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\def\class{36-726: 
Statistical Practice}
\def\term{Spring 2007}

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\begin{center}
{\large Statistical Practice}
\end{center}

\begin{center}
\Large
36-726 \hfill Spring, 2007
\end{center}
\begin{center}
{\large Course Policies and Syllabus}
\end{center}

\noindent\begin{tabular}{@{}l@{: } ll@{: } l}
{\bf Instructor} &
 Brian Junker &
{\bf Class Location} & 
231B Baker Hall
\\
{\bf Office} & 
 132E Baker Hall &
{\bf Meeting Times} &
MWF 1:30--2:20
\\
{\bf E-mail} &
 brian@stat.cmu.edu & 
{\bf Office Hours} &
By Appointment
\\
{\bf Webpage} &
 http://www.stat.cmu.edu/\~{}brian/726
\\
{\bf Phone} &
 x8874
\\
\end{tabular}

~\\

\noindent {\large \bf Overview}

\vspace{.1in} Most statisticians in industry, government, and academia
spend a fair bit of their time providing advice and analyzing data for
colleagues whose specialties are in some other field.  Often, the
contribution of the statistician in a scientific collaboration is to
sharpen the focus of an investigation by identifying what may, and
what may not, be learned from a particular set of data.  It is part of
the appeal of our discipline that it is application-oriented, yet in
most coursework there is inadequate time to take the applications
seriously and discover what they are about.  The main purpose of this
course is to give students some experience in digging more deeply into
scientific problems that involve statistical thinking.  It is a course
intended to aid you in making the transition from being a student of
statistics to being a statistics professional.

Whether collaborating with a researcher who has a problem that could
motivate development of new methodologies, or helping someone analyze
and interpret their data using conventional techniques, communication
skills are essential for success.  Through practice and discussion,
the class will help you to improve your ability to interact with and
understand a client.  A practicing statistician must be able to
summarize results and advice in a clear and useful manner, both
verbally and in writing. Work on projects in this course should help
you improve these skills as well.  Often, a lasting benefit to the
investigator will be the education he or she receives from the
statistician. This is another aspect of the process that improves with
experience, and which you should be conscious of in your work here.

\vspace{.1in} 

\noindent {\large \bf Textbooks, Resources, and Statistical Methods}

There are no assigned textbooks for this course.  Those of you who can
do your project work in R will appreciate having a current edition of
Venables \& Ripley's {\em Modern Applied Statistics with S} handy.
Web resources for R and other statistical packages will also be useful.

One good general rule about statistical analysis is K.I.S.S. (keep it
simple statistics), since results from simpler methods---when they are
appropriate---are always easier to interpret and explain.  Often you
can ``get'' everything from well-made graphs or other data summaries,
and sophisticated modeling and inference just confirms what you
already see.

Nevertheless many of you may need to learn new methods or adapt old
ones for your data analyses.  There will be no formal lectures on
methods in the class.  In class or in individual group meetings, I can
suggest ideas and resources for you to pursue on your own.

\vspace{.1in} 

\newpage

\noindent {\large \bf Objectives}
\vspace{.1in}

This course aims to help you develop your skills so that you will be
able to:
\begin{list}{$\bullet$}{}\parsep=0pt\itemsep=0pt\parskip=0pt
\item Determine the nature of an investigator's question, and a good
way to formulate it statistically;
\item Focus listening and speaking on important statistical issues;
and
\item Write about statistics for people who will use what you have
written.
\end{list}
In addition, we will read and discuss various ethical, practical, and
interpersonal aspects of consulting so that you can develop your own
sense of values as a practicing statistician.

