Stat 550 - Regression, Spring 2017

Instructor: Chris Williams, Room 414 Brink Hall Phone: 885-2802 or 885-6742

Meeting times: T Th 2:00-3:15 TLC 144

Prerequisites: Math 330 (Linear Algebra), Stat 451 (Probability), Coreq of Stat
452 (Mathematical Statistics) or equivalent coursework.

Office Hours: T Th 1:00-1:50 or by appointment.

email: chrisw@uidaho.edu

Text: Applied Regression Analysis and Generalized Linear Models, third edition, by John Fox.
A website for the text is at:
http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-3E/index.html
. Among other items, online appendices on linear algebra, probability, and mathematical statistics
for the text
are available at:

http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-3E/Appendices.pdf

Webpage:
http://www.webpages.uidaho.edu/~chrisw/stat550live/

The webpage will contain announcements, summaries of lectures, lists of assignments with
due dates, and other information.

Objectives: Theory and application of regression models including linear,
nonlinear, and mixed models, and generalized linear models. Topics include model specification,
point and interval estimators, exact and asymptotic sampling distributions,
tests of general linear hypotheses, prediction, influence, multicollinearity,
assessment of model fit, and model selection.

Learning Outcomes: Understand the matrix-based formulation of the general linear model, including continuous and categorical predictors; Know the Gauss-Markov theorem; Ability to use matrix calculations to obtain the properties of least-squares estimators for the general linear model; Ability to use the geometrical representation of the general linear model to explain model properties; Ability to apply diagnostics for the general linear model and understand how to address problems revealed by diagnostics; Understand the nonlinear regression model and inference for this model; Understand the generalized linear model and the use of likelihood-based approaches for inference with this model; Understand the linear mixed model with normal errorsLecture Outline: We will cover chapters 5-15 and chapter 17 in the text plus additional topics, to be detailed in the lecture schedule.

We will use SAS and/or R throughout the course.

Learning Outcomes will be assessed via homework sets, exams, and the project presentation.

Grading: Homework: 15%; Test 1 25%; Test 2 25%; Project 10%; Final 25% (Monday, May 8 at 12:30 pm). Several homework sets will be assigned, but they will not all be handed in for a grade.

Academic Honesty: You should be aware policies of the University of Idaho concerning academic honesty (see Article II of the Student Code of Conduct). Breaches of academic honesty will not be tolerated and will result in an F for the course and referral to the Dean of Students for further disciplinary action.

Disability Support Services Reasonable Accommodations Statement: Reasonable accommodations are available for students who have documented temporary or permanent disabilities. All accommodations must be approved through Disability Support Services located in the Idaho Commons Building, Room 306 in order to notify your instructor(s) as soon as possible regarding accommodation(s) needed for the course. Phone: 885-6307, email at dss@uidaho.edu, website at www.access.uidaho.edu .