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Regression Analysis

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 9:00-11:00. 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.

 

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  
Goodness-of-t for logistic regression ILRA 13.2.4, 13.2.5
Logistic regression of case-control 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
Homeworks
25%
Write-ups of lab problems 30%
Midterm Exam
20%
Final exam (25%) 25%

 

 
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