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Program Requirements
Mentor: Professor R. Steele; Department of Mathematics and Statistics, Faculty of Science
This program is comprised of 39 credits.
Students entering the Major concentration in Statistics are normally expected to have completed MATH 133, MATH 140, and MATH 141 or their equivalents. Otherwise they will be required to make up any deficiencies in these courses over and above the 39 credits required by the program.
Required Courses (27 credits)

MATH 222 Calculus 3 (3 credits)
Overview
Mathematics & Statistics (Sci) : Taylor series, Taylor's theorem in one and several variables. Review of vector geometry. Partial differentiation, directional derivative. Extreme of functions of 2 or 3 variables. Parametric curves and arc length. Polar and spherical coordinates. Multiple integrals.
Terms: Fall 2013, Winter 2014, Summer 2014
Instructors: Nathan Grieve, Payman L Kassaei (Fall) Dmitry Jakobson (Winter) Sidney Trudeau (Summer)

MATH 223 Linear Algebra (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications.
Terms: Fall 2013, Winter 2014
Instructors: William Cavendish (Fall) William Cavendish (Winter)

MATH 242 Analysis 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : A rigorous presentation of sequences and of real numbers and basic properties of continuous and differentiable functions on the real line.
Terms: Fall 2013
Instructors: Ivo Klemes, Stephen W Drury (Fall)
Fall
Prerequisite: MATH 141

MATH 243 Analysis 2 (3 credits)
Overview
Mathematics & Statistics (Sci) : Infinite series; series of functions; power series. The Riemann integral in one variable. A rigorous development of the elementary functions.
Terms: Winter 2014
Instructors: Annalisa Panati (Winter)
Winter
Prerequisite: MATH 242

MATH 314 Advanced Calculus (3 credits)
Overview
Mathematics & Statistics (Sci) : Derivative as a matrix. Chain rule. Implicit functions. Constrained maxima and minima. Jacobians. Multiple integration. Line and surface integrals. Theorems of Green, Stokes and Gauss. Fourier series with applications.
Terms: Fall 2013, Winter 2014, Summer 2014
Instructors: Ivo Klemes (Fall) Sidney Trudeau (Winter)

MATH 323 Probability (3 credits)
Overview
Mathematics & Statistics (Sci) : Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem.
Terms: Fall 2013, Winter 2014, Summer 2014
Instructors: Masoud AsgharianDastenaei (Fall) William J Anderson (Winter) Djivede Kelome (Summer)

MATH 324 Statistics (3 credits) *
Overview
Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.
Terms: Fall 2013, Winter 2014
Instructors: William J Anderson (Fall) Masoud AsgharianDastenaei (Winter)
Fall and Winter
Prerequisite: MATH 323 or equivalent
Restriction: Not open to students who have taken or are taking MATH 357
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.

MATH 423 Regression and Analysis of Variance (3 credits)
Overview
Mathematics & Statistics (Sci) : Leastsquares estimators and their properties. Analysis of variance. Linear models with general covariance. Multivariate normal and chisquared distributions; quadratic forms. General linear hypothesis: Ftest and ttest. Prediction and confidence intervals. Transformations and residual plot. Balanced designs.
Terms: Fall 2013
Instructors: Abbas Khalili Mahmoudabadi (Fall)

MGSC 373 Operations Research 1 (3 credits)
Overview
Management Science : A realistic experience of analytical models which have been successfully applied in several areas of managerial decisionmaking like marketing, finance and IS. Emphasis on the formulation of problems, their solution approaches, limitations, underlying assumptions and practical use. Topics include: decision analysis, project management, simulation, linear and integer programming, sensitivity analysis.
Terms: Fall 2013
Instructors: Brian E Smith (Fall)
* Credits for MATH 324 are counted toward Management Core, where they replace MGCR 271. MGCR 271 cannot be taken for credit after credit for MATH 324 has been obtained.
Complementary Courses (12 credits)
6 credits selected from:

MGSC 479 Applied Optimization (3 credits)
Overview
Management Science : Applications of optimization models to management problems, including Linear Programming, Integer Programming and Nonlinear Programming.
Terms: This course is not scheduled for the 20132014 academic year.
Instructors: There are no professors associated with this course for the 20132014 academic year.
Prerequisite: MGSC 373.

MGSC 575 Applied Time Series Analysis Managerial Forecasting (3 credits)
Overview
Management Science : Management applications of time series analysis. Starting with ratiotomoving average methods, the course deals successively with Census 2, exponential smoothing methods, the methodology introduced by Box and Jenkins, spectral analysis and timeseries regression techniques. Computational aspects and applications of the methodology are emphasized.
Terms: This course is not scheduled for the 20132014 academic year.
Instructors: There are no professors associated with this course for the 20132014 academic year.

