Stat 552


ST552 Winter 2016

Course Name: Statistical Methods II
Course Number: ST 552
Credits: This course combines approximately 120 hours of lecture, lab and assignments for 4 credits.
Term: Winter 2016
Prerequisites: ST 551

Charlotte Wickham, 255 Weniger
Office hours: Tue & Thu 2-3pm Weniger 255

Matt Higham

Course content

Course description

A survey of the basic concepts in regression analysis and its applications. Topics include simple and multiple regression, regression diagnostics, transformation of variables and collinearity problems. Students will be expected to demonstrate solid understanding of the use of statistical regression and the supporting theory as well as implementation and reporting of regression models in practice.

Measurable Student Learning Outcomes

By the end of the course, students will be able to:

  • Set up a multiple linear regression model in matrix form
  • Derive the properties for the least squares estimators for the linear regression model
  • Fit linear regression models for a data set using R and report in a non-technical manner
  • Conduct hypothesis tests and construct confidence intervals for the regression coefficients and interpret the ANOVA table
  • Test a general linear hypothesis and compare full versus reduced models
  • Detect and discuss the consequences of violations of regression assumptions.

Tentative Schedule

Week Topic
1 Review of simple linear regression
2 Matrix set up of multiple linear regression
3-4 Inference in multiple linear regression
5 Categorical factors
6 Diagnostics
7 Violations of assumptions
8 Model selection
9 Transformations, nonlinear regression and logistic regression
10 Other topics

Evaluation of Student Performance

Your final grade will be a weighted combination of homework (60%), an in class midterm (20%) and the final exam (20%).

Homeworks: Weekly homeworks may consist of readings, mathematical derivations, simulations and complete data analyses. Homeworks will be made available on the class website and, unless specified otherwise, handed in at the start of class on Friday. Late homeworks will not be accepted. Your lowest homework score will be dropped.

Midterm: In class Friday Feb 5th 2015. A study guide and last year’s midterm will be made available at the end of week 4.

Final Exam: Friday March 18th 2015 9:30-11:20am

Learning Resources

All lecture notes, homework assignments and additional resources will be posted on the class website:
I will use canvas ( to send announcements and record grades. You will also need to submit electronic copies of homework in canvas. You are also welcome to use the discussion board on canvas.

Textbook : Linear Models with R, 2nd Edition (2014) by Julian J. Faraway

This book is available online through the OSU library.

Other books you may find useful:

  • Applied Regression Analysis: A Research Tool by John O. Rawlings, Sastry G. Pantula and David A. Dickey.

  • Introduction to Linear Regression Analysis by Douglas C. Montgomery, Elizabeth A. Peck and G. Geoffrey Vining.

  • Statistical Models: Theory and Practice, by David A. Freedman

  • Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill

University and Department policies

Disability statement

Accommodations are collaborative efforts between students, faculty and Disability Access Services (DAS). Students with accommodations approved through DAS are responsible for contacting me prior to or during the first week of the term to discuss accommodations. Students who believe they are eligible for accommodations but who have not yet obtained approval through DAS should contact DAS immediately at (541) 737-4098.

Academic integrity

Academic dishonesty is a serious offense and will be addressed following the guidelines set out in the Academic Regulations of OSU (go to, click on Registration Information then Academic Regulations, and read AR 15).

The Student Conduct Code defines Academic dishonesty as

… an act of deception in which a Student seeks to claim credit for the work or effort of another person, or uses unauthorized materials or fabricated information in any academic work or research, either through the Student’s own efforts or the efforts of another.

Examples include, but are not limited to, the following:

  • verbatim copying of another student’s homework assignment
  • copying off another student’s exam
  • using prohibited materials (e.g., cell phone, cheat sheet) during an exam
  • communicating with another student during an exam
  • changing answers on an exam after the exam has been graded
  • unattributed use of material copied from an article, textbook, or web site
  • continuing to write on an exam after the instructor or TA has asked for the exams to be handed in.

You are responsible for knowing what academic dishonesty is, and for avoiding it. Ignorance of these rules does not absolve you from responsibility.