迴歸分析(一): 度教師自評: 甜度: ★★★✩✩ | 涼度: ★★✩✩✩
第一週上課,老師會說明個人教學理念、授課風格及本課程設計安排,若自覺得不合適不喜歡不想配合或這們課無法達到您的預期或有不得已的苦衷(請告知),請勿選修或請期中棄修,感謝~感恩!! 這裡有教師【歷年教學意見調查】及【政大課程評價網】供您選課參考,也可至【迪卡搜尋漢銘】。

Subject:迴歸分析 (一) Regression Analysis (I) [110-2: 2022/09~2023/01]  (英語授課)

Instructor: Wu, Han-Ming (吳漢銘) (Associate Professor, Department of Statistics, National Chengchi University)

Office: College of Commerce, Room 261237,  Extension: 81237。

Office Hour 一/14:00~16:00E-mail: wuhm@g.nccu.edu.tw

Course Department:Commerce/B/0 (商院選修)。Type of Credit: Elective。Credit(s):3。科目代號: 300807001。

Session: Thursday 09:10-12:00 (四234), 商館260207。(Capacity: 80人)

Prerequisite: Statistics

Hands-on course (practicum)(演習課,TA): 

    • 助教: 陳柏維 (email, 統碩一)。開學後由助教調查合適之時間,再借教室。
    • 助教課自由參加。
    • 以隔週上為原則,是否加課及教學內容依助教決定。

 

Announcement

  • TOP: [2022/07/13] Score Sheet(Latest updated: 2022/11/29)
  • TOP: [2022/09/04] 線上上課固定連結(MS Teams): http://shorturl.at/lSWY3 。(連結已更正。課後錄影檔連結於FB群組中公佈)

 

  • [2022/11/24] Mid-term Exam Question Sheet | Mid-term Exam Solution Sheet | Bonus Test Solution Sheet
  • [2022/11/10] Download Bonus Test Sheet (zip file) or  Browse the files here. (R程式加分考)
  • [2022/11/10] Mid-term Exam's Seating (期中考座位表,考前10分鐘公告).
  • [2022/11/04] 111-1學期期中TA課教學評量問卷,請同學踴躍填寫。(!!!此教學評量問卷是針對TA而非授課教師,請特別留意!!!)
    The 111-1 Midterm Evaluation of Teaching Assistant Class has already started. Please fill out surveys in class. Please Be Advised: This survey focuses on the TA and not instructors, please take note of this!
  • [2022/11/02] 期中考試11/10(四): 範圍: Chap1-Chap3
    • 紙筆考試時間: 9:10~10:50 (100分鐘),紙筆考試請自行攜帶計算機,其它規則同小考(1)。請依座位表入坐。
    • 程式加分考試(自由參加,Open Book,開放網路,勿與他人通訊)時間: 11:00~12:00 (60分鐘)。程式考試請自行攜帶筆電(請確認可正常使用),若有需要,請帶電源延長線。如何上傳,詳見考卷電子檔。
  • [2022/11/02] 小考(1)題目題目參考答案
  • [2022/10/26]111學年度第1學期各開課科目「期中教學意見調查」作業將自10月31日(週一)起展開調查,持續至11月6日(週日)止,敬請轉知同學踴躍填答。
    政大網站首頁登入iNCCU,於校園資訊系統 → 校務系統Web入口 → 學生資訊系統 → 學術服務項下,點選「本學期教學意見調查」連結。
  • [2022/10/14] Quiz 1 on 20 Oct, 10:10~11:50。Scope: Ch1 ~ Ch2。Bring your calculator, "Smart Phone, Laptop, Tablet" are not allowed during the quiz.
    (備註: (a)可帶普通或工程用計算機,不可用手機或具程式功能之計算機,(b)小考不指定座位,請提早入座,(c)可使用鉛筆/原子筆作答,(d)需以英文作答。)
  • [2022/09/24] 老師9/24新冠確診,9/29之正課,改為遠距上課。
  • [2022/09/04] Download the course lecture, exercises and past quiz/exam. (請勿跟老師索取考古題解答或前學期上課之錄影)
  • [2022/09/04] 欲加簽本課程的同學,請列印「選課加簽單」電子檔,email給老師簽名同意加簽。(限修人數滿,即不再加簽)
  • [2022/09/04] (!!重要!!) 請修課同學加入FB Messenger課程聊天室: 「111-1-迴歸分析 (一)」。(同學必需是此聊天室群組成員,點選連結才可直接進入)
    (加入方法: (1) 已在聊天室之同學可將未加入的同學加入,或(2) 同學們FB私訊助教 (請註明課名),請助教幫忙加入。 )
  • [2022/07/13] Teaching plan。Note that the 「Tentative Syllabus」is subject to change depending on class progress and other factors。課程大綱及規定,請以本頁(教師教學網站)為準。

 

