迴歸分析 (一): 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理想,請勿選修或請期中棄修。
這裡有教師歷年教學意見調查,供您選課參考。

Subject:迴歸分析 (一) Regression Analysis (I) [110-2: 2022/02~2022/06]  (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 。Type of Credit: Elective。Credit(s):3。科目代號: 304008001。

Session: Thursday 09:10-12:00, 商館260205。(Capacity: xx人)

Prerequisite: Statistics

Hands-on course (practicum)(演習課,TA): 

    • A班: 二78 (16:10~18:00), 研究大樓250307助教: 凃于珊 (email, 統計所碩一,Office Hour: Tue 12:00~13:00,地點: 商館九樓統計系辦前開放討論區)
    • B班: 五78 (16:10~18:00), 研究大樓250105助教: 陳柏維 (email, 統計所碩一,Office Hour: Mon 12:00~13:00,地點: 商館九樓統計系辦前開放討論區)
    • A、B班學生名單。(A、B班可自由參加。擇一參加或全部參加或不參加皆可)(助教課以隔週上為原則,是否加課及教學內容依各助教決定。)

Announcement


 

  • [2022/05/16] 依學校公告,本學期所有課程自5月16日(一)起全面實施遠距教學至學期結束。(助教課及考試也皆為遠距)
    https://www.nccu.edu.tw/p/406-1000-11692,r30.php?Lang=zh-tw
  • [2022/05/12] 上傳答題卷之格式要求
  • [2022/05/11] 緊急通知!! (改成線上小考)(以下內容已修正,5/11,22:02)
    剛統計系助理來電通知,這兩週全校教學實施遠距演練,其中也包含考試,故明天考試一律線上考。
     - 日期時間: 05/12(四),10:40~12:00。
     - 請使用MS Teams登入我們平時遠距的上課網址: https://bit.ly/3MwVYLj。老師會線上點名。需開攝影機(靜音)。
     - 下載考卷及上傳考卷: http://www.hmwu.idv.tw/,點選【作業考試上傳區】,帳號: reg110,密碼: xxxx。
     - 考卷請用類A4大小之白紙作答,需寫上學號及姓名,寫完請拍照上傳。
     - 考卷檔名: 學號-姓名-頁碼.jpg或學號-姓名-頁碼.pdf, 例如: 1234-吳漢銘-1.jpg
     - 建議使用DocScan APP拍照掃瞄功能,拍照請務必清晰、考卷需滿版平整,周遭不可有雜物入鏡。
     - 確診或密切接觸者同學,無法參加此次線上小考者,請於一星期內找助教(線上或實體)補考。若需延補考,也先通知助教。
  • [2022/05/08] 小考(2),5/12(四),時間: 10:40~12:00 (80分鐘)。範圍: Chap5
     - A班: 商館260205室,B班: 學思樓040103室。
     - 請直接到場自由選座,請記得帶計算機。
     - 確診者或密切接觸者,請私訊老師,告知要同時線上考,或擇日(線上/體體皆可)補考。
     - ★★★ 當天9:10~10:20要線上正課 ★★★
  • [2022/05/04] 5/19原為「校慶運動會,當日停課」,但本次要補4/21(四)的課!!
  • [2022/05/04] Quiz (2), Time: 5/12(Thu), 11:00 10:40~12:00,Scope:chap5。(實體考試,有可能會分兩間教室考,待公告。)
  • [2022/04/26] 鑑於疫情日益嚴重,為降低感染新冠肺炎風險,由同學投票過半數同意, 自4/28(四) 起實施「同步遠距教學」(使用 MS Teams): 上課網址:https://bit.ly/3MwVYLj
  • [2022/04/14] 期中考R程式加分考,下載考卷(壓縮檔),或按我瀏覽。
  • [2022/04/14] 期中考座位表考座位表
  • [2022/04/07] 期中教學意見調查開始 : 「為加強師生溝通,促進教學相長,本學期各開課科目「期中教學意見調查」自111年4月4日(週一)開始,截止期限為111年4月10日(週日),敬請同學把握時限踴躍填答。」
  • [2022/04/07] Mid-term Exam: Scope : Chap1-Chap3。Date: 04/14 (Thu)。 Place: A班: 商館260205B班: 研究250306
    • Paper Test: 9:10~10:40 (90 minutes)(Calculator is allowed) 。[依考試座位表入坐]
    • Bonus Test for R programming (Open Book, Free to Join):10:50~11:50 (60 minutes)(需帶筆電應考。如果需要請自行攜帶延長線)。
      程式加分考上傳答案卷方法:
      • 上傳網址:「 http://ftp.hmwu.idv.tw:8080/login.html?lang=tchinese 」或老師教學網站首頁,點選【作業考試上傳區
