Course Descriptions

An elementary introduction to the concepts of probability, including: sets, Venn diagrams, definition of probability, algebra of probabilities, counting principles, some discrete random variables and their distributions, graphical displays, expected values, the normal distribution, the Central Limit Theorem, applications, some statistical concepts.

Weekly hours: 3 Lecture hours
Prerequisite(s): Foundations of Mathematics 30 or Pre-Calculus 30.
Note: Credit will not be granted for STAT 103.3 if it is taken concurrently with or after STAT 241.3 or STAT 242.3. Please refer to the Statistics Course Regulations in the Arts & Science section of the Course and Program Catalogue.

Laws of probability, discrete and continuous random variables and their distributions, moments, functions of random variables and their distributions, Central Limit Theorem.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 110.3, MATH 133.4 or MATH 176.3; and MATH 116.3, MATH 134.3 or MATH 177.3.

Sampling theory, estimation, confidence intervals, testing hypotheses, goodness of fit, analysis of variance, regression and correlation.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 241.3
Note(s): Students may take STAT 242.3 for credit after GE 210.3, PLSC 214.3, STAT 245.3 or STAT 246.3, but may not take GE 210.3, PLSC 214.3, STAT 245.3 or STAT 246.3 for credit after completing STAT 242.3 for credit. Please refer to the Statistics Course Regulations in the Arts and Science section of the Course and Program Catalogue.

Statistical concepts and techniques including graphing of distributions, measures of location and variability, measures of association, regression, probability, confidence intervals, hypothesis testing. Students should consult with their department before enrolling in this course to determine the status of this course in their program.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): A course in a social science or Foundations of Mathematics 30 or Pre-Calculus 30.
Note(s): Students with credit for COMM 104.3, ECON 204, EPSE 441.3, GEOG 301, PSY 233.3, SOC 225.3 or equivalent may not take this course for credit. Students may not take STAT 244 for credit either concurrently with or following STAT 242, STAT 245, STAT 246 or ECON 204. Please refer to the Statistics Course Regulations in the Arts and Science section of the Course and Program Catalogue.

An introduction to basic statistical methods including frequency distributions, elementary probability, confidence intervals and tests of significance, analysis of variance, regression and correlation, contingency tables, goodness of fit.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): One of MATH 100.6, MATH 104.3, MATH 110.3, MATH 121.3, MATH 123.3, MATH 125.3, MATH 133.4, MATH 176.3, or STAT 103.3.
Note(s): Students with credit for GE 210.3, PLSC 214.3, STAT 242.3, or STAT 246.3 may not take this course for credit. Students may take STAT 242.3 for credit in a subsequent term. Please refer to the Statistics Course Regulations in the Arts and Science section of the Course and Program Catalogue.

An introduction to statistical techniques with emphasis on methods particularly applicable to biological and health sciences. Topics include: descriptive statistics, estimation and hypothesis testing, linear and logistic regression, contingency tables, life tables, and experimental design. Computerized data analysis will be an essential component of the labs.

Weekly hours: 3 Lecture hours and 2 Practicum/Lab hours
Prerequisite(s): Foundations of Mathematics 30 or Pre-Calculus 30; and BIOL 120.3 and 121.3 or permission of the department.
Note: One of MATH 104.3, MATH 110.3, MATH 176.3, or STAT 103.3 is recommended but not essential. Students with credit for GE 210.3, PLSC 214.3, STAT 242.3, or STAT 245.3 may not take this course for credit. Students may take STAT 242.3 for credit in a subsequent term. Please refer to the Statistics Course Regulations in the Arts and Science section of the Course and Program Catalogue.

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Lecture hours

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Lecture hours

Random variables and their distributions; independence; moments and moment generating functions; conditional probability; Markov chains; stationary time-series.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 241.

Probability spaces; conditional probability and independence; discrete and continuous random variables; standard probability models; expectations; moment generating functions; sums and functions of random variables; sampling distributions; asymptotic distributions. Deals with basic probability concepts at a moderately rigorous level.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 225 or 276; STAT 241 and 242.
Note: Students with credit for STAT 340 may not take this course for credit.

