Statistics Courses

STAT 100 - Chance and Data Analysis (3)

Chance phenomena and data analysis are studied through simulation and examination of real world contexts including sports, investment, lotteries and environmental issues. Intended to be particularly accessible to students who are not specializing in Statistics. Students may not obtain credit for STAT 100 if they already have credit for - or are taking concurrently - any upper division STAT course. Quantitative/Breadth-Science.

STAT 180 - Career Development Seminar for Statistics and Actuarial Science (1)

A seminar primarily for students undertaking a major or an honours program in Statistics. Visiting speakers share experience relevant to Statistics students and provide useful education and career advice. Prerequisite: Enrollment in the Statistics or Actuarial Science major or honours program, or STAT 270 with a minimum grade of C-.

STAT 201 - Statistics for the Life Sciences (3)

Research methodology and associated statistical analysis techniques for students with training in the life sciences. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 201 if they already have credit for - or are taking concurrently - STAT 101, 203, 205, 285, or any upper division STAT course. Quantitative.

STAT 203 - Introduction to Statistics for the Social Sciences (3)

Descriptive and inferential statistics aimed at students in the social sciences. Scales of measurement. Descriptive statistics. Measures of association. Hypothesis tests and confidence intervals. Students in Sociology and Anthropology are expected to take SA 255 before this course. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units including a research methods course such as SA 255, CRIM 220, POL 200W, or equivalent. Students cannot obtain credit for STAT 203 if they already have credit for - or are taking concurrently - STAT 101, 201, 205, 285, or any upper division STAT course. Quantitative.

STAT 205 - Introduction to Statistics (3)

The collection, description, analysis and summary of data, including the concepts of frequency distribution, parameter estimation and hypothesis testing. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 205 if they already have credit for - or are taking concurrently - STAT 101, 201, 203, 285, or any upper division STAT course. Quantitative.

STAT 240 - Introduction to Data Science (3)

Introduction to modern tools and methods for data acquisition, management, and visualization capable of scaling to Big Data. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, all with a minimum grade of C- or permission of the instructor. STAT 260 is also recommended. Quantitative.

STAT 260 - Introductory R for Data Science (2)

An introduction to the R programming language for data science. Exploring data: visualization, transformation and summaries. Data wrangling: reading, tidying, and data types. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 261. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

STAT 261 - Laboratory for Introductory R for Data Science (1)

A hands-on application of the R programming language for data science. Using the R concepts covered in STAT 260, students will explore (visualize, transform, and summarize) and wrangle (read and tidy) data. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 260. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

STAT 270 - Introduction to Probability and Statistics (3)

Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158, with a minimum grade of C-. Students wishing an intuitive appreciation of a broad range of statistical strategies may wish to take STAT 100 first. Quantitative.

STAT 271 - Probability and Statistics for Computing Science (3)

This is an introductory course in probability and statistics that is designed for Computer Science students. Mainly covers basic probability theory and statistical methods for designing and analyzing computing algorithms and systems. Topics include continuous probability distributions, random variables, multivariate normal distributions, parameter estimation and inference theory, as well as design and analysis of statistical studies, including hypothesis testing and presentation of statistical data. Prerequisite: CMPT 210 with a minimum grade of C-.

STAT 285 - Intermediate Probability and Statistics (3)

This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158, all with a minimum grade of C-. Quantitative.

STAT 290 - Selected Topics in Probability and Statistics (3)

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.

STAT 300W - Statistics Communication (3)

Guided experiences in written and oral communication of statistical ideas and results with both scientific and lay audiences. Prerequisite: Admission to the major or honours programs in statistics; STAT 350 with a minimum grade of C- or permission of the instructor; prior completion of a lower division W course. Writing.

STAT 302 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take the course for further credit. Quantitative.

STAT 305 - Introduction to Biostatistical Methods for Health Sciences (3)

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take this course for further credit. Quantitative.

STAT 310 - Introduction to Data Science for the Social Sciences (2)

An introduction to modern tools and methods for data acquisition, management, visualization, and machine learning, capable of scaling to Big Data. No prior computer programming experience required. Examples will draw from the social sciences. This course may not be used to satisfy the upper division requirements of the statistics honours, major, or minor programs. Prerequisite: 60 units in subjects outside of the Faculties of Science and Applied Sciences and one of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a minimum grade of C-. Corequisite: STAT 311. Students who have taken STAT 240, STAT 440, or any 200-level or higher CMPT course first may not then take this course for further credit. Quantitative.

