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people:gang:regression_i [2015/08/28 12:08]
gang [Software]
people:gang:regression_i [2015/11/23 12:27] (current)
gang [Presentation Schedule]
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 +<WRAP center box>
 +####Math 531 Regression I.####\\ ###Fall 2015###
 +</​WRAP>​
 +~~META:​title=Math 531 Regression I.~~
  
 +  * **Instructor:​** [[people:​gang:​]]
 +  * **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 [Math531] in the subject line of your email, or your email may not be read promptly.__**
 +
 +
 +===== Prerequisite =====
 +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 =====
 +  - Basic theories of linear regression models: estimation, statistical inference, prediction, model diagnosis,​model selection, etc.
 +  - Proficient use of programming language R with applications to regression models.
 +  - Basic training in scientific writing.
 +  - Basic training in presentation.
 +
 +<WRAP center round important 60%>
 +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.
 +</​WRAP>​
 +
 +===== Recommended Texts =====
 +
 +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: [[http://​www.maths.bath.ac.uk/​~jjf23/​LMR/​scripts2/​|R codes]]
 +  ​
 +  * **Recommended additional reading**
 +      * Sheather (2009). A Modern Approach to Regression with R. (Springer Texts in Statistics)
 +
 +===== 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.
 +  - [[https://​cran.r-project.org/​doc/​manuals/​R-intro.pdf|An Introduction to R]]
 +  - [[http://​www.cyclismo.org/​tutorial/​R/​|R tutorial by Kelly Black]]
 +  - Here is a pointer to [[http://​www.r-bloggers.com/​|R blogs]].
 +
 +Please install R before the beginning of the semester. In addition to R, some may find RStudio to be handy.
 +Downloads:
 +  * [[http://​cran.cnr.berkeley.edu/​|R]] - mirror hosted at UC Berkeley.
 +  * [[http://​www.rstudio.com/​products/​rstudio/​download/​|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.**
 +===== 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 (20%)**: October 23rd (tentative, subject to change)
 +  * **Final Exam (30%)**: TBA.
 +  * **Team project (30%)**: [[people:​gang:​regression_i:​requirement|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 :)