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課程簡介 Course Introduction
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開課年度學期 Year / Term
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114 學年度 第 1 學期
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開課班級 Department
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教育學系教育數位評量與數據分析碩士班 教育學系數位評量教管碩一碩二合選
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授課方式 Instructional Method
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課堂教學 、 中文
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課程電腦代號 Course Reference Number
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112041
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課程名稱(中文) Course Title(Chinese)
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迴歸分析
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課程名稱(英文) Course Title(English)
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Regression Analysis
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學分數/時數 Credit Hours
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3 /
3
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必(選)修 Requirement / Elective Course
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選修
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授課老師 Instructor
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鄒慧英
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助教 Teaching Assistant
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上課時間 Meeting Time
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星期四，節次3、4、5
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上課教室 Classroom
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ZB304
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Office Hours
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鄒慧英：4444/89AB
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| 獲獎及補助情形 Awards and Grants |
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| 聯合國永續發展目標 (SDGs跨域類別) Sustainable Development Goals, SDGs |
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課程目標 Learning Objectives
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1.Stating the required assumptions, describing the procedures for estimating important parameters, explaining how to make and interpret inferences about these parameters, and providing examples illustrating the use of the techniques of multiple regression analysis.
2.Describing the statistical test appropriate for an overall test, a test for addition of a single variable, and a test for addition of a group of variables.
3.Describing the essential features of regression by multiple correlations, partial correlations, and multiple-partial correlations.
4.Describing two concepts-confounding and interaction relevant to quantify the relationship of one or more independent variables to a dependent variable.
5.Providing a general overview of regression diagnostics, including methods for analyzing residuals, assessing the influence of outliers, and assessing collinearity.
6.Describing available techniques to deal with the polynomial model, such as centering and the use of orthogonal polynomials.
7.Applying dummy variables: comparing several regression equations by use of a single multiple regression model.
8.Describing the strategies for selecting the best model when the primary goal of analysis is prediction, also a strategy for modeling in situations where the validity of the estimates of one or more regression coefficients is of primary importance.
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先修 ( 前置 ) 課程 Prerequisite
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| 彈性教學規劃 Flexible Teaching/Planning Schedules |
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課程大綱 Course Syllabus
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| 週次 Week |
課程單元大綱 Unit |
教學方式 Instructional Method/Style/Teaching Style |
參考資料或相關作業 References or Related Materials |
評量方式 Grading |
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1
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Introduction to Regression Anlaysis
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09/11/2025
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KKMN, Chapters 4~6; KNNL, Chapter 2
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2
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Multiple regression analysis
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09/18/2025
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KKMN, Chapter 8; KNNL, Chapter 6~7.1
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3
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Testing hypotheses in multiple regression
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09/25/2025
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KKMN, Chapter 9; KNNL, Chapter 7.2~7.3
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4
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Testing hypotheses in multiple regression
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10/02/2025
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KKMN, Chapter 9; KNNL, Chapter 7.2~7.3
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5
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Correlations: Multiple, partial, and semipartial
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10/09/2025
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KKMN, Chapter 10; KNNL, Chapter 7.4
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6
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SPSS / R practice
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10/16/2025
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7
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SPSS / R practice
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10/23/2025
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8
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Midterm Exam
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10/30/2025
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9
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Confounding and interaction in regression
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11/06/2025
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KKMR, Chapter 11
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10
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Dummy variables in regression
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11/13/2025
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KKMR, Chapter 12
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11
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Regression diagnostics
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11/20/2025
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KKMR, Chapter 14; KNNL, Chapter 10
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12
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Selecting the best regression equation
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11/27/2025
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KKMR, Chapter 16; KNNL, Chapter 9
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13
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SPSS / R practice
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12/04/2025
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14
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SPSS / R practice
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12/11/2025
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15
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Final Exam
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12/18/2025
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16
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Holiday off
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12/25/2025
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17
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Holiday off
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01/01/2026
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18
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Midterm & Final Exam Discussion
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01/08/2026
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單一課程對應校能力指標程度 The Degree to Which Single Course Corresponds to School Competence
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| 編號 No. |
校核心能力 School Core Competencies |
符合程度 Degree of conformity |
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單一課程對應系能力指標程度 The Degree to Which Single Course Corresponds to Department Competence
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| 編號 No. |
類別 Category |
系核心能力 Department Core Competencies |
符合程度 Degree of conformity |
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01
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系所
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能分析與解釋量化與類別資料
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0
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02
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系所
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能批判量化研究設計
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0
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03
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系所
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能創新評量工具（碩）
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0
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04
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系所
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能整合科技進行測驗創新議題探討
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0
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05
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系所
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能發表測驗統計議題的論文
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0
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06
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系所
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能提供基礎水準測驗與統計問題的諮詢服務（碩）
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0
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單一課程對應院能力指標程度 The Degree to Which Single Course Corresponds to College Competence
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| 編號 No. |
院核心能力 College Core Competencies |
符合程度 Degree of conformity |
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教科書或參考用書 Textbooks or Reference Books
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館藏書名 Library Books
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備註 Remarks
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1.Kleinbaum, Kupper, Nizam, & Rosenberg(2013). Applied Regression Analysis and Other Multivariable Methods (5th). Cengage Learning.
2.Kutner, Nachtsheim, Neter, & Li (2005). Applied Linear Statistical Models (5th ed.). McGraw Hill.
3.Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and Prediction (3rd). Thomson learning, Inc.
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※請尊重智慧財產權，不得非法影印教科書※
※ Please respect intellectual property rights and do not illegally photocopy textbooks. ※
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教學方法 Teaching Method
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教學方法 Teaching Method
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百分比 Percentage
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講述
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50 %
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討論
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20 %
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實作練習
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30 %
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| 總和 Total |
100 % |
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成績評量方式 Grading
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| 評量方式 Grading |
百分比 Percentage |
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作業撰寫
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40 %
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期中考
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25 %
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期末考
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25 %
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口試
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10 %
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| 總和 Total |
100 % |
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課程大綱補充資料 Supplementary Material of Course Syllabus
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