| source University of Toronto, Mississauga (X) |
level |
department Statistics (X) |
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. [
Score: 9.805407 Details | Listing | Web page
Acquaints students with the statistical principles that managers need in order to extract information from numerical data, and to understand the formal principles of decision-making under conditions of uncertainty. Covers descriptive statistics, elementary probability, expected values, sampling distributions, point and interval estimation, hypothesis testing for normal and binomial data, and multiple regression analysis. [
Score: 9.805407 Details | Listing | Web page
Interest, discount and present values, as applied to determine prices and values of annuities, mortgages, bonds, equities; loan repayment schedules and consumer finance payments in general; yield rates on investments given the costs on investments. [
Score: 9.805407 Details | Listing | Web page
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. [
Score: 9.805407 Details | Listing | Web page
A sequel to STA220H, 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. [
Score: 9.805407 Details | Listing | Web page
Replaced by
Score: 9.805407 Details | Listing | Web page
This course covers probability including its role in statistical modeling. 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. [
Score: 9.805407 Details | Listing | Web page
(Replaces
Score: 9.805407 Details | Listing | Web page
A sequel to
Score: 9.805407 Details | Listing | Web page
This courses provides a richly rewarding opportunity for students in their second year to work in the research project of a professor in return for 299Y 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
Score: 9.805407 Details | Listing | Web page
An introduction to the principles and procedures of statistics for the forensic sciences. The course covers both classical and Bayesian methodologies. Topics from classical statistics include data presentation, statistical distributions, estimation, hypothesis testing, introduction to ANOVA, introduction to regression, and contingency tables. Topics from Bayesian statistics include subjective probability, conditional probabilities, prior and posterior probabilities. Statistical computing will be required. [
Score: 9.805407 Details | Listing | Web page
A continuation of
Score: 9.805407 Details | Listing | Web page
Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used.
Score: 9.805407 Details | Listing | Web page
Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used.
Score: 9.805407 Details | Listing | Web page
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,
Score: 9.805407 Details | Listing | Web page
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. [
Score: 9.805407 Details | Listing | Web page
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. [
Score: 9.805407 Details | Listing | Web page
(Replaces
Score: 9.805407 Details | Listing | Web page
Research project.
Score: 9.805407 Details | Listing | Web page
Introduction to a topic of current interest in statistics. Content will vary from year to year. Enrolment by permission of instructor only.
Score: 9.805407 Details | Listing | Web page
Topics from modern statistics for applied sciences.
Score: 9.805407 Details | Listing | Web page
This course replaces
Score: 9.805407 Details | Listing | Web page
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. [
Score: 9.805407 Details | Listing | Web page
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. [
Score: 9.805407 Details | Listing | Web page
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, smoothing and density estimation, analysis of censored data, introduced as needed in the context of case studies. [
Score: 9.805407 Details | Listing | Web page