Textbook

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 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 a public folder. 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.

Homework

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 bhartlau@bowdoin.edu before class.

Exams

Exam 1	Thursday, Oct. 13
Exam 2	Thursday, Nov. 17
Final Exam	Take home

Applied Project

You will be asked to solve an applied regression problem for a business. 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 near the end of the course.  Your executive summaries will be due in early December.

Grades

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%
Problem Session Presentations	10%
Exam 1	20%
Exam 2	20%
Applied Project	10%
Final Exam	20%

Class participation will be used to help make borderline decisions.

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