\vspace{0.15in}

\noindent {\large \bf Projects}
\vspace{.1in}

By having project presentations at the end of Fall Semester in 36-701,
we hope that projects and teams will get underway quickly near the
beginning of sprong semester, giving you the maximum time to interact
with your clients and their data, and produce the most sensible
analysis you can.  The project teams that we set up last fall are as
follows:

\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project A: &  Astrostatistics \\
\bf Investigator: & Andrew Connolly, Dept of Physics \& Astronomy,
U. Pitt; \& Google\\
\bf Team:  & Han Liu, Joey Richard
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project B: &  Biochemical Markers of Neuron Growth \\
\bf Investigator: & Alison Barth, Biological Sciences, Carnegie Mellon \\
\bf Team:  & Drew Crossett, Steven Wasik
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project C: &  Neonatal Ferret Visual System \\
\bf Investigator: & Bill Eddy, Statistics, Carnegie Mellon \\
\bf Team:  & Gabrielle Fijas, Gary Klein
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project D: &  Analysis and Design of Alumni Surveys \\
\bf Investigator: & Judy Cole, Associate Vice President for Alumni
Relations, Carnegie Mellon \\
\bf Team:  & Yi Jiang, Zhanwu Liu
\end{tabular}
\end{center}

\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project E: &  Project Listen in Ghana \\
\bf Investigator: & Jack Mostow, Robotics, Carnegie Mellon \\
\bf Team:  & Elise Olson, Nathaniel Anozie, April Galyardt
\end{tabular}
\end{center}


\begin{center}
\begin{tabular}{p{1in}p{5in}}
\bf Project F: &  Identifying Neurons by Firing Features \\
\bf Investigator: & Marc Sommer, Dept. of Neuroscience, U. Pitt \\
\bf Team:  & Bellone Gaia, Shuhei Okomura
\end{tabular}
\end{center}

\clearpage

\vspace{0.15in}
\noindent {\large \bf Format, Projects, Grading}
\vspace{.1in}

Your team of two or three students will work on the on problems
brought in by our clients last fall.  Each team will work with one
client on one project for the term; at the end of the term each team
will present its project/results to the class and produce a written
research report for the investigator (and the instructors),
summarizing the work that they've done.

Here are some major events in the course.  Exact dates will be
announced in class.

\begin{itemize} % parsep=1em\itemsep=0pt\parskip=0pt

\item Class meetings for the first week of classes will be for
organization and orientation.  We will talk a bit about the consulting
process, and also set up regular meeting times for the teams.

\item During the second week of class, each team will give a short
(10-15 min) presentation of their project, based on (a) the
investigator's presentation last fall; (b) your first meeting(s) with
the investigator; and (c) any initial work or thought you've had on
the project.

\item Student teams will arrange one or more meetings with the
investigator, as needed, and begin working on the problem.

\item Approximately once every 4 weeks, one member of each team will
give the entire class a review and update on each project. {\em Each
team member will be expected to do this at least once; the same person
may not give two monthly reports in a row.}

\item A written interim report on your project will be due to me on
March 9, 2007, {\bf by email} [I will be in Washington DC on that
day].  I will read these reports and give you feedback on March 19,
2007.  I may also ask the team to organize interim team presentations
to the class.

\item Toward the end of the semester, teams will make final
presentations to the entire class.  All of you will be expected to
write a summary of each team's oral presentation.

\item Final written reports are due to the instructors and the
investigators no later than the last day of classes, May 4, 2007.  We
will discuss how these final reports might be organized as the course
progresses.

\end{itemize}
As you can see, most of the class periods will be taken up by
presentations by outside investigators or student teams.  You are
expected to participate actively in class and to interact both in
class and outside class with the investigators.  In addition to
working on projects you will also be doing various reading, writing,
and presentation assignments to hone your skills as a consumer and
producer of statistical research.

\vspace{.15in}

\noindent {\large \bf Readings}
\vspace{.1in}

When we are not having presentations by investigators or students, we
will be discussing various aspects of statistical practice.  Some
representative readings follow.  I will assign readings from this
list or elsewhere, for you to present and discuss in class.

\clearpage

\vspace{.15in}

\noindent {\bf Communication \& Writing:}
\begin{list}{}{\setlength{\itemindent}{-.25in}\setlength{\labelwidth}{0in}
        \setlength{\leftmargin}{.25in}}
\parsep=0pt\itemsep=0pt\parskip=0pt 
\item Alley, M. (1996). {\em The Craft of Scientific Writing, 3rd ed.}
New York: Springer-Verlag.  There are many examples at
\ul{http://www.writing.eng.vt.edu} and
\ul{http://www.me.vt.edu/people/faculty/alley.html}.
\item Hoadley, A. Bruce and Kettenring, J.R. (1990)
Communications between statisticians and engineers/physical
scientists.  (Com: pp.249--274).
{\em Technometrics}, {\bf 32}, 243--247.

