For more detailed course information, click on a course title below.
Statistics (STAT) 631 Introduction to Statistical Methods in Health Informatics (0.50) LEC
Course ID: 013825
Exploratory data analysis and data visualization. Confounding, censoring, selection bias, study designs and meta-analysis. Statistical modelling for continuous and binary data. Use of a statistics package, such as SAS, to analyze case studies will be important throughout. This is open only to students registered in the Masters of Health Informatics plan.
Statistics (STAT) 814 Systematic Review and Meta-Analysis (0.50) LEC,TUT
Course ID: 014953
This course will provide students with an overview of the rationale and stages involved in the conduct of a formal systematic review and meta-analysis of a well-defined clinical/health research question. The overarching aim is to provide students with the tools to critically appraise and conduct a systematic review and meta-analysis. Students will largely work in pairs to progress through each step involved (with feedback from instructors at each stage) and to produce a final systematic review and meta-analysis to be presented/submitted at the end of the course. Course Objectives: 1. To demonstrate an understanding of the rationale underlying a systematic review and meta-analysis and relevance to clinical care and health policy; 2. To critically appraise a systematic review; 3. To develop a focused research question amenable to a systematic review; 4. To develop and implement a comprehensive and systematic literature search strategy; 5. To determine and apply procedures for including/excluding potential studies for a systematic review and meta-analysis; 6. To develop and implement a data abstraction process and study database; 7. To demonstrate an understanding of the fundamental statistical/biostatistical issues relevant to the conduct of a formal systematic review and meta-analysis. 8. To perform statistical analyses for a systematic review and meta-analysis and complete/present a final report demonstrating all stages involved. Students will need to meet with Course Coordinators and provide of appropriate previous experience with linear and/or logistic regression techniques.
Statistics (STAT) 830 Experimental Design (0.50) LEC
Course ID: 010065
Review of experimental designs in a regression setting; analysis of variance; replication, balance, blocking, randomization, and interaction; one-way layout, two-way layout, and Latin square as special cases; factorial structure of treatments; covariates; treatment contrasts; two-level fractional factorial designs; fixed versus random effects; split-plot and repeated-measures designs; other topics.
Statistics (STAT) 831 Generalized Linear Models and Applications (0.50) LEC
Course ID: 003087
Review of normal linear regression and maximum likelihood estimation. Computational methods, including Newton-Raphson and iteratively reweighted least squares. Binomial regression; the role of the link function. Goodness-of-fit, goodness-of-link, leverage. Poisson regression models. Generalized linear models. Other topics in regression modelling.
Statistics (STAT) 833 Stochastic Processes (0.50) LEC
Course ID: 003088
Random walks, renewal theory and processes and their application, Markov chains, branching processes, statistical inference for Markov chains.
Statistics (STAT) 835 Statistical Methods for Process Improvement (0.50) LEC,TUT
Course ID: 003089
Statistical methods for improving processes based on observational data. Assessment of measurement systems. Strategies for variation reduction. Process monitoring, control and adjustment. Clue generation techniques for determining the sources of variability. Variation transmission.
Statistics (STAT) 836 Introduction to the Analysis of Spatial Data in Health Research (0.50) LEC
Course ID: 014078
The objective of this course is to develop understanding and working knowledge of spatial models and analysis of spatial data. The course provides an introduction to the rudiments of statistaical inference based on spatially correlated data. Methods of estimation and testing will be developed for geostatistical models based on variograms and spatial autoregressive models. Concepts and application of methods will be emphasized through case studies and projects with health applications.
Statistics (STAT) 837 Analysis of Longitudinal Data in Health Research (0.50) LEC
Course ID: 014081
This course will provide an introduction to principles and methods for the analysis of longitudinal data. Conditional and random effect of modeling approaches to regression analysis will be covered, as well as semiparametric methods based on generalized estimating equations. The importance of model assessment and parameter interpretation will be emphasized. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies. Students will be required to think critically about appropriate strategies for data analysis. Analysis will be carried out with appropriate statistical software.
Statistics (STAT) 840 Computational Inference (0.50) LEC
Course ID: 003090
Introduction to and application of computational methods in statistical inference. Monte Carlo evaluation of statistical procedures, exploration of the likelihood function through graphical and optimization techniques including EM. Bootstrapping, Markov Chain Monte Carlo, and other computationally intensive methods.
