Statistics, Applied

Statistics, Applied Programs | Statistics, Applied Courses

Faculty List

Professor Emeriti
A. Weir, B.Sc., M.Sc., Ph.D

Professors
O. Aghababaei Jazi, B.Sc., M.Sc., Ph.D.
L. Al Labadi, B.Sc., M. Sc., Ph.D.
L.J. Brunner, B.A., Ph.D., M.A., Ph.D.
D. Kong, B.Sc., Ph.D.
A. Nosedal-Sánchez, B.Sc., M.Sc., Ph.D.
S. Volgushev, Ph.D.

Chair
Ilia Binder
Room 3016, Deerfield Hall
905-828-3834
chairmcs.utm@utoronto.ca

Vice-Chair, Mathematics and Statistics
Jacopo De Simoi
Room 3040, Deerfield Hall
905-569-5698
jacopods@math.utoronto.ca

Associate Chair, Statistics
Alvaro Nosedal Sanchez
Room 3030, Deerfield Hall
905-828-3812
al.nosedalsanchez@utoronto.ca

Academic Advisor and Undergraduate Program Administrator
Laura Ferlito
laura.ferlito@utoronto.ca
www.utm.utoronto.ca/math-cs-stats

 

Statistical methods have applications in almost all areas of science, medicine, engineering, business, politics, psychology, law, and the environment. A practicing statistician is involved in a diversity of projects: testing the effectiveness of a new vaccine, working on the human genome project, forecasting stock yields, examining the effectiveness of television advertising, predicting election results.

Today we are bombarded with information from quantitative studies, information generated from the application of statistical methodologies. While much of this information is valid, some of it is not. An understanding of applied statistics will make you a critical consumer of numbers presented by the media. A basic knowledge of statistics should be an integral part of everyone's education.

The Applied Statistics Specialist Program at U of T Mississauga provides students with a solid foundation in the fundamental aspects of probability and introduces students to a broad range of applied statistics methodologies. The Major and Minor Programs in Applied Statistics consist largely of STA courses, and may be combined with programs in other subjects.

Introductory Applied Statistics Courses: Non-Calculus Based

U of T Mississauga Statistics courses, STA220H5 and STA221H5 are non-calculus entry-level introductions to statistics. Rough equivalents to these courses are offered by the Biology, Economics, Psychology and Sociology department. These courses are not intended for students planning to pursue a degree in statistics, mathematics, or computer sciences. Some departments have changed which statistics courses they allow for program requirements (such as Biology).  To learn more, please consult with the respective departments.

Introductory Statistics and Probability Courses: Calculus-Based

U of T Mississauga Statistics courses STA246H5, STA256H5, STA258H5 and STA260H5 form a calculus-based introduction to probability and applied statistics. STA256H5, STA258H5 and STA260H5 are intended for students planning to pursue a degree in statistics, mathematics, or computer science. Various other departments accept these courses in place of a non-calculus based introduction to applied statistics course. IMPORTANT NOTE: STA246H5 cannot be used towards any program(s) in Applied Statistics or Mathematics. This course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements. STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses.

A wide variety of upper-level courses is available to students who have the proper prerequisites. Students should feel free to consult the department regarding course selection.

Students should also review the Degree Requirements section prior to selecting courses

Program website: https://www.utm.utoronto.ca/math-cs-stats

Statistics, Applied Programs

Applied Statistics - Specialist (Science)

Applied Statistics - Specialist (Science)

Enrolment Requirements:

Limited Enrolment — Enrolment in the Specialist program is limited to students with a minimum of 4.0 credits, including:

For students applying in 2023-2024 for program entry in the 2024-2025 Academic Year:

  1. STA107H5 (with a minimum grade of 60%) or STA256H5;
  2. MAT134H5 or MAT136H5 or MAT137Y5 or MAT139H5 or MAT233H5 (minimum 60%) or MAT157Y5 or MAT159H5; and
  3. A minimum cumulative grade point average, to be determined annually.
  4. All students must complete 4.0 U of T credits before requesting this program. Courses with a grade of CR/NCR will not count as a part of the 4.0 credits required for program entry.

