everyone should
submit a project proposal  by October 28th, 2009. The proposal
should include a brief description of the data you plan to analyze
and your tentative plan of analysis.



\end{itemize}

\noindent You are not allowed to collaborate on the exams.  You can
share ideas on homework problem solutions but all the work presented
on solutions must absolutely be yours. If you discuss your solutions
with a classmate (or someone else), you should write that person's
name at the end of your homework.


\noindent \textbf{Computing.} We will use R
(\url{http://www.r-project.org}), a freely available
implementation of S language, as our computing environment.
Discussion sessions will include basic information on related R
functions.



 \clearpage

\begin{table}[h]
\begin{center}
\textbf{Tentative syllabus}
\vspace{0.5cm}
\small
\begin{tabular}{|c|l|l|}
\hline
\textbf{Week} & \textbf{Date} & \textbf{Topic}\\
\hline 1  & Sep 2,  W     & A general introduction to the concept of
regression and review on matrices and quadratic forms.  \\
\hline 2  & Sep 7, M    & LABOR DAY. \\
&       \underline{Sep 9, W}                       &  Linear models: definition and examples.\\
   &            & Least squares estimates in full-rank models.\\
   &     & Normal equations, calculus approach, and geometrical notes. \\
\hline
3 &  Sep 14, M    & Properties of least squares estimates. A data example in R.\\
  &               & Review on random vectors and matrices.\\
  &  Sep 16, W    & Generalized inverse: least squares estimates in \\
  &               & less-than full-rank models. \\
  &               & Estimation of the error term and $\sigma^2$, \\
  &               & Linear estimators. \\
\hline
4 & \underline{Sep  21, M}    & Introduction to Gaussian linear model.\\
  &               & Gaussian linear model: Estimates of $\beta$ and $\sigma^2$.\\
  & Sep  23, W    & Gauss-Markov theorem: full rank and extended case.\\
\hline
5 & Sep 28, M      & Linear regression models with linear restrictions\\
  &                           & on parameters.\\
  &               & Testing linear hypothesis:  t and F-tests, canonical form.\\
  &  Sep 30, W     & Linear regression diagnostics: diagnostic plots, goodness of fit,\\
  &               & transformations, and analysis of variance.\\
\hline
6 & \underline{Oct  5, M}    &Testing linear hypothesis:  t and F-tests, canonical form.\\
  & Oct  7, W    & Linear regression diagnostics.\\
\hline
7 & Oct 12, M     & \textbf{Midterm I} (in class). \\
  & Oct 14, W     & Linear regression diagnostics.\\
\hline
8 & \underline{Oct  19, M}   & Methods for dealing with multi collinearity: \\
  &               & PCR, ridge regression, PLS.\\
  & Oct   21, W  & Methods for dealing with multi collinearity.\\
\hline
9 & Oct 26, M    & Prediction and model selection in linear regression:\\
  &                           & Notions of loss functions and risk; best subset selection;\\
  &                           & stepwise methods; cross-validation.\\
   & Oct 28,  W    & Prediction and model selection in linear regression.\\
   &              & \textbf{Project proposals are due.}\\
\hline
10& \underline{Nov 2,   M}    & Prediction and model selection in linear regression.\\
  & Nov 4,   W    & Prediction and model selection in linear regression.\\
\hline
11& Nov  9,  M    & Generalized linear models: logistic, poisson, log-linear models. \\
  & Nov  11, W     &  Generalized linear models. \\
\hline
12& \underline{Nov  16, M}  & \textbf{Midterm II} (in class).\\
  & Nov  18, W   & Generalized linear models.\\
\hline
13& Nov 23, M      & Generalized linear models.\\
  & Nov 25, W      & Generalized linear models.\\
\hline
14 & Nov 30, M      & Generalized linear models.\\
   & Dec 2, W      & Generalized linear models.\\
\hline
15& Dec  7,  M    & Generalized linear models.\\
  & Dec  9,  W   & Generalized linear models.\\
\hline
16&  Dec 14, M   & \textbf{Final project poster presentation and paper are due.}\\
  &              & Time: 1-4:30pm. Location: 133 SMI. \\
\hline
17&  Dec 15, T   & \textbf{Poster critiques are due by 10:30am.}\\
\hline
\end{tabular}

\end{center}
\textbf{PS1:} Underlined dates correspond to days on which the
homework will be assigned.  The homework is due  a Friday after two weeks of
the assigned date and should be returned to the TA in the discussion session. \\

\textbf{PS2:} No extensions will be granted on the homework
assignments. In case of an emergency, contact me. The TA is not
authorized to give extensions on the homework.
\end{table}


%\begin{verbatim}
%Exams begin            Dec 16 (R)
%Commencement           Dec 19 (N)
%Exams end          Dec 23 (R)
%Last day grades in     Dec 27 (M)
%\end{verbatim}

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