Program Requirements
The program provides training in statistics with a mathematical core. Taken together with the B.A.; Supplementary Minor Concentration in Statistics, these two programs constitute an equivalent of the B.Sc.; Major in Statistics program offered by the Faculty of Science. With satisfactory performance in an appropriate selection of courses, these two programs can lead to the accreditation "A.Stat" from the Statistical Society of Canada, which is regarded as the entry level requirement for a statistician practicing in Canada. Students interested in this accreditation should consult an academic adviser.
Program Prerequisites
Students entering the B.A.; Major Concentration Statistics program are normally expected to have completed the courses below or their equivalent. Otherwise, they will be required to make up any deficiencies in these courses over and above the 36 credits of courses.

MATH 133 Linear Algebra and Geometry (3 credits)
Overview
Mathematics & Statistics (Sci) : Systems of linear equations, matrices, inverses, determinants; geometric vectors in three dimensions, dot product, cross product, lines and planes; introduction to vector spaces, linear dependence and independence, bases. Linear transformations. Eigenvalues and diagonalization.
Terms: Fall 2022, Winter 2023
Instructors: BélangerRioux, Rosalie; Mazakian, Hovsep; GerbelliGauthier, Mathilde; Alfieri, Antonio (Fall) Duchesne, Gabriel William (Winter)
3 hours lecture, 1 hour tutorial
Prerequisite: a course in functions
Restriction A: Not open to students who have taken MATH 221 or CEGEP objective 00UQ or equivalent.
Restriction B: Not open to students who have taken or are taking MATH 123, except by permission of the Department of Mathematics and Statistics.
Restriction C: Not open to students who are taking or have taken MATH 134.

MATH 140 Calculus 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.
Terms: Fall 2022, Winter 2023
Instructors: Trudeau, Sidney; Huang, Peiyuan; Mellick, Sam (Fall) CollinsWoodfin, Elizabeth (Winter)
3 hours lecture, 1 hour tutorial
Prerequisite: High School Calculus
Restriction: Not open to students who have taken MATH 120, MATH 139 or CEGEP objective 00UN or equivalent
Restriction: Not open to students who have taken or are taking MATH 122, except by permission of the Department of Mathematics and Statistics
Each Tutorial section is enrolment limited

MATH 141 Calculus 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.
Terms: Fall 2022, Winter 2023
Instructors: Macdonald, Jeremy; Xu, Peter (Fall) Trudeau, Sidney; Barill, Gavin; Mazakian, Hovsep (Winter)
According to the Faculty of Arts MultiTrack System degree requirements, option C, students registered in this program may also register in the B.A.; Supplementary Minor Concentration in Statistics; they must also then complete another minor concentration in a discipline other than Mathematics and Statistics.
For more information about the MultiTrack System options, please refer to Faculty of Arts regulations under "Faculty Degree Requirements," "About Program Requirements," and "Departmental Programs."
Guidelines for Course Selection
Students are strongly advised to complete all required courses by the end of U2.
Where appropriate, Honours courses may be substituted for equivalent courses. Students planning to pursue graduate studies are encouraged to make such substitutions.
Required Courses (24 credits)
* Students who have taken an equivalent of MATH 203 at CEGEP or elsewhere must replace it by another course from the Complementary course list.
** Students must take MATH 204 before taking MATH 324.

MATH 203 Principles of Statistics 1 (3 credits) *
Overview
Mathematics & Statistics (Sci) : Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms: Fall 2022, Winter 2023
Instructors: Sajjad, Alia; Kreitewolf, Jens (Fall) Nadarajah, Tharshanna (Winter)
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. Students should consult http://www.mcgill.ca/students/transfercredit for information regarding transfer credits for this course.

