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Statistics 235 –
Modern Data Analysis Methods |
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Instructor: |
Dan Gillen Assistant Professor Department of Statistics Office: 2226 Donald Bren Hall (DBH) Telephone: 949.824.9862 E-mail: dgillen@uci.edu WebPage: http://www.ics.uci.edu/~dgillen Office hours: Tuesday
3:30-4:30 & Wednesday11:00-12:00, and by appointment |
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Lectures: |
Tuesday and Thursday, 2:00-3:20, Room: CS 243 |
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Discussion: |
Friday, 2:00-2:50, Room: DBH 1423 |
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Prerequisites: |
Graduate
standing in Statistics or Statistics 120C or equivalent. or permission of
instructor |
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Description: |
This course will introduce a variety of modern tools
for data analysis. The course
will emphasize the use of computational and resampling techniques for data
analyses wherein the data do not conform to the standard toolbox of
regression models and/or the complexity of the modeling problem threatens the
validity of standard methods.
Topics to be considered will be bootstrap standard error estimation,
smoothing for visualization of data, and prediction. 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: |
Venables
WN and Ripley BD. (2003). Modern Applied Statistics with S (4th edition). Springer. |
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Supplementary
texts: |
á
Efron
B and Tibshirani RJ. (1993). An Introduction to the Bootstrap. London: Chapman &
Hall. á
Hastie
T, Tibshirani R, and Friedman J. (2001). The Elements of Statistical Learning. Springer. |
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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 5 homework assignments.
Assignments will typically be due 1 to 1.5-2 weeks from the day they
are handed out. |
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Midterm
Exam: |
Tentatively scheduled for Thursday, Nov 1st. The exam will be in-class (closed-book,
closed-note), and will cover material through the Thursday, October 25th
lecture. |
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Final
Exam: |
The final exam is scheduled for Thursday, Dec 13th. The final exam will be a take-home,
handed out on Thursday, Nov 29th and due on Thursday, Dec 13th
by 5pm. |
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Grading: |
Homework: Midterm: Final: |
30% 30% 40% |
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Course
Links: |
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