NATIONAL CHIAO TUNG UNIVERSITY
INSTITUTE OF STATISTICS
REGRESSION
ANALYSIS
FALL 2014
Instructor: 
GuanHua Huang, Ph.D. 

Office: 423 Joint Education Hall 

Phone: 035131334 

Email: ghuang@stat.nctu.edu.tw 
Class meetings: 
Tuesday 9:0012:00 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 Tuesday 9:0011:00. The
lectures will primarily review and reinforce major issues. There is a
laboratory session on Tuesday 11:1012:00. 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 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 
1 
A review
of basic statistical concepts 
ILRA APPENDIX
C.1, and an introductory statistics book 
2 
Measures
of association with emphasis on the difference of means 

3 
Basics of
linear regression analysis 
ILRA 2.1, 2.2, 2.3 except 2.3.3, 2.4, 2.11 
4 
Correlation 
ILRA 2.6, 2.12.2 
5 
Analysis
of variance (ANOVA) table and prediction of y 
ILRA 2.3.3, 2.5 
6 
Basics of
multiple linear regression 
ILRA 3.1, 3.2 
7 
Hypothesis
testing in multiple regression 
ILRA 3.3 
8 
Polynomial
terms and dummy variables 
ILRA 3.10, 7.1, 7.2.1, 7.2.2,
8.1, 8.2 
9 
Interaction
and confounding 

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 
11 
Variable
selection and model building 
ILRA Chapter 10 
12 
Relative
risk, odds ratio and significance testing for 2x2 tables 

13 
Introduction
to logistic regression 
ILRA 13.2.1, 13.2.2, 13.2.3,
13.2.4 
14 
Logistic
regression for contingency tables 

15 
Goodnessof 
ILRA 13.2.4, 13.2.5 
16 
Logistic
regression of casecontrol data and conditional
logistic regression 

17 
Analysis
of polytomous data 
ILRA 13.2.7 
18 
Generalized
linear models 
ILRA 13.4 
19 
Poisson
regression 
ILRA 13.3 