Type of Credit: Required
Credit(s)
Number of Students
The course will provide the fundamental methods and practical application skills in regression analysis and its generalizations. The topics includes: simple linear regression, multiple regression, inferences, model diagnostics and remedial measures, regression models for quantitative and qualitative predictors and logistic regression. The statistical software R (or SAS) will be used to demonstrate the real data analysis. Note that the course will be lectured in English, and the "Course Schedule & Requirements" provided below are subject to change depending on the actual progress of the class. (註：本課程為英語授課。每週課程進度與作業要求，會依實際授課狀況做調整)
能力項目說明
The course provides students the underlying foundations of regression modeling with applications. Students successfully completing this course should be able to: 1) understand basic mathematical concepts and principles of the linear regression model and its limitations, 2) diagnose and apply modeling concepts to some real data problems in regression, 3) familiarize the groundwork and correction tools of model inaptness, as well as their applications to practical problems, and 4) conduct the regression data analysis using statistical programs.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
學生學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
||||
|
1 |
Introduction; Simple Linear Regression |
Course Introduction, Chapter 1 |
Lecture |
3 |
2 |
|
2 |
Simple Linear Regression; Inferences in Regression Analysis |
Chapters 1 and 2 |
Lecture, HW |
3 |
4 |
|
3 |
Inferences in Regression Analysis; Correlation Analysis |
Chapter 2 |
Lecture, HW |
3 |
4 |
|
4 |
Model Diagnostics |
Chapter 3 |
Lecture |
3 |
5 |
|
5 |
Model Diagnostics |
Chapter 3 |
Lecture, HW |
3 |
4 |
|
6 |
Remedial Measures |
Chapters 1-3 |
Lecture, Quiz |
3 |
4 |
|
7 |
Matrix Approach to Regression |
Chapter 5 |
Lecture, HW |
3 |
4 |
|
8 |
Review; Data analysis using R (or SAS) |
Chapters 1-5 |
Discussion, Self-Learning |
3 |
5 |
|
9 |
Midterm Exam |
Chapters 1-5 |
|
0 |
8 |
|
10 |
Multiple Regression (I) |
Chapters 6 and 7 |
Lecture |
3 |
4 |
|
11 |
Multiple Regression (II) |
Chapters 6 and 7 |
Lecture, HW |
3 |
4 |
|
12 |
Multiple Regression (II) |
Chapters 7, 8, and 11 |
Lecture, HW |
3 |
4 |
|
13 |
Model Building (I); Model Selection and Validation |
Chapter 9 |
Lecture, HW |
3 |
4 |
|
14 |
Model Diagnostics |
Chapter 10 |
Lecture, Quiz |
3 |
5 |
|
15 |
Model Building (II); Remedial Measures |
Chapter 11 |
Lecture |
3 |
4 |
|
16 |
Logistic Regression |
Chapter 14 (optional if time permitted) |
Lecture |
3 |
4 |
|
17 |
Review; Data analysis using R (or SAS) |
Chapters 6-11 |
Discussion, Self-Learning |
3 |
5 |
|
18 |
Final Exam |
Chapters 6-11 |
|
0 |
8 |
In-class Attendance 10%, Quiz 30%, Midterm Exam 30%, Final Exam 30%
Required Textbook: Michael H. Kutner et al. (2019). Applied Linear Statistical Models: Applied Linear Regression Models (5th edition), Mcgraw-Hill Inc. (華泰文化).
Some References:
Moodle: http://moodle.nccu.edu.tw/ ALSM: https://cran.r-project.org/web/packages/ALSM/index.html