Faculty of Arts & Science 2015-2016 Calendar

Statistical Sciences

• Statistics Specialist | Statistics Major | Statistics Minor
• Applied Statistics Specialist
• Statistics Courses

Faculty

Professors Emeriti
D.F. Andrews, M Sc, Ph D
D.A.S. Fraser, BA, Ph D, FRSC
I. Guttman, MA, Ph D
M.S. Srivastava, M Sc, Ph D

Professor and Chair of the Department
J. Stafford, M Sc, Ph D

Associate Professor and Associate Chair - Graduate Studies
F. Yao, B Sc, M Sc, Ph D

Professors and Associate Chairs Undergraduate Studies
S. Broverman, B Sc, M Sc, Ph D, ASA, Actuarial Science
R. Neal, B Sc, Ph D, Statistics

University Professor
N.M. Reid, M Sc, Ph D, FRSC

Professors
R. Craiu, B Sc, Ph D
M.J. Evans, MA, Ph D (UTSC)
A. Feuerverger, B Sc, Ph D
K. Knight, M Sc, Ph D
X.S. Lin, M Sc, Ph D, ASA
P. McDunnough, M Sc, Ph D
J. Quastel, MS, Ph D
J.S. Rosenthal, MA, Ph D
L. Sun, B Sc. Ph D

Associate Professors
A. Badescu, B Sc, M Sc, Ph D
D. Brenner M Sc, Ph D
L.J. Brunner, MA, Ph D (UTM)
S. Jaimungal , BA Sc, M Sc, Ph D

Assistant Professors
D. Roy, B Sc, M Sc, Ph D (UTSC)
R. Salakhutdinov, B Sc, M Sc, Ph D
B. Virag, Ph D (UTSC)
Z. Zhou, B Sc, Ph D

Senior Lecturers
A. Gibbs, B. Math, B Ed, M Sc, Ph D
A.M. Vukov, MA, ASA

Lecturers
N. Taback, B Sc, M Sc, Ph D
V. Zhang, B Sc, M Sc, FSA, ACIA

Introduction

Statistical methods have applications in almost all areas of science, engineering, business, government, and industry. The practising statistician is involved in such diverse projects as designing clinical trials to test a new drug, economic model-building to evaluate the costs of a guaranteed-income scheme, predicting the outcome of a national election, planning a survey of television viewing habits, and estimating animal populations.

Today’s consumer is bombarded with the results of so many quantitative studies using statistical methodology that it is necessary to know something about statistics in order to be properly critical. A basic knowledge of statistics should be an integral part of everyone’s general education.

Probability theory is used to analyse the changing balance among the age-groups in a population as the birth rate changes, the control force needed to keep an aircraft on course through gusts of wind, the chance that the demand for electricity by all the customers served by a substation will exceed its capacity. These are just three of many phenomena that can be analysed in terms of randomness and probability.

The course offerings are intended not only for specialists in the theory of the subject but also to serve the needs of the many other disciplines that use statistical methods, e.g. in sample survey design and experimental design. Students following the Specialist Program are encouraged to include courses in major fields of application in their overall program. The Major Program can be profitably combined with specialization in another discipline. Students in these programs may also qualify for the A. Stat. designation from the Statistical Society of Canada.

Both applied and theoretical courses are offered in Statistics and Probability. The foundation courses STA220H1, STA221H1, STA247H1, STA248H1, STA255H1, STA257H1, and STA261H1 are distinguished primarily by their mathematical demands, as indicated by the prerequisites. Students interested in the Biological or Social Sciences will generally find the most relevant courses of the more advanced offerings to be STA302H1, STA303H1, STA304H1, STA305H1 and STA429H1. The probability course STA347H1 will be of interest to those whose field of application includes stochastic models.