STAT 311 - Data Science Laboratory for the Social Sciences (2)

A hands-on application of modern tools and methods for data acquisition, management, visualization, and machine learning, capable of scaling to Big Data. No prior computer programming experience required. Projects will draw from the social sciences and integrate application area insight into the analytic toolkit from STAT 310. This course may not be used to satisfy the upper division requirements of the statistics honours, major, or minor programs. Prerequisite: 60 units in subjects outside of the Faculties of Science and Applied Sciences and one of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a minimum grade of C-. Corequisite: STAT 310. Students who have taken STAT 240, STAT 440, or any 200-level or higher CMPT course first may not then take this course for further credit. Quantitative.

STAT 320 - Introduction to Data Science for the Life Sciences (2)

An introduction to modern tools and methods for data acquisition, management, visualization, and machine learning, capable of scaling to Big Data. No prior computer programming experience required. Examples will draw from the life sciences. This course may not be used to satisfy the upper division requirements of the Statistics honours, major, or minor programs. Prerequisite: One of STAT 201, STAT 203, STAT 205, or STAT 270, with a grade of at least C-. Corequisite: MBB 343. Students who have taken STAT 240, STAT 310, or STAT 440 first may not then take this course for further credit. Quantitative.

STAT 330 - Introduction to Mathematical Statistics (3)

Review of probability and distributions. Multivariate distributions. Distributions of functions of random variables. Limiting distributions. Inference. Sufficient statistics for the exponential family. Maximum likelihood. Bayes estimation, Fisher information, limiting distributions of MLEs. Likelihood ratio tests. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

STAT 336 - Job Practicum I (3)

This is the first term of work experience in a co-operative education program available to statistics students. Interested students should contact their departmental advisors as early in their career as possible for proper counselling. Units from this course do not count towards the units required for an SFU degree. The course will be graded on a pass/withdraw basis. A course fee is required. Prerequisite: Students must apply and receive permission from the co-op co-ordinator at least one but preferably two terms in advance. They will normally be required to have completed 45 units with a GPA of 2.5 before they may take this practicum course.

STAT 337 - Job Practicum II (3)

This is the second term of work experience in a co-operative education program available to statistics students. Units from this course do not count towards the units required for an SFU degree. The course will be graded on a pass/withdraw basis. A course fee is required. Prerequisite: STAT 336 or Job Practicum I from another department. Students must apply and receive permission from the co-op co-ordinator at least one term in advance.

STAT 342 - Introduction to Statistical Computing and Exploratory Data Analysis - SAS (2)

Introduces the SAS statistical package. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333, with a minimum grade of C-. Students with credit for STAT 340 may not take STAT 342 for further credit.

STAT 350 - Linear Models in Applied Statistics (3)

Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

STAT 360 - Advanced R for Data Science (2)

Advanced R programming methods for data science. Tools for reproducible research. Version control. Data structures, subsetting, functions, environments, and debugging. Functional programming. Code performance: profiling, memory, integrating R and C++. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 361.

STAT 361 - Laboratory for Advanced R for Data Science (1)

A hands-on application of advanced R programming methods for data science. Using the R concepts covered in STAT 360 and tools for reproducible research, students will work with different data structures, write functions, and debug and optimize the performance of their code. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 360.

STAT 380 - Introduction to Stochastic Processes (3)

Review of discrete and continuous probability models and relationships between them. Exploration of conditioning and conditional expectation. Markov chains. Random walks. Continuous time processes. Poisson process. Markov processes. Gaussian processes. Prerequisite: STAT 330, or all of: STAT 285, MATH 208W, and MATH 251, all with a minimum grade of C-. Quantitative.

STAT 390 - Selected Topics in Probability and Statistics (3)

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.

STAT 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or ECON 333, all with a minimum grade of C-. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. Quantitative.

STAT 410 - Statistical Analysis of Sample Surveys (3)

An introduction to the major sample survey designs and their mathematical justification. Associated statistical analyses. Prerequisite: STAT 350 with a minimum grade of C-. Quantitative.

STAT 430 - Statistical Design and Analysis of Experiments (3)

An extension of the designs discussed in STAT 350 to include more than one blocking variable, incomplete block designs, fractional factorial designs, and response surface methods. Prerequisite: STAT 350 with a minimum grade of C-. Quantitative.

STAT 436 - Job Practicum III (3)

This is the third term of work experience in a co-operative education program available to statistics students. Units from this course do not count towards the units required for an SFU degree. The course will be graded on a pass/withdraw basis. A course fee is required. Prerequisite: STAT 337 or Job Practicum II from another department. Students must apply and receive permission from the co-op co-ordinator at least one term in advance.

