
Instruction offered by members of the Department of Mathematics and Statistics in the Faculty of Science.
Department Head – M. Lamoureux
Note: Not every 400 and 500numbered Statistics course is offered every year. Check with the divisional office to plan for the upcoming cycle of offered courses.
Note: For listings of related courses, see Actuarial Science Applied Mathematics, Mathematics, and Pure Mathematics.
Note: Credit towards degree requirements will be given for only one of Engineering 319, Political Science 399, Psychology 312, Sociology 311, Statistics 205, 213 and 217, 327; that one being a course(s) appropriate to the degree program.
Note: Statistics 205, 213, 217, 327 are not available to students who have previous credit for one of Mathematics 321 or Statistics 321 or are concurrently enrolled in Mathematics 321 or Statistics 321.
Note: Commencing in Fall 2014, Mathematics 265, 267, 367, Mathematics 275, 277, 375 and 377 will replace respectively Mathematics 251, 253, 353, Applied Mathematics 217, 219, 307 and 309 and will serve as prerequisites for appropriate courses. In some special cases, Mathematics 267 will replace Mathematics 349 or 353. For these and other deviations from the general rule, see individual course entries for details. Mathematics 267 supplemented by Mathematics 177 will be accepted as equivalent to Mathematics 277.

Junior Courses
Students requiring one half course in Statistics should take Statistics 205.

Statistics
205

Introduction to Statistical Inquiry


The systematic progression of statistical principles needed to conduct a statistical investigation culminating in parameter estimation, hypothesis testing, statistical modelling, and design of experiments.
Course Hours:
H(31T)
Prerequisite(s):
Mathematics 301 or Pure Mathematics 30 or Mathematics II (offered by Continuing Education) or registration in the Faculty of Nursing.
Antirequisite(s):
Credit for only one of Statistics 205, 211, or 213 will be allowed.
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings. This course is highly recommended for Statistics Majors.

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Statistics
213

Introduction to Statistics I


Collection and presentation of data, introduction to probability, including Bayes' law, expectations and distributions. Properties of the normal curve. Introduction to estimation and hypothesis testing.
Course Hours:
H(311T)
Prerequisite(s):
Mathematics 301 or Pure Mathematics 30 or Mathematics II (offered by Continuing Education).
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings.

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Statistics
217

Introduction to Statistics II


Estimation of population parameters; confidence intervals for means; choice of sample size. Tests of hypotheses including 2sample tests and paired comparisons. The Chisquared tests for association and goodnessoffit. Regression and correlation; variance estimates; tests for regression and correlation coefficients. Nonparametric methods and associated tests. Time series, forecasting.
Course Hours:
H(311T)
Prerequisite(s):
Statistics 213 or consent of the Department.
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings.

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Statistics
321

Introduction to Probability


A calculus based introduction to probability theory and applications. Elements of probabilistic modelling, Basic probability computation techniques, Discrete and continuous random variables and distributions, Functions of random variables, Expectation and variance, Multivariate random variables, Conditional distributions, Covariance, Conditional expectation, Central Limit Theorem, Applications to realworld modelling.
Course Hours:
H(31T)
Prerequisite(s):
Mathematics 267 or 277 or 253 or 283 or Applied Mathematics 219.
Notes:
Statistics 205 is strongly recommended as preparation for this course for Statistics majors.
Also known as:
(formerly Mathematics 321)

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Statistics
323

Introduction to Theoretical Statistics


Statistics and their distributions. Introduction to statistical inference through point estimation and confidence interval estimation of a population parameter. Properties of statistics including unbiasedness and consistency in estimation. Single parameter hypothesis testing, Type I and Type II Errors. Multiparameter estimation through confidence interval estimation and hypothesis testing. The analysis of bivariate data through simple linear regression, including inferences on the parameters of the linear model and the analysis of variance.
Course Hours:
H(31T)
Prerequisite(s):
Mathematics 321 or Statistics 321.
Notes:
Prior or concurrent completion of Mathematics 353 or 381 is strongly recommended for students without credit of Mathematics 267 or 277.
Also known as:
(formerly Mathematics 323)

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Statistics
327

Statistics for the Physical and Environmental Sciences


Introduction to the collection of data. Probability and probability distributions. Single and Multisample estimation of distribution parameters. Regression and Goodness of Fit tests. Experimental Design and Analysis of Variance.
Course Hours:
H(31)
Prerequisite(s):
Mathematics 249 or 251 or 265 or 275 or 281 or Applied Mathematics 217.
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings.

