NATIONAL CHIAO TUNG UNIVERSITY
INSTITUTE OF STATISTICS
REGRESSION
ANALYSIS
FALL 2013
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Instructor: |
Guan-Hua Huang, Ph.D. |
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Office: 423 Joint Education Hall |
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Phone: 03-513-1334 |
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Email: ghuang@stat.nctu.edu.tw |
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Class meetings: |
Thursday 13:20-16:20 at 406
Joint Education
Hall |
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Office hours: |
By
appointment |
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Class website: |
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Credit: |
Three (3) credits |
COURSE
SUMMARY
The goals of this course are to introduce
regression analysis for continuous and discrete data. Topics include simple and
multiple linear regressions, inferences for regression coefficients,
confounding and interaction, regression diagnostics, logistic regressions, Poisson
regressions, and generalized linear models.
The course consists of lectures and laboratory
sessions. The lectures are given on Thursday 13:20-15:10. The lectures will primarily review and
reinforce major issues. There is a laboratory session on Thursday 15:30-16:20.
The laboratory exercise will be distributed prior to each class, and students
are expected to read each lab exercise at home. Each student will be assigned
to a lab group and discuss the exercise with group members in the lab. At the
end of the lab, there will be a seminar-type discussion. Each group is required
to hand in a write-up of laboratory problems.
The course uses the R software for statistical
computing. Students are expected to be familiar with the usage of the software.
HANDOUTS
AND TEXTBOOKS
Handouts corresponding to each lecture
will be available on the course website before each class. The required
textbooks for this course are
Montgomery, D.C., Peck, E.A., Vining, G.G. (2012). Introduction to Linear Regression Analysis (5th Edition). Wiley.
PREREQUISITES
Students
are expected to have background on undergraduate probability, and mathematical
statistics. Computer programming knowledge on R and/or C/C++ is required.
METHOD
OF STUDENT EVALUATION
The course grade will be based on homeworks (25%), write-ups of lab problems (30%), one midterm
exam (20%),
and one final exam (25%).
COURSE OUTLINE
Readings refer to:
Montgomery, D.C., Peck, E.A., Vining, G.G. (2012). Introduction to Linear
Regression Analysis (5th Edition). Wiley.
(ILRA)
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Module |
Topic |
Reading |
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0 |
Revisiting
means and review of fundamental |
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1 |
Measures
of association with emphasis on the difference of means |
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2 |
Basics of
linear regression analysis |
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3 |
Correlation |
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4 |
The
Analysis of Variance (ANOVA) Table |
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5 |
Multiple
regression |
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6 |
Direct
standardization |
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7 |
Testing
hypotheses in multiple regression |
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8 |
Polynomial
regression |
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9 |
Dummy
variables |
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10 |
Confounding
and Interaction |
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11 |
Regression
diagnosis |
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12 |
Model
selection for investigation of associations |
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13 |
Rates and
risks |
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14 |
Some
properties of the odds ratio and the relative risk |
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15 |
Significance
testing in 2x2 tables |
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16 |
Confidence
intervals for the odds ratio and the relative risk |
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17 |
Introduction
to logistic regression |
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18 |
Maximum
likelihood estimation |
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19 |
Control
of confounding with logistic regression analysis |
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20 |
Modeling
interaction effects with logistic regression |
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21 |
Logistic
regression for contingency tables |
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22 |
Goodness-of-fit
for logistic regression |
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23 |
Logistic
regression of case-control data |
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24 |
Poisson
regression |
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25 |
Generalized
linear models |
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