An interdisciplinary seminar designed to explore communication in public health including: written and oral communication of research to scientific and lay audiences, grant proposal and manuscript writing, poster and oral presentations. All PhD students are required to complete this course. Note: Credit may not be obtained for both PHS 603 and SPH 603.

In depth exploration of current topics in public health research including: epidemiology, health service delivery, health policy, sociobehavioural approaches, occupational and environmental health. All PhD students are required to complete this course. Note: Credit may not be obtained for both PHS 604 and SPH 604.

The course will introduce students interested in careers in health administration and policy development to relevant issues in health law and risk management in the context of the Canadian legal and public health care systems. It will start with a discussion of the Constitutional foundation of health law in Canada with an analysis of Canada's Federal political structure, Federal and Provincial jurisdictions in health care, and the influence of the Canadian Charter of Rights and Freedoms and the Canada Health Act. It will introduce students to administrative structures and related law. The second part of the course will offer a practical exploration of issues that may confront health managers and policy makers, including medical negligence; informed consent; employment and labour law; contract law (e.g., procurement contracts); public health information; privacy and confidentiality; and regulation of health professions. Students will present papers on special topics in public health law such as infectious disease management, HIV/AIDS, vaccines, tobacco control, food-borne illnesses, intersections with criminal justice, Indigenous peoples and public health, and genetics and public health. Note: Credit may not be obtained for both PHS 606 and SPH 605.

A critical, interdisciplinary review of psychosocial health. Theoretical and methodological implications from a variety of disciplinary perspectives are considered. Prerequisite: SPH 501 or consent of Instructor. Note: Credit may not be obtained for both HPS 608 and SPH 608. May contain alternate delivery sections; refer to the Tuition and Fees page in the University Regulations section of the Calendar.

Theoretical approaches and practical issues regarding the provision of health care in Canada with a focus on Indigenous, refugee and immigrant families. Human ecological models, health promotion, and ethical issues will be examined within a framework of cultural diversity. Pre and corequisite: SPH 501 or consent of instructor. Note: Credit may not be obtained for both HECOL 618 and SPH 618. Note: Credit may not be obtained for both HPS 618 and SPH 618. May contain alternate delivery sections; refer to the Tuition and Fees page in the University Regulations section of the Calendar.

Advanced biostatistical methods used to analyze epidemiologic data with an emphasis on multivariable regression. Topics include multiple regression, unconditional and conditional logistic regression and proportional hazards regression. Prerequisite: SPH 519 or consent of Instructor. Note: Credit may not be obtained for both PHS 698 and SPH 619.

A critical examination of intervention strategies, implementation, and research evidence in health promotion practice. Note: Credit may not be obtained for both HPS 602 and SPH 622. May contain alternate delivery sections; refer to the Tuition and Fees page in the University Regulations section of the Calendar.

Theoretical understanding of qualitative and community-based research designs, including phenomenology, grounded theory, ethnography, biography and case study. Methods of data collection such as interviews, focus groups and participant observation. Strategies for data analysis and dissemination. Pre or corequisite: SPH 503 or consent of instructor. Note: Credit may not be obtained for both HPS 603 and SPH 623. Credit may not be obtained for both HECOL 603 and SPH 623. May contain alternate delivery sections; refer to the Tuition and Fees page in the University Regulations section of the Calendar.

Deals with the application of program evaluation for the health and social sciences fields. Emphasis is on the theory of program evaluation using various models, research design, and the application of these concepts by performing a program evaluation. Discussions will be centered around the ethics, reliability, validity, process, outcomes, and implications of various program evaluation models. Current and relevant publications in public health sciences complete this course. Prerequisite: SPH 630 or consent of Instructor. Note: Credit may not be obtained for both PHS 631 and SPH 631.

The aim of this course is to enable students to increase their understanding of historical and current determinants of global health and of the interventions to reduce global health inequities. Note: Credit may not be obtained for both PHS 640 and SPH 640.