For students applying in 2024-2025 for program entry in the 2025-2026 Academic Year:

  1. STA107H5 (with a minimum grade of 60%) or STA256H5;
  2. MAT134H5 or MAT136H5 or MAT137Y5 or MAT139H5 or MAT233H5 (minimum 60%) or MAT157Y5 or MAT159H5;
  3. ISP100H5; and
  4. A minimum cumulative grade point average, to be determined annually.
  5. All students must complete 4.0 U of T credits before requesting this program. Courses with a grade of CR/NCR will not count as a part of the 4.0 credits required for program entry.

Completion Requirements:

12.0-13.0 credits are required.

First Year:

  1. CSC108H5
  2. MAT102H5
  3. [( MAT132H5 or MAT135H5 or MAT137H5 or MAT157H5) and ( MAT134H5 or MAT136H5 or MAT139H5 or MAT159H5)] or MAT137Y5 or MAT157Y5
  4. MAT223H5 or MAT240H5
  5. For students entering the program in 2025-2026 (and beyond): ISP100H5

Second Year:

  1. MAT232H5 or MAT233H5 or MAT257Y5
  2. MAT244H5
  3. STA256H5 and STA258H5 and STA260H5

Higher Years:

  1. STA302H5 and STA304H5 and STA305H5 and STA348H5
  2. 2.0 credits of STA at the 300/400 level
  3. 2.0 credits from CSC322H5 or ( CSC311H5 or CSC411H5) or MAT302H5 or MAT311H5 or MAT332H5 or MAT334H5 or MAT344H5 or MAT337H5
  4. 1.0 credit of STA

NOTES:

  1. MAT133Y5 is included in the credit count only if the student also completes MAT233H5 (in which case MAT232H5 is not required).
  2. Students are strongly encouraged to familiarize themselves with the 100-level calculus pre-requisites to select the correct courses.
  3. ECO220Y5 cannot be substituted for STA256H5 or STA258H5 or STA260H5.
  4. ECO227Y5 can be substituted for STA256H5 and STA258H5, but not for STA260H5.
  5. STA107H5 is highly recommended in first year, but it is not required.
  6. MAT337H5 is highly recommended for students intending to pursue graduate level studies in statistics.
  7. Students in the Applied Statistics Specialist may take at most 1.0 credit of Statistics Research Project Courses from STA378H5, STA398H5, STA478H5 and STA498H5.
  8. STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses. In addition, STA246H5 cannot be used towards any program(s) in Applied Statistics or Mathematics. The course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements.

ERSPE1540

Applied Statistics - Major (Science)

Applied Statistics - Major (Science)

Enrolment Requirements:

Limited Enrolment — Enrolment in the Major program is limited to students with a minimum of 4.0 credits, including:

For students applying in 2023-2024 for program entry in the 2024-2025 Academic Year:

  1. STA107H5 (with a minimum grade of 60%) or STA256H5;
  2. MAT134H5 or MAT136H5 or MAT139H5 or MAT159H5 or MAT137Y5 or MAT157Y5 or MAT233H5; and
  3. A minimum cumulative grade point average, to be determined annually.
  4. All students must complete 4.0 U of T credits before requesting this program. Courses with a grade of CR/NCR will not count as a part of the 4.0 credits required for program entry.

For students applying in 2024-2025 for program entry in the 2025-2026 Academic Year:

  1. STA107H5 (with a minimum grade of 60%) or STA256H5;
  2. MAT134H5 or MAT136H5 or MAT139H5 or MAT159H5 or MAT137Y5 or MAT157Y5 or MAT233H5;
  3. ISP100H5; and
  4. A minimum cumulative grade point average, to be determined annually.
  5. All students must complete 4.0 U of T credits before requesting this program. Courses with a grade of CR/NCR will not count as a part of the 4.0 credits required for program entry.

Completion Requirements:

7.0-8.0 credits are required.

