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Math 531 Regression I.
Fall 2015

  • Instructor: Ganggang Xu
  • Phone number: (607) 777-3550
  • Office: OW-133
  • Meeting time & location: MWF 1:10 - 2:10pm at OW 100E.
  • Office hours: MW 3:30-5:00pm or by appointment.
    If you need to reach me, please e-mail
    Please include [Math531] in the subject line of your email, or your email may not be read promptly.


Math 501 and Math 502, or equivalent. A course in linear algebra. Graduate students from outside of the mathematical department and senior undergraduate students may take this course with Instructor's approval.

Learning Objectives

  1. Basic theories of linear regression models: estimation, statistical inference, prediction, model diagnosis,model selection, etc.
  2. Proficient use of programming language R with applications to regression models.
  3. Basic training in scientific writing.
  4. Basic training in presentation.

This course is a 4-credit course, which means that students are expected to do at least 12.5 hours of course-related work or activity each week during the semester. This includes scheduled class lecture/discussion meeting times as well as time spent completing assigned readings, studying for tests and examinations, preparing written and computing assignments, and other course-related tasks.

The required texts is Faraway (2014) (see below for details).

  • Required text
    • Faraway (2014). Linear Models with R, Second Edition. (Chapman & Hall/CRC Texts in Statistical Science)
    • Link to R scripts of the book: R codes
  • Recommended additional reading
    • Sheather (2009). A Modern Approach to Regression with R. (Springer Texts in Statistics)


R is chosen to be the statistical software used in this course. There are many online resources where the students can learn the basics of R.

  1. Here is a pointer to R blogs.

Please install R before the beginning of the semester. In addition to R, some may find RStudio to be handy. Downloads:

  • R - mirror hosted at UC Berkeley.
  • R Studio - a more user friendly platform for R.

Note: This is not an R class. R will not even be taught in class. You are expect to learning R programming by yourself.


  • Homework (20%):
    1. Assigned every day. Don't skimp on the homework if you want a good grade.
    2. You may discuss the problems with each other in general terms, but you must write your own solution.
    3. All sources, including friends and colleagues, must be cited.
  • Midterm exam (20%): October 23rd (tentative, subject to change)
  • Final Exam (30%): TBA.
  • Team project (30%): Guidelines

Presentation Schedule

Dec. 2nd, 1:10pm - 1:40pm Wenbo Wang, Xin Qi and Xu Chu;

Dec. 2nd, 1:40pm - 2:10pm Ruiqi Liu, Junyi Dong, Lin Yao and Liping Gu;

Dec. 4th, 1:10pm - 1:40pm Changwei Zhou, Hao Xu and Baiyang Qi;

Dec. 4th, 1:40pm - 2:10pm Rachael Kline, Yuan Fang and Rui Gao;

Dec. 7th, 1:10pm - 1:40pm Wenming Deng, Chen Liang, Xiang Li;

Grading points:

Slides (40%): you need to prepare slides that are clear, concise with great visualization of your results; Do not include too much technical detail;

Team presentation performance (30%, graded individually): you need to tell a story about your project; try to be as organized as possible and get right into the points; Your presentation should not be longer than 25 minutes; otherwise, you will be penalized;

Question/Answer performance (30%): you need to reserve 5 minutes for questions. The way you handle all questions must clearly show that you have sufficient knowledge of what you are doing. Otherwise, you will be penalized. I also encourage questions from the crowd, and if you ask a good question I will take a note in my heart :)

people/gang/regression_i.txt · Last modified: 2015/11/23 12:27 by gang