政大教學大綱
10/28/24, 10:01 AM
教學大綱 Syllabus
科目名稱：迴歸分析（一）
Course Name:
Regression Analysis (I)
 學年學期：112-2
課程簡介
Spring Semester, 2024
Course Description
 科目代碼：304008001
Course No.304008001
3.0 80

學分數
修別：
必
Credit(s)
Type of
Credit:
Required
課程資料
Course Details
開課單位：統計二
Course Department:Statistics/B/2
授課老師：陳怡如
Instructor: CHEN YI-JU
預收人
數
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)
核心能力分析圖
Core Competence Analysis Chart
先修科目：無
Prerequisite(N/A)
雷達圖
上課時間：四234
Session: thu09-12
A
5
4
3
2
E
B
1
0
D
C
老師
https://newdoc.nccu.edu.tw/teaschm/1122/schmPrv.jsp-yy=112&smt=2&num=304008&gop=00&s=1.html
學生
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能力項目說明
A. 具備基礎數理能力、 商業分析創新思考能力與熟悉應
用具全球視野的統計方法 (To equip with fundamental
mathematical abilities, innovation thinking ability in
business analytics, and to be familiar with statistical
methods of global vision)
B. 具備道德、社會責任與全球永續發展議題之知能 (To
equip with knowledge of ethics, social responsibility, and
sustainable development goals (SDGs))
C. 具備溝通技巧、闡釋分析結果與進行決策之判斷(To
equip with communication skills, explain analysis results
and to make judgment on decisions)
D. 熟悉統計方法之應用及實務、統計軟體操作與程式設計
與熟悉運用基礎商業管理知識 (To be familiar with
application and practices of statistical methods,
implementation of statistical software and
programming, and fundamental business management
knowledge)
E. 具備團隊合作與領導能力 (To equip with teamwork
and leadership ability)
課程目標與學習
成效
Course Objectives & Learning
Outcomes
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.
每周課程進度與作業要求
Course Schedule & Requirements
教學週次
彈性補充教學週次
Flexible Supplemental Instruction Week
彈性補充教學類別
Flexible Supplemental Instruction Type
16+2週
第8週
Week 8
完成指定課後作業或作品
Completion of designated after-course
assignment or work
第 17 週
Week 17
完成指定課後作業或作品
Completion of designated after-course
assignment or work
Course Week
16+2 weeks
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學生學習投入時間
週次
課程主題
Week
Topic
1
Introduction;
教學活動與作
課程內容與指定閱讀
業
Student workload
expectation
Content and Reading
Teaching
課堂講授 課程前後
Assignment
Activities and
In-class Outside-ofHomework
Hours class Hours
Course Introduction,
Chapter 1
Lecture
3
2
2
Simple Linear Regression;
Inferences in Regression Chapters 1 and 2
Analysis
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
Chapters 1-5
R (or SAS)
Discussion, SelfLearning
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
Chapter 9
Selection and Validation
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
Lecture
time permitted)
3
4
17
Review; Data analysis using
Chapters 6-11
R (or SAS)
3
5
18
Final Exam
0
8
Simple Linear Regression
Discussion, SelfLearning
Chapters 6-11
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授課方式
Teaching Approach
80%
講述
Lecture
評量工具與策
略、評分標準成
效
0%
0%
10% 10%
討論
小組
活動
數位
學習
Discussion
Group
activity
Elearning
其
他：
Others:
In-class Attendance 10%, Quiz 30%, Midterm Exam 30%, Final
Exam 30%
Evaluation Criteria
指定/參考書目
Textbook & References
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:
1. Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining
(2021). Introduction to Linear Regression Analysis (6th Edition).
2. John Fox and Sanford Weisberg (2018). An R Companion to
Applied Regression (3rd Edition), SAGE Publications, Inc.
已申請之圖書館指定參考書目
課程相關連結
Course Related Links
Moodle: http://moodle.nccu.edu.tw/
ALSM: https://cran.r-project.org/web/packages/ALSM/index.html

課程附件
Course Attachments
https://newdoc.nccu.edu.tw/teaschm/1122/schmPrv.jsp-yy=112&smt=2&num=304008&gop=00&s=1.html
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課程進行中，使用智
慧型手機、平板等隨
身設備
需經教師同意始得使用
Approval
To Use Smart Devices During the
Class
By appointment.
授課教師Office
Hours及地點
Office Hours & Office Location
To be announced.
教學助理基本資料
Teaching Assistant Information
https://newdoc.nccu.edu.tw/teaschm/1122/schmPrv.jsp-yy=112&smt=2&num=304008&gop=00&s=1.html
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