This introductory course to global health project development familiarizes students with the logical frame planning approach. This planning method is a must by many international development agencies, e.g. the Canadian International Development Agency (CIDA), the World Bank and many others. Through various stages of problem analysis, objective analysis and the development of the logical frame with planning indictors and assumptions, course participants learn how to apply this method in the context of a developing country. Prerequisite: Permission of the Instructor. Note: Credit may not be obtained for both PHS 641 and SPH 641.

A required course for the Graduate Embedded Certificate in Health Economic Evaluation, this 3-credit course provides an introduction to health economic evaluation. Methodological areas covered include: (1) types of economic evaluation (including cost-effectiveness and cost-utility analysis); (2) defining the target population; (3) comparators; (4) the perspective of the evaluation; (5) time preference and discounting; (6) measuring and valuing health; (7) resource use and costs; (8) uncertainty and probabilistic analysis; (9) equity considerations; and (10) analysis and reporting of economic evaluations. Students will be introduced to Indigenous perspectives on measuring and valuing health, and those of other equity-seeking groups, and will consider the implications for health economic evaluations. By the end of the course, students will be familiar with current best practices for conducting health economic evaluations in Canada. Students will also learn how to construct basic decision analytic models, providing practical experience in applying these methods. No prior economics courses or experience required. Note: Credit may not be obtained for both PHS 671 and SPH 671.

A required course for the Graduate Embedded Certificate in Health Economic Evaluation, this 3-credit course will build upon SPH 671 to provide students with a more advanced understanding of health economic evaluation. Methodological areas covered include: (1) advanced methods for probabilistic analysis; (2) value of information analysis; (3) perspectives on social choice; (4) advanced discounting considerations; (5) controversies in measuring and valuing health; (6) distributional cost-effectiveness analysis; (7) measuring opportunity cost; and (8) advanced modelling methods. Further consideration will be given to incorporating equity-seeking groups' perspectives on measuring and valuing health into health economic evaluations. Students will also learn how to construct complex decision analytic models, providing an opportunity to apply the more advanced methods covered in this course. By the end of the course, students will be familiar with current practices for conducting health economic evaluations in numerous jurisdictions and contexts, including developed and developing countries, and in both public health and health care. Pre- or Co-requisite: SPH 671.

An overview of the nature, science and practicalities of health technology assessment (HTA), which can then be used as the basis for further work and research. Issues covered will include health care technologies and their management, methods used for assessment, sources of information and application of HTA findings to policy and administrative decisions. Emphasis placed on assessments that have been undertaken by national and regional agencies in Canada and other countries to provide information to governments, health care providers and others. Diagnostic, screening, rehabilitation and information technologies will be considered. Note: Credit may not be obtained for both PHS 673 and SPH 673.

Methods for efficiently and critically identifying, appraising, and applying the health sciences literature are learned in an interactive group setting. Topics include studies of prognosis, diagnosis, therapy, causation outcomes research, economic analysis, and systematic reviews. Prerequisite: SPH 596 or 597 or consent of Instructor. Note: Credit may not be obtained for both PHS 693 and SPH 693.

Epidemiologic methods related to specific study designs and general issues relating to the conduct of epidemiologic studies at an advanced level. Topics covered include confounding, interaction, misclassification, matching, ecologic studies, justification of the odds ratio in case-control studies, and age-period-cohort analysis. Prerequisite: SPH 519 and 596 or consent of Instructor. Note: Credit may not be obtained for both PHS 696 and SPH 696.

This course provides a broad introduction to the knowledge needed to investigate and control infectious diseases. It covers the description, causes and modeling of epidemic and endemic infections, as well as intervention and prevention strategies. Selected infectious diseases are used as case studies. These provide understanding of the natural history, evolution, investigation, methods of control, and the costs and benefits of interventions in a legal and ethical policy context. Prerequisites: SPH 596, or equivalent, or permission of Instructor.

