### Course Descriptions - Undergraduate Calendar 2016-2017

 S T A T I S T I C S

### STAT 200s

 STAT 202 LEC,TST,TUT 0.50 Course ID: 008859 Introductory Statistics for Scientists Elementary probability, populations, samples and distributions with biological examples. Methods for data summary and presentation. Estimation, hypothesis testing, two-sample techniques and paired comparisons, regression, correlation. [Offered: F,W] Prereq: Science or Knowledge Integration students only. Antireq: STAT 220, 230

 STAT 206 LEC,TUT 0.50 Course ID: 010128 Statistics for Software Engineering Empirical problem solving with applications to software engineering. An introduction to probability theory. An introduction to distribution theory and to methods of statistical inference, including confidence intervals and hypothesis testing. An introduction to regression. [Offered: F] Prereq: MATH 115, 119; Software Eng students only.

 STAT 211 LEC,TUT 0.50 Course ID: 008861 Introductory Statistics and Sampling for Accounting Descriptive statistics, probability, discrete and continuous random variables. Sampling distributions and simple hypothesis testing. Introduction to survey sampling. [Offered: W] Prereq: MATH 109; Arts/Acc and SciBiot/CA students only.

 STAT 220 LEC,TST,TUT 0.50 Course ID: 008862 Probability (Non-Specialist Level) The laws of probability, discrete and continuous random variables, expectation, central limit theorem. [Offered: F,W] Prereq: One of MATH 119, 128, 138, 148. Antireq: STAT 202, 230, 240

 STAT 221 LEC,TST,TUT 0.50 Course ID: 008863 Statistics (Non-Specialist Level) Empirical problem solving, measurement systems, causal relationships, statistical models, estimation, confidence intervals, tests of significance. [Offered: F, W] Prereq: (One of MATH 128,138,148)&(One of STAT 220, 230, 240).

 STAT 230 LEC,TST,TUT 0.50 Course ID: 008864 Probability This course provides an introduction to probability models including sample spaces, mutually exclusive and independent events, conditional probability and Bayes' Theorem. The named distributions (Discrete Uniform, Hypergeometric, Binomial, Negative Binomial, Geometric, Poisson, Continuous Uniform, Exponential, Normal (Gaussian), and Multinomial) are used to model real phenomena. Discrete and continuous univariate random variables and their distributions are discussed. Joint probability functions, marginal probability functions, and conditional probability functions of two or more discrete random variables and functions of random variables are also discussed. Students learn how to calculate and interpret means, variances and covariances particularly for the named distributions. The Central Limit Theorem is used to approximate probabilities. [Note: STAT 230 is normally taken in second year. Students with an average of at least 80% in Honours Mathematics courses in 1A may enrol in STAT 230 in 1B. To enrol in STAT 231, a grade of at least 50% in STAT 230 is required. However, to enrol in STAT 330, 333, 334, 341 or 340 a grade of at least 60% in STAT 230 is required. Offered: F,W,S] Prereq: MATH 135 with min. grade of 60% & ((MATH 127 or 128) with min. grade of 70% or (MATH 117 or MATH 137) with min. grade of 60% or MATH 147)); Level at least 1B Hon. Math or Math/Phys students only. Coreq: MATH 119or128or138or148. Antireq: STAT 220 Also offered Online

 STAT 231 LEC,TST,TUT 0.50 Course ID: 008865 Statistics This course provides a systematic approach to empirical problem solving which will enable students to critically assess the sampling protocol and conclusions of an empirical study including the possible sources of error in the study and whether evidence of a causal relationship can be reasonably concluded. The connection between the attributes of a population and the parameters in the named distributions covered in STAT 230 will be emphasized. Numerical and graphical techniques for summarizing data and checking the fit of a statistical model will be discussed. The method of maximum likelihood will be used to obtain point and interval estimates for the parameters of interest as well as testing hypotheses. The interpretation of confidence intervals and p-values will be emphasized. The Chi-squared and t distributions will be introduced and used to construct confidence intervals and tests of hypotheses including likelihood ratio tests. Contingency tables and Gaussian response models including the two sample Gaussian and simple linear regression will be used as examples. [Note: To satisfy any Statistics plan requirement a grade of at least 50% in STAT 231 is required. However, to enrol in STAT 331, 332, 371, 372, or 373, a grade of at least 60% in STAT 231 is required. Offered: F,W,S] Prereq: (MATH 119 or 128 or 138 or 148) and (STAT 220 with a grade of at least 70% or STAT 230 or 240); Honours Math or Math/Phys students. Antireq: STAT 221, 241