First Year:

  1. CSC108H5
  2. MAT102H5
  3. [( MAT132H5 or MAT135H5 or MAT137H5 or MAT157H5) and ( MAT134H5 or MAT136H5 or MAT139H5 or MAT159H5)] or MAT137Y5 or MAT157Y5
  4. MAT223H5 or MAT240H5
  5. For students entering the program in 2025-2026 (and beyond): ISP100H5

Second Year:

  1. MAT232H5 or MAT233H5 or MAT257Y5
  2. STA256H5 and STA258H5 and STA260H5

Higher Years:

  1. STA302H5 and STA304H5 and STA305H5
  2. 1.0 credit from any 300/400 level STA course or CSC322H5 or ( CSC311H5 or CSC411H5) or MAT302H5 or MAT311H5 or MAT332H5 or MAT334H5 or MAT344H5 or MAT337H5

NOTES:

  1. MAT133Y5 is included in the credit count only if the student also completes MAT233H5 (in which case MAT232H5 is not required).
  2. Students are strongly encouraged to familiarize themselves with the 100-level calculus pre-requisites to select the correct courses.
  3. ECO220Y5 cannot be substituted for STA256H5 or STA258H5 and/or STA260H5.
  4. ECO227Y5 can be substituted for STA256H5 and STA258H5, but not for STA260H5.
  5. STA107H5 is highly recommended in first year, but it is not required.
  6. MAT337H5 is highly recommended for students intending to pursue graduate level studies in statistics.
  7. Students in the Applied Statistics Major may take at most 0.5 credit of Statistics Research Project Course(s) from STA378H5, STA398H5, STA478H5 and STA498H5.
  8. STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses. In addition, STA246H5 cannot be used towards any program(s) in Applied Statistics or Mathematics. The course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements.

ERMAJ1540

Applied Statistics - Minor (Science)

Applied Statistics - Minor (Science)

Completion Requirements:

4.0 - 4.5 credits are required.

First Year: MAT133Y5 or [( MAT132H5 or MAT135H5 or MAT137H5 or MAT157H5) and ( MAT134H5 or MAT136H5 or MAT139H5 or MAT159H5)] or MAT137Y5 or MAT157Y5

Higher Years:

1. MAT232H5 or MAT233H5 or MAT257Y5
2. STA256H5 and STA258H5 and STA260H5
3. 1.0 additional credit of STA at the 300/400 level

NOTES:

  1. Students are strongly encouraged to familiarize themselves with the 100-level calculus pre-requisites to select the correct courses.
  2. ECO220Y5 cannot be substituted for STA256H5 and/or STA258H5 and/or STA260H5.
  3. ECO227Y5 can be substituted for STA256H5 and STA258H5, but not for STA260H5.
  4. STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses. In addition, STA246H5 cannot be used towards any program(s) in Applied Statistics or Mathematics. The course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements.
  5. Note that Statistics Research Project courses ( STA378H5, STA398H5, STA478H5 or STA498H5) may not count towards the Applied Statistics minor.

ERMIN1540

Statistics, Applied Courses

STA107H5 • An Introduction to Probability and Modelling

Introduction to the theory of probability, with emphasis on the construction of discrete probability models for applications. After this course, students are expected to understand the concept of randomness and aspects of its mathematical representation. Topics include random variables, Venn diagrams, discrete probability distributions, expectation and variance, independence, conditional probability, applications such as queues.

Exclusions: STA256H5 or STA257H1 or STAB52H3 or STA246H5 or STA237H1 or STA247H1 or ECO227Y5

Distribution Requirement: Science
Total Instructional Hours: 39L/12T
Mode of Delivery: In Class, Hybrid

STA220H5 • The Practice of Statistics I

An introductory course in statistical concepts and methods, emphasizing exploratory data analysis for univariate and bivariate data, sampling and experimental designs, basis probability models, estimation and tests of hypothesis in one-sample and comparative two-sample studies. A statistical computing package is used but no prior computing experience is assumed.

Exclusions: STA215H5 or STA218H5 or STA246H5 or STA256H5 or STA257H1 or PSY201H5 or STA220H1 or STA237H1 or STA247H1 or PSY201H1 or STAB22H3 or STAB52H3

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA221H5 • The Practice of Statistics II

A sequel to STA220H5, emphasizing major methods of data analysis such as analysis of variance for one factor and multiple factor designs, regression models, categorical and non-parametric methods.

Prerequisites: STA215H5 or STA220H5
Exclusions: STA221H1 or STA258H5 or STA248H1or STAB27H3 or STA302H5 or STA302H1 or STAC67H3 or BIO360H5 or ECO220Y5 or ECO220Y1 or ECO227Y5 or ECO227Y1 or PSY202H5 or PSY202H1 or PSYB08H3

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA246H5 • Computational Probability and Statistics

This course covers probability including its role in statistical and computational modeling. Topics include classical and computational perspectives on cumulative, mass and distribution functions, random variables, expectation, limiting results, the normal distribution. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions for simulation and statistical analysis. Additional techniques include resampling, hypothesis testing, model fit and cross validation. IMPORTANT NOTE: STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses. In addition, STA246H5 cannot count towards any program(s) in Mathematics or Applied Statistics. The course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements.

