Objective
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.
Outline
Topic

Supplementary

A review of basic statistical concepts 
ILRA APPENDIX C.1, and an introductory statistics book 
Measures of association with emphasis on the difference of means 

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

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 
Variable selection and model building 
ILRA Chapter 10 
Relative risk, odds ratio and significance testing for 2x2 tables 
ILRA 13.2.1, 13.2.2, 13.2.3, 13.2.4 
Introduction to logistic regression 

Logistic regression for contingency tables 

Goodnessoft for logistic regression 
ILRA 13.2.4, 13.2.5 
Logistic regression of casecontrol data and conditional logistic regression 

Analysis of polytomous data 
ILRA 13.2.7 
Generalized linear models 
ILRA 13.4 
Poisson regression 
ILRA 13.3 
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. (ILRA)
Grading
Item 
Ratio 

25% 
Writeups of lab problems 
30% 

20% 
Final exam (25%) 
25% 
