STAT/CSSS 504 Ė Winter 2015

Applied Regression

Class time, location

MW 2:30-3:20, THO125††††††††††††

Th 3:30-4:20, THO134

F 2:30-3:30,THO125 or SAV117, see timeline

Web page


Elena Erosheva  

C 14C, Padelford Hall


Office Hours: M, Th 1:30-2:30

Questions: E-mails are welcome. They will often be answered quite quickly, but this is not guaranteed. In particularly, I don't always check e-mail over weekends.


Teaching Assistant

Amrit Dhar


Office Hours:

T 9:30-10:30am; 12:00-1:00pm, Padelford Hall, B302 (Stat Lounge)


Course description

This course provides an introduction to the most frequently used statistical model, namely, linear regression. Topics include simple and multiple linear regression, least squares and weighted least squares estimation, hypothesis testing and statistical inference, detecting violations of assumptions and ways to deal with them, statistical model-building strategies, and introduction to logistic regression.



One of the following: (a) STAT 502, (b) STAT 421, (c) STAT 342, (d) STAT 390, (e) STAT/ECON/CSSS 481, (f) SOC 505; or (g) permission of instructor.


Course text (required)

         Weisberg, S. (2014). Applied Linear Regression, 4rd edition, Wiley.

Other course materials (optional)

         Faraway, J. J. (2015), Linear Models with R, 2nd edition, Chapman & Hall

         Fox, J. (1997), Applied Regression Analysis, Linear Models, and Related Methods, Sage.

         An R Companion to Applied Regression (Second Edition) by John Fox and Sanford Weisberg, Sage, 2011

         Supplemental journal articles for some topics in the second half of the course will be available electronically.

Lecture notes

Most days, I will post lecture notes in pdf at the class web page in the morning before each lecture, but this is not guaranteed. If available, you are welcome to print out your own copies or use electronic tools to write notes on the slides.

Typical weekly schedule









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Final grades will be based on: (a) homework assignments (35%, the lowest homework score not included); (b) midterm exam (25%) and (c) a group project including short project presentation (15%) and final poster presentation (25%). Poster presentations will take place during the scheduled final exam slot that may be extended to allow for sufficient review time. If not enough viable project ideas are proposed, there will be an in-class final exam and the class schedule will be revised accordingly.


Discussion sections and homework

Discussion sections will be a combination of hands on computing, short presentations by the TA, reviews of homework/midterm, and project group work.


Completed homework assignments will be typically due at the beginning of a Wednesday lecture. Submit hard (paper) copies. Grades for homework assignments turned in up to 24 hours late will be lowered by 25% of the total score for that homework. Homework assignments turned in late by more than 24 hours will receive zero points except for cases of documented emergencies. If you are unable to come to a Wednesday lecture, please complete and e-mail your assignment to the TA before the due time. In addition, the TA may ask you to provide a hard copy.


Class projects

The group project will involve identifying a research question and a data set, and carrying out a thorough regression analysis to address the research question. A tentative project timeline is as follows:

  • Project ideas are due on Wednesday, January 14, as part of Homework 2.
  • Best project ideas will be chosen for group projects.
  • Project teams will be assigned.
  • Project proposals are due on Friday, February 13.
  • Short project presentations are scheduled during the week of February 23.
  • The poster session will take place during the final exam slot on March 17.
  • Project teams will need to submit:
    1. Poster components (one file),
    2. Annotated complete R code for the project,
    3. One page describing contributions of each team member.


Class mailing list

The instructor and the TA will use a class mailing list. The email addresses of the registered students will be included in the mailing list automatically. You may post to the list from your email address.



Most of the homework assignments will involve computing. We will use R language in this course. R can be downloaded for free at The Comprehensive R Archive Network (CRAN) where good introductory documentation is also available. You may also check out the following texts that are available electronically through UW libraries :

         A Beginner's Guide to R by Zuur, Ieno and Meesters, Springer, 2009

         Introductory Statistics with R (Second Edition) by Dalgaard, Springer, 2008

         Software for Data Analysis: Programming with R by Chambers, Springer, 2008

         Data Manipulation with R by Spector, Springer, 2008


Students with disabilities

If you would like to request academic accommodations due to a disability, please contact Disabled Student Services, 448 Schmitz, 543-8924 (V/TTY).  If you have a letter from Disabled Student Services indicating you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations you might need for this class.