STAT 437 - Job Practicum IV (3)

This is the fourth term of work experience in a co-operative education program available to statistics students. Units from this course do not count towards the units required for an SFU degree. The course will be graded on a pass-withdraw basis. A course fee is required. Prerequisite: STAT 436 or Job Practicum III from another department. Students must apply and receive permission from the co-op co-ordinator at least one term in advance.

STAT 438 - Job Practicum V (3)

This is an optional fifth term of work experience in a co-operative education program available to statistics students. Units from this course do not count towards the units required for an SFU degree. The course will be graded on a pass/withdraw basis. A course fee is required. This course may be repeated for additive credit. Prerequisite: STAT 437 or Job Practicum IV from another department. Students must apply and receive permission from the co-op co-ordinator at least one term in advance.

STAT 440 - Learning from Big Data (3)

A data-first discovery of advanced statistical methods. Focus will be on a series of forecasting and prediction competitions, each based on a large real-world dataset. Additionally, practical tools for statistical modeling in real-world environments will be explored. Prerequisite: 90 units including STAT 350 with a minimum grade of C- and one of STAT 341, STAT 260, or CMPT 225, with a minimum grade of C-, or instructor approval. STAT 240 is also recommended.

STAT 445 - Applied Multivariate Analysis (3)

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 450 - Statistical Theory (3)

Distribution theory, methods for constructing tests, estimators, and confidence intervals with special attention to likelihood methods. Properties of the procedures including large sample theory. Prerequisite: STAT 330 with a minimum grade of C-. Quantitative.

STAT 452 - Statistical Learning and Prediction (3)

An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 460 - Bayesian Statistics (3)

The Bayesian approach to statistics is an alternative and increasingly popular way of quantifying uncertainty in the presence of data. This course considers comparative statistical inference, prior distributions, Bayesian computation, and applications. Prerequisite: STAT 330 and 350, with a minimum grade of C-. Quantitative.

STAT 475 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for the former STAT 402 or 602 may not take this course for further credit. Quantitative.

STAT 485 - Applied Time Series Analysis (3)

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. This course may not be taken for further credit by students who have credit for ECON 484. Quantitative.

STAT 490 - Selected Topics in Probability and Statistics (3)

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.

STAT 495 - Directed Studies in Probability and Statistics (3)

Independent reading or research on consultation with the supervising instructor. This course can be repeated for credit. Prerequisite: Written permission of the department undergraduate studies committee.

STAT 603 - Quantitative Analysis of Research Studies (5)

The use of statistical techniques and mathematical models in field research with special emphasis on experimentation, survey techniques, and statistical model construction. This course may not be used for the satisfaction of degree requirements in the Department of Statistics and Actuarial Science. Prerequisite: A course in statistics. Students may not obtain credit for STAT 603 if they already have credit for STAT 403. Students with credit for STAT 650 may not take this course for further credit.

STAT 604 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in experimental research. Prerequisite: Any course in Statistics. Open only to students in departments other than Statistics and Actuarial Science. Students with credit for STAT 302 may not take this course for further credit.

STAT 605 - Biostatistical Methods (3)

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. Prerequisite: Any course in Statistics. Open only to students in departments other than Statistics and Actuarial Science. Students with credit for STAT 305 may not take this course for further credit.

STAT 641 - Introduction to Statistical Computing and Exploratory Data Analysis - R (2)

Introduces the R statistical package in the context of statistical problems. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or STAT 604 or STAT 605 or ECON 333 or equivalent. Open only to students in departments other than Statistics and Actuarial Science. Students with credit for STAT 340 or STAT 341 may not take STAT 641 for further credit.

STAT 642 - Introduction to Statistical Computing and Exploratory Data Analysis - SAS (2)

Introduces the SAS statistical package. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or STAT 604 or STAT 605 or ECON 333 or equivalent. Open only to students in departments other than Statistics and Actuarial Science. Students with credit for STAT 340 or STAT 342 may not take STAT 642 for further credit.

STAT 645 - Applied Multivariate Analysis (3)

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or permission of instructor. Open only to graduate students in departments other than Statistics and Actuarial Science.

STAT 652 - Statistical Learning and Prediction (3)

An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Open only to graduate students in departments other than Statistics and ActSci. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or equivalent. Students with credit for STAT 452 may not take this course for further credit.

STAT 675 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or permission of instructor. Open only to graduate students in departments other than Statistics and Actuarial Science. Students with credit for STAT 402 or 602 may not take this course for further credit.

STAT 685 - Applied Time Series Analysis (3)

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or permission of instructor. Open only to graduate students in departments other than Statistics and Actuarial Science.