Statistics (STAT) 841 Statistical Learning - Classification (0.50) LEC
Course ID: 003091
Given known group membership, methods which learn from data how to classify objects into the groups are treated. Review of likelihood and posterior based discrimination. Main topics include logistic regression, neural networks, tree-based methods and nearest neighbour methods. Model assessment, training and tuning.
Statistics (STAT) 842 Data Visualization (0.50) LEC,TUT
Course ID: 012612
Visualization of high dimensional data including interactive methods directed at exploration and assessment of structure and dependencies in data. Methods for finding groups in data including traditional and modern methods of cluster analysis. Dimension reduction methods including multi-dimensional scaling, nonlinear and other methods.
Statistics (STAT) 844 Statistical Learning - Function Estimation (0.50) LEC
Course ID: 003092
Methods for finding surfaces in high dimensions from incomplete or noisy functional information. Both data adaptive and methods based on fixed parametric structure will be treated. Model assessment, training and tuning.
Statistics (STAT) 845 Statistical Concepts for Data Science (0.50) LEC
Course ID: 015587
This is a foundational survey course designed for Data Science students who do not have an undergraduate degree in statistics. It provides an understanding and appreciation of the essential statistical concepts needed for Data Science. It has modular design with the following modules: probability for statistics, concepts of statistical inference, simulation and Monte Carlo methods, regression modelling, planning empirical studies, and drawing conclusions from data. Emphasis will be on exposing students to the core concepts and illustrating them largely through examples.
Statistics (STAT) 847 Exploratory Data Analysis (0.50) LEC
Course ID: 015523
This course introduces Data Science students to exploratory data analysis with a focus on the concepts and tools necessary for understanding and successfully undertaking an end to end exploratory analysis of large and complex data. All stages of data collection, preparation and cleaning, importation into a statistical programming environment, data manipulation, as well as analysis and visualization will be treated. Material will be presented via case studies which will involve hands on programming and analysis. Methods and models (both statistical and computational) will be introduced as needed according to the nature of the data encountered in each case. The case studies will be chosen to give a breadth of experience to the student. A modern statistical programming language will be used throughout.
Statistics (STAT) 850 Estimation and Hypothesis Testing (0.50) LEC,TUT
Course ID: 003094
Discussion of inference problems under the headings of hypothesis testing and point and interval estimation. Frequentist and Bayesian approaches to inference. Construction and evaluation of tests and estimators. Large sample theory of point estimation.
Statistics (STAT) 854 Sampling Theory and Practice (0.50) LEC
Course ID: 003097
Sources of survey error. Probability sampling designs, estimation and efficiency comparisons. Distribution theory and confidence intervals. Generalized regression estimation. Software for survey analysis.
Statistics (STAT) 900 PhD Research Skills (0.50) LEC
Course ID: 015988
This course acts as a capstone on the coursework based part of the PhD program and as a stepping stone to the PhD proposal and to research. It is aimed at developing research skills: critically reading published research, summarizing and synthesizing areas of research, writing and orally presenting summaries of research problems, data sets and theoretical and applied results. The course is designed to be integrative across core areas of the student discipline.
Statistics (STAT) 901 Theory of Probability 1 (0.50) LEC
Course ID: 003101
Probability measures, random variables as measurable functions, expectation, independence, characteristic functions, limit theorems, applications.
Statistics (STAT) 902 Theory of Probability 2 (0.50) LEC
Course ID: 003102
Review of conditioning on sigma-fields; martingale theory (discrete and continuous-time) and applications; counting processes; Brownian motion; stochastic differential and integral equations and applications; general theory of Markov processes (including martingale problems and semigroup theory), diffusions; weak convergence of stochastic processes on function spaces; functional versions of the central limit theorem and strong laws; convergence of empirical processes.
Statistics (STAT) 906 Computer Intensive Methods for Stochastic Models in Finance (0.50) LEC
Course ID: 003104
Review of basic numerical methods. Simulation of random variables, stochastic processes and stochastic models in finance. Numerical solution of deterministic and stochastic differential equations. Valuation of complex financial instruments and derivative securities. Project and paper.
Statistics (STAT) 908 Statistical Inference (0.50) LEC
Course ID: 003105
Principles of Inference: sufficiency, conditionality, and likelihood; examples and counter examples; conditional inference and ancillarity. Theory of Hypothesis Testing: Neyman-Pearson lemma; similar tests; invariant tests. Asymptotic Theory: maximum likelihood and related theory; large-sample properties of parametric significance tests. Interval Estimation: confidence intervals and significance intervals; location and scale models, conditional intervals. Introduction to Decision Theory: loss and risk functions, admissibility; minimax and Bayes rules; prior and posterior analysis. The course content of Stat 850 is a presumed prerequisite for Stat 908.
