Regression Analysis -104 Academic Year
This course is provided by the NYCU Institute of Statistic .
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.
Textbook：
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)
For perfect learning results, please buy textbooks!
| Instructor(s) | Institute of Statistic Prof. Guan-Hua Huang |
|---|---|
| Course Credits | 3 Credits |
| Academic Year | 104 Academic Year |
| Level | Graduate Student |
| Prior Knowledge | Students are expected to have background on undergraduate probability, and mathematical statistics. Computer programming knowledge on R and/or C/C++ is required. |
| Related Resources | Course Video Course Syllabus Course Calendar |
| Week | Course Content | Course Video | Course Download |
|---|---|---|---|
| Introduction | Watch Online | MP4 Download | |
| Lecture 1 | Lecture 1-1: A review of basic statistical concepts | Watch Online | MP4 Download |
| Lecture 1-2: Measures of association with emphasis on the di erence of means | Watch Online | MP4 Download | |
| Lecture 2 | Lecture 2: Basics of linear regression analysis | Watch Online | MP4 Download |
| Lab 2 同學報告: 謝念廷 陳柏魁 許凱璋 席瑋辰 陶冠蘭 劉冠妤同學 | Watch Online | MP4 Download | |
| Lecture 4 | Lecture 4: Correlation | Watch Online | MP4 Download |
| Lab 4 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學 | Watch Online | MP4 Download | |
| Lecture 5 | Lecture 5-1: Analysis of variance (ANOVA) table and prediction of y | Watch Online | MP4 Download |
| Lecture 5-2: Basics of multiple linear regression | Watch Online | MP4 Download | |
| Lab 5 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學 | Watch Online | MP4 Download | |
| Lecture 6 | Lecture 6-1: Hypothesis testing in multiple regression | Watch Online | MP4 Download |
| Lecture 6-2: Polynomial terms and dummy variables | Watch Online | MP4 Download | |
| Lab 6 同學報告: 顏天保、劉又齊、藍玉朋、曾郁翔、唐心誠、林志豪 同學 | Watch Online | MP4 Download | |
| Lecture 7 | Lecture 7: Interaction and confounding | Watch Online | MP4 Download |
| Lecture 7 補充: Confounding and interaction in epidemiology | Watch Online | MP4 Download | |
| Lab 7 同學報告: 黃郁豪、梁思婕、張登凱、何杰翰、周佳瑜同學 | Watch Online | MP4 Download | |
| Lecture 8 | Lecture 8-1: Regression diagnosis | Watch Online | MP4 Download |
| Lecture 8-2: Variable selection and model building | Lecture 8-2: Variable selection and model building | Watch Online | MP4 Download |
| Lab 9 同學報告: 許凱璋同學 | Watch Online | MP4 Download | |
| Lab 9 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學 | Watch Online | MP4 Download | |
| Lecture 11 | Lecture 11: Relative risk, odds ratio and signi cance testing for 2*2 tables | Watch Online | MP4 Download |
| Lecture 12 | Lecture 12: Introduction to logistic regression | Watch Online | MP4 Download |
| Lab 12 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學 | Watch Online | MP4 Download | |
| Lecture 13 | Lecture 13-1: Logistic regression for contingency tables | Watch Online | MP4 Download |
| Lecture 13-2: Goodness-of- t for logistic regression | Watch Online | MP4 Download | |
| Lab 13 同學報告: 顏天保、劉又齊、藍玉朋、曾郁翔、唐心誠、林志豪同學 | Watch Online | MP4 Download | |
| Lecture 14 | Lecture 14: Logistic regression for case-control data and conditional logistic regression | Watch Online | MP4 Download |
| Lab 14 同學報告: 何杰翰、梁思婕、張登凱、周家瑜、黃郁豪同學 | Watch Online | MP4 Download | |
| Lecture 15 | Lecture 15: Analysis of polytomous data | Watch Online | MP4 Download |
| Lab 15 同學報告: 謝念廷 陳柏魁 許凱璋 席瑋辰 陶冠蘭 劉冠妤同學 | Watch Online | MP4 Download | |
| Lecture 16 | Lecture 16: Poisson regression and log-linear model | Watch Online | MP4 Download |
| Lab 16 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學 | Watch Online | MP4 Download | |
| Lecture 17 | Lecture 17: Generalized linear models | Watch Online | MP4 Download |
| Lab 17 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學 | Watch Online | MP4 Download |
Course Objectives
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.
Course Chapter
| 單元主題 | 內容綱要 |
| 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 |
Course Book List
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)
本課程行事曆提供課程進度與考試資訊。
單元 | 單元主題 | 閱讀資料 |
| 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 | ILRA 13.2.1, 13.2.2, 13.2.3, 13.2.4 |
| 13 | Introduction to logistic regression | |
| 14 | Logistic regression for contingency tables | |
| 15 | Goodness-of-t for logistic regression | ILRA 13.2.4, 13.2.5 |
| 16 | Logistic regression of case-control 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 |