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Statistics
421

Mathematical Statistics


An advanced examination of core concepts in mathematical statistics, including the multivariate normal distribution, limit distributions, sufficient statistics, completeness of families of distributions, exponential families, likelihood ratio tests, chisquare tests, And the analysis of variance. Additional topics and examples relating to sequential tests, nonparametric methods, Bayesian statistical modelling, and the general linear model may also be explored.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381.

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Statistics
423

Statistical Analysis of Sample Survey


Introduction to questionnaire design of sample surveys. Treatment of the various sampling methodologies used in population parameter estimation. Ratio and regression estimation. Sampling weights and variance estimation of statistics. Estimation of population size and density. Nonresponse.
Course Hours:
H(31T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, Engineering 319, Psychology 312, Sociology 311; or consent of the Department.

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Statistics
425

Statistical Design and Analysis of Experiments


Introduction to the design of experiments and the statistical analysis of data. Analysis of variance in the response variable and adequacy of the model. Multiple comparison methods. Extensions to completely randomized block, latinsquares, and factorial experimental design. Introduction to nested and splitplot design, with emphasis on statistical software usage.
Course Hours:
H(31T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, Engineering 319, Psychology 312, Sociology 311; or consent of the Department.

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Statistics
429

Linear Models and Their Applications


Multiple linear regression model, parameter estimation, simultaneous confidence intervals and general linear hypothesis testing. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and nonlinear regression. Applications to forecasting. Variable selection in highdimensional data using linear regression. Computer analysis of practical real world data.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 211 or 213.

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Statistics
437

Actuarial Models


Tails of distributions; measures of risk (Var, TVaR); characteristics of actuarial models; continuous models; discrete distributions and processes; frequency and severity with coverage modifications (deductibles, policy limits, coinsurance); aggregate loss models.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381.

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Statistics
505

Time Series Analysis


An introduction to the theory and tools to conduct time series analysis, with the emphasis on modelling and forecasting using a software. Stationarity, white noise, autocorrelation, partial autocorrelation, and linear predictor. Stationary ARIMA models, seasonality and trends. Model fitting, diagnostics and forecasting. Additional topics may include state space models, spectral analysis of time series, and GARCH models.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 429 or consent of the Department.

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Statistics
507

Introduction to Stochastic Processes


Markov chains. Limit distributions for ergodic and absorbing chains. Classification of states, irreducibility. The Poisson process and its generalizations. Continuoustime Markov chains. Brownian motion and stationary processes. Renewal theory.
Course Hours:
H(30)
Prerequisite(s):
Mathematics 321 or Statistics 321.
Also known as:
(formerly Statistics 407)

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Statistics
517

Practice of Statistics


A capstone course intended for students in their final year of study. The emphasis is on how to address real world scientific and social issues by applying the various statistical methods acquired in the earlier years in a unified and appropriate way. This involves method selection, data handling, statistical computing, consulting, report writing and oral presentation, team work, and ethics.
Course Hours:
H(31)
Prerequisite(s):
At least two of Statistics 423, 425, 429 and 505; or consent of the Department.
Antirequisite(s):
Not open to students with Statistics 513 or 515.

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Statistics
519

Bayesian Statistics


Fundamentals of Bayesian inference, single and multiparameter models, hierarchical models, regression models, generalized linear models, advanced computational methods, Markov chain Monte Carlo.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381; or consent of the Department.
Notes:
Completion of Statistics 421 is highly recommended as preparation for this course.

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Statistics
523

Nonparametric Statistics


Nonparametric estimation and tests of hypotheses. Distributionfree tests. Asymptotic Theory. Resampling method and density estimation.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381; or consent of the Department.
Notes:
May not be offered every year. Consult the department for listings.

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Statistics
525

Applied Multivariate Analysis


Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Multivariate statistical methods; principal components, factor analysis, discriminant analysis and classification, canonical correlation analysis, cluster analysis.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or consent of the Department.
Notes:
May not be offered every year. Consult the department for listings. Completion of Mathematics 311 or 313 is highly recommended as preparation for this course.