Statistics (STAT) 923 Multivariate Analysis (0.50) LEC
Course ID: 003113
Multivariate problems as extensions of univariate problems, discriminant analysis, canonical correlation and principle component analysis.
Statistics (STAT) 930 Time Series 2 (0.50) LEC
Course ID: 003117
Multiple time series modeling including transfer function and intervention analysis. Various special topics in time series such as outliers, robustness, order determination methods, Kalman filtering, sampling and aggregation, seasonal adjustments.
Statistics (STAT) 931 Causal Inference and Epidemiological Studies (0.50) LEC
Course ID: 014341
Causal inference in health research will be covered. Methods for the design and analysis of randomized controlled trials including randomization techniques, sample size and power calculations, and specialized additional topics including missing data, noncompliance, and ethics. The design and analysis of classical and modern epidemiological studies will then be discussed for settings in which randomization is not feasible. Causal inference methodologies for the analysis of observational data include propensity scores, marginal structural models and instrumental variables. Studies will be discussed from the epidemiological literature and other sources in the public domain. Simulations and data analyses will be carried out using software (e.g. R or SAS). Students will be trained and assessed in part based on the preparation of reports and delivery of presentations.
Statistics (STAT) 932 Classification and Prediction in High Dimensional Analysis in Health Research (0.50) LEC
Course ID: 014342
In this course, we will cover classification and prediction in health research with a view to applications to screening and diagnosis of disease. This will lead to methods for evaluating the performance of various types of statistical models and learning techniques/algorithms. Cross-validation and bootstrap approaches will be introduced for model performance evaluation, and we will discuss discrimination and calibration as different components of prediction performance. We will cover variable selection techniques, including for high-dimensional data, with an emphasis on regularization techniques such as the LASSO and its variants. Model validation, both internal and external, and model updating will be covered, and we will also discuss post-model selection inference. An important focus will be on biomarker evaluation for a given disease, potentially connected to therapy, and leading to coverage of precision/personalized medicine. Finally, there will be coverage on the importance of reproducible and replicable research. Examples from different problems in health, including genetics, will be presented, and software (e.g. R or SAS) will be used throughout the course.
Statistics (STAT) 935 Analysis of Survival Data (0.50) LEC
Course ID: 003120
This course deals with methods of analyzing data on the time to failure with particular emphasis on the use of regression models for such data. Both parameteric and semi-parametric regression models will be considered.
Statistics (STAT) 936 Analysis of Longitudinal Data (0.50) LEC
Course ID: 013084
This course covers methods for analyzing data in which repeated measures have been obtained for individuals in health studies over time. Different methods will be discussed to handle both continuous and discrete longitudinal response data, with examples from biomedical and population health datasets. Some of the approaches covered will include linear, non-linear, and generalized linear mixed effects models, as well as generalized estimating equations and transition models, with distinctions drawn between subject-specific and population averaged approaches for generalized linear longitudinal response data. Also, there will be coverage of exploratory methods, evaluation of model assumptions and adapting to assumption violations, approaches for handling missing data, and treatment of advanced topics such as semiparametric and nonparametric models for longitudinal data. Software (e.g. R or SAS) will be used throughout the course.
Statistics (STAT) 938 Statistical Consulting (0.50) LEC
Course ID: 003122
This course will cover some of the basic tools of a statistical consultant. Topics will include the use of statistical packages, problem-solving techniques, discussion of common statistical consulting problems, effective communication of statistical concepts and management of consulting sessions.
Statistics (STAT) 947 Topics in Biostatistics (0.50) LEC
Course ID: 010558
Topics of current interest
Statistics (STAT) 974 Financial Econometrics (0.50) LEC
Course ID: 014063
The focus of this course is on the statistical modelling, estimation and inference and forecasting of nonlinear financial time series, with a special emphasis on volatility and correlation of asset prices and returns. Topics to be covered normally include: review on distribution and dynamic behaviour of financial time series, univariate and multivariate GARCH processes, long-memory time-series processes, stochastic volatility models, modelling of extreme values, copulas, realized volatility and correlation modelling for ultra high frequency data and continuous time models.