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 2023
Instructors: Correa, Jose Andres (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 208 Introduction to Statistical Computing (3 credits)
Overview
Mathematics & Statistics (Sci) : Basic data management. Data visualization. Exploratory data analysis and descriptive statistics. Writing functions. Simulation and parallel computing. Communication data and documenting code for reproducible research.
Terms: Fall 2022
Instructors: Steele, Russell (Fall)
Prerequisite(s): MATH 133

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 2022, Winter 2023
Instructors: Paquette, Elliot; Wrobel, Konrad (Fall) Trudeau, Sidney (Winter)

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 2022, Winter 2023
Instructors: Macdonald, Jeremy; Pichot, Michael (Fall) Macdonald, Jeremy (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 2022
Instructors: Hundemer, Axel W (Fall)

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 2022, Winter 2023
Instructors: Nadarajah, Tharshanna; Sajjad, Alia (Fall) Sajjad, Alia; Asgharian, Masoud (Winter)

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 2022, Winter 2023
Instructors: Nadarajah, Tharshanna (Fall) Nadarajah, Tharshanna (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.
Complementary Courses (12 credits)
* Students can take either MATH 410 or MATH 420, but not both.

COMP 551 Applied Machine Learning (4 credits)
Overview
Computer Science (Sci) : Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.
Terms: Fall 2022, Winter 2023
Instructors: Li, Yue (Fall) Rabbany, Reihaneh (Winter)
Prerequisite(s): MATH 323 or ECSE 205 or ECSE 305 or equivalent
Restriction(s): Not open to students who have taken or are taking COMP 451. Not open to students who have taken or are taking ECSE 551.
Some background in Artificial Intelligence is recommended, e.g. COMP424 or ECSE526, but not required.

MATH 308 Fundamentals of Statistical Learning (3 credits)
Overview
Mathematics & Statistics (Sci) : Theory and application of various techniques for the exploration and analysis of multivariate data: principal component analysis, correspondence analysis, and other visualization and dimensionality reduction techniques; supervised and unsupervised learning; linear discriminant analysis, and clustering techniques. Data applications using appropriate software.
Terms: Winter 2023
Instructors: Alam, Shomoita (Winter)

MATH 410 Majors Project (3 credits) *
Overview
Mathematics & Statistics (Sci) : A supervised project.
Terms: Fall 2022, Winter 2023
Instructors: Kelome, Djivede; Khadra, Anmar; Stephens, David; Nave, JeanChristophe; Tan, Hongping; Yang, Archer Yi; Kolaczyk, Eric; Steele, Russell; Asgharian, Masoud; Ding, Yichuan Daniel (Fall) Kelome, Djivede (Winter)
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 420 Independent Study (3 credits) *
Overview
Mathematics & Statistics (Sci) : Reading projects permitting independent study under the guidance of a staff member specializing in a subject where no appropriate course is available. Arrangements must be made with an instructor and the Chair before registration.
Terms: Fall 2022, Winter 2023
Instructors: Kelome, Djivede; Yang, Archer Yi (Fall) Kamran, Niky (Winter)
Fall and Winter and Summer
Requires approval by the chair before registration
Please see regulations concerning Project Courses under Faculty Degree Requirements

MATH 423 Applied Regression (3 credits)
Overview
Mathematics & Statistics (Sci) : Multiple regression estimators and their properties. Hypothesis tests and confidence intervals. Analysis of variance. Prediction and prediction intervals. Model diagnostics. Model selection. Introduction to weighted least squares. Basic contingency table analysis. Introduction to logistic and Poisson regression. Applications to experimental and observational data.
Terms: Fall 2022
Instructors: Nadarajah, Tharshanna (Fall)

MATH 427 Statistical Quality Control (3 credits)
Overview
Mathematics & Statistics (Sci) : Introduction to quality management; variability and productivity. Quality measurement: capability analysis, gauge capability studies. Process control: control charts for variables and attributes. Process improvement: factorial designs, fractional replications, response surface methodology, Taguchi methods. Acceptance sampling: operating characteristic curves; single, multiple and sequential acceptance sampling plans for variables and attributes.
Terms: This course is not scheduled for the 20222023 academic year.
Instructors: There are no professors associated with this course for the 20222023 academic year.

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 2023
Instructors: AddarioBerry, Louigi Dana (Winter)

MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Exponential families, link functions. Inference and parameter estimation for generalized linear models; model selection using analysis of deviance. Residuals. Contingency table analysis, logistic regression, multinomial regression, Poisson regression, loglinear models. Multinomial models. Overdispersion and Quasilikelihood. Applications to experimental and observational data.
Terms: Winter 2023
Instructors: Neslehova, Johanna (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: Fall 2022
Instructors: Neslehova, Johanna (Fall)

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: Winter 2023
Instructors: Yang, Archer Yi (Winter)

MATH 540 Life Actuarial Mathematics (4 credits)
Overview
Mathematics & Statistics (Sci) : Life tables and distributions; force of mortality; premium, net premium, and reserve valuation for life insurance and annuity contracts (discrete and continuous case); cash flow analysis for portfolios of life insurance and annuities; asset liability management; numerical techniques for multiple decrement and state models; portfolio valuation of aggregate risks.
Terms: This course is not scheduled for the 20222023 academic year.
Instructors: There are no professors associated with this course for the 20222023 academic year.