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Statistics
529

Special Topics in Applied Statistics


Content of the course will vary from year to year. Consult the Statistics Department for information on choice of topics.
Course Hours:
H(31)
Prerequisite(s):
Consent of the Department.
MAY BE REPEATED FOR CREDIT

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Statistics
531

Monte Carlo Methods and Statistical Computing


Introduction to statistical computing; random numbers generation; Monte Carlo methods (variance reduction technique; computation of definite integrals); Optimizations; Numerical integrations.
Course Hours:
H(31)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381; or consent of the Department.

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Statistics
533

Survival Models


Nature and properties of survival models; methods of estimating tabular models from both complete and incomplete data samples including actuarial, moment and maximum likelihood techniques; estimations of life tables from general population data; KaplanMeier estimator and NelsonAllan estimator; the accelerated failure time model; the Cox proportional hazards model; model building and highdimensional survival data analysis.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 323 or Mathematics 323.
Also known as:
(formerly Statistics 433)

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Graduate Courses
Note: Some 500 and 600level statistics courses may have concurrent lectures. Extra work in these courses (e.g., extra assignments, advanced examination questions, a term project) will be required for credit at the 600 level.

Statistics
601

Topics in Probability and Statistics


The content of this course is decided from year to year in accordance with graduate student interest and instructor availability. Topics include but are not restricted to: Advanced Design of Experiments, Weak and Strong Approximation Theory, Asymptotic Statistical Methods, the Bootstrap and its Applications, Generalized Additive Models, Order Statistics and their Applications, Robust Statistics, Statistics for Spatial Data, Statistical Process Control, Time Series Models.
Course Hours:
H(30)
MAY BE REPEATED FOR CREDIT

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Statistics
603

Applied Statistics for Nursing Research


Descriptive statistics; probability theory; statistical estimation/inference; power analysis; regression analysis; anova; logistic regression analysis; nonparametric tests; factor analysis; discriminant analysis; Cox's Proportional Hazard Model.
Course Hours:
H(31)
Also known as:
(formerly Statistics 601.14)

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Statistics
619

Bayesian Statistics


Fundamentals of Bayesian inference, single and multiparameter models, hierarchical models, regression models, generalized linear models, advanced computational methods, Markov chain Monte Carlo.
Course Hours:
H(30)

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Statistics
621

Research Seminar


A professional skills course, focusing on the development of technical proficiencies that are essential for students to succeed in their future careers as practicing mathematicians in academia, government, or industry. The emphasis is on delivering professional presentations and using modern mathematical research tools. A high level of active student participation is required.
Course Hours:
Q(2S0)
MAY BE REPEATED FOR CREDIT
NOT INCLUDED IN GPA

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Statistics
625

Multivariate Analysis


Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Principal components. Factor Analysis. Discrimination and classification. Canonical correlation analysis.
Course Hours:
H(30)

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Statistics
633

Survival Models


Advanced topics in survival models such as the product limit estimator, the cox proportional hazards model, timedependent covariates, types of censorship.
Course Hours:
H(30)

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Statistics
635

Generalized Linear Models


Exponential family of distributions, binary data models, loglinear models, overdispersion, quasilikelihood methods, generalized additive models, longitudinal data and generalized estimating equations, model adequacy checks.
Course Hours:
H(30)

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Statistics
637

Nonlinear Regression


Topics include but are not restricted to selections from: linear approximations; model specification; various iterative techniques; assessing fit; multiresponse parameter estimation; models defined by systems of DEs; graphical summaries of inference regions; curvature measures.
Course Hours:
H(30)

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Statistics
639

Conference Course in Actuarial Modelling


Topics in advanced actuarial theory and practice, such as: insurance risk models; practical analysis of extreme values; advanced property and casualty rate making; actuarial aspects of financial theory.
Course Hours:
H(30)
MAY BE REPEATED FOR CREDIT

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Statistics
701

Theory of Probability I


Course Hours:
H(30)

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Statistics
703

Theory of Probability II


Course Hours:
H(30)

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Statistics
721

Theory of Estimation


Course Hours:
H(30)

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Statistics
723

Theory of Hypothesis Testing


Course Hours:
H(30)

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Statistics
761

Stochastic Processes I


Course Hours:
H(30)

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In addition to the numbered and titled courses shown above, the department offers a selection of advanced level graduate courses specifically designed to meet the needs of individuals or small groups of students at the advanced doctoral level. These courses are numbered in the series 800.01 to 899.99. Such offerings are, of course, conditional upon the availability of staff resources.
