Appendix A - Courses
Statistics
| STAT*6010 Strategies for Study Design and Regression Analysis U [0.50] | |
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| Exploratory data analysis and review of elementary statistical methods. Design and analysis strategies for both randomized and observational studies. Sample size and power computations. Mixed models. Missing data techniques. Linear, logistic and Poisson regression. The focus is on problem formulation and associated study designs and analyses for real-world problems. Statistical software (R and SAS) is used throughout. | |
| Prerequisite(s): | Honours degree with 1.5 stat credits, 1 math credit, or relevant work experience |
| Restriction(s): | Students registered in the Graduate Diploma in Applied Statistics. Cannot be used to satisfy departmental MSc/PhD requirements. |
| STAT*6020 Data Analysis and Statistical Inference U [0.50] | |
|---|---|
| Generalized linear and additive models, likelihood theory, Bayesian inference. Multilevel, longitudinal, and event history models. Methods for temporally and spatially correlated data. Although secure statistical foundations are laid down, the emphasis is on applications and experimental planning. Statistical software (R, SAS, BUGS) is used throughout. | |
| Restriction(s): | Students registered in the Graduate Diploma in Applied Statistics. Cannot be used to satisfy departmental MSc/PhD requirements. |
| STAT*6098 Graduate Diploma Project in Applied Statistics U [0.50] | |
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| A project leading to a technical report, which utilizes statistical principles and procedures in the solution of a substantive research problem. Completion of this course requires a formal presentation of the project to faculty and students. | |
| Restriction(s): | Students registered in the Graduate Diploma in Applied Statistics. Cannot be used to satisfy departmental MSc/PhD requirements. |
| STAT*6550 Computational Statistics U [0.50] |
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| This course covers the implementation of a variety of computational statistics techniques. These include random number generation, Monte Carlo methods, non-parametric techniques, Markov chain Monte Carlo methods, and the EM algorithm. A significant component of this course is the implementation of techniques. |
| STAT*6700 Stochastic Processes U [0.50] |
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| The content of this course is to introduce Brownian motion leading to the development of stochastic integrals thus providing a stochastic calculus. The content of this course will be delivered using concepts from measure theory and so familiarity with measures, measurable spaces, etc., will be assumed. |
| STAT*6721 Stochastic Modelling U [0.50] |
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| Topics include the Poisson process, renewal theory, Markov chains, Martingales, random walks, Brownian motion and other Markov processes. Methods will be applied to a variety of subject matter areas. |
| STAT*6741 Statistical Analysis for Reliability and Life Testing U [0.50] |
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| Statistical failure models, order statistics, point and interval estimation procedures for life time distributions, testing reliability hypotheses, Bayes methods in reliability, system reliability. |
| STAT*6761 Survival Analysis U [0.50] |
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| Kaplan-Meier estimation, life-table methods, the analysis of censored data, survival and hazard functions, a comparison of parametric and semi-parametric methods, longitudinal data analysis. |
| STAT*6801 Statistical Learning U [0.50] |
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| Topics include: nonparametric and semiparametric regression; kernel methods; regression splines; local polynomial models; generalized additive models; classification and regression trees; neural networks. This course deals with both the methodology and its application witha appropriate software. Areas of application include biology, economics, engineering and medicine. |
| STAT*6802 Generalized Linear Models and Extensions U [0.50] |
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| Topics include: generalized linear models; generalized linear mixed models; joint modelling of mean and dispersion; generalized estimating equations; modelling longitudinal categorical data; modelling clustered data. This course will focus both on theory and implementation using relevant statistical software. |
| STAT*6821 Multivariate Analysis U [0.50] |
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| This is an advanced course in multivariate analysis and one of the primary emphases will be on the derivation of some of the fundamental classical results of multivariate analysis. In addition, topics that are more current to the field will also be discussed such as: multivariate adaptive regression splines; projection pursuit regression; and wavelets. |
| STAT*6841 Statistical Inference U [0.50] |
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| Bayesian and likelihood methods, large sample theory, nuisance parameters, profile, conditional and marginal likelihoods, EM algorithms and other optimization methods, estimating functions, MonteCarlo methods for exploring posterior distributions and likelihoods, data augmentation, importance sampling and MCMC methods. |
| STAT*6850 Advanced Biometry U [0.50] |
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| Topics on advanced techniques for analyzing data from biological systems. In particular, univariate discrete models, stochastic processes as it relates to population dynamics and growth models with time dependencies, generalized discrete models for spatial patterns in wildlife, the theoretical foundation and recent results in aquatic bioassays, and other topics relating to the student's research interest. |
| STAT*6860 Linear Statistical Models U [0.50] |
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| Generalized inverses of matrices; distribution of quadratic and linear forms; regression or full rank model; models not of full rank; hypothesis testing and estimation for full and non-full rank cases; estimability and testability; reduction sums of squares; balanced and unbalanced data; mixed models; components of variance. |
| STAT*6870 Experimental Design U [0.50] |
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| This is an advanced course in experimental design which emphasizes proofs of some of the fundamental results in the topic. The topics will include: design principles; design linear models; designs with several factors; confounding in symmetrical factorials; fractional factorials. |
| STAT*6880 Sampling Theory U [0.50] |
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| Theory of equal and unequal probability sampling. Topics in: simple random, systematic, and stratified sampling; ratio and regression estimates; cluster sampling and subsampling; double sampling procedure and repetitive surveys; nonsampling errors. |
| STAT*6950 Statistical Methods for the Life Sciences F [0.50] |
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| Analysis of variance, completely randomized, randomized complete block and latin square designs; planned and unplanned treatment comparisons; random and fixed effects; factorial treatment arrangements; simple and multiple linear regression; analysis of covariance with emphasis on the life sciences. STAT*6950 and STAT*6960 are intended for graduate students of other departments and may not normally be taken for credit by mathematics and statistics graduate students. |
| STAT*6960 Design of Experiments and Data Analysis for the Life Sciences W [0.50] |
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| Principles of design; randomized complete block; latin square and extensions the split plot and extension; incomplete block designs; confounding and fractional replication of factorial arrangements; response surfaces the analysis of series of experiments; the general linear model; multiple regression and data analytic techniques. STAT*6950 and STAT*6960 are intended for graduate students of other departments and may not normally be taken for credit by mathematics and statistics graduate students. |
| STAT*6970 Statistical Consulting Internship U [0.25] |
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| This course provides experience in statistical consulting in a laboratory and seminar environment. The student will participate in providing statistical advice and/or statistical analyses and participate in seminar discussions of problems arising from research projects in various disciplines. |