|Instructor||Dr. James Hanley|
|Co-ordinates||tel: (514) 398-6270
|IMPORTANT||This is a FULL YEAR COURSE, meeting once-a-week over both Fall '16 & Winter '17|
To consolidate and reinforce data analysis and reporting skills. Students, who have already has a course on multivariable regression, will practice these skills and integrate them in a series of projects involving datasets assembled in the course of applied research.
By the end of this course, students will be able to:
|Target||PhD and advanced Masters students in the Biostatistics stream, as well as quantitative Epidemiology Masters and PhD students. Statistically-prepared students from other departments or universities are especially welcome.|
|Prerequisites||Biostatistics students should have completed MATH 533 and 523 or their equivalents, or have permission from the instructor. Epidemiology students should have completed EPIB 607 and EPIB 621 or their equivalents.|
|Format||The course will involve lectures, seminars, group discussions, and independent work.
The seminar component will be based on student presentations of papers followed by general
discussion among faculty and students. Students will be expected to do considerable independent work analyzing data and preparing reports.
Class sessions will include Lecture sessions (infrequently),
Seminar sessions, Lab sessions, and Presentation/Discussion sessions.
'Seminars' will involve student presentations and discussion of relevant papers.
All other reading is intended as background for the lectures and will not be directly discussed in class.
Class sessions indicated as 'lab' will be sessions to discuss projects/data analysis questions and work on data analyses.
If numbers permit, students will be paired into teams -- teams that will change from project to project.
|When||Wednesdays: 2:30-4:30 in Fall and 12:30-2:30 in Winter. First class: Wed. Sept 7.
|Where||Room 48, third Floor, Purvis Hall, 1020 Pine Ave. West [corner Pine]|
|No. of Credits||4|
|Assessment|| Homework Assignments - 80%
Students will complete several assignments related to datasets provided by the instructor.
Assignments will be graded with emphasis on analysis, interpretation, and discussion
(or as AB used to weight the reports, on Science, Statistics, and Writing) as opposed to
mechanical implementation of statistical and modeling techniques. Choice of appropriate statistical
approach is as important as implementation and the 'correct' final number.
To maximize what can be gained from a discussion, all students
must actively participate in class sessions.
Part of your education is to learn how to verbally express
statistical concepts in discussion and so we expect you to
make efforts to contribute positively to the discussions.
Grades will be assigned according to the following criteria:
• Clarity and conciseness of verbal contributions
• Thoughtfulness of the verbal contributions
• Insights evident in the verbal contributions. Detailed guidelines are given in the footnote below.
McGill University Senate resolution of January 29, 2003 on academic integrity...
McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures. For more details, consult the link below.
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