MGSC 578 Simulation of Management Systems (3 credits)
Overview
Management Science : Building simulation models of management systems. Design of simulation experiments and the analysis and implementation of results. Students are expected to design a complete simulation of a real problem using a standard simulation language.
Terms: Fall 2013
Instructors: Yasmina Maizi (Fall)
6 credits selected from:

MATH 204 Principles of Statistics 2 (3 credits) **
Overview
Mathematics & Statistics (Sci) : The concept of degrees of freedom and the analysis of variability. Planning of experiments. Experimental designs. Polynomial and multiple regressions. Statistical computer packages (no previous computing experience is needed). General statistical procedures requiring few assumptions about the probability model.
Terms: Winter 2014
Instructors: Russell Steele (Winter)
Winter
Prerequisite: MATH 203 or equivalent. No calculus prerequisites
Restriction: This course is intended for students in all disciplines. For extensive course restrictions covering statistics courses see Section 3.6.1 of the Arts and of the Science sections of the calendar regarding course overlaps.
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.

MATH 315 Ordinary Differential Equations (3 credits)
Overview
Mathematics & Statistics (Sci) : First order ordinary differential equations including elementary numerical methods. Linear differential equations. Laplace transforms. Series solutions.
Terms: Fall 2013, Winter 2014, Summer 2014
Instructors: Charles Roth (Fall) Gantumur Tsogtgerel (Winter)

MATH 340 Discrete Structures 2 (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of mathematical writing, proof techniques, graph theory and counting. Mathematical logic. Graph connectivity, planar graphs and colouring. Probability and graphs. Introductory group theory, isomorphisms and automorphisms of graphs. Enumeration and listing.
Terms: Winter 2014
Instructors: Adrian Roshan Vetta (Winter)

MATH 410 Majors Project (3 credits)
Overview
Mathematics & Statistics (Sci) : A supervised project.
Terms: Fall 2013, Winter 2014, Summer 2014
Instructors: Djivede Kelome (Fall) Djivede Kelome, Abbas Khalili Mahmoudabadi (Winter) Djivede Kelome, Christian Genest, Adrian Roshan Vetta, Masoud AsgharianDastenaei (Summer)
Prerequisite: Students must have 21 completed credits of the required mathematics courses in their program, including all required 200 level mathematics courses.
Requires departmental approval.

MATH 447 Introduction to Stochastic Processes (3 credits)
Overview
Mathematics & Statistics (Sci) : Conditional probability and conditional expectation, generating functions. Branching processes and random walk. Markov chains, transition matrices, classification of states, ergodic theorem, examples. Birth and death processes, queueing theory.
Terms: Winter 2014
Instructors: William J Anderson (Winter)

MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Modern discrete data analysis. Exponential families, orthogonality, link functions. Inference and model selection using analysis of deviance. Shrinkage (Bayesian, frequentist viewpoints). Smoothing. Residuals. Quasilikelihood. Contingency tables: logistic regression, loglinear models. Censored data. Applications to current problems in medicine, biological and physical sciences. R software.
Terms: Winter 2014
Instructors: Johanna Neslehova (Winter)

MATH 524 Nonparametric Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution free procedures for 2sample problem: Wilcoxon rank sum, SiegelTukey, Smirnov tests. Shift model: power and estimation. Single sample procedures: Sign, Wilcoxon signed rank tests. Nonparametric ANOVA: KruskalWallis, Friedman tests. Association: Spearman's rank correlation, Kendall's tau. Goodness of fit: Pearson's chisquare, likelihood ratio, KolmogorovSmirnov tests. Statistical software packages used.
Terms: This course is not scheduled for the 20132014 academic year.
Instructors: There are no professors associated with this course for the 20132014 academic year.

MATH 525 Sampling Theory and Applications (4 credits)
Overview
Mathematics & Statistics (Sci) : Simple random sampling, domains, ratio and regression estimators, superpopulation models, stratified sampling, optimal stratification, cluster sampling, sampling with unequal probabilities, multistage sampling, complex surveys, nonresponse.
Terms: Fall 2013
Instructors: Russell Steele (Fall)
** MATH 204 cannot be taken for credit after credit for MATH 324 has been obtained. The two courses can be taken concurrently. Students should consult the rules for credit for Statistics courses in the course overlap section.