Linear Regression Models (Math 416)

Spring 2017

Professor Bradley A. Hartlaub
Office 305 Rutherford B. Hayes Hall
Phone PBX 5405
Office Hours

MWF 10:00 - 11:00

Tuesday 9:00 - 11:00

Additional appointments are available; 
please don't hesitate to ask for help.


Kutner, M. H., Nachtsheim, C. J., and Neter, J. (2004), Applied Linear Regression Models, Fourth Edition, The McGraw-Hill Companies, Inc.

Statistical Package & Computing

R and RStudio will be used extensively throughout the course. Assignments and course announcements will be sent to you via e-mail or posted on the course web page. Data sets and programs will be placed in P:\Data\Math\Hartlaub\Regression. Proper maintenance of computer accounts, files, etc. is your responsibility.

Our class meets in a computer equipped classroom, and we will be using statistical software extensively in the course. During regular class hours, the use of computers is restricted to students enrolled in the course. Furthermore, the use of the computers is also restricted to activities deemed appropriate by the instructor. Playing computer games, reading e-mail, conversing in a chat room, surfing the web, and working on assignments for other courses are a few examples of inappropriate activities that can be distracting to the instructor and other students in the course. Inappropriate computer use may result in penalties ranging from warnings to loss of computer privileges for the period. In cases of extreme and/or repeated violations, grade penalties or expulsion from the course may result.

Learning Disabilities and Math Anxieties

If you have a disability and feel that you may have need for some type of academic accomotation(s) in order to participate fully in this class, please feel free to discuss your concerns with me in private and also self identify yourself to Erin Salva, Coordinator of Disability Services at PBX 5453 or via e-mail at

Title IX Responsibilities

Kenyon College seeks to provide an environment that is free of bias, discrimination, and harassment. If you have been the victim of sexual harassment/misconduct/assualt we encourage you to report this. If you report this to a faculty member, she or he must notify the Title IX coordinator about the basic facts of the incident (you may choose whether you or anyone involved is identified by name). For more information about your options at Kenyon College, please go to


Homework assignments will be given throughout the semester. I encourage you to work on as many problems as possible, including problems which have not been assigned. Subsets of the homework assignments will be collected and graded. Working with other students is encouraged, but each student must submit her/his own solution for problems to be collected.

Problem Sessions

During the semester we will have problem sessions regularly. These sessions are designed to improve your understanding of the concepts and enhance your mathematical maturity by requiring a clear, detailed presentation of the material to your peers. During these sessions, you will be responsible for solving an assigned problem and presenting the solution to the rest of the class. Answering all questions about your solution is a required part of the presentation. Being able to solve problems and being able to present the solutions to a group in a logical and coherent fashion are two different tasks. Our goal is to master both tasks.

Late Policy

Your work must be turned in at the beginning of the class period on the assigned due date. No credit will be given for late papers. If for any reason you cannot turn in your paper on the assigned date, you must contact me or send e-mail to before class.


Exam 1 Friday, March 3
Exam 2 Friday, April 21
Final Exam (Take-home) Monday, May 8 @ 6:30

Applied Project

You will be asked to solve an applied regression problem. The deadlines and more detailed instructions on the project will be announced in class.  However, the tentative plan is to incorporate the project into our schedule immediately after Spring Break.  Your executive summaries will be due in early April.


Your course grade will be based on your overall percentage. The categories used to determine your overall percentage and their respective weights are listed below.

Homework and Problem Sessions 20%
Exam 1 20%
Exam 2 20%
Applied Project 15%
Final Exam 25%

Class participation will be used to help make borderline decisions.

Course Outline
Chapter 1 Linear Regression with One Predictor Variable
Chapter 2 Inferences in Regression and Correlation Analysis
Chapter 3 Diagnostics and Remedial Measures
Chapter 4 Simultaneous Inferences and Other Topics in Regression Analysis
Chapter 5 Matrix Approach to Simple Linear Regression Analysis
Chapter 6 Multiple Regression - I
Chapter 7 Multiple Regression - II
Chapter 8 Regression Models for Quantitative and Qualitative Predictors
Chapter 9 Building the Regression Model I: Model Selection and Validation
Chapter 10 Building the Regression Model II: Diagnostics
Chapter 11 Building the Regression Model III: Remedial Measures
Chapter 13 Introduction to Nonlinear Regression and Neural Networks
Chapter 14 Logistic Regression, Poisson Regression, and Generalized Linear Models