2. 迴歸分析技術與應用(Techniques and Applications of Regression Analysis) 主要目的是探究一個或數個自變數(Independent variables;又稱Regressors) 和一個因變數(Dependent variable; 也稱Response)間的關係,進而建構一個適當的數學方程式(稱為迴歸方程式),並利用此迴歸方程式來解釋或預測因變數之值。同時,也評估迴歸方程式的好壞、對迴歸模式中的參數執行統計推論。
3. To grasp the essence of this course, we mathematically formulate and solve related Regression Analysis problem and build those programs with relevant teaching materials. This course instills “logic” in the minds of the students. The students will acquire both the knowledge and ideas of Regression Analysis, so that even if new technology emerges, they will be able to follow the changes smoothly.
■ 多元教學方式 (Muliti-Teaching Methods) 說明:除了課堂講授與考試測驗之外,本課程在學期中可能會運用到以下哪些教學方式,以期能進一步提升學生學習成效 Direction: In addition to course teaching and regular exams, which of the following methods may also be used to promote students’ learning outcome
■ 主要參考書籍/資料 (Textbooks and References)
(教科書遵守智慧財產權觀念不得非法影印) Textbook(教科書):
1. E-Book (本校圖書館有此教科書之電子資源)
Joe Suzuki, “Statistical Learning with Math and Python”,
ISBN 978-981-15-7876-2 ISBN 978-981-15-7877-9 (eBook)
2. 黃文隆,黃龍合編
“迴归分析”, 滄海書局出版(Tel 04-2708-8787)
ISBN 986-7287-08-8 (2014 三版)
Reference (參考書)
B. Abraham, “Introduction to Regression Modeling”, ISBN 978-0-514-420758 (paper book), 423 pages (2006).
■ 本課程是否有使用原文書 Does This Curriculum Use the Original Textbook (English) 是(Yes)
■ 教學進度(Course Schedule) - 期中考前後(2 Stage)
週次 Week
日期Date
1
112/09/10 ~ 112/09/16 9/11第1學期開始上課
期中考前
教學進度:
1. Reviews on Simple Linear Regression (SLR) Models
2. 最佳迴歸模式分析及線性轉換及線性化
3. Introduction to Statistical Hypothesis
4. Application Exercises I
5. SLR Model迴歸係數(β0 , β1) 統計假說檢定
2
112/09/17 ~ 112/09/23 9/22加退選課程結束(特殊加選及網路退選截止)
3
112/09/24 ~ 112/09/30 9/29中秋節(放假)
4
112/10/01 ~ 112/10/07 10/6特殊退選課程申請截止
5
112/10/08 ~ 112/10/14 10/9調整放假、10/10國慶日(放假)
6
112/10/15 ~ 112/10/21
7
112/10/22 ~ 112/10/28
8
112/10/29 ~ 112/11/04 11/2校慶紀念日、全校運動大會(停課照常上班)
9
112/11/05 ~ 112/11/11 期中考週
10
112/11/12 ~ 112/11/18
期中考後
教學進度:
6. Reviews on Inverse Matrix and Related Materials
7. Introduction to Multiple Regression Models
8. 線性迴歸分析模式之矩陣表示法
9. Classification: Logistic Regression and related materials