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STA Statistics CoursesSTA107H1 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. STA220H1 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. STA221H1 Continuation of 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. STA250H1 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. STA255H1 This courses deals with the mathematical aspects of some of the topics discussed in STA250H. Topics include discrete and continuous probability
distributions, conditional probability, expectation, sampling distributions, estimation
and testing, the linear model. STA257H1 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. STA261H1 A sequel to STA257H 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. STA299Y1 Credit course for supervised participation in faculty research project. See page 42 for details. STA302H1 Analysis of the multiple regression model by least squares; statistical properties of
the least square analysis, including the Gauss Markov theorem; estimate 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, multicollinearity, variable search method. STA322H1 Designing samples for valid inferences about populations at reasonable cost:
stratification, cluster/multi-stage sampling, unequal probability selection, ratio
estimation, control of non-sampling errors (e.g. non-response, sensitive questions,
interviewer bias). STA332H1 (formerly STA402H) 39L STA347H1 An overview of probability form 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. STA352Y1 An introduction to the theory of mathematical statistics. The topics include: a review
of some relevant concepts from the theory of probability, the theory of optimal
estimators, tests and confidence regions, large sample theory, likelihood theory,
distribution-free methods, Bayesian inference. STA398H0/399Y0 An instructor-supervised group project in an off-campus setting. See page 42 for details. STA410H1 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. STA422H1 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. STA429H1 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. STA437H1 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. STA438H1 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. STA442H1 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. STA447H1 Stochastic Processes (formerly STA348H) 39L STA450H1 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. STA457H1 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. STA496H1/497H1 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. STA498Y1/499Y1 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. |
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