Fall 2013; Ben Bolker, bolker@mcmaster.ca
bolker@mcmaster.ca. When e-mailing please include “4C03” in the title of your e-mail.We will use the open-source R language throughout the course. It will be most convenient if you can install R on your own computer. I hope that it will be possible for students to bring (or share) a laptop for work during class sessions. A backup device such as a memory stick is required in order to save your work.
The course will cover the basics of linear and (mostly) generalized linear models; I will assume familiarity with ANOVA and linear regression. While the theoretical framework will be presented, the emphasis will be on solving practical problems. Some special topics such as survival analysis and multilevel models will be touched on but not covered in complete detail.Assignments and assessment
The assignments for the course will consist of biweekly problem sets (a mixture of computational (R-based) and analytical work) (30%), a midterm exam (in-class or take-home to be determined: 30%) and a take-home final exam (35%). Class participation will count for 5%.
Grades will be posted on Avenue 2 Learn.
I reserve the right to change the weightings in the grading scheme. If changes are made, your grade will be calculated using the original weightings and the new weightings, and you will be given the higher of the two grades. At the end of the course the grades may be adjusted but this can only increase your grade and will be done uniformly. I will use the following grade chart to convert between letter grades, grade points and percentages:
| A+ | A | A- | B+ | B | B- | C+ | C | C- | D+ | D | D- | F |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12 | 11 | 10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1 | 0 |
| 90-100 | 85-89 | 80-84 | 77-79 | 73-76 | 70-72 | 67-69 | 63-66 | 60-62 | 57-59 | 53-56 | 50-52 | 0-49 |
(from p. 29 of the current Undergraduate calendar)
The course goals are to have (1) a basic working knowledge of the concepts and techniques of general linear modeling (i.e., the ability to answer conceptual questions and do simple derivations of the properties of estimators and tests) and (2) practical knowledge of how to fit and interpret generalized linear models in R.
The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all courses in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check their McMaster email and course websites weekly during the term and to note any changes.
If you are absent from the university for a minor medical reason, lasting fewer than 5 days, you may report your absence, once per term, without documentation, using the McMaster Student Absence Form. Absences for a longer duration or for other reasons must be reported to your Faculty/Program office, with documentation, and relief from term work may not necessarily be granted. When using the MSAF, report your absence to course_email@mcmaster.ca. You must then contact the instructor immediately (normally within 2 working days) by email (see above for contact information) to learn what relief may be granted for the work you have missed, and relevant details such as revised deadlines, or time and location of a make-up exam. Please note that the MSAF may not be used for term work worth 30% or more, nor can it be used for the final examination.
If you must miss a lecture, it is your responsibility to find out what was covered. The best way to do this is to borrow a classmate’s notes, read them over, and then ask your instructor if there is something that you do not understand.
All assignments are due in class on the specified date, or e-mailed or posted to Avenue 2 Learn prior to class, unless otherwise stated. I reserve the right to penalize late work by 10% per day.
The expectations for this class are fairly simple: however, if you have any questions please ask. See the McMaster Office of Academic Integrity’s web page for general information.
Dobson, Annette J., and Adrian Barnett. 2008. An Introduction to Generalized Linear Models, Third Edition. 3rd ed.. Chapman and Hall/CRC. http://amzn.com/1584889500.