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{\Large  {\bf Advanced Data Analysis II}}
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36-402 \hfill Spring 1996
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{\large Course Policies and Syllabus}
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Instructor & Office & Telephone & e-mail & Office Hours \\
Mike Meyer & BH 229G & x8-3108 & mikem@stat.cmu.edu& After class,  or just drop by. \\
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Lectures & T Th 9:00-10:20 & PH A18B \\
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\paragraph{ Prerequisites:}
I will assume that you understand confidence intervals,
hypothesis testing, basic distributions
such as the normal, $t$, $\chi^2$ and $F$ distributions, and that you
have (more or less) mastered the material in 36-401, especially the
material about regression models and diagnostics.

I expect a {\em strong\/} desire to analyze data and a willingness to be an
active participant in the class.  I will try to find interesting data
analysis projects for each of you to work on, and will then encourage
you to talk about them.  You will have to use several computer packages
during the semester, and that means using the campus Unix systems.  You can
probably do everything in either SAS or Splus, but I encourage you to
learn both packages.  There will be at least one assignment that
requires you to use SAS.   

\paragraph{Textbook:}  {\em Modern Applied Statistics with S-Plus} by W.N. Venables and
   B.D. Ripley, 1994, Springer

\paragraph{Other Useful Books:}\ \\
{\em Statistical Models in S} by J.M. Chambers and T. Hastie, 1992,
Wadsworth.\\
{\em Statistics for Experimenters} by George E.P. Box, William
J. Hunter, and J. Stuart Hunter, 1978, Wiley (A good book for Anova).



\paragraph{Probable Topics}

We have several choices of material to cover.  I'm open to
suggestions, but I clearly have my own preferences.
\begin{enumerate}
\item ANOVA and designed experiments.  In many ways this is just a
  continuation and special case of multiple linear regression, but
  there is some new language to learn and the approach to the problems
  is sometimes different.  We will do this, for at least 4, and maybe
  more, weeks.

\item Categorial Data Analysis, including logistic regression, and the
  so-called generalized linear models.  This covers the analysis of
  data with binary responses, or counts as the response variable.  If
  we do this, it will take between 3 and 5 weeks.

\item Robust statistical methods and non-linear regression.   This
  depends on your interests.  It should take a couple of weeks.

\item Modern regression methods, including additive models and
  projection-pursuit.  Depending on how carefully we do this, it can
  take from 2 to 5 weeks.

\item Continuous multivariate analysis.  Data where there are multiple
  continuous-valued responses.  This can be useful, but it will take
  many weeks to do well.  Possible topics include, Discriminant
  Analysis, Principal Components, Factor Analysis (if I can figure it
  out), Multivariate ANOVA, Clustering Methods, Multivariate Graphics.


\item 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.

\item 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.

\end{enumerate}

Left to my own devices, I'll cover ANOVA, generalized linear models
(with an emphasis on categorical data), and perhaps some survival
analysis.  Speak up with your opinion.

\paragraph{Class Organization}
I intend to give formal lectures but I will try to talk about (your)
case-studies as often as possible.
I usually hand out class notes and handouts where possible.

\paragraph{Overview}

This course will emphasize the conceptual basis of several data
analytical techniques.  We will not be overly concerned with
computational or mathematical details, but nor will we shy from
difficult theory when it is needed.  I will stress the fact that 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 first plotting
  the data.} Also bear in mind that there is never one right way to do
an analysis---typically, many analyses are performed.

\paragraph{ Homework.}

There will be approximately weekly assignments and a project.  I
encourage you to work together on the assignments but the work that
you hand in should be your own.  The assignments will make use of the
computer packages.  I will teach you the minimum you need to know
about these computer packages---enough to do the work, but not enough
to think yourself an expert.  Statistical computing packages are very
easy to use.

{\bf Late homework will not be accepted.  You want your homeworks back
  quickly.  The only way I can do that is if the homework is turned in
  on time. }


NOTE: Your assignments should discuss the results of your analyses.
Your conclusions and interpretation of the results are the most
important part of your write-up.  {\bf Never hand in raw computer
  output.} Cut out plots, tables etc. from the output and include them
in your report as needed.  Include a short summary that someone not
versed in statistics could read.

\paragraph{Assessment} \ \\
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Homework: & 50\% \\
Project: & 25\% \\
Take-home final: & 25\% \\
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