STAT 5100 – Linear Regression and Time Series
Spring 2013
ESLC 046
Dr. Chris Corcoran
Office: Animal Science 223
Phone: 797-4012
Email: chris.corcoran@usu.edu
Course Website: http://www.math.usu.edu/~corcoran
Office Hours: MWF 11:00-12:00 – call, email, or stop by if you need to see me at another time. (Due to a monthly
teleconference, there will be no office hours on third Mondays.)
Course Objectives: Regression methods are ubiquitous in all areas of applied research, allowing analysts to assess how
the distributions of measured (so-called dependent or outcome) variables depend on other (independent or explanatory)
variables. This class provides a broad introduction specifically to linear regression, which is typically used when the
outcome variable under investigation is quantitative, having an interval-valued or continuous distribution. After a review
of random variables and inference for means of random variables, we will focus on simple regression models, interpreting
the effect of an explanatory variable, inference for model effects, and diagnostics for checking model assumptions. We
will then extend these concepts to multiple regression models (with more than one explanatory factor). Time permitting,
we will also briefly discuss models for time series (with repeated outcome measures) and regression models for
categorical variables.
Required Texts: Applied Linear Regression Models (4th edition), Kutner et al. (Optional – NOT AVAILABLE AT
THE USU BOOKSTORE: The Little SAS Book, 5th edition, Delwiche and Slaughter.)
Homework: will be given approximately every 1-2 weeks. Assignments will be graded and returned to you. Many of the
homework problems will require the use of computer software. SAS will be the package of choice for this class (i.e., my
examples will use SAS), although you may use any software with which you have a working knowledge. SAS help will
also be provided by our TA for Stat 5100 and Stat 5200, Anvar Suyundikov (anvar_suyundikov@yahoo.com). SAS labs
are scheduled for Tuesdays and Thursdays between 10:30-12:00, in the Family Life computing lab (room L201). Late
homework will be penalized one point if turned in after class on the date due, and one additional point for each day that
lapses after the due date (including weekends and holidays) before the assignment is turned in.
Exams: There will be four exams, weighted equally, that will be given in class on February 1, March 1, March 29, and
April 19 (all Fridays). I will drop the lowest of the four scores. There will be no make-up exams under any circumstances.
There will be no final exam.
Final Project: There will be a final project, involving groups of 4-5 students. The project will consist of a meaningful
(but relatively brief) analysis of data that your group has selected, using methods discussed in class. You will submit a
write-up and prepare a poster that will be presented to the class during the last day of lecture (April 26th). You must be
present for your group’s poster presentation to receive credit for the project. More guidelines will be given as the
semester progresses, although you are welcome to ask questions about the project at any time.
Grades: Your final score will be weighted as follows: Homework 30%, Exams 50%, Final Project 20%. There is no set
grading scale, although you are guaranteed at least an A- if your final score is above a 90%.
Disabilities: If a student has a disability that will likely require some accommodation by the instructor, the student must
contact the instructor and document the disability through the Disability Resource Center, preferably during the first week
of the course. Any requests for special considerations relating to attendance, pedagogy, taking of exams, etc., must be
discussed with, and approved by the instructor. In cooperation with the Disability Resource Center, course materials can
be provided in alternative formats – large print, audio, diskette, or Braille.
Add/Drop Policy: The last day to add this class is the 15th day of the semester (January 28th). Attending this class
beyond that date without being officially registered will not be approved by the Dean's Office.
TOPICS
• Review
o Random variables and distributions (including the normal distribution)
o One- and two-sample procedures for comparing means
o One-way ANOVA
• The simple linear regression model
o Interpreting the model
o Fitting the model
o Inference for model parameters
o Inference for predictions
o Diagnostic procedures
• The multivariable linear regression model
o Controlling for experimental factors
o Interpreting model coefficients
o F tests
o Goodness of fit
o Nominal factors
o Model building
o Diagnostics
• Models for time series
• Generalized linear regression models
o Components of a GLM
o Logistic regression