Administrative health data have been used widely for decision making and research in Canada and the world. Analysis of these data required knowledge of data features and unique analytical skills since data are not collected for research purposes. This course will help hone students data management and analytical skills to answer research questions using health systems data. Note: Credit may not be obtained for both PHS 699 and SPH 699.

Advanced biostatistical methods for the design and analysis with a special emphasis on applications for health sciences research. Topics include multinomial and ordinal logistic regression, Poisson and negative binomial regression, longitudinal and correlated data analysis methods (including generalized estimating equations and random-effects models), advanced survival analysis, principal component and factor analyses, and propensity score analysis. Prerequisites: SPH 619 or permission of the Instructor. Note: Credit may not be obtained for both PHS 798 and SPH 719.

This is an advanced epidemiology methods course with emphasis on causal inference. Topics covered include causal inference in observational studies, causal diagrams, effect modification, interaction, selection and measurement bias in causal modelling, propensity score analysis, inverse probability weighting and marginal structural models, standardization and the parametric g-formula, instrumental variable estimation, and mediation analysis. The overall goal of this course is to provide an understanding of concepts and practical applications of causal inference and prepare graduates to understand and apply these concepts in epidemiological research. Prerequisites: SPH 619 and 696 or consent of Instructor. Note: Credit may not be obtained for both PHS 766 and SPH 766.

Explores how the elements of story employed by the Gospel writers and editors shaped their understanding of the person of Jesus and his followers. Note: Not open to students with credit in CHRTP 305.

Explores how the Sacred is experienced and expressed through the visual arts, music and dance. Note: Not open to students with credit in CHRTP 311.

A public theology overview of contemporary issues in faith and society. This course underscores the relevance of faith and spirituality to the changing texture of society. Note: Not open to students with credit in CHRTP 312.

Women's relationship to and place in Christianity is explored. Women's attempts to critique and transform received tradition and/or to develop alternative forms of religious life are examined. Note: Not open to students with credit in CHRTP 314.

Considering theological themes in movies, poetry, fiction, and graphic novels that echo lived experiences. Note: Not open to students with credit in CHRTP 315.

Deepen awareness of personal, social, cultural, and spiritual dimensions of sexuality. Explored in light of feminist, queer, traditional and contemporary theological thought. Note: Not open to students with credit in CHRTP 316.

Examines the sacred nature of creation and our inter-connectedness with the non-human world. Explores ways to respond to the urgent call of the earth.

Explores contemplative/meditative practices that foster calm, insight, and compassion.

Discussion of topics relevant to theology, spirituality, spiritual care, and/or ministry studies. May be repeated for credit when course content differs. .

Directed reading or research in a chosen area of theology, spirituality, spiritual care, and/or ministry studies. May be repeated for credit when course content differs.

Explores spirituality as a personal and social response to the human quest for integration and transcendence. Multi-faith and secular perspectives are examined.

Explores ways to cultivate inner resources needed for the embodiment of non-violence. Considers peacemaking through the lenses of spirituality and ethical responsibility.

Discussion of topics relevant to theology, spirituality, spiritual care, and/or ministry studies. May be repeated for credit when course content differs.

Directed reading or research in a chosen area of theology, spirituality, spiritual care, and/or ministry studies. May be repeated for credit when course content differs.

Explores spirituality as a personal and social response to the human quest for integration and transcendence. Multi-faith and secular perspectives are examined.

Explores ways to cultivate inner resources needed for the embodiment of non-violence. Considers peacemaking through the lenses of spirituality and ethical responsibility.

Data collection and presentation, descriptive statistics. Probability distributions, sampling distributions and the central limit theorem. Point estimation and hypothesis testing. Correlation and regression analysis. Goodness of fit and contingency table. Prerequisite: Mathematics 30-1 or 30-2. Notes: (1) Credit can be obtained in at most one of STAT 151, STAT 161, and STAT 235. (2) This course may not be taken for credit if credit has been obtained in STAT 222, STAT 266, STAT 276, KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151, SOC 210 or SOC 320.

