An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests.

Statistical inference, experimental design, sampling design, confidence intervals and hypothesis tests for means and proportions, regression and correlation.

Descriptive statistics and graphs, probability and distributions. Sampling, hypothesis testing, and confidence intervals. Experimental design and analysis of variance. Regression and correlation, including multiple regression. Applications emphasized.

An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals and hypothesis tests. Emphasis will be placed on health-related applications.

An introduction to statistics with emphasis on the applied probability models used in Electrical and Civil Engineering and elsewhere. Topics covered include samples, probability, probability distributions, estimation (including comparison of means), correlation and regression.

A data-driven introduction to statistics intended primarily for students in Chemical and Mechanical Engineering. Exploratory data analysis, probability, the Binomial, Poisson, Normal, Chi-Square and F distributions. Estimation, correlation and regression (model building and parameter estimation), analysis of variance, design of experiments.

An introductory course in the application of statistical methods, intended for students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation.

Modeling deterministic systems with differential equations: first and second order ODEs, systems of linear differential equations. Laplace transforms and moment generating functions. Modeling stochastic systems with Markov chains: discrete and continuous time chains, Chapman-Kolmogorov equations, ergodic theorems.

Probability axioms, conditional probability, Bayes' theorem. Random variables motivated by real data and examples. Parametric univariate models as data reduction and description strategies. Multivariate distributions, expectation and variance. Likelihood function will be defined and exploited as a means of estimating parameters in certain simple situations.

An introduction to the theory of statistics with strong links to data as well as its probabilistic underpinnings. Topics covered include estimation and hypothesis testing, sampling distributions, linear regression, experimental design, law of large numbers and central limit theorem.

An introduction to programming using a high level language (currently R).

A continuation of the study of multivariate probability and stochastic processes. This course builds on the background developed in the second year courses, and focuses on the more advanced aspects of multivariate probability, namely transformations where the domain of random variables must be carefully considered.

A case study approach to how data are collected in science, social science and medicine, including the methods of designed experiments, sample surveys, observational studies and administrative records.

Point estimation: sufficiency, completeness, consistency, unbiasedness, Cramer-Rao inequality, Rao-Blackwell theorem, Hypotheses tests: uniformly most powerful tests, likelihood ratio tests.

Simple and multiple linear regression models and their use to model data using computing including model specification and assumptions, inference and estimation, use of indicator variables, regression diagnostics, model building and selection. Introduction to forecasting and time series.

Estimation and tests for generalized linear models, including residual analysis and the use of statistical packages. Logistic regression, log-linear models. Additional topics may include generalized estimating equations, quasi-likelihood and generalized additive models.

Applied linear modelling emphasizing data analysis using software including statistical inference review, visualization, multiple regression, logistic regression, and extensions. Core topics include assumptions, estimation, confidence/prediction intervals, hypothesis testing, diagnostics, indicator variables, cross validation, prediction, model building and model assessment. Other topics may include random effects or smoothing methods.

Continuous-time Markov chains, applications to phase-type distributions, Markov chain Monte Carlo simulation and queuing theory.

An introduction to data analytics consulting in the context of Problem, Plan, Data, Analysis and Conclusion, including interpersonal techniques; communication; teamwork; project management; copyright, intellectual property, compensation and negotiation; robust data analysis; and ethics. A large portion of the course will be conducted in a seminar format with student participation.

Completely randomized designs, randomized complete and incomplete block designs, factorial and fractional factorial designs, latin square designs, hierarchical designs, random and fixed effect models.

Modern methods of data analysis including linear and generalized linear models, modern nonparametric regression, principal component analysis, multilevel modelling and bootstrapping.

Simple random sampling with and without replacement, stratification, systematic sampling, cluster and multistage clustering, ratio and regression estimation, models in surveys, survey design, estimation and analysis.

A review of multiple regression including assumptions, estimation and inference, diagnostics, and modelling with factors. Variable selection techniques including cross-validation. Smoothing techniques, generalized additive models, and the incorporation of random effects and/or serial auto-correlated error structures.

ARIMA models, seasonality, dynamic regression, model building using an interactive computer package, forecasting, intervention analysis, control, applications in econometrics, business, and other areas.

Review of fundamental concepts in statistical computing, including programming, optimization methods and Monte Carlo simulations. A selection of advanced topics such as bootstrapping, robust methods, statistical graphics, Markov chain Monte Carlo, nonlinear regression, relational databases, time series analysis, and spatial statistics.

A course description will be available from the department at the time of registration.

A course description will be available from the department at the time of registration.

This course aims to develop important business skills that are often not emphasized in the formal education of quantitative financial professionals. The course focuses on four main topic areas: how businesses work, financial statement analysis, oral and written communications skills, and leadership and people management.

The student will work on a project under faculty supervision. The project may involve an extension, or more detailed coverage, of material presented in other courses. Credit for the course will involve a written report as well as an oral presentation.