Statistics Courses

Key to Course Descriptions.

For Distribution Requirement purposes STA220H1, 221H1, 250H1, 255H1, AND257H1 have NO distribution requirement status; STA429H1 is a SCIENCE or SOCIAL SCIENCE course; all other STA courses are classified as SCIENCE courses.

| Course Winter Timetable |

First Year Seminar [24S]

First Year Seminar [48S]

Undergraduate seminar that focuses on specific ideas, questions, phenomena or controversies, taught by a regular Faculty member deeply engaged in the discipline. Open only to newly admitted first year students. It may serve as a distribution requirement course; Details here..

An Introduction to Probability and Modelling [36L, 12T]

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, the central limit theorem, applications to the analysis of algorithms and simulating systems such as queues.
Exclusion:ECO220Y1, ECO227Y1/STA247H1STA255H1/STA257H1/
Co-requisite: MAT135Y1/MAT137Y1/MAT157Y1(MAT137Y1/MAT157Y1 is strongly recommended; MAT133Y1 is not acceptable)

The Practice of Statistics I [36L, 12T]

An introductory course in statistical concepts and methods, emphasizing exploratory data analysis for univariate and bivariate data, sampling and experimental designs, basic 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.
Exclusion:ECO220Y1/ECO227Y1/GGR270H1/PSY201H1/ SOC300Y1/STA250H1/STA261H1/STA248H1
Prerequisite: Grade 12 Mathematics and one University course in the physical, social, or life sciences
STA220H1 does not count as a distribution requirement course.

The Practice of Statistics II [36L, 12T]

Continuation of STA220H1, 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.
Exclusion:ECO220Y1/ECO227Y1/GGR270H1 /PSY202H1/SOC300Y1/STA261H1/STA250H1/STA248H1
Prerequisite: STA220H1
STA221H1 does not count as a distribution requirement course.

Probability with Computer Applications [36L, 12T]

Introduction to the theory of probability, with emphasis on applications in computer science. The topics covered include random variables, discrete and continuous probability distributions, expectation and variance, independence, conditional probability, normal, exponential, binomial, and Poisson distributions, the central limit theorem, sampling distributions, estimation and testing, applications to the analysis of algorithms, and simulating systems such as queues.
Prerequisite: MAT135Y1/MAT137Y1/MAT157Y1; CSC108H1/CSC148H1
Exclusion: ECO227Y1/STA255H1/STA257H1

Statistics for Computer Scientists [36L, 12T]

A survey of statistical methodology with emphasis on data analysis and applications. The topics covered include descriptive statistics , data collection and the design of experiments, univariate and multivariate design, tests of significance and confidence intervals, power, multiple regressions and the analysis of variance, and count data. Students learn to use a statistical computer package as part of the course.
Prerequisite: STA247H1/STA255H1/STA257H1; CSC108H1/CSC148H1
Exclusion:ECO220Y1/ECO227Y1/GGR 270Y11/PSY201H1/SOC300Y1/STA220H1/STA221H1/STA250H1/STA261H1

Statistical Concepts [36L, 12T]

A survey of statistical methodology with emphasis on data analysis and applications. The topics covered include descriptive statistics, basic probability, simulation, data collection and the design of experiments, tests of significance and confidence intervals, power, multiple regression and the analysis of variance, and count data. Students learn to use a statistical computer package as part of the course.
Exclusion:ECO220Y1/ECO227Y1/GGR270Y1/PSY201H1/ SOC300Y1/STA220H1/STA261H1/STA221H1/STA248H1
Prerequisite: MAT133Y1/MAT135Y1/MAT137Y1/MAT157Y1

Statistical Theory [36L, 12T]

This courses deals with the mathematical aspects of some of the topics discussed in STA250H1. Topics include discrete and continuous probability distributions,
conditional probability, expectation, sampling distributions, estimation and testing, the linear model.
Exclusion: ECO220Y1/ECO227Y1/STA257H1/261H1/247H1/248H1
Prerequisite: STA250H1/221H1, MAT135Y1/137Y1/ 157Y1
STA255H1 does not count as a distribution requirement course.

Probability and Statistics I [36L, 12T]

This course covers probability including its role in statistical modelling. Topics include probability distributions, expectation, continuous and discrete random variables and vectors, distribution functions. Basic limiting results and the normal distribution presented with a view to their applications in statistics.

Exclusion: ECO227Y1/STA255H1/STA247H1
Prerequisite: MAT135Y1/MAT137Y1/MAT157Y1 (MAT137Y1/MAT157Y1 is strongly recommended)
Co-requisite: MAT235Y1/MAT237Y1/MAT257Y1 (MAT237Y1/MAT257Y1 is strongly recommended), MAT223H1/MAT240H1
STA257H1 does not count as a distribution requirement course.


Probability and Statistics II [36L, 12T]

A sequel to STA257H1 giving an introduction to current statistical theory and methods. Topics include: estimation, testing, and confidence intervals; unbiasedness, sufficiency, likelihood; simple linear and generalized linear models.
Exclusion: ECO227Y1/STA248H1/255H1
Prerequisite: STA257H1
Co-requisite: MAT235Y1/237Y1/257Y1, MAT223H1/240H1

Research Opportunity Program

Credit course for supervised participation in faculty research project. Details here.


