This Course and Program Catalogue is effective from May 2015 to April 2016.

Not all courses described in the Course and Program Catalogue are offered each year. For a list of course offerings in 2015-2016, please consult the class search website.

For general registration information, please visit students.usask.ca.

As of 2005-2006, certain course abbreviations have changed. Students with credit for a course under its former label may not take the relabeled course for credit.

The following conventions are used for course numbering:

- 010-099 represent non-degree level courses
- 100-699 represent undergraduate degree level courses
- 700-999 represent graduate degree level courses

Course Term and Instructional Code Designations are outlined here.

Please use the following form to look up courses and find detailed information on course prerequisites, corequisites, and other special notes. To view all 100-level courses in a subject, select a Subject Code and type 1% in the Course Number field. (200-level = 2%, etc.)

Elementary Probability

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.

Probability Theory

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

Statistical Theory and Methodology

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

Elementary Statistical Concepts

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.

Introduction to Statistical Methods

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.

Introduction to Biostatistics

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.

Special Topics

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

Special Topics

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

Probability and Stochastic Processes

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

Mathematical Statistics

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.

Applied Regression Analysis

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.

Design and Analysis of Experiments

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.

Multivariate Analysis

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

Sampling Techniques

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.

Time Series Analysis

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.

Special Topics

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

Special Topics

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

Statistical Inference

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.

Linear Statistical Models

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.

Special Topics

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

Special Topics

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

Computational Statistics

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.

Advanced Experimental Design

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

Probability Theory

Probability spaces and random variables. Distribution functions. Convergence of random variables. Characteristic functions. Fundamental limit theorems. Conditional expectation.

Statistical Methods for Research

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

Special Topics in Probability and Statistics

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.

Multivariate Data Analysis

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.

Mathematical Statistics and Inference

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.

Linear Models

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

Special Topics

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

Special Topics

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

- Agriculture & Bioresources
- Arts & Science
- Centre for Continuing & Distance Education
- Dentistry
- Education
- Edwards School of Business
- Engineering
- Graduate Studies
- Kinesiology
- Law
- Medicine
- Nursing
- Pharmacy & Nutrition
- Physical Therapy
- School of Environment and Sustainability
- St. Thomas More College
- Theological and Affliated
- Veterinary Medicine