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Statistics STAT

STAT 505 507 517 519 523 525 529 531 533 601 603 619 621 625 633 635 637 639 701 703 721 723 761

Instruction offered by members of the Department of Mathematics and Statistics in the Faculty of Science.

Department Head - M. Lamoureux

Undergraduate Courses Only where appropriate to a studentâ€™s program may graduate credit be received for courses numbered 500-599.

Statistics 505

Time Series Analysis

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.

Statistics 507

Applied Probability

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)

Statistics 517

Practice of Statistics

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.

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(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.

Statistics 523

Nonparametric Statistics

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.

Statistics 525

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(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.

Statistics 529

Special Topics in Applied Statistics

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

Statistics 531

Monte Carlo Methods and Statistical Computing

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.

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.
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)

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.

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(3-0)
MAY BE REPEATED FOR CREDIT

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(3-1)
Also known as: (formerly Statistics 601.14)

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(3-0)
Notes: Lectures may run concurrently with Statistics 519.

Statistics 621

Research Seminar

AÂ 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

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(3-0)
Notes: Lectures may run concurrently with Statistics 525.

Statistics 633

Survival Models

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)

Statistics 635

Generalized Linear Models

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)

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(3-0)

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(3-0)
MAY BE REPEATED FOR CREDIT

Statistics 701

Theory of Probability I

Course Hours: H(3-0)

Statistics 703

Theory of Probability II

Course Hours: H(3-0)

Statistics 721

Theory of Estimation

Course Hours: H(3-0)

Statistics 723

Theory of Hypothesis Testing

Course Hours: H(3-0)

Statistics 761

Stochastic Processes I

Course Hours: H(3-0)

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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