Data collection and presentation, descriptive statistics. Probability distributions, sampling distributions and the central limit theorem. Point estimation and hypothesis testing. Correlation and regression analysis. Goodness of fit and contingency table. Prerequisite: Mathematics 30-1 or 30-2. Notes: (1) Credit can be obtained in at most one of STAT 151, STAT 161, and STAT 235. (2) This course may not be taken for credit if credit has been obtained in STAT 222, STAT 266, STAT 276, KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151, SOC 210 or SOC 320.

Data collection and presentation, descriptive statistics. Probability distributions, sampling distributions and the central limit theorem. Point estimation and hypothesis testing. Correlation and regression analysis. Goodness of fit and contingency table. Prerequisite: Mathematics 30-1 or 30-2. Notes: (1) Credit can be obtained in at most one of STAT 151, STAT 161, and STAT 235. (2) This course may not be taken for credit if credit has been obtained in STAT 222, STAT 266, STAT 276, KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151, SOC 210 or SOC 320.

Data collection and presentation, descriptive statistics. Probability distributions, sampling distributions and the central limit theorem. Point estimation and hypothesis testing. Correlation and regression analysis. Goodness of fit and contingency table. Use of a microcomputer software package for statistical analyses in business and economics. Prerequisite: Mathematics 30-1 or 30-2. Notes: (1) Credit can be obtained in at most one of STAT 151, STAT 161, and STAT 235. (2) This course may not be taken for credit if credit has been obtained in obtained in STAT 222, STAT 266, STAT 276, KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151 or SOC 320.

Induction; principles of counting, multinomial coefficients, negative binomial distribution; maximum likelihood estimation, probability axioms; conditional probability, Bayes' rule; independence; probability mass, distribution, and moment generating functions; strong law of large numbers; conditional expectation estimators; gambler's ruin; transience and recurrence; compound processes; applications. Corequisite: One of MATH 101, 118, 136, 146, or 156. Prerequisite: One of MATH 125 or 127. Notes: (1) Credit can be obtained in at most two of STAT 181, STAT 265, or STAT 281. (2) Credit cannot be obtained in STAT 181 if credit has already been obtained in MATH 181.

Descriptive data analysis. Calculus of Probability. Binomial, multinomial, Poisson, normal, beta, exponential, gamma, hypergeometric, and Weibull distributions. Sampling distributions. Estimation, testing hypotheses, goodness-of-fit tests, and one-way analysis of variance. Linear correlation and regression. Sampling. Quality control. Use of a microcomputer software package for statistical analyses in engineering applications. Prerequisite: MATH 100. Corequisite: MATH 101. Notes: (1) This course may not be taken for credit if credit has already been obtained in one of STAT 151, 161, 222, 265, 266, 276, 281; KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151 or SOC 320. (2) Intended for Engineering students. (2) Intended for Engineering students. Other students who take this course will receive 3.0 units.

Descriptive data analysis. Calculus of Probability. Binomial, multinomial, Poisson, normal, beta, exponential, gamma, hypergeometric, and Weibull distributions. Sampling distributions. Estimation, testing hypotheses, goodness-of-fit tests, and one-way analysis of variance. Linear correlation and regression. Sampling. Quality control. Use of a microcomputer software package for statistical analyses in engineering applications. Prerequisite: MATH 100. Corequisite: MATH 101. Notes: (1) This course may not be taken for credit if credit has already been obtained in one of STAT 151, 161, 222, 265, 266, 276, 281; KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151 or SOC 320. (2) Intended for Engineering students. (2) Intended for Engineering students. Other students who take this course will receive 3.0 units.

