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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 500-numbered 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 Educational Psychology 301, Engineering 319, Political Science 399, Psychology 312, Sociology 311, Statistics 205, 211, 213 and 217, 327; that one being a course(s) appropriate to the degree program.
Note: Statistics 205, 211, 213, 217, 327 are not available to students who have previous credit for Mathematics 321 or are concurrently enrolled in Mathematics 321.
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Junior Courses
Students requiring one half course in Statistics should take Statistics 205.
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Statistics
205
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Introduction to Statistical Inquiry
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The systematic progression of statistical principles needed to conduct a statistical investigation culminating in parameter estimation, hypothesis testing, statistical modeling, and design of experiments.
Course Hours:
H(3-1T)
Prerequisite(s):
Mathematics 30-1 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
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Introduction to Statistics I
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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(3-1-1T)
Prerequisite(s):
Mathematics 30-1 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
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Introduction to Statistics II
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Estimation of population parameters; confidence intervals for means; choice of sample size. Tests of hypotheses including 2-sample tests and paired comparisons. The Chi-squared tests for association and goodness-of-fit. Regression and correlation; variance estimates; tests for regression and correlation coefficients. Non-parametric methods and associated tests. Time series, forecasting.
Course Hours:
H(3-1-1T)
Prerequisite(s):
Statistics 213 or consent of the Division.
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings.
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Statistics
323
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Introduction to Mathematical Statistics
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Bivariate distributions. Sampling distributions. Chi-squared, F and t distributions. Estimation. Hypothesis tests (proportions, means, variance, chi-square). Method of moments. Maximum likelihood estimators. Neyman-Pearson lemma. Likelihood ratio tests. Elementary regression and correlation.
Course Hours:
H(3-1T)
Prerequisite(s):
Mathematics 321.
Notes:
Prior or concurrent completion of Mathematics 353 or 381 is strongly recommended.
Also known as:
(formerly Mathematics 323)
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Statistics
327
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Statistics for the Physical and Environmental Sciences
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Introduction to the collection of data. Probability and probability distributions. Single and Multi-sample estimation of distribution parameters. Regression and Goodness of Fit tests. Experimental Design and Analysis of Variance.
Course Hours:
H(3-1)
Prerequisite(s):
Mathematics 249 or 251 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
409
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Theoretical Probability
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Elementary measure theory, zero-one laws, weak and strong laws of large numbers, characteristic functions, central limit theorems and infinitely divisible distributions.
Course Hours:
H(3-0)
Prerequisite(s):
Statistics 323 or Mathematics 323 and Mathematics 353 or 381.
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Statistics
419
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Information Theory and Coding Theory
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Information sources, entropy, channel capacity, Shannon's theorems, coding theory, error-correcting codes.
Course Hours:
H(3-0)
Prerequisite(s):
Mathematics 311 or 313, and 321 or any Statistics course, or consent of the Division.
Antirequisite(s):
Credit for both Statistics 419 (Pure Mathematics 419) and Statistics 405 will not be allowed.
Also known as:
(Pure Mathematics 419)
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Statistics
421
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Mathematical Statistics
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Multivariate Normal distribution. Limit distributions. Sufficient statistics. Completeness of families of distributions. Exponential families. Likelihood ratio tests. Chi-square tests. Analysis of variance. Sequential tests. Introduction to nonparametric methods, Bayesian theory, the general linear model.
Course Hours:
H(3-0)
Prerequisite(s):
Statistics 323 or Mathematics 323, and Mathematics 353 or 381.
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Statistics
423
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Sampling Theory of Surveys
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Principles of sampling. Questionnaire design. Various types of sampling designs: simple random, stratified, systematic, cluster, multi-stage cluster. Ratio and regression estimates. Estimation of required sample size. Estimation of population size and density. Problems of nonresponse.
Course Hours:
H(3-1T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, 333, 357, Educational Psychology 301, Engineering 319, Mathematics 323, Psychology 312, Sociology 311 or consent of the Division.
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Statistics
425
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Experimental Design
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The objective and structure of an experiment, cause and effect, randomization, the estimation of error, replication, interaction, confounding. Using a computer as an aid in the analysis.
Course Hours:
H(3-1T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, 333, 357, Educational Psychology 301, Engineering 319, Mathematics 323, Psychology 312, Sociology 311 or consent of the Division.
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Statistics
429
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Applied Regression Analysis
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Multiple linear regression model including parameter estimation, simultaneous confidence intervals and general linear hypothesis testing using matrix algebra. Applications to forecasting. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Computer analysis of practical real world data.
Course Hours:
H(3-1T)
Prerequisite(s):
Statistics 323 or Mathematics 323 and Mathematics 353 or 381.
Notes:
Statistics 421 is highly recommended as preparation.
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Statistics
437
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Actuarial Models
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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(3-1T)
Prerequisite(s):
Statistics 323 or Mathematics 323 and Mathematics 353 or 381.
