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Statistics
The master of science (MSc) program offers a wide range of statistical techniques and provides experience in practical statistics application. It teaches statistical expertise for careers in either theoretical or applied statistics.
The MSc program in statistics combines applied and theoretical training in state of the art statistical methodology, hands-on consulting experiences, a project in data analysis or in the development of new statistical methodology, and the opportunity to gain work experience through co-operative education. The program prepares graduates for careers as statisticians in industry, government, consulting, and research organizations. In addition, graduates receive the foundational training to continue on to PhD studies.
間眅埶AV Requirements
See the Section 1.3 for further information.
Applicants whose first language is not English normally submit Test of English as a Foreign Language (TOEFL) results.
Applicants with degrees in areas other than statistics are encouraged to apply provided they have some formal training in statistical theory and practice.
Program Requirements
The MSc in Statistics requires a total of 36 units consisting of a 6 unit project and a further 30 units of course work of which at least 24 must be at the graduate level.
Normally these courses must include
This course is designed to give students some practical experience as a statistical consultant through classroom discussion of issues in consulting and participation in the department's Statistical Consulting Service under the direction of faculty members or the director.
Students will participate in the department's Statistical Consulting Service under the direction of faculty members or the director.
The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.
A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference. Prerequisite: STAT 350 or equivalent.
The theory and application of statistical methodology for analyzing non-normal responses. Special emphasis on contingency tables, logistic regression, and log-linear models. Other topics can include mixed-effects models and models for overdispersed data. Prerequisite: STAT 830 and STAT 850 or permission of instructor.
An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines. Prerequisite: STAT 830 and STAT 853 or permission of instructor.
An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed. Prerequisite: STAT 830 or equivalent or permission of instructor.
Project
All students submit and successfuly defend a 6 unit project (STAT 898-6) based on a statistical analysis problem. See the for further information.
Program Length
Students with a good undergraduate background in statistics will normally complete the course work in up to four terms. The project, including the defense, is expected to require up to two terms. Students with backgrounds in other disciplines, or with an inadequate background in statistics, may be required to complete certain undergraduate courses in the department in addition to the requirements shown above.
Diploma in Financial Engineering
is designed for students in the MSc program who would like to develop applied skills in the field of finance.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the (residence, course work, academic progress, supervision, research competence requirement, completion time, and degree completion), as well as the specific requirements for the program in which they are enrolled, as shown above.