 |
Problem of the Week
Math Club
BUGCAT 2020
Zassenhaus Conference
Hilton Memorial Lecture
Sidebar
people:gang:regression_ii
Table of Contents
Math 532 Regression II.
Spring 2016
- Instructor: Ganggang Xu
- Email: gang@math.binghamton.edu
- 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 gang@math.binghamton.edu.
Please include [Math532] in the subject line of your email, or your email may not be read promptly.
Prerequisite
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
- More advanced techniques of regression models: nonparameteric regression models, mixed effects models and Generalized linear models
- Proficient use of programming language R with applications to regression models.
- More advanced training in scientific writing.
- 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.
Recommended Texts
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)
- Link to the book website: http://www.maths.bath.ac.uk/~jjf23/ELM/index.html
- Recommended additional reading
- James et al. (2013). An Introduction to Statistical Learning with Applications in R.
- Free PDF copy available online: http://www-bcf.usc.edu/~gareth/ISL/
Software
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.
- 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:
Note: This is not an R class. R will not even be taught in class. You are expect to learning R programming by yourself.
Grading
- Homework (20%):
- Assigned every day. Don't skimp on the homework if you want a good grade.
- You may discuss the problems with each other in general terms, but you must write your own solution.
- All sources, including friends and colleagues, must be cited.
- Midterm exam (25%): March 23rd (tentative, subject to change)
- Final Exam (25%): May 17th, 8:00am-4:00pm.
- Team project (30%): Guidelines
Presentation Schedule
May 4th: Team 1: Dong/Gu
May 6th: Team 1: Kline/Yuan/Gao; Team 2: Zhou
May 9th: Team 1: Wang/Qi/Chu; Team 2: Yao/Liu
May 11st:Team 1: Xu/Qi; Team 2: Cheng/Deng/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_ii.txt · Last modified: 2016/05/10 22:05 by gang