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Statistics
505
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Time Series Analysis
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Trend fitting, auto-regressive schemes, moving average models, periodograms, second-order stationary processes, ARCH models, statistical software for time series. Additional topics may include Bayesian analysis, spectral theory, Kalman filtering.
Course Hours:
H(3-1T)
Prerequisite(s):
Statistics 429 or consent of the Division.
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Statistics
507
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Applied Probability
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Markov chains. Limit distributions for ergodic and absorbing chains. Classification of states, irreducibility. The Poisson process and its generalizations. Continuous-time Markov chains. Brownian motion and stationary processes. Renewal theory. Introduction to basic simulation methods.
Course Hours:
H(3-0)
Prerequisite(s):
Mathematics 321.
Also known as:
(formerly Statistics 407)
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Statistics
517
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Practice of Statistics
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Intended for students in their final year of study. Introduction to real-world statistical practice. Model selection. Messy data. Statistical software. Report writing and presentation. Working in groups. Ethical considerations in statistics.
Course Hours:
H(3-1)
Prerequisite(s):
Statistics 429 or consent of the Division.
Antirequisite(s):
Not open to students with Statistics 513 or 515.
Notes:
Prior or concurrent completion of Statistics 429 is strongly recommended.
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Statistics
519
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Bayesian Statistics
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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(3-0)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 353 or 381; or consent of the Division.
Notes:
Statistics 421 is highly recommended as preparation.
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Statistics
523
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Nonparametric Statistics
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Nonparametric estimation and tests of hypotheses. Distributions useful to handle nonparametric inference. Distribution-free tests. Asymptotic Theory.
Course Hours:
H(3-0)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 353 or 381; or consent of the Division.
Notes:
May not be offered every year. Consult the department for listings.
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Statistics
525
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Multivariate Analysis
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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(3-0)
Prerequisite(s):
Statistics 421 or consent of the Division.
Notes:
May not be offered every year. Consult the department for listings. Mathematics 311 is highly recommended as preparation.
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Statistics
529
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Special Topics in Applied Statistics
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Content of the course will vary from year to year. Consult the Statistics Division for information on choice of topics.
Course Hours:
H(3-1)
Prerequisite(s):
Consent of the Division.
MAY BE REPEATED FOR CREDIT
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Statistics
531
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Monte Carlo Methods and Statistical Computing
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Introduction to a variety of statistical languages and packages and introductory statistical programming in SPLUS. Pseudo-random variate generation. Bootstrapping. Variance reduction techniques. Computation of definite integrals. Model design and simulation, with applications.
Course Hours:
H(3-1)
Prerequisite(s):
Statistics 323 or Mathematics 323; and Mathematics 353 or 381; or consent of the Division.
Notes:
Statistics 421 is highly recommended as preparation.
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Statistics
533
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Survival Models
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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.
Course Hours:
H(3-1T)
Prerequisite(s):
Statistics 323 or Mathematics 323; Mathematics 353 or 381; and Actuarial Science 327.
Also known as:
(formerly Statistics 433)
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Graduate Courses
Note: Some 500- and 600-level 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.
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Statistics
601
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Topics in Probability and Statistics
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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(3-0)
MAY BE REPEATED FOR CREDIT
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Statistics
603
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Applied Statistics for Nursing Research
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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(3-1)
Also known as:
(formerly Statistics 601.14)
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Statistics
619
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Bayesian Statistics
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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(3-0)
Notes:
Lectures may run concurrently with Statistics 519.
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Statistics
621
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Research Seminar
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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(2S-0)
MAY BE REPEATED FOR CREDIT
NOT INCLUDED IN GPA
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Statistics
625
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Multivariate Analysis
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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(3-0)
Notes:
Lectures may run concurrently with Statistics 525.
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Statistics
633
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Survival Models
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Advanced topics in survival models such as the product limit estimator, the cox proportional hazards model, time-dependent covariates, types of censorship.
Course Hours:
H(3-0)
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Statistics
635
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Generalized Linear Models
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Exponential family of distributions, binary data models, loglinear models, overdispersion, quasi-likelihood methods, generalized additive models, longitudinal data and generalized estimating equations, model adequacy checks.
Course Hours:
H(3-0)
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Statistics
637
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Nonlinear Regression
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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(3-0)
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Statistics
639
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Conference Course in Actuarial Modelling
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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(3-0)
MAY BE REPEATED FOR CREDIT
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Statistics
701
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Theory of Probability I
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Course Hours:
H(3-0)
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Statistics
703
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Theory of Probability II
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Course Hours:
H(3-0)
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Statistics
721
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Theory of Estimation
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Course Hours:
H(3-0)
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Statistics
723
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Theory of Hypothesis Testing
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Course Hours:
H(3-0)
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Statistics
761
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Stochastic Processes I
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Course Hours:
H(3-0)
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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.
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COURSES OF INSTRUCTION