Course Description

A linear regression model is a relationship between an outcome and a set of predictors of interest based on the linear assumptions. It is the most important statistical analysis tool for data scientists. This course introduces the fundamental theories, methods and practical application skills in regression analysis and their generalizations. The textbook used in this course is "Michael H. Kutner et al. (2019), Applied Linear Statistical Models: Applied Linear Regression Models, Mcgraw-Hill Inc., (5th edition)". The topics in this semester cover the simple linear regression, multiple regression, inferences, model diagnostics and remedial measures, regression models for quantitative and qualitative predictors and logistic regression. In addition, students will learn how to use R/RStudio to perform the real data analysis and interpret the results. Note that the main teaching method in this class is lecturing in English. (The "Course Schedule & Requirements" below is subject to change according to the actual progress of the class.)

 

Course Objectives & Learning Outcomes

After completing this course, students will be able to (1) understand the basic mathematical concepts and principles of the linear regression models and their limitations; (2) evaluate and diagnose the regression models; (3) apply corrections to some real data problems in regression; (4) conduct the analysis to develop an optimal regression model using R/RStudio software.

 

Tentative Syllabus (the syllabus is always subject to change according to the needs of the course as the professor sees fit):

Week Month/Day Topics

Notes

1 09/15 Course Introduction, Ch 1: Simple Linear Regression (SLR)
2 09/22  
Ch2: Inferences in Regression
3 09/29 Ch2: Correlation Analysis
4 10/06 Ch3: Model Diagnostics
5 10/13 Ch3: Remedial Measure
6 10/20 Ch4: Simultaneous Inferences quiz (1): ch1~ch2
7 10/27 Ch5: Matrix Approach to SLR  
8 11/03 Case studies  (I), Exercise using R (I)
9 11/10

Mid-term Exam: Ch1~Ch3

Midterm Exam

10 11/17 Ch6: Multiple Regression (I)
11 11/24 Ch7: Multiple Regression (II)
12 12/01 Ch8: Regression Models for Quantitative and Qualitative Predictors  
13 12/08 Ch9: Model Selection and Validation quiz (2)
14 12/15 Ch10: Model Diagnostics  
15 12/22
Ch11: Model Remedial Measures

16 12/29 Ch14: Logistic Regression (Optional)  
17 01/05
Case studies (II), Exercise using R (II)
18 01/12
Final Exam: Ch6-Ch11 (Ch14) Final Exam

Textbook: Michael H. Kutner et al. (2019), Applied Linear Statistical Models: Applied Linear Regression Models, Mcgraw-Hill Inc., (5th edition)
(購買方式: (1) 華泰文化。(2) 巨流政大書城)
★專屬賣場連結: http://eshop.hwatai.com.tw/SalePage/Index/RQZgpnPeoTd2eLRQIk0tYA==
Ø華泰eShop商店連結: https://eshop.hwatai.com.tw/V2/Activity/24622?layout=official

Michael H. Kutner et al. (2019), Applied Linear Statistical Models: Applied Linear Regression Models, Mcgraw-Hill Inc., (5th edition)( 華泰文化)

Reference


Grading Scheme:(調整配分需經
全班大多數修課同學同意)

  • Quizzes:30 % (Two quizzes, each 15%)。
  • Midtem exam:30 %。
  • Final exam:40 %
  • TA 0%。
  • HW 0%。
  • Attendance (including TA class) 0%。
  • Bonus Test: 10% * 2。
  • EXtra (up to 0% ~10%): in-class performance/discussion, learning attitude, and so on。(No adjustment made for personal reasons)。(期末求分信及訊息,老師不予回應,不便之處尚請見諒!)
     

Notes (in class)

  • The lecture is based on the use of projector and handwriting tablet. Please print the lecture notes before class.
  • Rules on leave-taking by students. (缺課、曠課相關規定,依校規辦理)。
  • Treat each other with mutual respect in the classroom. (上課以「互相尊重」為最高原則並盡到「告知老師」的義務。)
  •  What you can do in the class: (1) whispered discussion, (2) go to toilet quietly, (3) eating and drinking (without alcohol) but keeping the classroom clean, (4) use laptop or tablet to take notes or pitcures.
  • What you can't do in the class: (1) play cell phone or tablet (please mute the phone), (2) chat, sleep, play cards, smoke。
  • If you have any questions, please contact TA or Lecturer directly or using e-mail or FB

 

Notes (quizzes、grading)

  • The time for the quiz is scheduled at the ordinary class. See previous exam for sample questions。
  • The make-up quiz/exam is not allowed for no particular reason. Only one make-up is limited  out of 3 quizzes.
  • Cheating in exams is absolutely prohibited. Any form of cheating on an exam will result in a 0 for all the rest exams.
  • The scores for the attendance is extra (which is not contained in 100%).
  • (對成績有疑問,請於當次成績公佈後一星期內連絡老師。)