      • 登入帳號(account): reg110。密碼(password): xxxx (不是4個x,於FB群組公告)
      • 登入後有「上傳測試資料夾」,可試試上傳WORD文件,看能否成功。
      • 請上傳答案卷,檔名:「學號-姓名-Reg-Midterm.docx」(學號及姓名,改成自己的)
      • 若上傳檔案格式錯誤,內容亂碼,空檔等等問題。請自行負責。
      • 如果上傳網站出現「`You can modify the html file, but please keep the link www.wftpserver.com at least.`」,請將滑鼠移至「網址列」後,按「Enter」即可。若再不行,請換其它瀏覽器(IE/Edge/Firefox/Chrome)。
      • 有問題者,請FB私訊老師。
  • [2022/04/07] Quiz(1) Question Sheet Quiz(1) Solution Sheet
  • [2022/03/17] Quiz (1), Time: 3/24(Thu), 11:00~12:00,Scope:chap1~chap2.7。
  • [2022/03/03] 本學期三次小考排考時間為正課第三堂(即星期四正課第4節 )。
  • [2022/02/17] Vote for the TA class time (即日起至2/19日上)。
  • [2022/02/17] This semester, classes will mainly be held in person。The link for the class video will be announced on FB Messenger。
  • [2022/01/28] (!!重要!!) 請修課同學加入FB Messenger課程聊天室: 「110-2-迴歸分析 (一)」。
    (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 凃于珊陳柏維 (請註明課名),請助教幫忙加入。 )
  • [2022/01/28] Download the handouts and exercises below.
  • [2022/01/28] 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 02/17 Course Introduction, Ch 1: Simple Linear Regression (SLR)
2 02/24  
Ch2: Inferences in Regression
3 03/03 Ch2: Correlation Analysis
4 03/10 Ch3: Model Diagnostics
5 03/17 Ch3: Remedial Measure
6 03/24 Ch4: Simultaneous Inferences quiz (1): chap1~2.7
7 03/31 Ch5: Matrix Approach to SLR  
8 04/07 Case studies  (I), Exercise using R (I)
9 04/14

Mid-term Exam: Ch1~Ch3

Midterm Exam

10 04/21 Ch6: Multiple Regression (I)
11 04/28 Ch7: Multiple Regression (II)
12 05/05 Ch8: Regression Models for Quantitative and Qualitative Predictors quiz (2):
13 05/12 Ch9: Model Selection and Validation quiz (2): chap5
14 05/19 Ch10: Model Diagnostics 原為「校慶運動會停課」,
但本次要補4/21(四)的課!!
15 05/26
Ch11: Model Remedial Measures

quiz (3):

16 06/02 Ch14: Logistic Regression (Optional) quiz (3):
17 06/09
Case studies (II), Exercise using R (II)
18 06/16
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)

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 % (Three quizzes, each 10%)。
  • 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)。
     

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 TA class. There will be about 3~4 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%).
  • The students should attend the classes at least 2/3 of all classes during all the semester so that he/she could get these extra scores.
    (對成績有疑問,請於當次成績公佈後一星期內連絡老師。)