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\begin{center}\Large\bf
\course \\
\semester \\[0.5em]
\Large\verb|Student Center 201              TR 9:00--10:20| \\[0.5em]
\Large\it
                          Course Policies and Syllabus
\end{center}

\head{Vital Information}

\noindent
\begin{tabular}{p{2.5in}p{1.0in}p{2.5in}}

\hspace*{0.25em}
\begin{tabular}[t]{l}
{\it Instructor:} \\ 
\hline
Brian Junker 	 \\
232C Baker Hall  \\
268-8873 	 \\
{\tt brian@stat.cmu.edu} \\
\ \\
{\it Office Hours:} \\
\hline
\multicolumn{1}{l}{Please feel free to drop in.  If I am} \\
\multicolumn{1}{l}{busy, I will schedule a better time.}
\end{tabular}

& &

\begin{tabular}[t]{l}
{\it Teaching Assistant:} \\
\hline
%\mbox{\hbox {\vbox to 4.8em {\phantom{XXXXXXXXXXXXXXXXXXXX}}}}
Ashish Sanil\\
Student Center 218\\
268-1889 \\
{\tt ashish@stat.cmu.edu} \\
\ \\
{\it Office Hours:} \\
\hline
%\mbox{\hbox {\vbox to 1.2em {\phantom{XXXXXXXXXXXXXXXXXXXX}}}}
%\\
Also by appointment.
\end{tabular}

\end{tabular}

\head{Texts}

The texts for this course are

\vspace*{-1.0em}
\begin{description}\itemsep=0pt\parsep=0pt\parskip=0pt

\item[\quad Required:] Venables, W. N. and Ripley, B. D. (1994).  {\em Modern 
	applied statistics with Splus.}  New York: Springer-Verlag.

\item[\quad Recommended:] Krause, A. and Olson, M. (1997). {\em The basics 
	of S and SPLUS.} New York: Springer.

\end{description}
\vspace*{-1.0em}

You may already own Venables and Ripley, since it was recommended for
401. I will also use excerpts from

\vspace*{-1.0em}
\begin{itemize}\itemsep=0pt\parsep=0pt\parskip=0pt

\item Mathsoft, Inc.  (1997).  {\em S-PLUS 4: Guide to Statistics.}
Seattle, WA: Author.

\end{itemize}
\vspace*{-1.0em}

Some other books that are useful for this course are listed at the end
of the syllabus.
 
\head{Prerequisites}

\noindent
I assume that:
\begin{itemize}\parsep=0pt\parskip=0pt\itemsep=0pt

\item You have (more or less) mastered the material in 36-401,
especially the material about regression models and diagnostics.  

\item You can deal with Unix on Andrew.  The primary computer package
for the course is SPLUS, but we will also be using other packages such
as SAS and Minitab.  All of these are available on the campus Unix
systems (if you can find them on PC's and Mac's on campus, you are
welcome to use them there instead/too).

\item You have a strong desire to analyze data and a willingness to
participate in class.

\end{itemize}

\head{Course Description} 

\noindent
Advanced Data Analysis (ADA) I and II are key courses in the
Statistics undergraduate program.\\[-0.5em]

In ADA I (36-401) you should have learned to apply the theory of
linear regression analysis---including confidence intervals and
hypothesis testing, basic distributions such as the normal, $t$,
$\chi^2$ and $F$ distributions, basic facts and techniques for lienar
regression, including the material about regression models and
diagnostics---in order to investigate model assumptions, and take
appropriate action if the assumptions don't hold.  In addition you
should have learned something about using SPLUS, and about writing
literate data analysis reports.

In ADA II (36-402), we will further develop those skills in exploring
data, building and fitting models, investigating model assumptions,
interpreting results from statistical models, and report writing.  We
will begin by talking about some topics that are closely related to
linear regression, and move farther and farther from linear regression
as the semester progresses.

We will emphasize the conceptual basis of the modeling and data
analysis techniques, not the computational details.  However, when
difficult theory is needed to understand something we will use it.
These techniques, like all statistical techniques, involve
assumptions.  A typical statistical analysis begins with simple
graphical and descriptive analyses followed by the application of
formal statistical techniques.  The analysis must then be followed up
by diagnostics that check whether the assumptions have been violated.
Most good analyses include a heavy dose of graphical techniques.  The
golden rule in this course is: {\bf Never do a statistical analysis
without plotting the data.} Also bear in mind that there is never just
one right way to do an analysis---typically, many analyses are
performed, even by a single person looking at a single data set to
answer a single question.

