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迴歸分析(一): 甜涼度教師自評: 甜度: ★★★✩✩ | 涼度: ★★✩✩✩ |
Subject：迴歸分析 (一) Regression Analysis (I) [111-2: 2023/02~2023/06] (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： 一/14:00~16:00。 E-mail: wuhm@g.nccu.edu.tw
Course Department：統計二 。Type of Credit: Required。Credit(s)：3。科目代號: 304008001。
Session: Thursday 09:10-12:00 (四234), 商館260205。(Capacity: 80人)
Prerequisite: Statistics
Hands-on course (practicum)(演習課，TA):
Announcement：
- TOP: [2023/01/19] Score Sheet。(Latest updated: 2023/06/22)
- [2023/06/20] 期末考R程式加分考考卷下載、或瀏覽這裡。
- [2023/06/20] 期末考分兩班考: A班商館260205、B班商館260311。座位表 (考前10分鐘公告)。
- [2023/06/09] 期末考(Final Exam): Scope : Chap5-Chap14，有教過的部份。
Date: 06/15 (Thu)。 座位分兩班，請依座位表入坐。A班商館260205、B班商館260311。(考前10分鐘公佈)
Paper Test: 9:10~10:50 (100 minutes)(Calculator is allowed，可算log, exp的) 。
Bonus Test for R programming (Open Book, Free to Join):11:00~12:00 (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): reg111。密碼(password): xxxx (不是4個x，於FB群組公告)
- 登入後有「上傳測試用資料夾」，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-Final.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。
- 如果上傳網站出現空白頁，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。
- [2023/06/05] Quiz(2) Question Sheet。 Quiz(2) Solution Sheet。
- [2023/06/01] (補課通知) 日期: 2023/06/05(一)，時間: 13:10~16:00。內容: 上進度。上課方式: 線上遠距，會錄影，自由參加。視訊網址(MS Teams): https://bit.ly/3nsRZrY 。
- [2023/06/01] Chap5~Chap14, 課本範例R程式碼。[講解影片在上課影片放置處]
- [2023/05/24] Quiz (2)分兩班考: A班商館260205、B班商館260311。自由入座，左右儘量不要坐太靠近。
- [2023/05/21] Quiz (2), Time: 5/25(Thu), 10:10~11:50，Scope:chap5~chap6。Place: 分兩班考: 借到教室再公佈。
Bring your calculator, Note that the "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。) - [2023/05/16] 請同學上網填寫「期末教學意見問卷」，期間112年5月22日(一) ~ 112年6月11日(日)。
- 請同學務必上網填寫建設性的意見(不管是正評、負評)，讓本課程更好。請儘量具體舉出本課程的優點、缺點、值得保持的地方及可以改善的地方，感謝~感恩。
- 方式: 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。
- [2023/05/02] 期中考考題、期中考參考解答、R程式加分考參考解答。
- [2023/04/27] 停課通知: 5/4(四)停課一次，擇日補課。
- [2023/04/20] 期中考R程式加分考考卷下載、或瀏覽這裡。
- [2023/04/20] 期中考分兩班考: A班商館260102、B班商館260205。座位表 (考前10分鐘公告)。
- [2023/04/11] Mid-term Exam: Scope : Chap1-Chap3。
Date: 04/20 (Thu)。 座位分兩班。
Paper Test: 9:10~10:50 (100 minutes)(Calculator is allowed) 。[依考試座位表入坐]
Bonus Test for R programming (Open Book, Free to Join):11:00~12:00 (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): reg111。密碼(password): xxxx (不是4個x，於FB群組公告)
- 登入後有「上傳測試用資料夾」，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。
- 如果上傳網站出現空白頁，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。
- [2023/04/08] Datasets used in the textbook used in the textbook.
