## Statistics CoursesFor 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. |

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..
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.
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.
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.
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.
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.
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.
This courses deals with the mathematical aspects of some of the topics
discussed in STA250H1. Topics include discrete and continuous probability distributions,
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.
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.
Credit course for supervised participation in
faculty research project.
Details here.
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.
Analysis of variance for one-and two-way
layouts, logistic regression,
loglinear models, Longitudinal data, introduction to time series.
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.
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. 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.
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.
An instructor-supervised
group project in an off-campus setting. Details here.
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.
Modern methods of nonparametric
inference, with special emphasis on bootstrap
methods, and including density estimation, kernel regression,
smoothing methods and functional data analysis.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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. |