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people:gang:regression_ii_sp2018

Math 532 Regression II.

Spring 2018

**Instructor:**Ganggang Xu**Email:**gang@math.binghamton.edu**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 gang@math.binghamton.edu.

*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.**

- More advanced techniques of regression models: Linear 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.

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/

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.

- 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.**

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

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

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: 2018/01/16 18:41 by gang

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