NATIONAL CHIAO TUNG UNIVERSITY
INSTITUTE OF STATISTICS
REGRESSION ANALYSIS
FALL 2016
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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: |
Thursday 13:20-16:20 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 Thursday 13:20-15:10. The lectures will primarily review and reinforce major issues. There is a laboratory session on Thursday 15:30-16:20. 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 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 |