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Math 532 Regression II.
Spring 2018

  • Instructor: Ganggang Xu
  • Phone number: (607) 777-3550
  • Office: WH-133
  • Meeting time & location: MWF 1:10 - 2:10pm at WH 100E.
  • Office hours: MW 3:30-5:00pm or by appointment.
    If you need to reach me, please e-mail
    Please include [Math532] in the subject line of your email, or your email may not be read promptly.


Math 502 and Math 531, 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. More advanced techniques of regression models: Linear mixed effects models and Generalized linear models
  2. Proficient use of programming language R with applications to regression models.
  3. More advanced training in scientific writing.
  4. More advanced 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 (2005) (see below for details).

  • Required text
    • Faraway (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. (Chapman & Hall/CRC Texts in Statistical Science)
  • Recommended additional reading


1. 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.

2. All homework must be completed using Latex, unless otherwise instructed.


  • 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 (25%): March 28th (tentative, subject to change)
  • Final Exam (25%): TBD
  • Team project (30%): Guidelines

Presentation Schedule

* 04/30/2018: 1:10-1:40 pm Baozhen Wang, Xinhai Zhang and Shengfu Zhang;

* 04/30/2018: 1:40-2:10 pm Haoran Ding and Weiyu Zhou;

* 05/02/2018: 1:10-1:40 pm Joshua Segal and Jacob Boni;

* 05/02/2018: 1:40-2:10 pm Huang Siming, Ren Zepin, and Li Kexuan;

* 05/04/2018: 1:10-1:40 pm Joseph Njuki and Huize Xie;

* 05/04/2018: 1:40-2:10 pm Fan Yang and Ying Wu;

* 05/07/2018: 1:10-1:40 pm Zhou Wang, Xueyan Zhang and Siruo Zhang;

* 05/07/2018: 1:40-2:10 pm Dalton Pawliczak and Amanda Park;

* 05/08/2018: 1:30-2:00 pm Zeyang Wang, Ziyang Liu and Qianru Zhang; (tentative)

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_ii_sp2018.txt · Last modified: 2018/04/28 22:45 by gang