\head{Course Work}

\noindent
In this course there will be approximately weekly ``finger exercises''
and three or four ``mini-projects.''  I encourage you to work together
on the finger exercises; rules for collaboration on the projects are
different and I will hand those rules out with the project
assignments.

There will also be reading assignments, from Venables and Ripley, and
from other material that I hand out or make available on-line.  This
reading is very important; there won't be enough time for me to go
over everything in detail in class.

All assignments and projects in this course will make use of computer
packages.  I will go over the main points of the computer packages in
class, but teaching yourself computing details that I don't have time
to cover in class is also part of this course.  Nowadays, statistical
computing packages have excellent on-line help facilities and are
generally very easy to use.

There will be no midterm or final exams.

\head{Grading}

\begin{center}
\begin{tabular}{lr}
Mini-Projects: & 60\% \\
Finger Exercises: & 20\% \\
Classroom Participation: & 20\% \\
\hline
Total & 100\%
\end{tabular}
\end{center}

Each mini-project will involve taking some data and question(s),
analyzing the data to answer the question(s), and writing a report on
your analyses and conclusions, using \LaTeX, MS-Word, WordPerfect, or
some other good word processing software.  

Homework and project writeups should discuss the results of your
analyses.  Your conclusions and interpretations are the most important
part of your write-up.  {\bf Never hand in raw computer output.} Cut
and paste (physically with Elmer's or electronically) plots, tables,
etc., from the stat package output and include them in your report as
needed.  Include a short summary that someone not versed in statistics
could read.

For at least the first mini-project, I will provide the data and the
question(s).  As the course progresses I want the mini-projects to
revolve around data and questions that you bring in.  

{\em So start thinking about where you are going to get data!}

\head{Possible Topics}

Here is a menu of topics for the course.  I also indicate the relevant
chapters in Venables and Ripley, and about how long I expect each
topic to take.

From the eight topics listed below we need to select about 4--5 topics
for the semester.

{\small

\begin{enumerate}

\item \ulem{ANOVA/ANCOVA and designed experiments.}  Linear regression
  gives us tools for thinking about, designing and analyzing
  experiements and other studies.  Historically, though, experimental
  design and ANOVA were developed separately from multiple linear
  regression (because computing used to be expensive), and so the
  language and approaches to problems are somewhat different from what
  you learned in ADA I.

  {\em Venables and Ripley, Chapter 6, plus supplementary materials;
  about 4 weeks}

\item \ulem{Generalized linear models, logistic regression and
  categorical data analysis.}  Normal-errors linear regression deals
  with continuous response variables that can take on any positive or
  negative values.  This is the adaptation of linear regression models
  to deal with responses that are categories (yes/no,
  good/better/best, red/green/blue), counts, or waiting times.

  {\em Venables and Ripley, Chapter 7, plus supplementary materials;
  about 4 weeks}

\item \ulem{Robust statistical methods and non-linear regression}.
  How can one deal with outliers ``automatically'' in linear
  regression?  What if the response variables and predictors are all
  continuous, but the response function is very nonlinear, like $ y =
  a\cdot e^{bx} + \epsilon$ (such nonlinear relationships arise
  frequently in physics/chemistry/biology applications, for example).
  
  {\em Venables and Ripley, Chapters 8 and 9; 2--4 weeks, depending on
  interest.}

\item \ulem{Random effects and mixed effects models.} In standard
  linear regression models there is only one source of error or random
  variation.  What do you do when there are several sources of error?
  For example, in studies of public education, the performance of an
  individual student is the sum of his/her own performance, plus
  influences from teachers, the school, the school district, the
  state, etc., so different students (from different teachers,
  schools, etc.)  should have different ``error terms'' in the linear
  regression model.  How do we deal with this?

  {\em Venables and Ripley, Chapter 10; 2--4 weeks, depending on
  interest.}

\item \ulem{Modern regression methods}, including additive models and
  projection-pursuit.  This would also be a natural place to discuss
  some topics in {\em machine learning} (neural networks,
  reinforcement learning and classification, etc.)

