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： 一/14:00~16:00。 E-mail: firstname.lastname@example.org
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: xx人)
Hands-on course (practicum)(演習課，TA):
- 助教: 待公告。開學後由助教調查合適之時間，再借教室。
- [2022/08/??] 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)
- [2022/07/13] 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。
- [2022/07/13] (!!重要!!) 請修課同學加入FB Messenger課程聊天室: 「111-1-迴歸分析 (一)」。(需為此聊天室群組成員，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。
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):
- 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
- 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。
- 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%).