
Instruction offered by members of the Department of Mathematics and Statistics in the Faculty of Science.
Department Head  T. Bisztriczky
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 Anthropology 307, Applied Psychology 301 and 303, Engineering 319, Political Science 399, Psychology 312, Sociology 311, Statistics 201 and 211, 213 and 217, 327, 333, 357; that one being a course(s) appropriate to the degree program.
Note: Statistics 201, 211, 213, 217, 327, 333, 357 are not available to students who have previous credit for Mathematics 321 or are concurrently enrolled in Mathematics 321.

Junior Courses
Students requiring one half course in Statistics should take Statistics 211.

Statistics
211

Concepts of Statistics


The systematic treatment of fundamental statistical ideas culminating in the discussion of parameter estimation and hypotheses testing.
Course Hours:
H(31T)
Prerequisite(s):
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
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):
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 Division.
Notes:
See the statements regarding credit which appear at the beginning of the Statistics course listings.

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Statistics
323

Introduction to Mathematical Statistics


Bivariate distributions. Sampling distributions. Chisquared, F and t distributions. Estimation. Hypothesis tests (proportions, means, variance, chisquare). Method of moments. Maximum likelihood estimators. NeymanPearson lemma. Likelihood ratio tests. Elementary regression and correlation.
Course Hours:
H(31T)
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

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 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

Theoretical Probability


Elementary measure theory, zeroone laws, weak and strong laws of large numbers, characteristic functions, central limit theorems and infinitely divisible distributions.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323 and Mathematics 353.

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Statistics
419

Information Theory and Coding Theory


Information sources, entropy, channel capacity, Shannon's theorems, coding theory, errorcorrecting codes.
Course Hours:
H(30)
Prerequisite(s):
Mathematics 311 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

Mathematical Statistics


Multivariate Normal distribution. Limit distributions. Sufficient statistics. Completeness of families of distributions. Exponential families. Likelihood ratio tests. Chisquare tests. Analysis of variance. Sequential tests. Introduction to nonparametric methods, Bayesian theory, the general linear model.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323, and Mathematics 353.

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Statistics
423

Sampling Theory of Surveys


Principles of sampling. Questionnaire design. Various types of sampling designs: simple random, stratified, systematic, cluster, multistage cluster. Ratio and regression estimates. Estimation of required sample size. Estimation of population size and density. Problems of nonresponse.
Course Hours:
H(31T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, 333, 357, Applied Psychology 301, Engineering 319, Mathematics 323, Psychology 312, Sociology 311 or consent of the Division.

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Statistics
425

Experimental Design


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(31T)
Prerequisite(s):
Any one of Statistics 217, 323, 327, 333, 357, Applied Psychology 301, Engineering 319, Mathematics 323, Psychology 312, Sociology 311 or consent of the Division.

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Statistics
429

Applied Regression Analysis


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 nonlinear regression. Computer analysis of practical real world data.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 323 or Mathematics 323 and Mathematics 353.
Notes:
Statistics 421 is highly recommended as preparation.

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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 353.

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Statistics
505

Time Series Analysis


Trend fitting, autoregressive schemes, moving average models, periodograms, secondorder stationary processes, ARCH models, statistical software for time series. Additional topics may include Bayesian analysis, spectral theory, Kalman filtering.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 429 or consent of the Division.

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Statistics
507

Applied Probability


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. Introduction to basic simulation methods.
Course Hours:
H(30)
Prerequisite(s):
Mathematics 321.
Also known as:
(formerly Statistics 407)

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Statistics
517

Practice of Statistics


Intended for students in their final year of study. Introduction to realworld statistical practice. Model selection. Messy data. Statistical software. Report writing and presentation. Working in groups. Ethical considerations in statistics.
Course Hours:
H(31)
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

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 353 or consent of the Division.
Notes:
Statistics 421 is highly recommended as preparation.

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Statistics
523

Nonparametric Statistics


Nonparametric estimation and tests of hypotheses. Distributions useful to handle nonparametric inference. Distributionfree tests. Asymptotic Theory.
Course Hours:
H(30)
Prerequisite(s):
Statistics 323 or Mathematics 323 and 353 or consent of the Division.
Notes:
May not be offered every year. Consult the department for listings.

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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(30)
Prerequisite(s):
Statistics 421 or consent of the Division.
Notes:
May not be offered every year. Consult the department for listings.

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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(31)
Prerequisite(s):
Consent of the Division.
MAY BE REPEATED FOR CREDIT

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Statistics
531

Monte Carlo Methods and Statistical Computing


Introduction to a variety of statistical languages and packages and introductory statistical programming in SPLUS. Pseudorandom variate generation. Bootstrapping. Variance reduction techniques. Computation of definite integrals. Model design and simulation, with applications.
Course Hours:
H(31)
Prerequisite(s):
Statistics 323 or Mathematics 323 and 353 or consent of the Division.
Notes:
Statistics 421 is highly recommended as preparation.

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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.
Course Hours:
H(31T)
Prerequisite(s):
Statistics 323 or Mathematics 323, 353, and Actuarial Science 327.
Notes:
(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)
Notes:
Lectures may run concurrently with Statistics 519.

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Statistics
621

Research Seminar


Reports on studies of the literature or of current research.
Course Hours:
Q(2S0)
Notes:
All graduate students in Mathematics and Statistics are required to participate in one of Applied Mathematics 621, Pure Mathematics 621, Statistics 621 each semester.
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)
Notes:
Lectures may run concurrently with Statistics 525.

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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.
