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Master of Science (M.Sc.); Biostatistics (Thesis) (48 credits)

Offered by: Epidemiology and Biostatistics     Degree: Master of Science

Program Requirements

Training in statistical theory and methods, applied data analysis, scientific collaboration, communication, and report writing by coursework and thesis.

Thesis Courses (24 credits)

  • BIOS 690 M.Sc. Thesis (24 credits)

    Offered by: Epidemiology and Biostatistics (Faculty of Medicine)

    Administered by: Graduate Studies

    Overview

    Biostatistics : A review, appraisal of the performance, or application of, selected biostatistical methods, carried out under supervision.

    Terms: Fall 2013, Winter 2014, Summer 2014

    Instructors: There are no professors associated with this course for the 2013-2014 academic year.

Required Courses (24 credits)

Students exempted from any of the courses listed below must replace them with complementary course credits, at the 500 level or higher, chosen in consultation with the student's academic adviser or supervisor.

  • BIOS 601 Epidemiology: Introduction and statistical models (4 credits)

    Offered by: Epidemiology and Biostatistics (Faculty of Medicine)

    Administered by: Graduate Studies

    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 2013

    Instructors: James Anthony Hanley (Fall)

    • Prerequisites: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
  • BIOS 602 Epidemiology: Regression Models (4 credits)

    Offered by: Epidemiology and Biostatistics (Faculty of Medicine)

    Administered by: Graduate Studies

    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 2014

    Instructors: Olli Saarela (Winter)

    • Prerequisites: Permission of instructor. MATH 556 and BIOS 601, or their equivalents.
  • MATH 523 Generalized Linear Models (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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. Sliced inverse regression. Contingency tables: logistic regression, log-linear models. Censored data. Applications to current problems in medicine, biological and physical sciences. GLIM, S, software.

    Terms: Winter 2014

    Instructors: Johanna Neslehova (Winter)

    • Winter
    • Prerequisite: MATH 423
    • Restriction: Not open to students who have taken MATH 426
  • MATH 533 Honours Regression and Analysis of Variance (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.

    Terms: Fall 2013

    Instructors: Abbas Khalili Mahmoudabadi (Fall)

    • Prerequisites: MATH 357, MATH 247 or MATH 251.
    • Restriction: Not open to have taken or are taking MATH 423.
    • Note: An additional project or projects assigned by the instructor that require a more detailed treatment of the major results and concepts covered in MATH 423.
  • MATH 556 Mathematical Statistics 1 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2013

    Instructors: Johanna Neslehova (Fall)

    • Fall
    • Prerequisite: MATH 357 or equivalent
  • MATH 557 Mathematical Statistics 2 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2014

    Instructors: Masoud Asgharian-Dastenaei (Winter)

    • Winter
    • Prerequisite: MATH 556
Faculty of Medicine—2013-2014 (last updated Jul. 30, 2013) (disclaimer)