 STAT 240 LEC,TST 0.50 Course ID: 008866 Probability (Advanced Level) STAT 240 is an advanced-level enriched version of STAT 230. [Note: STAT 240 is normally taken in second year. Students with an average of at least 80% in Honours Mathematics courses in 1A may enrol in STAT 240 in 1B. Students with a cumulative math average of at least 80% are encouraged to register in STAT 240. Offered: F] Prereq: MATH 137 with a grade of at least 60% or MATH 147; Level at least 1B Honours Mathematics students only. Coreq: MATH 138 or 148. Antireq: STAT 220, 230

 STAT 241 LEC,TST,TUT 0.50 Course ID: 008867 Statistics (Advanced Level) STAT 241 is an advanced-level enriched version of STAT 231. [Note: Students with a cumulative math average of at least 80% are encouraged to register in STAT 241. Offered: W] Prereq: MATH 138/148 & STAT 230/240; Hon Math only. Antireq: STAT 221,231.

### STAT 300s

 STAT 316 LEC,TUT 0.50 Course ID: 004384 Introduction to Statistical Problem Solving by Computer This is an applications oriented course which prepares the nonmathematical student to use the computer as a research tool. Topics include aids for statistical analysis and the preparation of documents such as reports and theses. The course provides sufficient background for application to other problems specific to the individual's field. [Offered: W] Prereq: One of ECON 221, ENVS 278, ISS/SDS 250R, KIN 222, PSCI 214/314, PSYCH 292, REC 371, SOC/LS 280, any STAT course; Not open to Honours Mathematics students. Antireq: STAT 331, 371

 STAT 321 LEC,TUT 0.50 Course ID: 008870 Regression and Forecasting (Non-Specialist Level) Modeling the relationship between a response variable and several explanatory variables via regression models. Model diagnostics and improvement. Using regression models for forecasting, Exponential smoothing. Simple time series modeling. [Offered: W] Prereq: (MATH 225/126 or 235 or 245) and (STAT 221 or 231 or 241). Antireq: ECON 321, STAT 331, 371, 373, 443

 STAT 322 LEC,TUT 0.50 Course ID: 008871 Sampling and Experimental Design (Non-Specialist Level) Planning sample surveys; simple random sampling; stratified sampling. Observational and experimental studies. Blocking, randomization, factorial designs. Analysis of variance. Applications of design principles. [Offered: F] Prereq: STAT 221 or 231 or 241. Antireq: STAT 332, 372

 STAT 330 LEC,TUT 0.50 Course ID: 008872 Mathematical Statistics This course provides a mathematically rigorous treatment for topics covered in STAT 230 and 231, and to make essential extensions to the multivariate case. Maximum likelihood estimation. Random variables and distribution theory. Generating functions. Functions of random variables. Limiting distributions. Large sample theory of likelihood methods. Likelihood ratio tests. Joint probability (density) functions, marginal probability (density) functions, and conditional probability (density) functions of two or more random variables are discussed. Topics covered include independence of random variables, conditional expectation and the determination of the distribution of functions of random variables using the cumulative distribution method, change of variable and moment generating functions. Properties of the Multinomial and Bivariate Normal distributions are proved. Limiting distributions, including convergence in probability and convergence in distribution, are discussed. Important results, including the Weak Law of Large Numbers, Central Limit Theorem, Slutsky's theorem, and the Delta Method, are introduced with applications. The maximum likelihood method is discussed for the multi-parameter case. Asymptotic properties of the maximum likelihood estimator are examined and used to construct confidence intervals or regions. Tests for simple and composite hypotheses are constructed using the Likelihood Ratio Test. [Offered: F,W,S] Prereq: MATH 237 or 247, (STAT 230 with a grade of at least 60% or STAT 240), STAT 231 or 241; Not open to General Mathematics students. Antireq: STAT 334

 STAT 331 LEC,TUT 0.50 Course ID: 008873 Applied Linear Models Modeling the relationship between a response variable and several explanatory variables (an output-input system) via regression models. Least squares algorithm for estimation of parameters. Hypothesis testing and prediction. Model diagnostics and improvement. Algorithms for variable selection. Nonlinear regression and other methods. [Offered: F,W,S] Prereq: MATH 235 or 245, (STAT 231 with a grade of at least 60%) or STAT 241 or (SYDE 212 with a grade of at least 70%). Antireq: ECON 321, STAT 321, 371, 373, SYDE 334

