STAT 515: Theory and Applications of Nonlinear Models
Class meets: MWF 9:00 am  9:50 am in: PHY 39
Instructor: Ranjan Maitra (Ronjoan Moitro)

Office:123 Snedecor Hall
 Phone: (515)2947757
 Email:maitra
 Office Hours: 1011 am, or by appointment
Course Prerequisites:
 Stat 511
 Stat 543 or Stat 447
Grading Scheme:
 Homeworks: 30%
 Projects: 30%
 Midterm: 20%
 Final: 20%
Course Description: This course is designed for Ph. D.level and
advanced Masterslevel students. In this course, we will study extensions of
linear models and into nonlinear models. We shall do this by developing
theory and methodology of generalized linear models. We will extend our
development to cover additive models and generalized additive
models. Finally, we will cover the completely nonparametric regression
and classification tree methodology. We will also apply our
understanding of linear and nonlinear models into analyzing datasets
derived from realworld applications. We will also study how to
communicate our results and conclusions to nonstatistical audiences.
Textbook: Because all the material is spread out over
three to four books, there are no required textbooks for this
class. However, the following books are recommended:
 Generalized Linear Models by P. McCullagh and
J. A. Nelder, Chapman & Hall, 1989.
 Generalized Additive Models by T. J. Hastie and
R. J. Tibshirani, Chapman & Hall, 1990.
 Classification and Regression Trees by L. Breiman,
J. Friedman, R. Olshen and C. Stone, Chapman & Hall, 1984.
 Statistical Models in S by J. M. Chambers and
T. J. Hastie(Ed.), Wadsworth & Brooks/Cole, 1991.
 Modern Applied Statistics in S by W. N. Venables and
B. D. Ripley, SpringerVerlag, 2002.
Statistical Software:
The statistical software used throughout this class will be R. R is very
similar to Splus but comes under the GNU Public License. It is
a comprehensive statistical software package freely available from
http://www.rproject.org/. R is developed by a team of
international researchers and operates under the GNU Public License
and is free. It is very similar, though not the exact same software as
the commercially available Splus. Most commands in Splus work with
R. All lab machines running Windows and Linux have R installed. Since the
software is freely available, you may download it from the above web
site and use it on your home computer. You may use either the Windows
version or the Unix/Linux version. Please note that your
installation of R is at your own risk, though the department systems
administrators can perhaps help. Also, you may use Splus, in lieu
of R, though the latter provides for more flexibility. Please note that
SAS is often not an option for many of the topics we shall be
covering.
Homeworks: Homeworks will be handed out every two weeks. This
will mostly consist of applying and exploring the concepts learnt in class.
A considerable part of the homework will involve computer work.
Examinations:
There will be one midterm examination whose date wil be announced in
consultation with the students. The scores in this exam will contribute to
20% of your final grade. The exam will have an inclass theory component
and a takehome data analysis component. There will be a takehome final
examination, worth 20% of the final grade and on the lines of a project,
due on the last day of classes. Each student will have an oral defense
of his/her project report on Wednesday, December 17, 2003 between 7:30
and 9:30 am (exact schedules to be announced later).
Projects:
There will be three data analysis projects assigned during this
semester. The goal in each of these projects will be to learn how to
analyze a given realworld data set, to understand the scientific or
research question being asked of the statistician, to formulate and
provide a reasonable statistical approach to the problem, and to
present a wellwritten report to a nonstatistical audience. The three
projects are structured as follows: In the first project, we will have
extensive discussions, led by the instructor, on how to proceed with
the given dataset. For the second project, I will adopt a slightly
more handsfree approach, where the students will discuss a reasonable
approaches for the project. I will moderate the discussion as well as
answer questions. Each of the first two projects is worth 15% of the
final grade. The third and final project, together with the oral
defense, is worth 20% of your grade. In this final project, you will be
prohibited from discussing the matter with anyone excluding the
instructor who may choose not to
answer your questions. This final project will be the same as your final
exam. Please note that for all three projects, you
are individually responsible for performing the statistical analysis,
and for writing the final report . Each report will be graded on a
15point scale, with 5 points each for (a) the validity of the
statistical analysis, (b) the scientific component, and
(c) quality of the writeup in communicating the results to a
nonstatistical audience.
Course Homepage: The course homepage will be located on
the WWW at
http://www.public.iastate.edu/~maitra/stat515/fall2003.html.
I will try and keep this homepage as upto date as possible. However,
you are still responsible for any announcements made in class.