Enquiries: 100 St. George Street, Sidney Smith Hall, Room 6018 (416-978-3452)

Associate Chair, Undergraduate Studies: Statistics - Professor R. Neal; e-mail: ugchair.stats@utstat.utoronto.ca

Associate Chair, Undergraduate Studies: Actuarial Science - Professor S. Broverman; e-mail: ugchair.actsci@utstat.utoronto.ca

Statistical Sciences Programs

(11.0 full courses or their equivalent)

First Year:
CSC108H1/CSC120H1/CSC148H1, MAT137Y1/MAT157Y1

Second Year:
MAT223H1/MAT240H1, MAT224H1/MAT247H1, MAT237Y1/MAT257Y1; STA257H1, STA261H1
(MAT223H1/MAT240H1 recommended in 1st year) while CSC148H1 might well be taken in 2nd year)

Higher Years:
1. STA302H1, STA303H1, STA347H1, STA355H1, STA410H1, STA442H1
2. 2 full year courses from the given list: (STA414H1/CSC411H1), STA422H1, STA437H1, STA447H1, STA452H1, STA453H1, STA457H1, STA465H1, STA480H1
3. 1.5 full year courses from: ACT451H1, ACT452H1, ACT460H1; APM412H1;
MAT327H1, MAT334H1/MAT354H1, MAT337H1/MAT357H1, MAT301H1/MAT347Y1
CSC207H1, CSC310H1, CSC336H1/CSC436H1,
STA300 + level courses

(6.5 full courses or their equivalent, including at least one STA 400-series course)

First Year:

CSC108H1/CSC120H1/CSC148H1 (may be taken in 2nd year); (MAT135H1,MAT136H1)/MAT137Y1/MAT157Y1

Second Year:
MAT223H1/MAT240H1, MAT235Y1/MAT237Y1/MAT257Y1; (STA220H1/ECO220Y1, STA255H1)/ (STA247H1, STA248H1)/(STA257H1, STA261H1)/ECO227Y1

(MAT223H1/MAT240H1 recommended in 1st year, MAT221H1 is not allowed)

Higher Years:
1. STA302H1
2. 3 half (H) course equivalents from all available STA300+ level courses (For example, a student interested in economics/commerce/finance might think to include STA304H1, STA347H1, STA457H1 in their programme, while someone engaged in a life science might entertain STA303H1, STA305H1, STA437H1. On the other hand, a student with an interest in pure math might choose to focus on applications of that subject matter to theoretical probability and statistics, selecting STA347H1, STA355H1 towards a major in statistics.)
3. 1 half (H) course equivalent from the available STA400+ level courses (For those anticipating a future professional need to analyze large arrays of data STA410H1, STA414H1 are certainly very worthy of consideration.)

(4 full courses or their equivalent)

First Year:
MAT133Y1 (70%)/(MAT135H1, MAT136H1)/MAT135Y1/MAT137Y1/MAT157Y1 ((MAT135H1, MAT136H1)/MAT137Y1/MAT157Y1 is strongly recommended)

Second Year:
MAT221H1 (70%)/MAT223H1/MAT240H1, (STA220H1/ECO220Y1, STA255H1)/(STA247H1, STA248H1)/(STA257H1, STA261H1)/ECO227Y1

MAT221H1 (70%)/MAT223H1/MAT240H1 recommended in 1st year

Higher Years:
STA302H1
2. 2 half (H) course equivalents from all available STA300+ level courses

(10.0 FCEs plus a concentration in another discipline requiring 2.0-3.5 FCEs)

First year:
1.  CSC108H1/CSC120H1/CSC148H1, (MAT135H1, MAT136H1)/MAT137Y1/MAT157Y1

2.  Recommended: introductory course in area of concentration

Second year
3.  MAT223H1/MAT240H1, MAT235Y1/MAT237Y1/MAT257Y1, (STA220H1,STA255H1)/(STA247H1,STA248H1)/(STA257H1,STA261H1)
MAT223H1/MAT240H1 can be taken in first year.

Upper years:
4.  STA302H1, STA303H1, STA304H1/STA305H1, STA355H1, STA410H1, STA437H1, STA442H1, STA490Y1
5.  0.5 FCEs from STA 300+-level offerings
6.  1.0 FCEs from the following list:
MAT224H1/MAT247H1, MAT244H1/MAT267H1
APM236H1/APM346H1/APM462H1
CSC148H1/CSC207H1

Disciplinary Focuses

To enrol in one or more focuses, students must first be enrolled in the Applied Statistics Specialist program. Enrolment instructions can be found on the Arts & Science Current Students subject POSt enrolment web site. Focuses can be chosen on ROSI after admission to the program, which begins in July.