 STAT 332 LEC,TUT 0.50 Course ID: 008874 Sampling and Experimental Design Designing sample surveys. Probability sampling designs. Estimation with elementary designs. Observational and experimental studies. Blocking, randomization, factorial designs. Analysis of variance. Designing for comparison of groups. [Offered: F,W,S] Prereq: (STAT 231 with a grade of at least 60%) or STAT 241 or (SYDE 212 with a grade of at least 70%); Not open to General Mathematics students. Antireq: BIOL 361, STAT 322, 372

 STAT 333 LEC,TUT 0.50 Course ID: 008875 Applied Probability Review of basic probability. Generating functions. Theory of recurrent events. Markov chains, Markov processes, and their applications. [Offered: F,W,S] Prereq: STAT 230 with a grade of at least 60% or STAT 240; Level at least 3A; Not open to General Mathematics students. Antireq: STAT 334

 STAT 334 LEC,TUT 0.50 Course ID: 012662 Probability Models for Business and Accounting Random variables and distribution theory, conditional expectations, moment and probability generating functions, change of variables, random walks, Markov chains, Markov processes. [Offered F,S] Prereq: MATH 237 or 247, (STAT 230 with a grade of at least 60% or STAT 240); STAT 231 or 241; Business/Math double degree, Mathematics/Accounting or Math/Business students only. Antireq: STAT 330, 333

 STAT 337 LEC,TUT 0.50 Course ID: 013320 Introduction to Medical Statistics This course will provide an introduction to statistical methods in health research. Topics to be covered include types of medical data, measures of disease prevalence and incidence, age and sex adjustment of disease rates, sensitivity and specificity of diagnostic tests, ROC curves, measures of association between risk factors and disease, major sources of medical data in the Canadian context including surveys, registries, and clinical studies such as cohort studies, clinical trials and case-control studies. Papers from the medical literature will be used throughout to illustrate the concepts. Introduction to SAS for data analysis and an introduction to database management tools. [Offered: F] Prereq: (STAT 221 with a grade of at least 60%) or STAT 231 or 241. Antireq: HLTH 333, STAT 232

 STAT 340 LEC,TUT 0.50 Course ID: 004408 Computer Simulation of Complex Systems Building and validation of stochastic simulation models useful in computing, operations research, engineering and science. Related design and estimation problems. Variance reduction. The implementation and the analysis of the results. [Offered: W,S] Prereq: (One of CS 116, 126/124, 134, 136, 138, 145, SYDE 221/322) and (STAT 230 with a grade of at least 60% or STAT 240) and (STAT 231 or 241); Not open to Computer Science or General Mathematics students. Antireq: CM 361/STAT 341, CS 437, 457.

 STAT 341 LEC 0.50 Course ID: 011431 Computational Statistics and Data Analysis Approximation and optimization of noisy functions. Simulation from univariate and multivariate distributions, multivariate normal distribution, mixture distributions and introduction to Markov Monte Carlo. Introduction to supervised statistical learning including discrimination methods. [Offered: F] Prereq: MATH 237 or 247, (STAT 230 with a grade of at least 60% or STAT 240), STAT 231 or 241; Not open to General Mathematics students. Antireq: CS 437/STAT 340

 STAT 371 LEC,TUT 0.50 Course ID: 011723 Applied Linear Models and Process Improvement for Business Practical and theoretical aspects of simple and multiple linear regression models. Model building, fitting and assessment. Process thinking and improvement. Strategies for variation reduction such as control charting. Process monitoring, control and adjustment. Applications to problems in business. [Offered: F,W,S] Prereq: (MATH 235 or 245) and (STAT 231 with a grade of at least 60% or STAT 241); Bus/Math dbl degree, Math/Bus, Math/FARM, Math/ITM, or Math Optimization - Business Spec students only. Antireq: ECON 321, STAT 321, 331, 373

 STAT 372 LEC,TUT 0.50 Course ID: 011724 Survey Sampling and Experimental Design Techniques for Business Design and analysis of surveys. Management of sample and non-sample error. Simple random sampling and stratified random sampling. Additional topics in survey sampling. Observational and experimental studies. Principles for the design of experiments. Analysis of Variance, factorial experiments and interaction. Application to problems in business. [Offered: F,W,S] Prereq: STAT 231 with a grade of at least 60% or STAT 241; Business/Math Double Degree, Math/Business, Math/FARM, Math/ITM or Mathematical Optimization - Business Specialization students only. Antireq: STAT 322, 332

 STAT 373 LEC,TUT 0.50 Course ID: 012225 Regression and Forecasting Methods in Finance Application of regression and time series models in finance; multiple regression; algebraic and geometric representation of least squares; inference methods - confidence intervals and hypothesis tests, ANOVA, prediction; model building and assessment; time series modeling; autoregressive AR(1) models - fitting, assessment and prediction; moving average smoothing, seasonal adjustment; non-stationarity and differencing. [Offered: F] Prereq: STAT 231 with a grade of 60% or STAT 241; Computing & Financial Management students and Mathematics/Accounting students who began F06 or later. Antireq: STAT 321, 331, 371, 443

