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Begins
April 6th 2012
Aim
of the Course: This
3-week course will show you
how to use R to create
models for use in
classification and
prediction. You will be
introduced to advanced
graphing methods as needed.
Modeling techniques include
OLS, LAD, and EIV
regression, quantile
regression, and decision
trees (CART). Model
validation is emphasized.
Who Should Take This Course:
Anyone familiar with R
who encounters statistics in
their work and wishes to
program their own procedures
in a convenient,
widely-used, open source
(free) language.
Instructor: Dr. Phillip
Good, former Calloway
Professor of Computer
Science at the University of
Georgia (Fort Valley) and
graduate of the program in
mathematical statistics at
UC Berkeley, is the author
of Introduction to
Statistics via Resampling
Methods and R (Wiley, 2005),
Common Errors in Statistics
(and How to Avoid Them)
(Wiley, 2003 with James
Hardin), Resampling Methods
(Birkhauser, 2nd ed, 2005),
Permutation, Parametric, and
Bootstrap Tests of
Hypotheses (Springer, 3rd
ed, 2004), Manager's Guide
to Design and Conduct of
Clinical Trials (Wiley, 2nd
ed. 2005), and Applying
Statistics in the Courtroom
(CRC, 2001). He has given
tutorials at the Joint
Statistical Meetings (U.S.)
and Deming Conference,
lectured in Australia,
Belgium, Bulgaria, France,
Holland, Ireland, Slovenia,
and Spain, and was twice a
traveling lecturer for the
American Statistical
Association. This is his
seventh (7th) year of
providing on-line
interactive courses.
Prerequisite:
Prerequisite:
You should have familiarity
with basic statistical
concepts or the
equivalent. You should
have some familiarity with R
as in our course Introduction
to R. (And will
get a $50 discount if you
sign up for
"Introduction" as
well as "Modeling with
R".)
Organization
of the Course:
The course takes place over
the Internet. During
each course week, you
participate at times of your
own choosing - there are no
set times when you must be
online. Course participants
will be given an alias and
access to a private bulletin
board that serves as a forum
for discussion of ideas,
problem solving, and
interaction with the
instructor. The course is
scheduled to take place over
three weeks. Estimated
weekly time requirements for
this course - an hour and
half for the lecture, an
hour and a half for
preparation, and another
three hours for homework and
review.
At the beginning of
each week, participants
receive the relevant
material, in addition to
answers to exercises from
the previous session. During
the week, participants are
expected to go over the
course materials and work
through exercises.
Discussion among
participants is encouraged.
The instructor will provide
answers and comments.
Optional
Text: Participants may wish
to purchase and make use of Introduction
to Statistics via Resampling
Methods and R/S-Plus
(Wiley, 2005; Chapter 7).
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Session
I: Linear Regression and
Advanced Graphics
Ordinary Least Squares
Interpretation of Output
Plotting residuals,
Plots with multiple
lines, Side-by-side
plots
Stepwise Regression
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Session
2: Alternatives to OLS
Regression
Deming Regression
LAD
Quantile Regression
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Session
3: Decision Trees
Construction, Pruning,
Prediction
Validation Methods
Trees vs. Regression
Which Modeling Technique?
One
week is allowed after
Session 3 to give
participants the opportunity
to clarify any questions
arising from this or
previous sessions.
Full
cost of course is
$229. Early-bird
discount may apply. Students,
faculty and research workers
at academic institutions are
eligible for a discount of
$50. Just send an
email to courses@statcourse.com
from your academic email
account to receive a
discount coupon.
Immediately
after your payment is
credited, you will receive
an email giving you a
password, sign up
instructions, and the web
address (URL) of the course
material. Note that
you will not be able to
access this address until
the start date of the
course.
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