Health Studies:  (2.5 FCE)
UNI209H1, UNI211H1, UNI373H1, UNI330H1/UNI411H1/UNI464H1

Global Health: (2.5 FCE)
BIO120H1, BIO130H1, HMB203H1, HMB323H1, HMB342H1/HMB433H1
(Recommended: HMB433H1)

Health and Disease: (3.0 FCE)
BIO120H1, BIO130H1, HMB202H1, HMB265H1, HMB302H1, HMB321H1/HMB322H1/HMB422H1

Fundamental Genetics and its Applications: (3.0 FCE)
BIO120H1, BIO130H1, HMB201H1, HMB265H1, HMB301H1, HMB321H1/HMB421H1/HMB441H1
(Recommended: HMB421H1)

Neuroscience: (3.5 FCE)
BIO120H1, BIO130H1, PSY100H1, HMB200H1/HMB220H1, HMB265H1, HMB300H1, HMB420H1/ HMB440H1
(Recommended: HMB420H1)

Social Psychology: (2.0 FCE)
PSY100H1, PSY220H1, PSY322H1, PSY326H1/PSY321H1/PSY424H1/PSY426H1/PSY405H1/PSY406H1

Cognitive Psychology: (2.0 FCE)
PSY100H1, PSY270H1, PSY493H1, PSY342H1/PSY405H1/PSY406H1

Sociolinguistics: (3 FCE)
LIN100Y1; 2 of LIN228H1, LIN229H1, LIN232H1 or LIN241H1; LIN351H1 and LIN456H1

Psycholinguistics: (3 FCE)
LIN100Y1; 2 of LIN228H1, LIN229H1, LIN232H1 or LIN241H1; 2 of JLP374, JLP315 or JLP471

Astronomy & Astrophysics: (2.5 or 3.0 FCE)

(PHY131H1,PHY132H1)/(PHY151H1,PHY152H1); AST221H1, AST222H1; (PHY252H1, AST320H1)/AST325H1/AST326Y1

Sociology: (2.5 FCE)
SOC101Y1 (minimum grade of 65%) or SOC102H1+SOC103H1 (minimum combined average grade of 65%); SOC200H1; one of SOC303H1/SOC312H1/SOC355H1; 0.5 credit SOC course at 400-level. Students interested in advanced study in Sociology should consider additional courses, in particular SOC201H1 and SOC203H1. 

Statistical Sciences Courses

The 199Y1 and 199H1 seminars are designed to provide the opportunity to work closely with an instructor in a class of no more than twenty-four students. These interactive seminars are intended to stimulate the students’ curiosity and provide an opportunity to get to know a member of the professorial staff in a seminar environment during the first year of study. Details can be found at www.artsci.utoronto.ca/current/course/fyh-1/.

This course, intended for students considering a program in Statistical Sciences, discusses the crucial role played by statistical reasoning in solving challenging problems from natural science, social science, technology, health care, and public policy, using a combination of logical thinking, mathematics, computer simulation, and oral and written discussion and analysis.

This course teaches non-science students the importance of quantitative reasoning to many different areas.  It explores a variety of applications to such diverse subjects as economics, gambling, politics, poetry, graphics, music, medicine, demographics, sports, secret codes, and more, using only basic high school level mathematics combined with logical thinking.

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. (Note:STA220H1 does not count as a distribution requirement course)

Section L0201 (Enrironmental Sciences) and L0301 (Health and Life Sciences) have a focus on applications in a specific area, but any section will satisfy a STA220H1 program requirement. The prerequisites are the same for all sections.

This course will also be Available Online

For the section available online: The online section of the course will use web-based delivery of lectures and online assessments, and will require participation in online discussions and problem solving using a range of communication tools.  The final eam will require student attendance on the St. George campus.

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 (Note: STA221H1 does not count as a distribution requirement course).

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 (Note: STA247H1 does not count as a distribution requirement course).