\item Boen, James R. and Zahn, Douglas A. (1982) {\em The Human Side
        of Statistical Consulting.}  Wadsworth, Belmont CA. {\em out of
        print}
\item Ehrenberg, A.S.C. (1981) The problem of numeracy. {\em The American
        Statistician}, {\bf 35}, 67--71.
\item --------- (1982) Writing technical papers or reports.
        {\em The American Statistician}, {\bf 36}, 326--329.
\item Gopen, George D. and Swan, Judith A. (1990) The science of
        scientific writing. {\em American Scientist}, {\bf 78}, 550--558.
\end{list}

\noindent 
{\bf Scientific Thinking:}
\begin{description}
\parsep=0pt\itemsep=0pt\parskip=0pt
\item Chatfield, Christopher (1991)
Avoiding statistical pitfalls (with discussion).
{\em Statistical Science}, {\bf 6}, 240--268.

\item Bolles, Robert C. (1988) Why you should avoid statistics.
        {\em Biological Psychiatry}, {\bf 23}, 79--85.
\item Platt, John R. (1964) Strong Inference.  {\em Science}, {\bf 146},
\item Cohen, Jacob (1990) Things I have learned (so far).
        {\em American Psychologist}, {\bf 45}, 1304--1312.
\end{description}

\noindent {\bf Ethics:}
\begin{description}
\parsep=0pt\itemsep=0pt\parskip=0pt
\item Committee on Professional Ethics, American Statistical
Association (1999). {\em Ethical guidelines for statistical practice.}
http://www.amstat.org/profession/index.cfm?fuseaction=ethicalstatistics
Additional resources available at http://www.tcnj.edu/\~{}asaethic 

\item Ellenberg, Jonas H. (1983) Ethical guidelines for statistical
        practice: a historical perspective.  {\em The American Statistician},
        {\bf 37}, 1--4.
\item Finney, David J. (1991) Ethical aspects of statistical practice.
        {\em Biometrika}, {\bf 47}, 331--339.
\end{description}

\noindent {\bf Consulting and Statistical Practice:}
\begin{description}
\parsep=0pt\itemsep=0pt\parskip=0pt
\item Hunter, William G. (1981) The practice of statistics: The real world
        is an idea whose time has come.  {\em The American Statistician},
        {\bf 35}, 72--76.
\item Joiner, Brian L. (1975, 1979) Some advice to young statisticians;
        Consultant's check list.  Unpublished papers.
\item Kirk, Roger E. (1991)
Statistical consulting in a university: Dealing with people and
other challenges.  {\em The American Statistician},
{\bf 45}, 28--34.
\item Marquardt, Donald W. (1979) Statistical consulting in industry.
        {\em The American Statistician}, {\bf 33}, 102--107.

\item Tweedie, R. (1998).  Consulting: Real problems, real
interactions, real outcomes, {\em Statistical Science, 13,} 1--29.


\item  Zahn,  D.~A. and Isenberg, D.~J. (1983).
 Nonstatistical aspects of statistical consulting, {\em The American
  Statistician, 37,}  297--302.

\end{description}

% \vspace{.15in}


The Office of Equal Opportunity Services provides support services for both
physically disabled and learning disabled students.  For individualized
academic adjustment based on a documented disability, contact Equal
Opportunity Services at eos@andrew.cmu.edu or (412) 268-2012.


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

  These presentations should have roughly three sections:
\begin{description}\parsep=0pt\itemsep=0pt\parskip=0pt

\item[The data:] What are the data like?  Summarize the data
qualitatively, numerically, graphically.  If the data are not yet
available, explain why not and when they are likely to be available,
and what form you have been led to expect the data set in.

\item[The questions:] What are the research questions that your client
is posing?  How important are these questions to your client?  How
have you been able to help the client specify these questions?  How
well adapted to the data are the client's questions?  What (if any)
statistical methods might be appropriate?

\item[The difficulties:] What are you unsure about in this project;
where could you stand to get more clarity (from the client about the
problem; from your understanding of statistics; or \ldots)?  Are there
specific difficulties that you can foresee in completing this project?

\end{description}
The order of the teams will be DEACBF.

