NATIONAL CHIAO TUNG UNIVERSITY
INSTITUTE OF STATISTICS
REGRESSION
ANALYSIS
FALL 2013
Instructor: 
GuanHua Huang, Ph.D. 

Office: 423 Joint Education Hall 

Phone: 035131334 

Email: ghuang@stat.nctu.edu.tw 
Class meetings: 
Thursday 13:2016:20 at 406
Joint Education
Hall 
Office hours: 
By
appointment 
Class website: 

Credit: 
Three (3) credits 
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 13:2015:10. The lectures will primarily review and
reinforce major issues. There is a laboratory session on Thursday 15:3016:20.
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 seminartype discussion. Each group is required
to hand in a writeup 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 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.
Students
are expected to have background on undergraduate probability, and mathematical
statistics. Computer programming knowledge on R and/or C/C++ is required.
The course grade will be based on homeworks (25%), writeups 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)
Module 
Topic 
Reading 
0 
Revisiting
means and review of fundamental 

1 
Measures
of association with emphasis on the difference of means 

2 
Basics of
linear regression analysis 

3 
Correlation 

4 
The
Analysis of Variance (ANOVA) Table 

5 
Multiple
regression 

6 
Direct
standardization 

7 
Testing
hypotheses in multiple regression 

8 
Polynomial
regression 

9 
Dummy
variables 

10 
Confounding
and Interaction 

11 
Regression
diagnosis 

12 
Model
selection for investigation of associations 

13 
Rates and
risks 

14 
Some
properties of the odds ratio and the relative risk 

15 
Significance
testing in 2x2 tables 

16 
Confidence
intervals for the odds ratio and the relative risk 

17 
Introduction
to logistic regression 

18 
Maximum
likelihood estimation 

19 
Control
of confounding with logistic regression analysis 

20 
Modeling
interaction effects with logistic regression 

21 
Logistic
regression for contingency tables 

22 
Goodnessoffit
for logistic regression 

23 
Logistic
regression of casecontrol data 

24 
Poisson
regression 

25 
Generalized
linear models 
