Program Requirements
Training in statistical theory and methods, applied data analysis, scientific collaboration, communication, and report writing by coursework and project.
Research Project (6 credits)
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BIOS 630 Research Project/Practicum in Biostatistics (6 credits)
Overview
Biostatistics : Critical appraisal of the biostatistical literature related to a specific statistical methodology. Topic to be approved by faculty member who will direct student and evaluate the paper.
Terms: Fall 2015, Winter 2016
Instructors: There are no professors associated with this course for the 2015-2016 academic year.
Restriction: Limited to non-thesis M.Sc. students who have completed requirements.
Required Courses (24 credits)
Students exempted from any of the courses listed below must replace them with additional complementary course credits.
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BIOS 601 Epidemiology: Introduction and statistical models (4 credits)
Overview
Biostatistics : Examples of applications of statistics and probability in epidemiologic research. Source of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.
Terms: Fall 2015
Instructors: Hanley, James Anthony (Fall)
Prerequisites: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
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BIOS 602 Epidemiology: Regression Models (4 credits)
Overview
Biostatistics : Multivariable regression models for proportions, rates and their differences/ratios; Conditional logic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox's method; Rate ratio estimation when "time-dependent" membership in contrasted categories.
Terms: Winter 2016
Instructors: Moodie, Erica (Winter)
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MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Modern discrete data analysis. Exponential families, orthogonality, link functions. Inference and model selection using analysis of deviance. Shrinkage (Bayesian, frequentist viewpoints). Smoothing. Residuals. Quasi-likelihood. Contingency tables: logistic regression, log-linear models. Censored data. Applications to current problems in medicine, biological and physical sciences. R software.
Terms: Winter 2016
Instructors: Neslehova, Johanna (Winter)
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MATH 533 Honours Regression and Analysis of Variance (4 credits)
Overview
Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.
Terms: Fall 2015
Instructors: Stephens, David (Fall)
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MATH 556 Mathematical Statistics 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including location-scale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.
Terms: Fall 2015
Instructors: Asgharian-Dastenaei, Masoud (Fall)
Fall
Prerequisite: MATH 357 or equivalent
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MATH 557 Mathematical Statistics 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : Sampling theory (including large-sample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.
Terms: Winter 2016
Instructors: Khalili Mahmoudabadi, Abbas (Winter)
Winter
Prerequisite: MATH 556
Complementary Courses (18 credits)
18 credits of coursework, at the 500 level or higher, chosen in consultation with the student's academic adviser or supervisor.