Descriptive data analysis. Calculus of Probability. Binomial, multinomial, Poisson, normal, beta, exponential, gamma, hypergeometric, and Weibull distributions. Sampling distributions. Estimation, testing hypotheses, goodness-of-fit tests, and one-way analysis of variance. Linear correlation and regression. Sampling. Quality control. Use of a microcomputer software package for statistical analyses in engineering applications. Prerequisite: MATH 100. Corequisite: MATH 101. Notes: (1) This course may not be taken for credit if credit has already been obtained in one of STAT 151, 161, 222, 265, 266, 276, 281; KIN 109, PEDS 109, PSYCH 211, PTHER 352, SCI 151 or SOC 320. (2) Intended for Engineering students. (2) Intended for Engineering students. Other students who take this course will receive 3.0 units.

Methods in applied statistics including regression techniques, analysis of variance and covariance, and methods of data analysis. Applications are taken from Biological, Physical and Social Sciences, and Business. Prerequisite: One of STAT 141, 151, 161, 235 or SCI 151. Notes: (1) Credit can be obtained in at most one of STAT 252, 319, 337 or 341, or AREC 313. (2) This course may not be taken for credit if credit has already been obtained in STAT 368 or 378.

Sample space, events, combinatorial probability, conditional probability, independent events, Bayes Theorem, random variables, discrete random variables, expected values, moment generating function, inequalities, continuous distributions, multivariate distributions, independence. Corequisite: One of MATH 209, 214 or 217. Note: Credit can be obtained in at most two of STAT 181, STAT 281, or STAT 265.

Functions of random variables, sampling distributions, Central Limit Theorem, law of large numbers, statistical models for the data, likelihood, parameters and their interpretation, objectives of statistical inference, point and interval estimation, method of moments, basic notions of testing of hypotheses, errors of the first and second kind, significance level, power, p-value. Prerequisites: one of MATH 209, MATH 214, or MATH 217 and one of STAT 265 or STAT 281. Corequisites: One of MATH 225 or 227. Credit can only be obtained in one of STAT 266 or STAT 276.

Fundamental principles of statistical learning and inference for data science Understanding of types of analytics, probability, variability, relationship between variables, probability distributions, law of large numbers, Central Limit Theorem, hypothesis testing and statistical significance, and elementary theory of regression. Prerequisite: STAT 281 or STAT 265. Students presenting STAT 265 must also present one of MATH 117 or MATH 216 as corequisite. Credit can only be obtained in one of STAT 266 or STAT 276.

Review of binomial and negative binomial distributions; continuous random variables; uniform, exponential, and gamma distributions; conditional probability; properties of conditional expectation; stochastic processes; finite-dimensional distributions, Poisson approximation; Poisson measures; counting processes, Markov queues, customer time in queues; steady-state distributions; applications. Corequisite: One of MATH 209, 214, or 217. Notes: (1) Credit can be obtained in at most two of STAT 181, STAT 265, or STAT 281. (2) Credit cannot be obtained in STAT 281 if credit has already been obtained in STAT 371.

Methods of data analysis useful in Biostatistics including analysis of variance and covariance and nested designs, multiple regression, logistic regression and log-linear models. The concepts will be motivated by problems in the life sciences. Applications to real data will be emphasized through the use of a computer package. Prerequisite: STAT 151, STAT 161, or SCI 151 and a 200-level Biological Science course. Notes : (1) Credit can be obtained in at most one of STAT 252, STAT 337, and AREC 313. (2) This course may not be taken for credit if credit has already been obtained in STAT 368 or 378.

Time at death random variables, continuous and discrete insurances, endowments and varying annuities, net premiums and reserves. Prerequisites: MATH 253 and one of STAT 265 or STAT 281.

Simple random sampling from finite populations, stratified sampling, regression estimators, cluster sampling. Prerequisite: One of STAT 266 or STAT 276, or STAT 235 with consent of the Department. Note: This course may only be offered in alternate years.

Basic principles of experimental design, completely randomized design-one way ANOVA and ANCOVA, randomized block design, Latin square design, Multiple comparisons. Nested designs. Factorial experiments. Prerequisite: One of STAT 266 or STAT 276, or STAT 235 with consent of the Department.