Prerequisites: CSC148H5 and (MAT134H5 or MAT134Y5 or MAT135Y5 or MAT136H5 or MAT137Y5 or MAT139H5 or MAT157Y5 or MAT159H5 or a minimum 65% in MAT133Y5)
Exclusions: STA256H5 or STA237H1 or STA247H1 or STA257H1 or STAB52H3 or ECO227Y5
Recommended Preparation: MAT232H5 or MAT233H5

Distribution Requirement: Science
Total Instructional Hours: 36L/12P
Mode of Delivery: In Class

STA256H5 • Probability and Statistics I

This course covers probability including its role in statistical modeling. Topics include probability distributions, expectation, discrete and continuous random variables and vectors, distribution functions, distributions of functions of random variables, limit theorems, the central limit theorem.

Prerequisites: MAT134H5 or MAT136H5 or MAT137Y5 or MAT139H5 or MAT157Y5 or MAT159H5 or a minimum 65% in MAT133Y5
Exclusions: ECO227Y5 or STA257H1 or ECO227Y1 or STAB52H3
Recommended Preparation: MAT232H5 or MAT233H5

Distribution Requirement: Science
Total Instructional Hours: 39L/12T
Mode of Delivery: In Class

STA258H5 • Statistics with Applied Probability

A survey of statistical methodology with emphasis on the relationship between data analysis and probability theory. Topics covered include descriptive statistics, limit theorems, sampling distribution, point and interval estimation, hypothesis testing, contingency tables and count data, simple linear regression. A statistical computer package will be used.

Prerequisites: STA256H5
Exclusions: ECO227Y5 or ECO227Y1 or STA248H1 or STA255H1

Distribution Requirement: Science
Total Instructional Hours: 39L/12T
Mode of Delivery: In Class

STA260H5 • Probability and Statistics II

A sequel to STA256H5 introducing current statistical theory and methodology. Topics include: Sampling distributions, point estimation, confidence intervals, testing (Neyman-Pearson Theorem, uniformly most powerful test, likelihood ratio tests), unbiasedness, consistency, sufficiency, complete statistics, and exponential family; Fisher Information and the Cramer-Rao inequality; simple linear models.

Prerequisites: STA256H5 or ECO227Y5
Exclusions: STAB57H3 or STA261H5 or STA261H1 or STAC58H3 or STA238H1

Distribution Requirement: Science
Total Instructional Hours: 39L/12T

STA302H5 • Regression Analysis

Analysis of the multiple regression model by least squares; statistical properties of the least square analysis, including estimation of error; residual and regression sums of squares; distribution theory under normality of the observations; confidence regions and intervals; tests for normality; variance stabilizing transformations, multicolinearity, variable search methods.

Prerequisites: STA260H5 and (MAT223H5 or MAT240H5)
Exclusions: STA302H1 or STAC67H3
Recommended Preparation: STA258H5
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 39L/12T
Mode of Delivery: In Class

STA304H5 • Surveys, Sampling and Observational Data

The sample survey is a widely used technique for obtaining information about a large population at relatively small cost. Only probability samples can provide both an estimator and a measure of sampling error from the data itself. In addition to sampling error, non-sampling errors (refusals, not-at-home, lies, inaccuracies, etc.) are always present, and can produce serious biases. The course covers: design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation, observational data; correlation vs. causation, missing data, sources of bias.

Prerequisites: STA258H5 or STA260H5 or STA238H1 or STA255H1 or ECO227Y5
Exclusions: STA304H1 or STAC50H3 or STAC53H3
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 39L/12T
Mode of Delivery: In Class

STA305H5 • Experimental Design

This course covers topics in the design and analysis of experiments. The topics covered include analysis of variance, randomization, confounding, block designs, factorial designs, orthogonal polynomials and response surface methods. Applications include agricultural experiments, laboratory experiments, and industrial experiments, including quality control techniques.