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 regression and the analysis of variance, and count data. Students learn to use a statistical computer package as part of the course (Note: STA248H1 does not count as a distribution requirement course).

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 (Note: STA255H1 does not count as a distribution requirement course).

This course, and its sequel, STA261H1, are mathematically quite challenging, the target audience  includes anyone proceeding directly to a specialist degree in statistics, as well as anyone with serious and special interest in some other of the identifiably statistical-physical sciences.  Topics, albeit very rigorously covered, are, nevertheless, very standard introductory ones: abstract probability and expectation, discrete and continuous random variables and vectors, with the special mathematics of distribution and density functions, all realized in the special examples of ordinary statistical practice: the binomial, poisson and geometric group, and the gaussian (normal), gamma, chi-squared complex.

A sequel to STA257H1, providing a rigorous introduction to the logical foundations of statistical inference and the practical methodology engendered. Topics include: statistical models, parameters, samples and estimates; the general concept of statistical confidence with applications to the discrete case and the construction of confidence intervals and more general regions in both the univariate and vector-valued cases; hypothesis testing; the likelihood function and its applications; time permitting: the basics of data analysis, unbiasedness, sufficiency, linear models and regression (Note: STA261H1 does not count as a distribution requirement course).

Credit course for supervised participation in faculty research project. Details at http://www.artsci.utoronto.ca/current/course/rop. Not eligible for CR/NCR option.

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, randomized 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.

STA355H1 provides a unifying structure for the methods taught in other courses, and will enable students to read methodological research articles or articles with a large methodological component. Topics covered include statistical models and distributions; fundamentals of inference: estimation, hypothesis testing, and significance levels; likelihood functions and likelihood-based inference; prior distributions and Bayesian inference.

Bayesian inference has become an important applied technique and is especially valued to solve complex problems. This course first examines the basics of Bayesian inference. From there, this course looks at modern, computational methods and how to make inferences on complex data problems.

An instructor-supervised group project in an off-campus setting. Details at http://www.artsci.utoronto.ca/current/course/399. Not eligible for CR/NCR option.

An instructor-supervised group project in an off-campus setting. Details at http://www.artsci.utoronto.ca/current/course/399. Not eligible for CR/NCR option.

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.

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 neighbour methods for unsupervised learning.

This course examines current theory of statistical inference, particularly likehood-based methods and Bayesian methods with an emphasis on resolving present conflicts; log-model expansion and asymptotics are primary tools.

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.

Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models, introduction to generalized linear models, graphical methods, additional topics such as random effects models, designed experiments, model selection,  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.

Statistical theory and its applications at an advanced mathematical level. Topics include probability and distribution theory as it specifically pertains to the statistical analysis of data. Linear models and the geometry of data, least squares and the connection to conditional expectation. The basic concept of inference and the likelihood function.

Continuation of STA452H1:  statistical theory and its applications at an advanced mathematical level. Topics include classical estimation, theory with methods based on the likelihood function and the likelihood statistics. Testing hypothesis and the evaluation of conference from both a bayesian and frequentist point of view.

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.

Data acquisition trends in the environmental, physical and health sciences are increasingly spatial in character and novel in the sense that modern sophisticated methods are required for analysis. This course will cover different types of random spatial processes and how to incorporate them into mixed effects models for Normal and non-Normal data. Students will be trained in a variety of advanced techniques for analyzing complex spatial data and, upon completion, will be able to undertake a variety of analyses on spatially dependent data, understand which methods are appropriate for various research questions, and interpret and convey results in the light of the original questions posed.

Statistical analysis of genetic data is an important emerging research area with direct impact on population health. This course provides an introduction to the concepts and fundamentals of statistical genetics, including current research directions.  The course includes lectures and hands-on experience with R programming and state-of-the-art statistical genetics software packages.

Through case studies and collaboration with researchers in other disciplines, students develop skills in the collaborative practice of Statistics. Focus is on pragmatic solutions to practical issues including study design, dealing with common complications in data analysis, and ethical practice, with particular emphasis on written communication.

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. Not eligible for CR/NCR option.

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. Not eligible for CR/NCR option.

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. Not eligible for CR/NCR option.

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. Not eligible for CR/NCR option.