  {\em Venables and Ripley, Chapters 11 and 17; 2--4 weeks, depending
  on interest.}

\item \ulem{Continuous multivariate analysis}.  Data where there are
  multiple continuous-valued responses.  Possible topics include,
  Discriminant Analysis, Principal Components, Factor Analysis,
  Multivariate ANOVA, Clustering Methods, Multivariate Graphics.

  {\em Venables and Ripley, Chapters 13--14; 2--4 weeks depending on
  interest.} 

\item \ulem{Survival Analysis.}  How to look at a clinical trial and
  try to understand, say, how effective a drug is.  Possible topics
  include Survival Analysis and Hazard Rates, Censoring, Parametric
  and Non-parametric models, Goodness of Fit.

  {\em Venables and Ripley, Chapter 12; 2--4 weeks depending on
  interest.}

\item \ulem{Time series.}  Data recorded in time where an important
  concern is understanding the serial correlation of the data. We
  might explore both the time and frequency domain.

  {\em Venables and Ripley, Chapter 15; 2--4 weeks depending on
  interest.}

\end{enumerate}

}

\head{Plan of Action}

I will begin the course talking about ANOVA/ANCOVA, designed
experiments, and generalized linear models.  Where we go from there
can depend largely on your interests.

\head{Computing, Data Sets, Web Page, Communications}

\begin{itemize}

\item Data sets, functions, and other files for this class are kept in
  \data; access the same area on the Web via \www.

\item Both the TA ({\tt ashish@stat.cmu.edu}) and the instructor ({\tt
brian@stat.cmu.edu}) read email regularly.  Please feel free to send
us email with questions, comments, etc., anytime.  Also, please feel
free to drop by our offices or schedule special appointments by email
anytime.

\end{itemize}

\head{Other Useful Texts}

The following texts will occasionally be useful in this course. Most
are in the E\&S Library, in the DeGroot Library, on the Web, or on my
bookshelves.  Amazon books ({\tt http://www.amazon.com}) is a good way
to order a copy quickly, if you like.

{\small

\begin{description}

\item[\em Books about Data Analysis and Modeling] \

\begin{description}\parsep=0pt\parskip=0pt\itemsep=0pt

\item Agresti, A. (1990). {\em Categorical data analysis.}  New York:
Wiley.

\item Box, G. E. P., Hunter, W. G. and Hunter, J. S. (1978).  {\em
Statistics for experimenters:  An introduction to design, data
analysis and model building.} New York: Wiley.

\item Cryer, J. D. (1986).  {\em Time series analysis.}  Boston:
Duxbury Press.

\item Gelman, A., Carlin, J., Stern, H. and Rubin, D. (1995).
{\em Bayesian data analysis}. London and New York: Chapman and Hall.

\item Johnson, R. A. and Wichern, D. W. (1992).  {\em Applied multivariate
statistical analysis\/}, 3rd edition.  Prentice-Hall, Englewood Cliffs
NJ.

\item Mitchell, T. (1997). {\em Machine learning.}  Burr Ridge, IL:
McGraw Hill.

\item Myers, R. H. (1990) {\em Classical and modern regression with
applications.} (second or current edition).  Boston: PWS-Kent
(Duxbury).

\item Neter, J., Wasserman, W. and Kutner, M. H. (1990).  {\em Applied
linear statistical models, 3$^{rd}$ Ed.}  Homewood, IL: Irwin.

\end{description}

\item[\em Books about SPLUS] \

\begin{description}\parsep=0pt\parskip=0pt\itemsep=0pt

\item Spector, Phil.  (1994).  {\em An Introduction to S and Splus.}  Belmont
CA: Duxbury Press.

This text gives a gentle introduction to S and Splus, with relatively
few rich statistical examples.  If you can't figure out how to do
something in Splus, often browsing through this book will give you
the hint you need.  Almost the same info is available on the Web from
\statlib{} in two formats: {\tt sguide.ps1} is formatted in full-sized
8.5$\times$11 inch pages, and {\tt sguide.ps2} is formatted in
4.25$\times$5.5 inch pages, to save paper.

This was a recommended text for 36-401 last fall, so you probably
already own a copy.

