Class Web Page: http://www.stat.berkeley.edu/classes/s243/
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Introduction (1 lecture)
Basic Unix Commands (2 lectures)
C Programming Language (8-10 lectures)
Algorithms for Mean and Variance (1 lecture)
Random Number Generation (3 lectures)
Matrix Storage and Operations (3 lectures)
Regression Calculations (2 lectures)
Matrix Decompositions (4 lectures)
R Programming Language (5 lectures)
Minimization Methods (3 lectures)
Non-linear Regression (2 lectures)
Any remaining lectures are spent on special topics such as program
maintenance, object oriented programming, and advanced UNIX programming
techniques.
The grade for this course is based on four computer projects
which will be assigned throughout the semester. If you feel you
have a project which would be more relevant than one of the
assignments,
feel free to suggest it as an alternative to an assignment given
in class.
All students will be provided with a computer account on the Statistical
Computer Facilities (SCF) network of SUN, Linux and Mac computers. The
computer room in 342 Evans provides iMacs, and computers are also available
in Room 432 Evans;
you can also
remotely log in to the SCF system from other campus computers or from
home. If you
wish to do your assignments on some other computer, keep in mind that
required programs may be stored on the SCF system, and it is your
responsibility to get the programs to another computer. Additionally, some
of the assignments are oriented towards the UNIX operating system, so if you
wish to
use a non-UNIX computer, you should make sure that the necessary resources
are available.
None of the following texts are required, but interested students may
want to consider the following books, not only for this course, but as
a useful part of their professional libraries:
Gentle, James E.: Numerical Linear Algebra for Applications in Statistics, Springer, New York(1998)
Gentle, James E.: Random Number Generation and Monte Carlo Methods, Springer, New York(1998)
Kernighan, Brian W. & Pike, Rob: The UNIX Programming Environment,
Prentice Hall, New Jersey(1984)
Kernighan, Brian W. & Pike, Rob: The Practice of Programming, Addison-Wesley, Reading(1999)
Kernighan, Brian W. & Ritchie, Dennis M.: The C Programming Language,
Second Edition, Prentice Hall, New Jersey(1988)
Kennedy, William J. & Gentle, James E.: Statistical Computing, Marcel Dekker, New York (1980)
Thisted, Ronald A.: Elements of Statistical Computing, Chapman and Hall,
New York (1988)
Phil Spector
Evans 495
email: spector@stat
Guidelines for Assignments
The grade for this course is determined by four computer projects related
to the material covered in class. Some of the assignments may seem
deceptively easy, but try to avoid putting the assignments off until the
last possible minute. One of the most important things to learn about
programming is that it is an unpredictable venture, and a simple task
often takes more time than you would think at first glance. The purpose
of the assignments is to give you an opportunity to write real programs
which solve real problems. Your goal should not be to simply put together
a program which gets the right answer for a particular set of data, but
to develop a programming style which will allow you to be comfortable in
solving problems which you will encounter in your future work.
You may find it useful to use a word processing program like
LaTeX when writing your reports, but this is not required.
If you have
an interest in learning how to produce attractive electronically typeset
documents, this may be a good time to learn, but the focus of these
assignments is not to produce a pretty report.
Each assignment should consist of the following sections:
An introduction, explaining in your own words what the goal of
the program is, and a brief overview of your strategy in solving the
problem. In other words, this first section should outline the
reasoning you used as you figured out how to get the assignment
completed.
You should include in your assignments the complete source code of the
program which you wrote to solve to the problem.
Please provide a copy of the actual output of your program, as well
as a copy of any input data, or a description of the data if it is
very large or provided as part of the assignment. If there are parts
of the output which are not self-explanatory, please be sure to
annotate them so I can figure out what you are doing.
Each assignment should contain a conclusion, which answers any specific
questions raised in the assignment, as well as reporting on any
interesting findings which you made while you were working on the
assignment. If you feel you've encountered a principle or concept which
has helped you understand things better, please don't hesitate to mention
it, both for your own clarification, and so that I can get a better idea
of how you are approaching the tasks at hand.
The prefered method
for submitting your assignments is to email me (at spector@stat.berkeley.edu),
with a clear indication in the subject line that you are submitting an
assignment for Stat 243. Your submission should have a PDF, OpenOffice
document, or
Word file containing your report. If necessary, you can include a single
archive (zip, rar, tar, etc.) containing any other files
which you feel are relevant.
Your report need not be in any standardized format, but all of the
above information should be included, and you may find it convenient
to organize your work into the four sections described above.
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