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STA Statistics Courses SCI199Y1 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
breadth requirement course; see page 44. STA107H1 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. STA220H does not count as a distribution requirement
course. The Practice of Statistics II 39L, 13T STA221H does not count as a distribution requirement
course. Statistics for Biologists 39L, 13T JBS229H does not count as a distribution requirement
course. Statistical Concepts 39L, 13T STA250H does not count as a distribution requirement
course Statistical Theory 39L, 13T STA255H does not count as a distribution requirement
course. Probability and Statistics I 39L, 13T STA257H does not count as a distribution requirement
course. Probability and Statistics II 39L, 13T STA299Y1
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
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. STA429H does not count towards any STA programs Applied Multivariate Statistics 26L, 13P 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 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). 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 TBA STA498Y1/499Y1 TBA |
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