This course introduces students to statistical software, such as R and SAS, and its use in analyzing data. Emphasis will be placed on communication and explanation of findings. Students will be required to write a statistical report.

The principles of proper collection of data for statistical analysis, and techniques to adjust statistical analyses when these principles cannot be implemented. Topics include: relationships among variables, causal relationships, confounding, random sampling, experimental designs, observational studies, experiments, causal inference, meta-analysis. Statistical analyses using SAS or R.

Students enrolled in the Minor program in Applied Statistics should take STAC53H3 instead.

Statistical models for categorical data. Contingency tables, generalized linear models, logistic regression, multinomial responses, logit models for nominal responses, log-linear models for two-way tables, three-way tables and higher dimensions, models for matched pairs, repeated categorical response data, correlated and clustered responses. Statistical analyses using SAS or R.

This course introduces the principles, objectives and methodologies of data collection. The course focuses on understanding the rationale for the various approaches to collecting data and choosing appropriate statistical techniques for data analysis. Topics covered include elements of sampling problems, simple random sampling, stratified sampling, ratio, regression, and difference estimation, systematic sampling, cluster sampling, elements of designed experiments, completely randomized design, randomized block design, and factorial experiments. The R statistical software package is used to illustrate statistical examples in the course. Emphasis is placed on the effective communication of statistical results.

Principles of statistical reasoning and theories of statistical analysis. Topics include: statistical models, likelihood theory, repeated sampling theories of inference, prior elicitation, Bayesian theories of inference, decision theory, asymptotic theory, model checking, and checking for prior-data conflict. Advantages and disadvantages of the different theories.

This course continues the development of probability theory begun in STAB52H3. Topics covered include finite dimensional distributions and the existence theorem, discrete time Markov chains, discrete time martingales, the multivariate normal distribution, Gaussian processes and Brownian motion.

This course continues the development of probability theory begun in STAC62H3. Probability models covered include branching processes, birth and death processes, renewal processes, Poisson processes, queuing theory, random walks and Brownian motion.

A fundamental statistical technique widely used in various disciples. The topics include simple and multiple linear regression analysis, geometric representation of regression, inference on regression parameters, model assumptions and diagnostics, model selection, remedial measures including weighted least squares, instruction in the use of statistical software.

A mathematical treatment of option pricing. Building on Brownian motion, the course introduces stochastic integrals and Itô calculus, which are used to develop the Black-Scholes framework for option pricing. The theory is extended to pricing general derivatives and is illustrated through applications to risk management.

The course discusses many advanced statistical methods used in the life and social sciences. Emphasis is on learning how to become a critical interpreter of these methodologies while keeping mathematical requirements low. Topics covered include multiple regression, logistic regression, discriminant and cluster analysis, principal components and factor analysis.

Linear algebra for statistics. Multivariate distributions, the multivariate normal and some associated distribution theory. Multivariate regression analysis. Canonical correlation analysis. Principal components analysis. Factor analysis. Cluster and discriminant analysis. Multidimensional scaling. Instruction in the use of SAS.

An overview of methods and problems in the analysis of time series data. Topics covered include descriptive methods, filtering and smoothing time series, identification and estimation of times series models, forecasting, seasonal adjustment, spectral estimation and GARCH models for volatility.

Statistical aspects of supervised learning: regression, regularization methods, parametric and nonparametric classification methods, including Gaussian processes for regression and support vector machines for classification, model averaging, model selection, and mixture models for unsupervised learning. Some advanced methods will include Bayesian networks and graphical models.

A survey of statistical techniques used in finance. Topics include mean-variance and multi-factor analysis, simulation methods for option pricing, Value-at-Risk and related risk-management methods, and statistical arbitrage. A computer package will be used to illustrate the techniques using real financial data.

Presents theoretical foundations of machine learning. Risk, empirical risk minimization, PAC learnability and its generalizations, uniform convergence, VC dimension, structural risk minimization, regularization, linear models and their generalizations, ensemble methods, stochastic gradient descent, stability, online learning.

Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. Big data sets include data with high-dimensional features and massive sample size. This course introduces the statistical principles and computational tools for analyzing big data: the process of acquiring and processing large datasets to find hidden patterns and gain better understanding and prediction, and of communicating the obtained results for maximal impact. Topics include optimization algorithms, inferential analysis, predictive analysis, and exploratory analysis.

Correlation does not imply causation. Then, how can we make causal claims? To answer this question, this course introduces theoretical foundations and modern statistical and graphical tools for making causal inference. Topics include potential outcomes and counterfactuals, measures of treatment effects, causal graphical models, confounding adjustment, instrumental variables, principal stratification, mediation and interference.

Topics of interest in Statistics, as selected by the instructor. The exact topics can vary from year to year. Enrolment is by permission of the instructor only.

This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and must be completed by the last day of classes in the session in which it is taken.

This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and must be completed by the last day of classes in the session in which it is taken.

A significant project in any area of statistics. The project may be undertaken individually or in small groups. This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and the project must be completed by the last day of classes in the session in which it is taken.

A significant project in any area of statistics. The project may be undertaken individually or in small groups. This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and the project must be completed by the last day of classes in the session in which it is taken.

A general introduction to theatre as a social institution and collaborative performing art. Through a combination of lectures, discussions, class exercises, and excursions to see theatre together throughout Toronto, this course will investigate why and how people commit their lives to make theatre. It will also orient students to the four areas of focus in the Theatre and Performance program's curriculum, providing a background for further theatre studies.

An introduction to the actor’s craft. This course provides an experiential study of the basic physical, vocal, psychological and analytical tools of the actor/performer, through a series of group and individual exercises.

This course challenges students to "wrestle" with the Western canon that has dominated the practice of theatre-making in colonized North America. In wrestling with it, students will become more conversant in its forms and norms, and thus better able to enter into dialogue with other theatre practitioners and scholars. They also learn to probe and challenge dominant practices, locating them within the cultural spheres and power structures that led to their initial development.

Intercultural & Global Theatre will be a study of theatre and performance as a forum for cultural representation past and present. Students will think together about some thorny issues of intercultural encounter and emerge with a fuller understanding of the importance of context and audience in interpreting performances that are more likely than ever to travel beyond the place they were created.

This course explores the history of performance on this part of Turtle Island as a way of reimagining its future. Through a series of case studies, students will grow their understanding of theatre a powerful arena for both shoring up and dismantling myths of the "imagined nation" of Canada. With a special focus on Indigenous-settler relations and the contributions of immigrant communities to diversifying the stories and aesthetics of the stage, the course will reveal theatre as an excellent forum for reckoning with the past and re-storying our shared future.

By performing characters and staging scenes in scripted plays, students in this course develop and hone the physical, psychological, analytical, and vocal skills of actors.

This course engages students in an experiential study of devised theatre, a contemporary practice wherein a creative team (including actors, designers, writers, dramaturgs, and often a director) collaboratively create an original performance without a preexisting script. We will explore how an ensemble uses improvisation, self-scripted vignettes, movement/dance, and found materials to create an original piece of theatre.

This course introduces students to improvisation across a range of theatrical contexts. In a sequence of short units, the course will explore improv comedy, improvisation-based devising work, and the improvisation structures commonly used in the context of applied theatre work (including forum theatre and playback theatre). Simultaneously, students will read scholarly literature that addresses the ethical dilemmas, cultural collisions, and practical conundrums raised by these forms. Students will reflect on their own experiences as improvisers through the vocabulary that has been developed in this literature.