Professor Marian Frazier
Office 309-A
Rutherford B. Hayes Hall
Phone PBX 5267
Website www2.kenyon.edu/Depts/Math/FrazierM/
Office Hours
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
SAS will be used extensively throughout the course. A thorough list of SAS commands and procedures can be found on the class website. This file will be invaluable, especially if you are not already familiar with SAS. Another useful resource is Rebecca Elliot’s Learning SAS in the Computer Lab (the lab manual from Math 206). 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\Frazier\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.
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.
As a “spectator” during a problem session, you have responsibilities as well. Just as in any other upper-level Mathematics course (e.g., Probability, Real Analysis), students are expected to be present in both body and mind: contribute to class discussions; ask good questions; and actively participate when another student is presenting work at the board. Students presenting their work are not meant to replace a seasoned, polished lecture that would be given by an experienced instructor. Nor should they be made to. They are counting on their fellow students to help them by making clarifying suggestions and asking questions. I will feel free to ask questions of persons who are sitting down.
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 frazierm@kenyon.edu before class.
Exams
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Exam 1 |
Thursday, Feb. 19 |
|
Exam 2 |
Thursday, April 16 |
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Final Exam (Take-home) |
Thursday, May 7 Due at 8:30 am |
Applied Project
You will be asked to solve an applied regression problem for a marketing company. 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.
Academic Honesty
In general, the rules set forth in
the 2008-2009 Course of Study apply.
Presenting the work of others as your own is strictly prohibited. In the case of problem sessions/homework, you
may collaborate with others in discussing how a problem may be solved (in fact,
this is encouraged), but your write-up must be your own. In the case of the applied project and the
take-home Final, you are on your honor not to discuss any aspect of
these assignments with anyone but me until all projects/exams have been turned
in. If you are not sure about some aspect of
this policy, please see me before turning in an assignment.
Learning Disabilities
If you have a disability and feel that you may have need for some type of academic accommodation(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 salvae@kenyon.edu.
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% |
|
Exam 1 |
20% |
|
Exam 2 |
20% |
|
Applied Project |
15% |
|
Final Exam |
25% |
Class participation will be used to help make borderline decisions.
Course Outline
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Chapter 1 |
Linear Regression with One Predictor Variable |
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Chapter 2 |
Inferences in Regression and Correlation Analysis |
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Chapter 3 |
Diagnostics and Remedial Measures |
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Chapter 4 |
Simultaneous Inferences and Other Topics in Regression Analysis |
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Chapter 5 |
Matrix Approach to Simple Linear Regression Analysis |
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Chapter 6 |
Multiple Regression - I |
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Chapter 7 |
Multiple Regression - II |
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Chapter 8 |
Regression Models for Quantitative and Qualitative Predictors |
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Chapter 9 |
Building the Regression Model I: Model Selection and Validation |
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Chapter 10 |
Building the Regression Model II: Diagnostics |
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Chapter 11 |
Building the Regression Model III: Remedial Measures |
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Chapter 13 |
Introduction to Nonlinear Regression and Neural Networks |
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Chapter 14 |
Logistic Regression, Poisson Regression, and Generalized Linear Models |