Problem solving of classical probability questions, random walk, gambler's ruin, Markov chains, branching processes. Selected topics of the instructor's choice. Prerequisite: STAT 265. Note: Credit can be obtained in at most one of STAT 281 or STAT 371.

Laws of large numbers, weak convergence, some asymptotic results, delta method, maximum likelihood estimation, testing, UMP tests, LR tests, nonparametric methods (sign test, rank test), robustness, statistics and their sensitivity properties, prior and posterior distributions, Bayesian inference, conjugate priors, Bayes estimators. Prerequisites: STAT 266 or STAT 276.

Simple linear regression analysis, inference on regression parameters, residual analysis, prediction intervals, weighted least squares. Multiple regression analysis, inference about regression parameters, multicollinearity and its effects, indicator variables, selection of independent variables. Non-linear regression. Prerequisite: One of STAT 266 or STAT 276, or STAT 235 with consent of the Department.

Survey of contemporary languages/environments suitable for algorithms of Statistics and Data Science. Introduction to Monte Carlo methods, random number generation and numerical integration in statistical context and optimization for both smooth and constrained alternatives, tailored to specific applications in statistics and machine learning. Prerequisites: One of STAT 265 or STAT 281 and one of STAT 266 or STAT 276, or consent of the Department.

Survival models, model estimation from complete and incomplete data samples, parametric survival models with concomitant variables, estimation of life tables from general population data. Prerequisites: STAT 372 and 378.

Methods of data analysis useful in applied research, including repeated measures and longitudinal data analysis, non-linear regression, survival analysis, multivariate techniques. Applications to real data will be emphasized, including case studies and real data applications. Each researcher works on a project to present, highlighting the methods used in the project. Prerequisite: STAT 252 or 337 or consent of the instructor.

Review of linear and nonlinear regression and brief introduction to generalized linear models, the course covers selected methods of dimension reduction (principal components, factor analysis, canonical correlations), of unsupervised (clustering, multidimensional scaling ordination) and supervised classification (discriminant analysis, logistic regression, nearest neighbours - including, among others, the machine learning methods like classification trees, neural networks, and support vector machines). Prerequisite: STAT 378.

Classical ruin theory, individual risk models, collective risk models, models for loss severity: parametric models, tail behavior, models for loss frequency, mixed Poisson models; compound Poisson models, convolutions and recursive methods, probability and moment generating functions. Prerequisite: One of STAT 371 or STAT 281.

Probability spaces, algebra of events. Elements of combinatorial analysis. Conditional probability, stochastic independence. Special discrete and continuous distributions. Random variables, moments, transformations. Basic limit theorems. Prerequisite: One of STAT 371 or STAT 281.

Stationary series, spectral analysis, models in time series: autoregressive, moving average, ARMA and ARIMA. Smoothing series, computational techniques and computer packages for time series. Prerequisites: STAT 372 and 378. Note: This course may only be offered in alternate years.

This course is designed to give credit to mature and able students for reading in areas not covered by courses, under the supervision of a staff member. A student, or group of students, wishing to use this course should find a staff member willing to supervise the proposed reading program. A detailed description of the material to be covered should be submitted to the Chair of the Department Honors Committee. (This should include a description of testing methods to be used.) The program will require the approval of both the Honors Committee, and the Chair of the Department. The students' mastery of the material of the course will be tested by a written or oral examination. This course may be taken in Fall or Winter and may be taken any number of times, subject always to the approval mentioned above. Prerequisite: Any 300-level STAT course.

This topics course is designed for new course offerings that may be offered in a given term. Prerequisites: One of STAT 266 or 276. Additional prerequisites may be required. Note: Credit for this course may be obtained more than once.

This course provides students in Major, Specialization and Honors programs an opportunity to pursue research in statistics under the direction of a member of the Department. Course requirements include at least one oral presentation and a written final report. Students interested in taking this course should contact the course coordinator two months in advance. Credit for this course may be obtained more than once. Prerequisites: a 300-level STAT course and consent of the course coordinator.