MATH 541 Nonlife Actuarial Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Stochastic models and inference for loss severity and claim frequency distributions; computational techniques for the aggregation of independent risks (Panjer's algorithm, FFT, etc.); risk measures and quantitative risk management applications; models and inference for multivariate data, heavytail distributions, and extremes; dynamic risk models based on stochastic processes and ruin theory.
Terms: This course is not scheduled for the 20222023 academic year.
Instructors: There are no professors associated with this course for the 20222023 academic year.

MATH 545 Introduction to Time Series Analysis (4 credits)
Overview
Mathematics & Statistics (Sci) : Stationary processes; estimation and forecasting of ARMA models; nonstationary and seasonal models; statespace models; financial time series models; multivariate time series models; introduction to spectral analysis; long memory models.
Terms: This course is not scheduled for the 20222023 academic year.
Instructors: There are no professors associated with this course for the 20222023 academic year.

MATH 556 Mathematical Statistics 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including locationscale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.
Terms: Fall 2022
Instructors: Stephens, David (Fall)
Fall
Prerequisite: MATH 357 or equivalent

MATH 557 Mathematical Statistics 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : Sampling theory (including largesample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.
Terms: Winter 2023
Instructors: Asgharian, Masoud (Winter)
Winter
Prerequisite: MATH 556

MATH 558 Design of Experiments (4 credits)
Overview
Mathematics & Statistics (Sci) : Introduction to concepts in statistically designed experiments. Randomization and replication. Completely randomized designs. Simple linear model and analysis of variance. Introduction to blocking. Orthogonal block designs. Models and analysis for block designs. Factorial designs and their analysis. Rowcolumn designs. Latin squares. Model and analysis for fixed row and column effects. Splitplot designs, model and analysis. Relations and operations on factors. Orthogonal factors. Orthogonal decomposition. Orthogonal plot structures. Hasse diagrams. Applications to real data and ethical issues.
Terms: Winter 2023
Instructors: Sajjad, Alia (Winter)

MATH 559 Bayesian Theory and Methods (4 credits)
Overview
Mathematics & Statistics (Sci) : Subjective probability, Bayesian statistical inference and decision making, de Finetti’s representation. Bayesian parametric methods, optimal decisions, conjugate models, methods of prior specification and elicitation, approximation methods. Hierarchical models. Computational approaches to inference, Markov chain Monte Carlo methods, Metropolis—Hastings. Nonparametric Bayesian inference.
Terms: This course is not scheduled for the 20222023 academic year.
Instructors: There are no professors associated with this course for the 20222023 academic year.

MATH 598 Topics in Probability and Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : This course covers a topic in probability and/or statistics.
Terms: Winter 2023
Instructors: Yang, Archer Yi; Steele, Russell; Kolaczyk, Eric (Winter)
Prerequisite(s): At least 30 credits in required or complementary courses from the Honours in Probability and Statistics program including MATH 356. Additional prerequisites may be imposed by the Department of Mathematics and Statistics depending on the nature of the topic.
Restriction(s): Requires permission of the Department of Mathematics and Statistics.

WCOM 314 Communicating Science (3 credits)
Overview
WCOM : Production of written and oral assignments (in English) designed to communicate scientific problems and findings to varied audiences Analysis of the disciplinary conventions of scientific discourse in terms of audience, purpose, organization, and style; comparative rhetorical analysis of academic and popular genres, including abstracts, lab reports, research papers, print and online journalism.
Terms: Fall 2022, Winter 2023
Instructors: HARDIN, KATHERINE; Hung, Yvonne; Kubler, Kyle; PimentelLopes, Kelly (Fall) Kubler, Kyle; Hung, Yvonne (Winter)
Restriction: Not open to students who have taken CCOM 314.