教學大綱 Syllabus

科目名稱：應用迴歸分析

Course Name: Applied Regression Analysis
  • 學年學期：112-1 Fall Semester, 2023
  • 科目代碼：354745001 Course No.354745001

修別：選

Type of Credit: Elective

3.0

學分數

Credit(s)

40

預收人數

Number of Students

課程資料Course Details
  • 開課單位：統計碩一 Course Department:Statistics/M/1
  • 授課老師：鄭宗記 Instructor: CHENG TSUNG-CHI
  • 先修科目：無Prerequisite(N/A)
  • 上課時間：四D56 Session: thu13-16

課程簡介Course Description

The course offers a solid coverage of the most important parts of the theory and application of regression models.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    1.  Introduction to Statistical Model  (Chapter 0)
    2.  Introduction: Regression and Model Building (Chapter 1)
    3.  Simple Linear Regression (Chapter 2)
    4.  Multiple Linear Regression (Chapter 3)
    5.  Model Adequacy Checking (Chapter 4)
    6.  Transformations and Weighting To Correct Model Inadequacies (Chapter 5)
    7.  Diagnostics For Leverage and Influence (Chapter 6)
    8.  Polynomial Regression Models (Chapter 7)
    9.  Indicator Variables (Chapter 8)
    10. Multicollinearity (Chapter 9)
    11. Variable Selection and Model Building (Chapter 10)
    12. Validation of Regression Models (Chapter 11)
    13. Introduction To Nonlinear Regression (Chapter 12)

    每周課程進度與作業要求 Course Schedule & Requirements

    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

    學習投入時數（含課堂及課程前後）：6~10小時

     

    Week 1  Introduction to the Course and Statistical Model

    Week 2  Introduction: Regression and Model Building  (Chapter 1)

    Week 3  Simple Linear Regression (Chapter 2)

    Week 4  Multiple Linear Regression (Chapter 3)

    Week 5  Multiple Linear Regression (Chapter 3)

    Week 6  Model Adequacy Checking (Chapter 4)

    Week 7  Model Adequacy Checking Chapter 4)

    Week 8 Transformations and Weighting to Correct Model Inadequacies (Chapter 5)

    Week 9  Diagnostics For Leverage and Influence (Chapter 6)

    Week 10  Polynomial Regression Models (Chapter 7)

    Week 11  Midterm Exam

    Week 12  Indicator Variables (Chapter 8)

    Week 13  Outliers and Influence (Chapter 9)

    Week 14  Multicollinearity (Chapter 9)

    Week 15  Variable Selection and Model Building (Chapter 10)

    Week 16  Validation of Regression Models (Chapter 11)

    Week 17  Introduction To Nonlinear Regression (Chapter 12)

    Week 18  Final Exam

    （以上進度內容可能調整）

     

    授課方式Teaching Approach

    70%

    講述 Lecture

    20%

    討論 Discussion

    10%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    Coursework and class participation (20%)

                           Midterm exam (30%)

                           Final report (and possible presentation) (20%)

                            Final exam (30%)

    指定/參考書目Textbook & References

    Textbook:

    Montgomery, D. C., Peck, E. A., and Vining, G. G. (2021). Introduction to Linear Regression Analysis, 6th ed., John Wiley & Sons.

     

    References:

    1. Atkinson, A. C. (1985) Plots, Transformations and Regression, Oxford: Clarendon Press.
    2. Atkinson, A. C. and Riani, M. (2000) Robust Diagnostic and Regression Analysis, New York: Springer-Verlag.
    3. Cook, R. D. and Weisberg, S. (1999) Applied Regression Including Computing and Graphics, New York: John Wiley.
    4. Draper, N. R. and Smith, H. (1998) Applied Regression Analysis, 3rd ed., New York: John Wiley.
    5. Harrell, F. E. (2001) Regression Modeling Strategies, New York: Springer-Verlag.
    6. Rawlings, J. O., Pantula, S. G. and Dickey, D. A. (1998) Applied Regression Analysis: A Research Tool, 2nd ed., New York: Springer-Verlag.
    7. Seber, G. A. F. (1989) Nonlinear regression, New York: John Wiley.
    8. Seber, G. A. F., and Lee, A. J. (2003) Linear regression Analysis, New York: John Wiley.
    9. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S, 4th ed., New York: Springer-Verlag
    10. Weisberg, S. (2014) Applied Linear Regression, 4th ed., New York: John Wiley.

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