Prerequisites: STA302H5 or ECO375H5
Exclusions: STA305H1
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 39L/12T

STA312H5 • Topics in Statistics

Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Prerequisites: Appropriate prerequisite requirement(s) will be available on the UTM timetable along with the topic title prior to course registration.
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA313H5 • Topics in Statistics

Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Prerequisites: Appropriate prerequisite requirement(s) will be available on the UTM timetable along with the topic title prior to course registration.
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA314H5 • Introduction to Statistical Learning

A thorough introduction to the basic ideas in supervised statistical learning with a focus on regression and a brief introduction to classification. Methods covered will include multiple linear regression and its extensions, k-nn regression, variable selection and regularization via AIC,BIC, Ridge and lasso penalties, non-parametric methods including basis expansions, local regression and splines, generalized additive models, tree-based methods, bagging, boosting and random forests. Content will be discussed from a statistical angle, putting emphasis on uncertainty quantification and the impact of randomness in the data on the outcome of any learning procedure. A detailed discussion of the main statistical ideas behind crossvalidation, sample splitting and re-sampling methods will be given. Throughout the course, R will be used as software, a brief introduction will be given in the beginning.

Prerequisites: (MAT223H5 or MAT240H5) and (STA258H5 or ECO375H5) and STA260H5
Corequisites: STA302H5
Exclusions: STA314H1
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA315H5 • Advanced Statistical Learning

The second part of the course will focus on basic ideas in classification problems including discriminant analysis and support vector machine, and unsupervised learning techniques such as clustering, principal component analysis, independent component analysis and multidimensional scaling. The course will also cover the modern statistics in the "big data" area. The high dimensional problems when p >> n and n >> p will be introduced. In addition, the students will be formed as groups to do data analysis projects on statistical machine learning and present their findings in class. This will prepare them for future careers in industry or academia.

Prerequisites: STA314H5
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA348H5 • Introduction to Stochastic Processes

Discrete Markov chains with a finite number of states, random walks, single-server queues, continuous-time Markov chains, Poisson processes, branching processes, birth and death process, M/M/n queues, Monte-Carlo simulation may be introduced.

Prerequisites: (STA260H5 or STA238H1 or ECO227Y5) and (MAT223H5 or MAT240H5)
Exclusions: STA347H1 or STA447H1 or STAC63H3
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA360H5 • Introduction to Bayesian Statistics

A thorough introduction to statistics from a Bayesian perspective. Methods covered will include: the rules of probability, including joint, marginal, and conditional probability; discrete and continuous random variables; discrete and continuous random variables; Bayesian inferences for means and proportions; the simple linear regression model analyzed in a Bayesian manner; and (time permitting) a brief introduction to numerical methods such as the Gibbs sampler. Throughout the course, R will be used as software, a brief introduction will be given in the beginning.

Prerequisites: STA258H5 or STA260H5 or ECO227Y5 or STA238H1 or STA255H1 or ECO227Y1
Exclusions: STA313H5 (Winter 2020 and Winter 2022) or STA365H1
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA378H5 • Statistics Research Project

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

Prerequisites: (STA260H5 or STA261H1 or STAB57H3) and (permission of instructor and department) and a minimum CGPA of 3.0.
Enrolment Limits: Enrolment is restricted to students in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

STA380H5 • Computational Statistics

Computational methods play a central role in modern statistics and machine learning. This course aims to give an overview of some of the computational techniques that are useful in statistics. Topics include methods of generating random variables, Monte Carlo integration and variance reduction, Monte Carlo methods in inference, bootstrap and jackknife, resampling application, permutation tests, probability density estimation, and optimization.

Prerequisites: STA260H5 or STA238H1
Exclusions: STA312H5 (Winter 2020 and Winter 2022) or STA410H1
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T

STA388H5 • Topics in Statistics

Introduction to a topic of current interest in statistics. Content will vary from year to year. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Prerequisites: Appropriate prerequisite requirement(s) will be available on the UTM timetable along with the topic title prior to course registration.
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA398H5 • Statistics Research Project

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

Prerequisites: (STA260H5 or STA261H1 or STAB57H3) and (permission of instructor and department) and a minimum CGPA of 3.0.
Enrolment Limits: Enrolment is restricted to students in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

STA399H5 • Research Opportunity Program

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 399H course credit. Students enrolled have an opportunity to become involved in original research, learn research methods and share in the excitement and discovery of acquiring new knowledge. Participating faculty members post their project descriptions for the following summer and fall/winter sessions in early February and students are invited to apply in early March. See Research Opportunity Program (ROP) for more details.