- [2023/04/06] Chap1~Chap3, 課本範例R程式碼。
- [2023/04/02] Quiz(1) Question Sheet。 Quiz(1) Solution Sheet。
- [2023/03/29] 請修課同學上網填寫「111-2學期期中【教學助理(TA)】教學評量問卷」
填寫時間：112/3/28(二)00:00~112/4/7(五)23:59。填寫網址：http://ctldta.nccu.edu.tw/
填寫說明：(1) 請留意，此教學評量問卷是「針對TA」而「非授課教師」！ (2) TA與授課老師登入可看到所屬課程填答情形(不具名)。 - [2023/03/29] (補課通知) 日期: 2023/04/06(四)，時間: 9:10~12:00。內容: chap5 & chap01~05之範例R程式。上課方式: 線上遠距，會錄影，自由參加。視訊網址: http://bit.ly/3nsRZrY 。
- [2023/03/17] Quiz (1), Time: 3/23(Thu), 10:10~11:50，Scope:chap1~chap2.3。Place: A班商館260206、B班商館260102。
Bring your calculator, Note that the "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。) - [2023/02/06] ★★★ 重要 ★★★，第一週因老師有事，故停課一次，擇日補課。第一次上課日期為: 2/23(四)。欲加簽/退選的同學，請將加簽單/退選單電子檔email給老師簽名。
- [2023/01/24] Download the course lecture: [教師版] [學生版] (請勿來跟老師要考古題解答)(請勿來跟老師要前學期上課的錄影)
- [2023/01/19] 講義勾選之習題簡答/提示，已公告於助教網址。
- [2023/01/19] 加簽事項: 若限修人數85人已滿，則不再加簽。欲加簽本課程的同學，請
- (1) 第一週上課，拿單子給老師簽名，或
- (2) 列印「選課加簽單」電子檔，email給老師簽名同意加簽。
- [2023/01/19] (!!重要!!) 請修課同學加入課程FB私密社團:「111-2-迴歸分析 (一)課程FB社團」。
- 同學有課程問題，可於課程社團發問，由助教/教師回答。
- 助教/教師有即時消息、考題提示、上課影片網址或上傳帳號密碼資訊，會發佈在課程FB社團。
- 助教/教師不接受FB私訊或email發問課程問題。(原因第一週上課會解釋)。 若有個人問題，可FB私訊或email助教或老師。
- [2023/01/19] 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 | 02/16 | Course Introduction, Ch 1: Simple Linear Regression (SLR) | |
| 2 | 02/23 |
Ch2: Inferences in Regression | |
| 3 | 03/02 | Ch2: Correlation Analysis | |
| 4 | 03/09 | Ch3: Model Diagnostics | |
| 5 | 03/16 | Ch3: Remedial Measure | |
| 6 | 03/23 | Ch4: Simultaneous Inferences | quiz (1) |
| 7 | 03/30 | Ch5: Matrix Approach to SLR | |
| 8 | 04/06 (放假) | Ch6: Multiple Regression (I) | [補課，自由參加，上課會錄影] |
| 9 | 04/13 |
(原校定期中考) |
|
| 10 | 04/20 | Mid-term Exam | 本課程期中考 |
| 11 | 04/27 | Ch7: Multiple Regression (II) | |
| 12 | 05/04 | Ch8: Regression Models for Quantitative and Qualitative Predictors | |
| 13 | 05/11 | Ch9: Model Selection and Validation | |
| 14 | 05/18 | Ch10: Model Diagnostics | |
| 15 | 05/25 |
Ch11: Model Remedial Measures |
quiz (2) |
| 16 | 06/01 | Ch14: Logistic Regression (Optional) | |
| 17 | 06/08 |
Case studies (II), Exercise using R (II) | |
| 18 | 06/15 |
Final Exam: |
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==
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Reference：
- John Fox, Sanford Weisberg, 2018, An R Companion to Applied Regression (3rd Edition), SAGE Publications, Inc
- ALSM: https://cran.r-project.org/web/packages/ALSM/index.html
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：30 % 。
- TA： 0%。
- HW： 0%。
- Attendance： 10%。
- Bonus Test (R加分考): 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 personal 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 all quizzes/exams.
(所有考試，時間會排在正課。考試無特殊原因不得補考。)(特殊原因請先報告老師，然後找助教補考) - 補考計分方式(不論補考原因為何)，依學校規定(20%~40% OFF): 補考分數 = (實得分數 - 60) x 0.8 + 60。補考成績在60分以下者，按實得分數計算。
- Cheating in exams is absolutely prohibited. Any form of cheating on an exam will result in a 0 for all the rest exams.
- (對成績有疑問，請於當次成績公佈後一星期內連絡老師。)