NATIONAL CHIAO TUNG UNIVERSITY
INSTITUTE OF STATISTICS
REGRESSION
ANALYSIS
FALL 2015
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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: |
Tuesday 9:00-12:00 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 Tuesday 9:00-11:00. The
lectures will primarily review and reinforce major issues. There is a
laboratory session on Tuesday 11:10-12:00. 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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1 |
A review
of basic statistical concepts |
ILRA APPENDIX
C.1, and an introductory statistics book |
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2 |
Measures
of association with emphasis on the difference of means |
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3 |
Basics of
linear regression analysis |
ILRA 2.1, 2.2, 2.3 except 2.3.3, 2.4, 2.11 |
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4 |
Correlation |
ILRA 2.6, 2.12.2 |
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5 |
Analysis
of variance (ANOVA) table and prediction of y |
ILRA 2.3.3, 2.5 |
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6 |
Basics of
multiple linear regression |
ILRA 3.1, 3.2 |
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7 |
Hypothesis
testing in multiple regression |
ILRA 3.3 |
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8 |
Polynomial
terms and dummy variables |
ILRA 3.10, 7.1, 7.2.1, 7.2.2,
8.1, 8.2 |
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9 |
Interaction
and confounding |
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10 |
Regression
diagnosis |
ILRA 4.1, 4.2,
4.4, 5.1, 5.2, 5.3, 5.4, 5.5, 6.1, 6.2, 6.3 |
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11 |
Variable
selection and model building |
ILRA Chapter 10 |
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12 |
Relative
risk, odds ratio and significance testing for 2x2 tables |
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13 |
Introduction
to logistic regression |
ILRA 13.2.1, 13.2.2, 13.2.3,
13.2.4 |
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14 |
Logistic
regression for contingency tables |
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15 |
Goodness-of- |
ILRA 13.2.4, 13.2.5 |
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16 |
Logistic
regression for case-control data and conditional logistic regression |
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17 |
Analysis
of polytomous data |
ILRA 13.2.7 |
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18 |
Generalized
linear models |
ILRA 13.4 |
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19 |
Poisson
regression |
ILRA 13.3 |