Class time,
location MW 2:303:20, THO125 Th 3:304:20, THO134 F 2:303:30, THO125
or SAV117, see timeline 
Web page 
Instructor Elena Erosheva C 14C, Padelford Hall erosheva[at]uw.edu 
Office Hours: M, Th
1:302:30 Questions: Emails are welcome. They will often be answered quite
quickly, but this is not guaranteed. In particularly, I don't always check
email over weekends. 
Teaching Assistant Amrit Dhar adhar[at]uw.edu 
Office Hours: T 9:3010:30am; 12:001:00pm, Padelford Hall, B302 (Stat Lounge) 
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 modelbuilding 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, 2^{nd} 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. 
Time 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
9:3010:30 
Amrit’s office hour 

121 

Amrit’s office hour 



1:302:30 
Elena’s office hour 


Elena’s office hour 

2:303:20 
Class time 
Class time 

Class time 

3:304:30 
Class time 
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 inclass final exam and the class schedule will be
revised accordingly. 
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 email your assignment to the TA before the due time. In addition, the TA may ask you to provide a hard copy. 
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:

The
instructor and the TA will use a class mailing list. The uw.edu email
addresses of the registered students will be included in the mailing list
automatically. You may post to the list from your uw.edu 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 
If
you would like to request academic accommodations due to a disability, please
contact Disabled Student Services, 448 Schmitz, 5438924 (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. 