GUIDELINES FOR STUDENTS
ENROLLED IN THE M.Sc. PROGRAM IN STATISTICS
What follows is drawn from departmental rules, regulations and
practices, and from Graduate Studies regulations. More details can
be obtained from the Statistics
Program Coordinator (Dr N. Balakrishnan), the Chair of the Department
of Mathematics and Statistics (Dr. H. Boden) and the regulations
in Sections
1-6 of the Graduate Calendar. Students must acquaint themselves
with the relevant parts of the Graduate Calendar. The guidelines
below should explain general procedures although they may be adjusted
in particular cases.
The M.Sc. in Statistics can be obtained either by coursework or by thesis.
- Coursework : completion of 8
1-term graduate courses (consisting of 3 compulsory and 5 elective
courses) and attendance to the Statistics Seminar during the Fall and Winter terms. The degree requirements are normally completed in 2 academic terms.
- Thesis: complete 6 1-semester graduate courses (consisting of 3 compulsory
and 3 elective courses) and a thesis. Equivalent in work to 2 1-semester
courses, the thesis is written under the supervision of a faculty
member of the program in a topic of mutual interest to student and
supervisor. All master statistics students are required to attend
the weekly Statistics Seminar during the Fall and Winter terms. The degree requirements are normally completed in 3 academic terms.
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Students must have their selection of courses approved by the Statistics
Program Coordinator. Students are advised to select a project or thesis
supervisor before the end of their first term. The Statistics Program
Coordinator should be informed of this choice as soon as possible.
A student who fails a prescribed graduate, prerequisite undergraduate,
or required extra course, must withdraw from the university unless the
Faculty Committee on Graduate Admissions and Study approves a recommendation
to the contrary from the Committee for Graduate Study in Statistics.
It is essential that a student submitting an M.Sc. thesis or project
follows faithfully the procedures set forth on page nine of the Graduate
Calendar.
All students must attend the Statistics
Seminar, held Tuesdays at 3:30 p.m.
Courses Available For The 2009/10
Academic Year
* indicates a half-course. The term in which it is given is indicated
in parentheses (I = Fall, II = Winter, III = Summer).
Statistics: M.Sc. students are required
to take S743, S752*, S770*.
- S743(I & II)Foundations of Statistics/Hoppe
- S744*(I)Ordered Data Analysis/Balakrishnan
- S744*(II) Topics/Feng
- S752*(I) Design of Experiments/Childs
- S753*(II) Advanced Survey Sampling/Zhu
- S754*(I) Stochastic Processes and Applications/Hoppe
- S770*(I&II) Statistics Seminar/Macdonald
Mathematics:
- M772*(II) Topics in Financial Mathematics/Grasselli
Undergraduate Statistics Courses for Graduate
Credit:
Students may take any of the following courses for graduate credit, provided
that they have not already completed an equivalent undergraduate course.
For the M.Sc. in Statistics, at most two 600-level courses may be taken
for graduate credit.
- S6A3*(I) Time Series/Viveros-Aguilera
- S6D3*(II) Intermediate Probability Theory/Feng
- S6F3* (II) Categorical Data Analysis/Sarr
- S6M3*(I) Multivariate Analysis/Viveros-Aguilera
- S6P3*(II) Advanced Applied Statistics/Macdonald
Clinical Health Sciences (Health Research
Methodology):
- CHS-HRM721* Fundamentals of Health Research and Evaluation
Methods
- CHS-HRM730* Introduction to Research Methods for Randomized Controlled
Trials
- CHS-HRM731* Special Topics in the Analysis of Health Data
- CHS-HRM733* Statistical and Methodologic Issues in Controlled
Clinical Trials
Enrolment in these courses is limited and permission of the instructor
is required.
Computer Science:
Students with appropriate prerequisites may select a half-course in
Computer Science.
- CS6EB3* Database Management System Design
Chemical Engineering:
- CE762* Time Series Analysis and Process Identification
- CE765* Multivariate Statistical Methods for Process Analysis and
Monitoring
Electrical Engineering:
- ECE760* Stochastic Processes
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