Introduction to mathematical techniques commonly used in theoretical Statistics, with applications. Applications of diagonalization results for real symmetric matrices, and of continuity, differentiation, Riemann-Stieltjes integration and multivariable calculus to the theory of Statistics including least squares estimation, generating functions, distribution theory. Prerequisite: consent of Department.

Introduction to contemporary computational culture: reproducible coding, literate programming. Monte Carlo methods: random number generation, variance reduction, numerical integration, statistical simulations. Optimization (linear search, gradient descent, Newton-Raphson, method of scoring, and their specifics in the statistical context), EM algorithm. Fundamentals of convex optimization with constraints. Prerequisites: consent of the instructor.

This course is designed to equip students with essential statistical knowledge and skills necessary for the successful clinical trial design and analysis. This course covers a wide range of statistical topics specific to clinical trials, including intention-to-treat versus efficacy trials, principles of sampling and exclusion, methods of allocation and techniques of randomization, parallel versus cross over design, cluster randomization designs, statistical analysis planning, external and internal validation, and reports of statistical findings. Additionally, the course will explore other selected topics related to logistical issues in the management of clinical trials. Prerequisite: consent of the instructor. Notes: Students outside of the course-based MSc with a specialization in Biostatistics need permission from the Department to enroll in this course. Thesis-based graduate students in Mathematical and Statistical Sciences cannot take this course for credit.

This course is a continuation of Statistics for Clinical Trials I, with a focus on statistical computation and data analysis techniques specifically tailored for clinical trials. Students will work with the R and SAS statistical programming languages to gain a comprehensive understanding of these methods in the clinical trials context. The primary goal is to equip graduate students with the statistical skills required for data analysis in clinical trials. Successful students will become proficient in using statistical computational tools to analyze real-world clinical datasets and will be exposed to advanced statistical techniques and best practices for data storage, management, and analysis. Key statistical topics covered in this course include sampling designs, chi-square tests, linear models, mixed-effects models for repeated measurements and survival analysis. Prerequisite: STAT 514. Notes: Students outside of the course-based MSc with a specialization in Biostatistics need permission from the Department to enroll in this course. Thesis-based graduate students in Mathematical and Statistical Sciences cannot take this course for credit.

Survival and hazard functions, censoring, truncation. Non-parametric, parametric and semi-parametric approaches to survival analysis including Kaplan-Meier estimation and Cox's proportional hazards model. Prerequisite: STAT 372 or consent of Department.

Review of basic statistical concepts of inference and probability theory. Includes applied methods of Linear and non-linear regression and analysis of variance for designed experiments, multiple comparisons, correlations, modeling and variable selection, multicollinearity, predictions, confounding and Simpson's paradox. Includes case studies and real data applications. Each researcher works on a project to present, highlighting the methods used in the project. Prerequisite: STAT 437 equivalent or consent of the instructor.

The course focuses on statistical learning techniques, in particular those of supervised classification, both from statistical (logistic regression, discriminant analysis, nearest neighbours, and others) and machine learning background (tree-based methods, neural networks, support vector machines), with the emphasis on decision-theoretic underpinnings and other statistical aspects, flexible model building (regularization with penalties), and algorithmic solutions. Selected methods of unsupervised classification (clustering) and some related regression methods are covered as well. Prerequisite: Consent of the instructor.

Classical ruin theory, individual risk models, collective risk models, models for loss severity: parametric models, tail behavior, models for loss frequency, mixed Poisson models; compound Poisson models, convolutions and recursive methods, probability and moment generating functions. Prerequisite: STAT 371 or equivalent. Note: Cannot be used for credit towards a thesis-based graduate program in the Department of Mathematical and Statistical Sciences.

