University of Calgary : Statistics STAT

Statistics STAT

STAT 205 213 217 321 323 327 421 423 425 429 431 505 507 517 519 523 525 529 531 533 541 543 600 601 603 619 625 631 633 635 637 641 701 703 721 723

Instruction offered by members of the Department of Mathematics and Statistics in the Faculty of Science.

Notes:

Not every 400- and 500-numbered Statistics course is offered every year. Check with the divisional office to plan for the upcoming cycle of offered courses.
Credit towards degree requirements will be given for only one of Engineering 319, Political Science 399, Psychology 300, 301, 312, Sociology 311, Statistics 205, 213/217, 327; that one being a course(s) appropriate to the degree program.
For listings of related courses, see Actuarial Science, and Mathematics.

Junior Courses

Students requiring one course (3 units) in Statistics should take Statistics 205.

Statistics 205

Introduction to Statistical Inquiry

The systematic progression of statistical principles needed to conduct a statistical investigation culminating in parameter estimation, hypothesis testing, statistical modelling, and design of experiments.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Mathematics 30-1 or Mathematics 2 (offered by Continuing Education) or registration in the Faculty of Nursing.
Antirequisite(s): Credit for Statistics 205 and any one of Statistics 211, 213, 217, 327, Political Science 399, Psychology 300, 301, 312, or Sociology 311 will not be allowed. Students may not register in, or have credit for, Statistics 205 if they have previous credit for one of Statistics 321 or Engineering 319 or are concurrently enrolled in Statistics 321 or Engineering 319.

Statistics 213

Introduction to Statistics I

Collection and presentation of data, introduction to probability, including Bayes' law, expectations and distributions. Properties of the normal curve. Introduction to estimation and hypothesis testing.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Mathematics 30-1 or Mathematics 2 (offered by Continuing Education).
Antirequisite(s): Credit for Statistics 213 and any one of Statistics 205, Statistics 327, Political Science 399, Psychology 300, 301, 312, or Sociology 311 will not be allowed. Not available to students who have previous credit for one of Statistics 321 or Engineering 319 or are concurrently enrolled in Statistics 321 or Engineering 319.

Statistics 217

Introduction to Statistics II

Estimation of population parameters; confidence intervals for means; choice of sample size. Tests of hypotheses including 2-sample tests and paired comparisons. The Chi-squared tests for association and goodness-of-fit. Regression and correlation; variance estimates; tests for regression and correlation coefficients. Non-parametric methods and associated tests. Time series, forecasting.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Statistics 213.
Antirequisite(s): Credit for Statistics 217 and any one of Statistics 205, 327, Political Science 399, Psychology 300, 301, 312, or Sociology 311 will not be allowed. Not available to students who have previous credit for one of Statistics 321 or Engineering 319 or are concurrently enrolled in Statistics 321 or Engineering 319.

Senior Courses

Statistics 321

Introduction to Probability

A calculus-based introduction to probability theory and applications. Elements of probabilistic modelling, Basic probability computation techniques, Discrete and continuous random variables and distributions, Functions of random variables, Expectation and variance, Multivariate random variables, Conditional distributions, Covariance, Conditional expectation, Central Limit Theorem, Applications to real-world modelling.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Mathematics 267 or 277.
Antirequisite(s): Credit for Statistics 321 and Engineering 319 will not be allowed.
Notes: Statistics 205, 213, 217, and 327 are not available to students who have previous credit for one of Statistics 321 or Engineering 319 or are concurrently enrolled in Statistics 321 or Engineering 319.
Also known as: (formerly Mathematics 321)

Statistics 323

Introduction to Theoretical Statistics

Statistics and their distributions. Introduction to statistical inference through point estimation and confidence interval estimation of a population parameter. Properties of statistics including unbiasedness and consistency in estimation. Single parameter hypothesis testing, Type I and Type II Error. Multi-parameter estimation through confidence interval estimation and hypothesis testing. The analysis of bivariate data through simple linear regression, including inferences on the parameters of the linear model and the analysis of variance. Chi-square test of independence and goodness of fit test.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 321.
Antirequisite(s): Credit for Statistics 323 and Data Science 305 will not be allowed.
Also known as: (formerly Mathematics 323)

Statistics 327

Statistics for the Physical and Environmental Sciences

Introduction to the collection of data. Probability and probability distributions. Single and Multi-sample estimation of distribution parameters. Regression and Goodness of Fit tests. Experimental Design and Analysis of Variance.
Course Hours: 3 units; H(3-1)
Prerequisite(s): One of Mathematics 249, 265 or 275.
Antirequisite(s): Credit for Statistics 327 and any one of Statistics 205, 213, 217, Political Science 399, Psychology 300, 301, 312, or Sociology 311 will not be allowed.
Notes: Statistics 327 is not available to students who have previous credit for one of Statistics 321 or Engineering 319 or are concurrently enrolled in Statistics 321 or Engineering 319.