Applied regression analysis involving the extensive use of computer software. Includes: linear regression; multiple regression; stepwise methods; residual analysis; robustness considerations; multicollinearity; biased procedures; non-linear regression.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 242 or STAT 245 or STAT 246.
Note: Students with credit for ECON 404 may not take this course for credit.

An introduction to the principles of experimental design and analysis of variance. Includes: randomization, blocking, factorial experiments, confounding, random effects, analysis of covariance. Emphasis will be on fundamental principles and data analysis techniques rather than on mathematical theory.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 242 or STAT 245 or STAT 246.

The multivariate normal distribution, multivariate analysis of variance, discriminant analysis, classification procedures, multiple covariance analysis, factor analysis, computer applications.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 164 (formerly MATH 264) or MATH 266, STAT 241, and one of STAT 344 or STAT 345.

Theory and applications of sampling from finite populations. Includes: simple random sampling, stratified random sampling, cluster sampling, systematic sampling, probability proportionate to size sampling, and the difference, ratio and regression methods of estimation.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 242 or STAT 245 or STAT 246.

An introduction to statistical time series analysis. Includes: trend analysis, seasonal variation, stationary and non-stationary time series models, serial correlation, forecasting and regression analysis of time series data.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 241, and STAT 344 or 345.

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours

This course will cover topics in probability and statistics not discussed in other courses. Possible subjects include: large deviation theory, stochastic calculus and Ito’s formula, stochastic coupling and convergence rates, asymptotic techniques in probability and statistics, and advanced Markov chain algorithms.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): Permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

This course will cover topics in using computers to solve statistical problems. Possible subjects include: computational methods/toolkits for data wrangling, exploration, visualization and analysis with R/Python; R/python for data science; computational techniques (e.g. optimization, integration, algebra) for statistical inference; computing intensive statistical methods (e.g. bootstrapping methods, sample-size determination, Monte Carlo methods).

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): Permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

This course will cover topics in Applied Statistics not discussed in other courses. Possible subjects include: exploratory data analysis, survival analysis, longitudinal data analysis, spatial statistics, non-parametric methods, and data mining and visualization.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 344.3 or permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

Parametric estimation, maximum likelihood estimators, unbiased estimators, UMVUE, confidence intervals and regions, tests of hypotheses, Neyman Pearson Lemma, generalized likelihood ratio tests, chi-square tests, Bayes estimators.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 342.

A rigorous examination of the general linear model using vector space theory. Includes: generalized inverses; orthogonal projections; quadratic forms; Gauss-Markov theorem and its generalizations; BLUE estimators; Non-full rank models; estimability considerations.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 164 (formerly MATH 264) or MATH 266, STAT 342, and STAT 344 or 345.

Based on a foundation of mathematical and statistical theory, the course covers a series of statistical methods for supervised learning and unsupervised learning, focusing on applications to real data using statistical software. The topics include: resampling methods such as Cross-Validation and Bootstrap; regression and classification including Linear Regression, Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN); model selection and regularization including Best Set Selection, Lasso, Elastic Net; non-linear models including Generalized Additive Models (GAM); tree-based methods including Decision Trees, Bagging, Random Forest; Support Vector Machines (SVM); dimension reduction and clustering including Principle Component Analysis (PCA), K-Means, Hierarchical Clustering; Ensemble Learning including Boosting, Stacking, Multi-Task Prediction; and an introduction to Deep Learning.

Weekly hours: 3 Lecture hours and 1.5 Practicum/Lab hours
Prerequisite(s): STAT 344.3 or STAT 345.3 or CMPT 317.3 or CMPT 318.3
Note: Students with credit for STAT 498.3 Machine Learning or STAT 847 may not take this course for credit.

The multivariate normal distribution, multivariate analysis of variance, discriminant analysis, classification procedures, multiple covariance analysis, factor analysis, computer applications.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 164 (formerly MATH 264) or MATH 266, STAT 241, and one of STAT 344 or STAT 345.
Note: Students with credit for STAT 346.3 may not receive credit for this course.