\item Mathsoft, Inc.  (1997).  {\em S-PLUS User's Guide, Version 4.0}
Seattle, WA: Author.

Contains much of the same material that is in Spector's book above.

\item Mathsoft, Inc.  (1997).  {\em S-PLUS Programmer's Guide, Version 4.0}
Seattle, WA: Author.

Technical reference for Splus.  We probably won't need it, but I
mention it just in case.

\end{description}

\item[\em Other Useful Books.] \

\begin{description}\parsep=0pt\parskip=0pt\itemsep=0pt

\item Cody, R. P. and Smith, J. K. (1997).  {\em Applied statistics and the
SAS programming language.}  Upper Saddle River, NJ: Prentice-Hall.
% ISBN 0-13-743642-4

% No working statistician's education is complete without some exposure
% to SAS, perhaps the most ubiquitous major statistical computing
% package in use today.  This book is an example-driven introduction to
% SAS that you may find useful.
% \begin{quote}
{\em
This is sold on the main floor [not the basement] of the bookstore in
the University Center, in the computer books section.}
% \end{quote}

% {\em SAS Procedures Guide: Version 6\/}, 3rd Edition.  SAS Institute.

% {\em SAS/STAT User's Guide, Volume 1: Version 6\/}, 4th Edition.  SAS
% Institute.

% {\em SAS/STAT User's Guide, Volume 2: Version 6\/}, 4th Edition.  SAS
% Institute.

\item Young, Margaret L. and Levine, John. (1995).  {\em UNIX for dummies:
quick reference, 2nd Edition.}  Foster City, CA: IDG Books Worldwide.
% ISBN 1-56884-979-6

% This is a lighthearted (if that is possible!) but reasonably
% informative introduction to UNIX, the predominant operating system in
% our department.  Most of what you learn about UNIX you will have to
% learn on your own, so it is good to have a reference around.  If you
% don't like this one, the bookstore sells many other guides to UNIX.
% \begin{quote}
{\em
This is sold on the main floor [not the basement] of the bookstore in
the University Center, in the computer books section.}
% \end{quote}

\end{description}

\end{description}

}


\end{document}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\head{Other Courses}

For comparison, here are some WWW links to some other statistical
computing courses at about the same level as this course.  Other links
are provided on the course Web page (see below).

\begin{enumerate}\small

\comment{

\item {\tt http://www.stat.cmu.edu/\~{}brian/old-data/95-96/711/}

    711 when it was a half-semester course.  S-centric topics: machine
      precision, maximization, normal approx, numerical integration,
      simulation, EM.  Mostly followed Tanner's "Tools" book.

}

\item {\tt http://www.stat.lsa.umich.edu/\~{}faraway/syll506.html} \\
    {\tt http://www.stat.lsa.umich.edu/\~{}faraway/teaching.html}

    Crash course covering aspects of AFS, Unix, Splus, Maple, C, Fortran,
      TeX and LISP-STAT.

\item {\tt http://www.stat.berkeley.edu/classes/s243/syl1996/syl1996.html}
    
	 Phil Spector's intro to computing, covering aspects of C, S and SAS.

\item {\tt http://muskie.biostat.umn.edu/\~{}brad/5421/syll.html}

     S- and C-centric, covering Thisted Ch's 1-5 (basic computation), 
      Ripley Ch's 1-3 (basic simulation), and Spector's intro to S (graphics).

item {\tt http://www.stat.yale.edu/Courses/200.html}

    S-centric crash course on high-level computing and graphics.

\item {\tt http://www.stat.yale.edu/Courses/625.html} \\
    {\tt http://www.stat.yale.edu/Courses/625/index.html}

    A more high-level course: Splus and perl.
 
\item {\tt http://www.stat.ucla.edu/courses/graduate/M280/1996/bob280.html}

    SAS/BMDP-centric, focuses on methods: lin/nonlin regr, maximization, 
      EM/MCMC.

\end{enumerate}

\head{Scheduling}

I would very much like to meet twice a week for an hour and a half,
rather than three times a week for an hour.  Right now the best option
seems to be to extend class for 1/2 hr on Mondays and Wednesdays and
cancel class on Friday.  Please let me know if this {\em doesn't} work
for you.