Statistics 421

Mathematical Statistics

An advanced examination of core concepts in mathematical statistics, including the multivariate normal distribution, limit distributions, sufficient statistics, completeness of families of distributions, exponential families, likelihood ratio tests, chi-square tests, and the analysis of variance. Additional topics and examples relating to sequential tests, non-parametric methods, Bayesian statistical modelling, and the general linear model may also be explored.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 323.

Statistics 423

Statistical Analysis of Sample Survey

Introduction to questionnaire design of sample surveys. Treatment of the various sampling methodologies used in population parameter estimation. Ratio and regression estimation. Sampling weights and variance estimation of statistics. Estimation of population size and density. Non-response.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): One of Statistics 217, 323, 327, Data Science 305, Engineering 319, Psychology 300, 301, 312, or Sociology 311.
Notes: This course is not part of the Field of Mathematics.

Statistics 425

Statistical Design and Analysis of Experiments

Introduction to the design of experiments and the statistical analysis of data. Analysis of variance in the response variable and adequacy of the model. Multiple comparison methods. Extensions to completely randomized block, latin-squares, and factorial experimental design. Introduction to nested and split-plot design, with emphasis on statistical software usage.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): One of Statistics 217, 323, 327, Data Science 305, Engineering 319, Psychology 300, 301, 312, or Sociology 311.
Notes: This course is not part of the Field of Mathematics.

Statistics 429

Linear Models and Their Applications

Multiple linear regression model, parameter estimation, simultaneous confidence intervals and general linear hypothesis testing. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Applications to forecasting. Variable selection in high-dimensional data using linear regression. Computer analysis of practical real world data.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 323 or Data Science 305; and Mathematics 211 or 213.

Statistics 431

Introduction to Biostatistics

Fundamental topics in biostatistics, including descriptive statistics, graphical presentation of data, analysis of variance (ANOVA), study designs, contingency tables, measures of association, tests of significance, categorical data analysis, regression, time to event data analysis.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 323 or Data Science 305.

Statistics 505

Time Series Analysis

An introduction to the theory and tools to conduct time series analysis, with the emphasis on modelling and forecasting using a software. Stationarity, white noise, autocorrelation, partial autocorrelation, and linear predictor. Stationary ARIMA models, seasonality and trends. Model fitting, diagnostics and forecasting. Additional topics may include state space models, spectral analysis of time series, and GARCH models.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 429.

Statistics 507

Introduction to Stochastic Processes

Markov chains. Limit distributions for ergodic and absorbing chains. Classification of states, irreducibility. The Poisson process and its generalizations. Continuous-time Markov chains. Brownian motion and stationary processes. Renewal theory.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 321.
Also known as: (formerly Statistics 407)

Statistics 517

Practice of Statistics

A capstone course intended for students in their final year of study. The emphasis is on how to address real world scientific and social issues by applying the various statistical methods acquired in the earlier years in a unified and appropriate way. This involves method selection, data handling, statistical computing, consulting, report writing and oral presentation, team work, and ethics.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Two of Statistics 423, 425, 429 and 505.
Antirequisite(s): Credit for Statistics 517 and either 513 or 515 will not be allowed.

Statistics 519

Bayesian Statistics

Fundamentals of Bayesian inference, single and multiparameter models, hierarchical models, regression models, generalized linear models, advanced computational methods, Markov chain Monte Carlo.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 323; and Mathematics 267 or 277.
Antirequisite(s): Credit for Statistics 519 and 619 will not be allowed.

Statistics 523

Non-parametric Statistics

Non-parametric estimation and tests of hypotheses. Distribution-free tests. Asymptotic Theory. Re-sampling method and density estimation.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 323.
Notes: May not be offered every year. Consult the department for listings.

Statistics 525

Applied Multivariate Analysis

Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Multivariate statistical methods; principal components, factor analysis, discriminant analysis and classification, canonical correlation analysis, cluster analysis.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 323.
Antirequisite(s): Credit for Statistics 525 and 625 will not be allowed.
Notes: May not be offered every year. Consult the department for listings.

Statistics 529

Special Topics in Applied Statistics

Content of the course will vary from year-to-year. Consult the Department for information on choice of topics.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Consent of the Department.
MAY BE REPEATED FOR CREDIT

Statistics 531

Monte Carlo Methods and Statistical Computing

Introduction to statistical computing; random numbers generation; Monte Carlo methods (variance reduction technique; computation of definite integrals); Optimizations; Numerical integrations.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Statistics 323; Mathematics 267 or 277.

Statistics 533

Survival Models

Nature and properties of survival models; methods of estimating tabular models from both complete and incomplete data samples including actuarial, moment and maximum likelihood techniques; estimations of life tables from general population data; Kaplan-Meier estimator and Nelson-Allan estimator; the accelerated failure time model; the Cox proportional hazards model; model building and high-dimensional survival data analysis.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 323.
Antirequisite(s): Credit for Statistics 533 and 633 will not be allowed.
Also known as: (formerly Statistics 433)

Statistics 541

Categorical Data Analysis

Description and inference for binomial and multinomial observations using proportions and odds ratios; multi-way contingency tables; generalized linear models for discrete data; logistic regression for binary responses; multi-category logit models for nominal and ordinal responses; loglinear models, and inference for matched-pairs and correlated clustered data.
Course Hours: 3 units; H(3-1T)
Prerequisite(s): Statistics 429.