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours

This course is about computational techniques used in statistical inference. Topics will be selected from: computer arithmetic, Monte Carlo methods for statistical research, optimization methods for maximum likelihood estimation, numerical methods for Bayesian inference, Bayesian analysis using BUGS, bootstrap methods, matrix computations for linear models, and others. This course also serves as a tutorial on a statistical programming language, such as R or Matlab, with examples from statistical inference.

Weekly hours: 3 Lecture hours
Prerequisite(s): STAT 342, STAT 344, and STAT 442 or by permission of the instructor.
Note: Students with credit for STAT 846: Special Topics in Probability and Statistics; Special Topics in Computational Techniques in Statistics; and Special Topics in Computational Statistics may not take this course for credit.

The theory of experimental design, including randomization theory, construction of block designs and Latin squares, factorial designs, and optimal design theory.

Weekly hours: 3 Lecture hours
Prerequisite(s): Undergraduate courses in design and analysis of experiments, such as (STAT 345 or equivalent), mathematical statistics (STAT 342 or equivalent), and linear algebra (MATH 266 or equivalent) or permission of instructor.

Statistical methods as they apply to scientific research, including: Experimental design, blocking and confounding, analysis of multifactor experiments, multiple regression and model building.

Weekly hours: 3 Lecture hours
Prerequisite(s): STAT 242 or 245 or permission of the department.

Topics will be related to recent developments in statistics and probability (multivariate statistics, time series, experimental design, non-parametric statistics, etc.) of interest to the instructor and students.

Weekly hours: 3 Lecture hours
Note: Students may take this course more than once for credit, provided the topic covered in each offering differs substantially. Students must consult the Department to ensure that the topics covered are different.

Based on a mathematical and statistical theory foundation, the course introduces statistical methods for supervised and unsupervised learning, focusing on hands-on skills with statistical software, R, and applications to real data.

Weekly hours: 3 Lecture hours
Prerequisite(s): STAT 344.3 or STAT 345.3.
Note: This course is a hybrid course with STAT 447, and this course cannot be taken for credit after previously taking STAT 447.

A survey of methods for analyzing discrete and continuous multivariate data. Includes; Log-linear models, logistic regression, canonical correlation, discriminant analysis, cluster analysis, MANOVA, factor analysis.

Weekly hours: 3 Lecture hours
Prerequisite(s): COMM 395, STAT 345 and STAT 845 or permission of the department.

An overview of mathematical methods used in theoretical statistics with particular emphasis on inference. Will cover general probability distributions, generating functions, limit theorems, likelihood concepts, exponential families, decision theory, Bayesian and frequentist paradigms for estimation and testing, asymptotic theory.

Weekly hours: 3 Lecture hours and 1 Seminar/Discussion hours
Prerequisite(s): Undergraduate courses in mathematical statistics and inference, such as STAT 342 and STAT 442.

A rigorous development of the general linear model, using vector space theory. Generalized inverses, orthogonal projections, quadratic forms, Gauss-Markov theorem, estimability.

Weekly hours: 3 Lecture hours and 1 Seminar/Discussion hours
Prerequisite(s): An undergraduate course in mathematical statistics (STAT 342), linear algebra (MATH 266), and STAT 344 or 345.

Offered occasionally in special situations. Students interested in these courses should contact the department for more information.

Offered occasionally in special situations. Students interested in these courses should contact the department for more information.

Each graduate student in the Statistics MSc or PhD program must enroll in this course in every Fall and Winter term during their time in their program. Mandatory activities of the course may include, but are not limited to: Department Colloquium attendance; Statistics Graduate Seminar attendance; delivery of a short expository or research-based presentation in the Graduate Seminar (or another appropriate venue, with permission) at least once during the program; and participation in broader skills-building activities.

Students writing a Master's thesis must register for this course.

Students writing a Ph.D. thesis must register for this course.