Review of basic sampling schemes: simple random sampling, and stratified random sampling, and systematic sampling. Multistage sampling schemes. Estimation of nonlinear parameters: ratios, regression coefficients, and correlation coefficients. Variance estimation techniques: linearization, BRR, jackknife, and bootstrap. Selected topics: model-based estimation, regression analysis from complex survey data. Relevant computer packages. Prerequisites: STAT 361, 372, 471.

Sampling models and methods of inference for discrete data. Maximum likelihood estimation for complete contingency tables, measures of association and agreement. Goodness-of-fit. Incomplete tables. Analysis of square tables; symmetry and marginal homogeneity. Model selection and closeness of fit; practical aspects. Chi-square tests for categorical data from complex surveys. Prerequisite: STAT 372 or 471.

An introduction to the theory of statistical inference. Topics to include exponential families and general linear models, likelihood, sufficiency, ancillarity, interval and point estimation, asymptotic approximations. Optional topics as time allows, may include Bayesian methods, Robustness, resampling techniques. This course is intended primarily for MSc students. Prerequisite: STAT 471 or consent of Department.

The general linear model. Fully randomized designs, one-way layout, multiple comparisons. Block designs, Latin squares. Factorial designs confounding, fractions. Nested designs, randomization restrictions. Response surface methodology. Analysis of covariance. Prerequisite: STAT 368 and a 400-level STAT course.

Probability Measures, Lebesgue Measure on R, Random Variables, Probability Densities and Distributions, Lebesgue-Stieltjes integration, Independence, Convergence (almost surely, in probability, and in distribution), Product Probabilities, Key Inequalities (Markov, Chebyshev, Hölder, Jensen, and Paley-Zygmund), Characteristic functions, Law of Large Numbers, Central Limit Theorem, Concentration Inequalities, Applications to problems in analysis, geometry, and combinatorics. Prerequisite: STAT 512.

The multivariate normal distribution, multivariate regression and analysis of variance, classification, canonical correlation, principal components, factor analysis. Prerequisite: STAT 372 and STAT 512.

Multiple linear regression, ordinary and generalized least squares, partial and multiple correlation. Regression diagnostics, collinearity, model building. Nonlinear regression. Selected topics: robust and nonparametric regression, measurement error models. Prerequisites: STAT 378 and a 400-level statistics course.

Elements of stochastic processes. Discrete and continuous time Markov Chains; Birth and Death processes. Branching processes. Brownian Motion. General Stationary and Markov processes. Examples. Prerequisite: STAT 471 or consent of Instructor.

Data analysis, problem solving, oral communication with clients, issues in planning experiments and collecting data; practical aspects of consulting and report writing. Corequisite: STAT 568 and 578 or their equivalents.

Students will be supervised by an individual staff member to participate in areas of research interest of that staff member. Students can register only with the permission of the Chair of the Department in special circumstances. Will not be counted toward the minimum course requirement for graduate credits.

Advanced statistical design and methods. Topics include sampling, multivariate techniques, survival analysis, power and sample size, linear and non linear regression, longitudinal and repeated measures data, and analysis of seasonal or time series data. Topics to be covered can be influenced by the registered researchers. Includes critical review and case studies with real data applications. Each researcher works on a project to present, highlighting the methods used in the project. Prerequisite: STAT 537 or equivalent or consent of the instructor. Cannot be used for credit towards a graduate program in Statistics.

Modern methods of statistical inference. Various versions of likelihood: conditional, marginal, integrated, profile, partial, empirical. Estimating equations. Semi-parametric models. Foundational issues. Prerequisites: STAT 512 and 566.

Approximation techniques and asymptotic methods in statistics. Topics may include second and higher order expansions, asymptotics of likelihood based estimation and testing. Edgeworth expansions, exponential tilting, asymptotic relative efficiency, U-, M-, L-, and R-estimation. Prerequisites: STAT 566 or 664 and 512 or the equivalent.

Open only to students taking the MSc non-thesis option in statistics.

Open only to students taking the MSc non-thesis option in statistics.

Open only to students taking the MSc non-thesis option in statistics.