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Statistics 211 –
Statistical Methods II |
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!!!Click here for course announcements!!! |
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Instructor: |
Dan Gillen Assistant Professor Department of Statistics Office: 2226 Bren Hall Telephone: 949.824.9862 E-mail: dgillen@uci.edu WebPage: http://www.ics.uci.edu/~dgillen Office hours: Wednesday
11:00-12:00, Thursday 11:00-12:00, and by appointment |
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Lectures: |
Tuesday and Thursday, 9:30-10:50, Room: ICS 259 |
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Discussion: |
Friday, 3:00-3:50, Room: ICS 253 |
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Prerequisites: |
Statistics 210
(Statistical Methods I) or equivalent; or permission of instructor |
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Description: |
This course will provide an introduction to the
principles of generalized regression models, with an emphasis on categorical
data models. Categorical data occurs extensively in both observational and
experimental studies, as well as in industrial applications. The course will
focus on both theory and application of methods for data analysis. Problems
will be motivated from a scientific perspective. |
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Required
Text: |
McCullagh P. and Nelder, J. (1989). Generalized Linear Models, Second
Edition. Chapman & Hall/CRC. |
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Recommended
texts: |
(On reserve in the science library) á
Agresti,
A. (1996). Categorical Data
Analysis, Wiley-Interscience. á
Casella
G. and Berger R. (2001). Statistical
Inference, Second Edition. Duxbury Press. á
Lindsey,
J. (1997). Applying
Generalized Linear Models.
Springer-Verlag. á
Kutner,
M., Nachtsheim, C., Neter, J., and Li, W. (2005) Applied Linear Statistical Models. McGraw Hill Irwin. |
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Software/Computing: |
Examples that are presented in class are primarily
done using the R statistical package, and it is recommended that R be used
for homework assignments and exams. R is free software which can be
downloaded from the web at http://www.r-project.org/. R can be installed onto Windows, Mac,
or Unix machines. In addition, the student computer lab in CS 364 will have R
loaded onto all Windows machines.
The website also offers many tutorials regarding the use of R. If you wish, it is possible to use
other commercially available software packages such as Splus, Stata, Matlab,
or SAS. |
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Homework: |
There will be
a total of 8 homework assignments.
Assignments will typically be due 1 to 1.5 weeks from the day they are
handed out. |
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Midterm
Exam: |
Tentatively scheduled for Tuesday, February 19th.
The exam will be in-class (closed-book, closed-note), and will cover material through the
Tuesday, February 12th lecture. |
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Final
Exam: |
The final exam is scheduled for Thursday, March 20th. The final exam will be take-home and
will consist of two portions.
The first portion of the exam will consist of short answer questions similar
to a comprehensive homework assignment.
The second portion of the exam will be a complete statistical analysis
and report pertaining to a particular scientific question. The final exam will be handed out on
Tuesday, March 11th and due on Thursday March 20th by
3:30pm. |
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Grading: |
Homework: Midterm: Final: |
30% 30% 40% |
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Course
Links: |
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Home á
Syllabus |
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