Methods of Data Analysis I [36L]

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.
Prerequisite: STA248H1/261H1/ECO220Y1(70%)/ ECO227Y1/(STA257H1/(STA250H1, STA255H1))

Methods of Data Analysis II [36l]

Analysis of variance for one-and two-way layouts, logistic regression, loglinear models, Longitudinal data, introduction to time series.
Prerequisite: STA302H1

Surveys, Sampling and Observational Data (formerly STA322H1) [36L]

Design of surveys, sources of bias, randominized 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.
Exclusion: STA322H1
Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

Design and Analysis of Experiments (formerly STA332H1) [36L]

Experiments vs observational studies, experimental units. Designs with one source of variation. Complete randomized designs and randomized block designs. Factorial designs. Inferences for contrasts and means. Model assumptions. Crossed and nested treatment factors, random effects models. Analysis of variance and covariance. Sample size calculations.
Prerequisite: STA302H1/352Y1/ECO374H1/375H1
Exclusion: STA332H1, 402H1

Probability [36L]

An overview of probability from a non-measure theoretic point of view. Random variables/vectors; independence, conditional expectation/probability and consequences. Various types of convergence leading to proofs of the major theorems in basic probability. An introduction to simple stochastic processes such as Poisson and branching processes.
Prerequisite: STA247H1/STA255H1/257H1/(ECO227Y1, MAT237Y1/257Y1), MAT223H1/240H1; MAT235Y1/237Y1/257Y1
Note: STA257H1 and MAT237Y1/257Y1; MAT (MAT223H1, 224H1)/240H1 are very strongly recommended)

Introduction to Mathematical Statistics [72L]

Introduction to statistical theory and its application. Basic inference concepts. Likelihood function, Likelihood statistic. Simple large sample theory. Least squares and generalizations, survey of estimation methods. Testing hypotheses, p-values and confidence intervals. Bayesian-fequentist interface. Analysis of Variance from a vector-geometric viewpoint. Conditional inference.
Prerequisite: MAT223H1/240H1; MAT235Y1/237Y1/257Y1; STA (257H1,261H1)/ECO227Y1
Note: MAT 237Y1/257Y1; MAT223H1, 224H1)/240H1 very strongly recommended.

Independent Experiential Study Project

Independent Experiential Study Project

An instructor-supervised group project in an off-campus setting. Details here.

Statistical Computation [36L]

Programming in an interactive statistical environment. Generating random variates and evaluating statistical methods by simulation. Algorithms for linear models, maximum likelihood estimation, and Bayesian inference. Statistical algorithms such as the Kalman filter and the EM algorithm. Graphical display of data.
Prerequisite: STA302H1, CSC108H1

Nonparametric methods of inference [48L, 24P]

Modern methods of nonparametric inference, with special emphasis on bootstrap methods, and including density estimation, kernel regression, smoothing methods and functional data analysis.
Prerequisite: STA302H1, 352Y1

Statistical Methods for Data Mining and Machine Learning [48L, 24P]

Statistical aspects of supervised learning: regression with spline bases, regularization methods, parametric and nonparametric classification methods, nearest neighbours, cross-validation and model selection, generalized additive models, trees, model averaging, clustering and nearest neigtbour methods for unsupervised learning.
Prerequisite: CSC108H1, STA302H1/CSC411H1

Theory of Statistical Inference [36L]

The course discusses foundational aspects of various theories of statistics. Specific topics covered include: likelihood based inference, decision theory, fiducial and structural inference, Bayesian inference.
Prerequisite: STA352Y1


Advanced Statistics for the Life and Social Sciences [36L]

The course discusses many advanced statistical methods used in the life and social sciences. Emphasis is on learning how to become a critical interpreter of these methodologies while keeping mathematical requirements low. Topics covered include multiple regression, logistic regression, discriminant and cluster analysis, principal components and factor analysis.
Exclusion: All 300+ level STA courses except STA304H1
Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 /PSY202H1/SOC300Y1/STA221H1/250H1
STA429H1 does not count towards any STA programs

Methods for multivariate data [24L, 12P]

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 partial, multiple and canonical correlations; multivariate analysis of variance; profile analysis and curve fitting for repeated measurements; classification and the linear discriminant function.
Prerequisite: ECO374H1/ECO375H1/STA302H1/STA352Y1
Recommended preparation: APM233Y1/MAT223H1/MAT240H1

Theoretical Multivariate Statistics [36L]

An introductory survey of current multivariate analysis, multivariate normal distributions, distribution of multiple and partial correlations, Wishart distributions, distribution of Hotelling’s T2, testing and estimation of regression parameters, classification and discrimination.
Prerequisite: MAT223H1/MAT240H1, STA352Y1/STA437H1 (STA352Y1 strongly recommended)

Methods of Applied Statistics [36L]

Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models, introductions to generalized linear models, graphical methods, additional topics such as random effects models, split plot designs, analysis of censored data, introduced as needed in the context of case studies.
Prerequisite: ECO374H1/ECO375H1/STA302H1 ; STA305H1

Stochastic Processes (formerly STA348H1) [36L]

Discrete and continuous time processes with an emphasis on Markov, Gaussian and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods).

Exclusion: STA348H1
Prerequisite: STA347H1

Topics in Statistics [36L]

Topics of current research interest are covered. Topics change from year to year, and students should consult the department for information on material presented in a given year.

Time Series Analysis [36L]

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and smoothing time series, theory of stationary processes, identification and estimation of time series models, forecasting, seasonal adjustment, spectral estimation, bivariate time series models.
Prerequisite: ECO374H1/ECO375H1/STA302H1
Recommended preparation: MAT235Y1/MAT237Y1/MAT257Y1

Readings in Statistics [TBA]

Readings in Statistics [TBA]

Independent study under the direction of a faculty member. Persons wishing to take this course must have the permission of the Undergraduate Secretary and of the prospective supervisor.

Readings in Statistics [TBA]

Readings in Statistics [TBA]

Independent study under the direction of a faculty member. Persons wishing to take this course must have the permission of the Undergraduate Secretary and of the prospective supervisor.