### STAT 400s

 STAT 430 LEC,TUT 0.50 Course ID: 008880 Experimental Design 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. [Offered: F,S] Prereq: (STAT 331 or 371) and (STAT 332 or 372); Not open to General Mathematics students. Antireq: (for Arts and Environmental Studies students) BIOL 461, PSYCH 391

 STAT 431 LEC 0.50 Course ID: 008881 Generalized Linear Models and their Applications Review of the normal linear model and maximum likelihood estimation; regression models for binomial, Poisson and multinomial data; generalized linear models; and other topics in regression modelling. [Offered: F,W,S] Prereq: STAT 330, (331 or 371); Not open to General Mathematics students

 STAT 433 LEC 0.50 Course ID: 008882 Stochastic Processes Point processes. Renewal theory. Stationary processes. Selected topics. [Offered: F] Prereq: STAT 333; Not open to General Mathematics students

 STAT 435 LEC,TUT 0.50 Course ID: 011042 Statistical Methods for Process Improvements 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. [Offered: W] Department Consent Required Prereq: STAT 332 or 372; Not open to General Mathematics students

 STAT 436 LEC 0.50 Course ID: 013322 Introduction to the Analysis of Spatial Data in Health Research 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 statistical inference based on spatially correlated data. Methods of estimation and testing will be developed for geostatistical models based on variograms and spatial autogressive models. Concepts and application of methods will be emphasized through case studies and pro jects with health applications. [Note: First offering will be 2014. Offered: W] Prereq: STAT 431

 STAT 437 LEC 0.50 Course ID: 013321 Analysis of Longitudinal Data in Health Research This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Conditional and random effect modeling approaches to regression analysis will be covered, as well as semiparametric methods based on generalized estimating equations. Supporting statistical theory will be given at a level appropriate for a senior undergraduate student in Statistics. 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 SAS and S-PLUS/R software and the importance of providing clear interpretations to data analyses will be emphasized. [Note: First offering will be 2014. Offered: W] Prereq: STAT 431

 STAT 440 LEC 0.50 Course ID: 008883 Computational Inference 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. [Offered: W] Prereq: CM 361/STAT 341 or CS 437/STAT 340; Not open to General Mathematics students

 STAT 441 LEC 0.50 Course ID: 008884 Statistical Learning - Classification 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. [Offered: F] Prereq: CM 361/STAT 341 or (STAT 330 and 340); Not open to General Mathematics students

 STAT 442 LEC 0.50 Course ID: 011434 Data Visualization 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. [Offered: F] Prereq: STAT 231 or 241; Not open to General Mathematics students

 STAT 443 LEC,TUT 0.50 Course ID: 008885 Forecasting Model building. Multiple regression and forecasting. Exponential smoothing. Box-Jenkins models. Smoothing of seasonal data. [Offered: F,W,S] Prereq: STAT 331 or 371 or SYDE 334; Not open to General Mathematics students. Antireq: STAT 321, 373

 STAT 444 LEC 0.50 Course ID: 011436 Statistical Learning - Function Estimation 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. [Offered: W] Prereq: CM 361/STAT 341 or STAT 331 or 371; Not open to General Mathematics students

 STAT 450 LEC,TUT 0.50 Course ID: 008888 Estimation and Hypothesis Testing 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. [Offered: W] Prereq: STAT 330; Not open to General Mathematics students

 STAT 454 LEC 0.50 Course ID: 008890 Sampling Theory and Practice Sources of survey error. Probability sampling designs, estimation and efficiency comparisons. Distribution theory and confidence intervals. Generalized regression estimation. Software for survey analysis. [Offered: W] Prereq: STAT 332 or 372; Not open to General Mathematics students

 STAT 464 LEC 0.50 Course ID: 008891 Topics in Probability Theory Prereq: STAT 333; Not open to General Mathematics students

 STAT 466 LEC 0.50 Course ID: 008892 Topics in Statistics 1 Prereq: STAT 330, 331; Not open to General Mathematics students

 STAT 467 LEC 0.50 Course ID: 008893 Topics in Statistics 2 Prereq: Not open to General Mathematics students

 STAT 468 RDG 0.50 Course ID: 008894 Readings in Statistics 1 Prereq: Not open to General Mathematics students

 STAT 469 RDG 0.50 Course ID: 010728 Readings in Statistics 2 Prereq: Not open to General Mathematics students