STAT 811 - Statistical Consulting I (2)

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.

STAT 812 - Statistical Consulting II (2)

Students will participate in the department's Statistical Consulting Service under the direction of faculty members or the director.

STAT 830 - Statistical Theory I (4)

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.

STAT 831 - Statistical Theory II (4)

Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes. Prerequisite: STAT 830 or permission from the instructor.

STAT 832 - Applied Probability Models (4)

Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.

STAT 840 - Statistical Genetics and Genomics (4)

A mixed lecture and seminar-based course to introduce Statistics graduate students to statistical models and methods in Genetics and Genomics. Topics may include applications of statistical learning in: Quantitative Genetics, Population and Evolutionary Genetics, Computational Molecular Genetics, Human Genomics and Genetic Epidemiology. Prerequisite: STAT 450 or permission of the instructor.

STAT 841 - Advanced Design of Experiments (4)

An advanced treatment of experimental design. Topics can include: factorial designs, multi-stratum experiments, orthogonal arrays, optimal design and robust parameter design. Prerequisite: STAT 830 or permission of the instructor.

STAT 842 - Environmetrics (4)

A practical introduction to analyzing (complex) ecological data using modern statistical methods. A foundation for application of environmental models and methods in scientific research and policy. Prerequisite: STAT 830 or permission of the instructor.

STAT 843 - Functional Data Analysis (4)

An introduction to smoothing and modelling of functional data. Basis expansion methods, functional regression models and derivative estimation are covered. Prerequisite: STAT 830 or permission of the instructor.

STAT 850 - Linear Models and Applications (4)

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.

STAT 851 - Generalized Linear Models and Discrete Data Analysis (4)

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.

STAT 852 - Modern Methods in Applied Statistics (4)

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.

STAT 853 - Applications of Statistical Computing (4)

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.

STAT 854 - Biometrics: Methods in Biomedical Studies (4)

Principles, methods and applications of basic statistical approaches in biomedical studies are presented. Topics include introduction to epidemiology; design of cohort and case-control studies; experimental versus observational data, and cross-sectional versus longitudinal studies; issues of confounding, causation and missing data; design of clinical trials; data monitoring and interim analysis. Prerequisite: STAT 450 or permission of the instructor.

STAT 855 - Lifetime Data Analysis (4)

Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Prerequisite: STAT 450. Students with credit for STAT 806 may not take this course for further credit.

STAT 856 - Longitudinal Data Analysis (4)

Methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including unbalanced and incomplete data sets, characteristic of biomedical research are covered. Topics include covariance pattern models, random or mixed-effects models, multilevel models, generalized estimating equations, inference for multistate processes and counting processes, and methods for handling missing data. Prerequisite: STAT 450 or permission of the instructor.

STAT 857 - Space-Time Models (4)

The theory and application of statistical approaches for the analysis of spatial and time dependent data. Topics will include: point pattern analysis, spatial autocorrelation analysis, geostatistics, lattice processes, modeling spatial count and binary data, spatio-temporal models and time series analysis. Prerequisite: STAT 830 or permission of the instructor.

STAT 880 - Co-op I

First term of work experience in the Co-operative Education Program. Graded on a satisfactory/unsatisfactory basis.

STAT 881 - Co-op II

Second term of work experience in the Co-operative Education Program. Graded on a satisfactory/unsatisfactory basis.

STAT 882 - Co-op III

Third term of work experience in the Co-operative Education Program. Graded on a satisfactory/unsatisfactory basis.

STAT 890 - Statistics: Selected Topics (4)

STAT 891 - Seminar (2)

A course to be team taught by current and visiting faculty and with topics chosen to match the interests of the students.

STAT 894 - Reading (2)

Equivalent Courses: MATH894.

STAT 895 - Reading (4)

Reading. Variable Units 1, 2, 3 or 4 units. Students who have credit for MATH 895 may not take STAT 895 for further credit.

STAT 897 - PhD Comprehensive Exam

Candidates must pass a general examination and may not complete the general exam more than twice. This exam is normally completed within two terms of initial PhD enrollment. Graded on a satisfactory/unsatisfactory basis. Prerequisite: STAT PhD students.

STAT 898 - MSc Project (10)

Students are required to submit and successfully defend a project based on a statistical analysis problem or on the development of new statistical methodology. The project is examined as a thesis and must be submitted to the library. Graded on a satisfactory/unsatisfactory basis.

STAT 899 - PhD Thesis (6)

Graded on a satisfactory/unsatisfactory basis.