Statistics 543

Statistical Learning

Introduction and linear regression; classification; regularization; model assessment and selection; support vector machines; unsupervised learning; tree-based methods; additional topics selected by course instructor.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 429.
Antirequisite(s): Credit for Statistics 543 and 641 will not be allowed.

Graduate Courses

Statistics 600

Research Seminar

A professional skills course, focusing on the development of technical proficiencies that are essential for students to succeed in their future careers as practicing statistician in academia, government, or industry. The emphasis is on delivering professional presentations and using modern statistical research tools. A high level of active student participation is required.
Course Hours: 1.5 units; Q(3S-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Also known as: (formerly Statistics 621)
MAY BE REPEATED FOR CREDIT NOT INCLUDED IN GPA

Statistics 601

Topics in Probability and Statistics

The content of this course is decided from year-to-year in accordance with graduate student interest and instructor availability. Topics include but are not restricted to: Advanced Design of Experiments, Weak and Strong Approximation Theory, Asymptotic Statistical Methods, the Bootstrap and its Applications, Generalized Additive Models, Order Statistics and their Applications, Robust Statistics, Statistics for Spatial Data, Statistical Process Control, Time Series Models.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
MAY BE REPEATED FOR CREDIT

Statistics 603

Applied Statistics for Nursing Research

Descriptive statistics; probability theory; statistical estimation/inference; power analysis; regression analysis; anova; logistic regression analysis; non-parametric tests; factor analysis; discriminant analysis; Cox's Proportional Hazard Model.
Course Hours: 3 units; H(3-1)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Also known as: (formerly Statistics 601.14)

Statistics 619

Bayesian Statistics

Fundamentals of Bayesian inference, single and multiparameter models, hierarchical models, regression models, generalized linear models, advanced computational methods, Markov chain Monte Carlo.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Antirequisite(s): Credit for Statistics 619 and 519 will not be allowed.

Statistics 625

Multivariate Analysis

Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Principal components. Factor Analysis. Discrimination and classification. Canonical correlation analysis.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Antirequisite(s): Credit for Statistics 625 and 525 will not be allowed.

Statistics 631

Computational Statistics

Unconstrained optimization methods, simulation and random number generation, Bayesian inference and Monte Carlo methods, Markov chain Monte Carlo, non-parametric inference, classical inference and other topics. An emphasis will be placed on computational implementation of algorithms.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 633

Survival Models

Advanced topics in survival models such as the product limit estimator, the cox proportional hazards model, time-dependent covariates, types of censorship.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Antirequisite(s): Credit for Statistics 633 and 533 will not be allowed.

Statistics 635

Generalized Linear Models

Exponential family of distributions, binary data models, loglinear models, overdispersion, quasi-likelihood methods, generalized additive models, longitudinal data and generalized estimating equations, model adequacy checks.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 637

Non-linear Regression

Topics include but are not restricted to selections from: linear approximations; model specification; various iterative techniques; assessing fit; multiresponse parameter estimation; models defined by systems of differential equations; graphical summaries of inference regions; curvature measures.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 641

Statistical Learning

Introduction and Linear Regression; Classification; Regularization; Model Assessment and Selection; Support Vector Machines; Unsupervised Learning; Tree-Based Methods; Other Topics (e.g., Neural Networks, Graphical Models, High-Dimensional Data).
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.
Antirequisite(s): Credit for Statistics 641 and 543 will not be allowed.

Statistics 701

Theory of Probability I

Probability spaces, integration, expected value, laws of large numbers, weak convergence, characteristic functions, central limit theorems, limit theorems in Rd, conditional expectation, introduction to martingales.

Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 703

Theory of Probability II

Stopping times, renewal theory, martingales, almost sure convergence, Radon-Nikodym derivatives, Doob’s inequality, square integrable martingales, uniform integrability, Markov chains, stationary measure, Birkhoff’s Ergodic Theorem, Brownian motion, stopping times, hitting times, Donsker’s Theorem, Brownian bridge, laws of the iterated logarithm.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 701 and admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 721

Statistical Inference

Statistical models, likelihoods, maximum likelihood estimators, likelihood ratio, Wald and score tests, confidence intervals, bounds and regions, Bayesian estimation and testing, basic large sample theory, estimating equations, jackknife, bootstrap and permutation.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to a graduate program in Mathematics and Statistics or consent of the Department.

Statistics 723

Theory of Hypothesis Testing

Likelihood ratio (LR), union-intersection, most powerful, unbiased and invariant tests, Neyman-Pearson Lemma, Karlin-Rubin Theorem, confidence interval (CI), pivotal quantities, shortest length and shortest expected length CI, uniformly most accurate CI, confidence region, simultaneous CI, large-sample tests (Wald’s, score, LR tests), Bayesian hypothesis testing, analysis of variance and linear models.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 721 and admission to a graduate program in Mathematics and Statistics or consent of the Department.

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