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. (本課程為英文授課。每週教學內容與課程進度，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 |
|---|---|---|
|
Week |
Topic |
Content and Reading Assignment |
Teaching Activities and Homework |
|
1 |
Introduction; Simple Linear Regression |
Course Introduction, Chapter 1 |
Lecture |
|
2 |
Simple Linear Regression; Inferences in Regression Analysis |
Chapters 1 and 2 |
Lecture, HW |
|
3 |
Inferences in Regression Analysis; Correlation Analysis |
Chapter 2 |
Lecture, HW |
|
4 |
Model Diagnostics |
Chapter 3 |
Lecture |
|
5 |
Model Diagnostics; Remedial Measures |
Chapters 1-3 |
Lecture, HW |
|
6 |
Remedial Measures |
Chapters 1-3 |
Lecture, HW |
|
7 |
National Holiday |
|
|
|
8 |
Matrix Approach to Regression |
Chapter 5 |
Lecture, HW |
|
9 |
Midterm Exam |
Chapters 1-5 |
|
|
10 |
Multiple Regression (I) |
Chapters 6 and 7 |
Lecture |
|
11 |
Multiple Regression (I) |
Chapters 6 and 7 |
Lecture, HW |
|
12 |
Multiple Regression (II) |
Chapters 7, 8, and 11 |
Lecture, HW |
|
13 |
Model Building (I); Model Selection and Validation |
Chapter 9 |
Lecture, HW |
|
14 |
Model Diagnostics |
Chapter 10 |
Lecture, HW |
|
15 |
Model Building (II); Remedial Measures |
Chapter 11 |
Lecture |
|
16 |
Remedial Measures; Logistic Regression (optional) |
Chapters 11 and 14 (optional) |
Lecture |
|
17 |
Final Exam |
Chapters 6-11 |
|
|
18 |
Review; Data analysis using R (or SAS) |
Chapters 1-11 |
Discussion, Self-Learning |
In-Class Attendance and Participation 10%, Homework (Data Analysis) 20%, Midterm Exam 35%, Final Exam 35%
Note: The course schedule and requirements are subject to change based on the actual progress. (每週教學內容與課程進度，會依實際授課狀況做調整!)
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 Note: The course schedule and requirements are subject to change based on the actual progress. (每週教學內容與課程進度，會依實際授課狀況做調整!)