# Appendix A - Courses

## Statistics

STAT*6700 Stochastic Processes U [0.50]
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]
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]
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]
Kaplan-Meier estimation, life-table methods, the analysis of censored data, survival and hazard functions, a comparison of parametric and sem-parametric methods, longitudinal data analysis.
STAT*6801 Advanced Data Analysis I U [0.50]
Residual analysis, deletion residuals, influential points,added variable plots, constructed variables, families of transformations, jackknife and bootstrap methods, local linear regression, regression splines and cubic smoothing splines.
STAT*6802 Advanced Data Analysis II U [0.50]
Generalized linear and generalized additive models, linear and nonlinear mixed effects models, parameteric and semiparametric analysis of longitudinal and clustered data, generalized estimating equations, applications to categorical and spatial data.
STAT*6821 Multivariate Analysis U [0.50]
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]
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 samling and MCMC methods.
STAT*6850 Advanced Biometry U [0.50]
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]
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]
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]
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]
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*6960 Design of Experiments and Data Analysis for the Life Sciences * W [0.50]
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*6970 Statistical Consulting Internship U [0.25]
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.
STAT*7010 Strategies for Study Design and Regression Analysis F [0.50]
Exploratory data analysis and review of elementary statistical methods. Design and analysis strategies for both randomized and observational studies. Sample size and power computations. Mised 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): Cannot be used to satisfy departmental MSc/PhD course requirements.
STAT*7020 Data Analysis and Statiscal Inference W [0.50]
Generalized linear models, likelihood theory, generalized estimating equations. Generalized additve models. Linear and non-linear mixed models including multilevel and longitudinal models. Bayesian inference. Methods for handling temporally and spatially correlated data. Event history models. Although secure statistical foundations are laid down, the emphasis is on applications and issues such as matching, stratification, confounding, effect modication, and experimental planning. Statistical software (R, SAS and BUGS) is used throughout.
Prerequisite(s): STAT*7010 or permission of the instructor
Restriction(s): Cannot be used to satisfy departmental MSc/PhD course requirements.