Enrolment Limits: Restricted to students enrolled in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

STA399Y5 • Research Opportunity Program

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 399Y course credit. Students enrolled have an opportunity to become involved in original research, learn research methods and share in the excitement and discovery of acquiring new knowledge. Participating faculty members post their project descriptions for the following summer and fall/winter sessions in early February and students are invited to apply in early March. See Research Opportunity Program (ROP) for more details.

Enrolment Limits: Restricted to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Mode of Delivery: In Class

STA413H5 • Estimation and Testing

This course covers advanced topics in probability and mathematical statistics. Topics include convergence in probability, convergence in distribution, and convergence with probability one, sufficiency, completeness, Rao-Blackwell and Lehmann-Sheffe theorems, and asymptotics.

Prerequisites: STA260H5
Exclusions: STA452H1 or STA442H1 or STAC58C3
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA431H5 • Structural Equation Models

Random vectors and matrices, univariate and multivariate regression with measurement error, latent variables, model identification, the LISREL model, path analysis,confirmatory factor analysis, longitudinal data analysis,robustness of the normal model. A statistical computing package will be used.

Prerequisites: STA302H5 or STA302H
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA437H5 • Applied Multivariate Statistics

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical correlations; multivariate analysis of variance; profile analysis and curve fitting for repeated measurements; classification and the linear discriminant function. There will be extensive use of statistical computing packages.

Prerequisites: STA302H5 or ECO375H5
Exclusions: STA437H1 or STAD37H3
Enrolment Limits: Priority is given to students enrolled in Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA441H5 • Data Analysis

Vocabulary of data analysis, Tests of statistical significance, Principles of research design, Applications of statistical methods such as Multiple regression, Factorial ANOVA, Mixed linear models, Multivariate analysis of variance, Repeated measures, Logistic regression, Generalized linear models, Permutation tests and Bootstrapping.

Prerequisites: STA221H5 or STA302H5 or BIO360H5 or ECO375H5 or GGR376H5 or PSY202H5 or SOC350H5 or STA302H1 or STAC67H3
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA457H5 • Applied Time Series Analysis

This course develops the theory and methodology for the statistical analysis of time series. The methods may be broadly characterized as time domain methods based on correlation (Box-Jenkins), or frequency domain methods based on a decomposition of the series into cycles (Fourier). The course develops both of these to the point where they may be applied using standard statistical software. Model identification, estimation and forecasting are discussed. Applications in social and physical sciences are used.

Prerequisites: STA302H5 or ECO375H5
Exclusions: STA457H1 or STAD57H3
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA478H5 • Statistics Research Project

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

Prerequisites: (STA302H5 or STA302H1 or STAC67H3) and (permission of instructor and department) and a minimum CGPA of 3.0.
Enrolment Limits: Enrolment is restricted to students in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

STA488H5 • Topics in Statistics

Introduction to a topic of current interest in statistics. Content will vary from year to year.The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Prerequisites: Appropriate prerequisite requirement(s) will be available on the UTM timetable along with the topic title prior to course registration. 
Enrolment Limits: Priority is given to students enrolled in Applied Statistics Specialist or Major programs.

Distribution Requirement: Science
Total Instructional Hours: 36L/12T
Mode of Delivery: In Class

STA498H5 • Statistics Research Project

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

Prerequisites: (STA302H5 or STA302H1 or STAC67H3) and (permission of instructor or department) and a minimum CGPA of 3.0.
Enrolment Limits: Enrolment is restricted to students in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

STA499H5 • Research Opportunity Program

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 499H course credit. Students enrolled have an opportunity to become involved in original research, learn research methods and share in the excitement and discovery of acquiring new knowledge. Participating faculty members post their project descriptions for the following summer and fall/winter sessions in early February and students are invited to apply in early March. See Research Opportunity Program (ROP) for more details.

Enrolment Limits: Restricted to students enrolled in Applied Statistics Specialist or Major programs.

Course Experience: University-Based Experience
Distribution Requirement: Science
Mode of Delivery: In Class

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