# Statistics Course Descriptions

**
STAT 051 - Statistics Co-op Work Term #1**

Four-month co-op work term #1 approved by the department and arranged by the co-op coordinator.

**
STAT 052 - Statistics Co-op Work Term #2**

Four-month co-op work term #2 approved by the department and arranged by the co-op coordinator.
*** Prerequisite: STAT 051 ***

**
STAT 053 - Statistics Co-op Work Term #3**

Four-month co-op work term #3 approved by the department and arranged by the co-op coordinator.
*** Prerequisite: STAT 052 ***

**
STAT 054 - Statistics Co-op Work Term #4**

Four-month co-op work term #4 approved by the department and arranged by the co-op coordinator.
*** Prerequisite: STAT 053 ***

**
STAT 100 - Elementary Statistics for Applications**

An introduction to statistical methods; descriptive statistics; the normal distribution; basic techniques of statistical inference; confidence intervals and hypothesis tests for population means and proportions; simple linear regression; and one-way analysis of variance.
***Prerequisite: Foundations of Math 20 or Precalculus 20 or Apprenticeship & Workplace Math 30 or MATH A30 or AMTH 091 or MATH 101***
*Note: STAT 100 and STAT 200 are designed to provide a year-long introduction to statistical methodology with a view towards applications and are not intended for majors in statistics, actuarial science, or any other program requiring a detailed knowledge of statistics. Students who receive credit for STAT 100 may not receive credit for STAT 160*

**
STAT 160 - Introductory Statistics**

A comprehensive introduction to probability, probability distributions, sampling distributions, basic techniques of statistical inference, analysis of variance, linear regression, inference for categorical variables, and nonparametric statistics. ***Prerequisite: Precalculus 30, or MATH B30 and MATH C30, or MATH 127***
*Note: STAT 160 is designed to provide a comprehensive single semester introduction to statistical techniques and is intended for students majoring in statistics, actuarial science, or any other program requiring a detailed knowledge of statistics. Students who receive credit for STAT 160 may not receive credit for STAT 100 or STAT 200*

**
STAT 165 -
Introduction to Programming with Python**

An introduction to problem-solving techniques using Python. This course will introduce fundamental programming principles and topics: data types, expressions, control structures, elementary data structures, functions, files, and the mechanics of running, testing and debugging. These concepts will be applied to problem solving and applications in data analysis.
***Prerequisite: Foundations of Mathematics 30, Precalculus 20, Math B30, Math C30, or PMTH 092.***
*Note: Students may receive credit for one of CS 165 or STAT 165.*

**
STAT 200 - Intermediate Statistics for Applications**

A continuation of STAT 100; inference for two categorical variables; basic multiple linear regression; two-way analysis of variance; introduction to nonparametric methods; statistical process control; introduction to survey design.
***Prerequisite: STAT 100***
*Note: STAT 100 and STAT 200 are designed to provide a year-long introduction to statistical methodology with a view towards applications and are not intended for majors in statistics, actuarial science, or any other program requiring a detailed knowledge of statistics. Students who receive credit for STAT 200 may not receive credit for STAT 160*

**
STAT 217 - Introduction to Actuarial Mathematics**

Topics include: economics of insurance, applications of probability to problems of life insurance, life annuities, and life tables.
***Prerequiste: ACSC 116 or MATH 116, and STAT 251***
*Note: Students may receive credit for only one of ACSC 217 or STAT 217*

**
STAT 251 - Introduction to Probability**

Basic notions of probability; discrete and continuous random variables; expectation; moment generating functions; joint discrete random variables.
***Prerequisites: MATH 111 or MATH 112 and one of STAT 160 or STAT 200***
*Note: Students can receive credit for only one of Math 251 and Stat 251*

**
STAT 252 - Introduction to Statistical Inference**

Sampling distribution theory and the Central Limit Theorem; large sample theory; methods of estimation and hypothesis testing including maximum likelihood estimation, likelihood ratio testing, and confidence interval construction. ***Prerequisite: STAT 251.***

**
STAT 289 - Statistics for Engineers**

Topics include probability, discrete and continuous distributions, the central limit theorem, confidence intervals and hypothesis tests for one and two samples, linear regression and correlation.
***Prerequisite: MATH 111***
*Note: Designed for engineering students. Students who received credit for STAT 289 may not receive credit for STAT 100, 160, or 200.

**
STAT 300 - Statistical Learning and Predictive Modeling**

Selected topics and techniques in statistical learning and predictive modeling, including linear models, logistic regression models, regression trees, classification models and statistical software.
***Prerequisite: MATH 122, STAT 252, and CS 110***
*Note: Students can receive credit for only one of ACSC 300 and STAT 300*

**
STAT 301 - Introduction to Statistical Computing**

This course aims to provide students with an introduction to statistical computing. Topics include the basics of programing for statistics, data visualization, simulation of random variables, numerical optimization, statistical inference, and selected additional topics.
***Prerequisite: MATH 122, STAT 252, and CS 265.***

**
STAT 316 - Mathematics of Finance III**

This course covers the theory and pricing of financial derivates such as Puts and Calls, with particular emphasis on the Black-Scholes model.
***Prerequisite: ACSC 216 or MATH 216, and STAT 251***
*Note: Students can receive credit for only one of MATH 316, STAT 316 and ACSC 316*

**
STAT 317 - Actuarial Models I**

Probabilistic and deterministic contingency mathematics in life insurance and pensions. Topics include: benefit premiums, benefit reserves, multiple life functions, and multiple decrement models.
***Prerequisite: ACSC 217 or STAT 217***
*Note: Students may receive credit for only one of ACSC 317 or STAT 317*

**
STAT 318 - Actuarial Models II**

This course introduces collective risk models over an extended period. Stochastic processes are introduced, followed by definition and application of Markov chains. Introductory loss model material is also presented.
***Prerequisite: ACSC 317 or STAT 317***
*Note: Students may receive credit for only one of ACSC 318 or STAT 318*

**
STAT 342 - Biostatistics**

This course will present relevant, up-to-date coverage of research methodology using careful explanations of basic statistics and how they are used to address practical problems that arise in the medial and public health settings. Through this course, students will learn to interpret and examine data by applying common statistical tools to the biostatistical, medical, and public health fields.
***Prerequisite: STAT 160 or STAT 200.***
*Note: Students with credit in BIOL 341 cannot take STAT 342 for credit.*

**
STAT 351 - Intermediate Probability**

Multivariate random variables; conditioning; order statistics; the multivariate normal distribution; the Poisson process.
***Prerequisite: MATH 213 and STAT 251.***

**
STAT 354 - Linear Statistical Methods**

Simple linear regression; multiple linear regression; diagnostics and remedial measures for regression models; remedial measures and alternative regression techniques; multicollinearity diagnostics.
***Prerequisite: STAT 252 and CS 110 and MATH 122.***

**
STAT 357 - Sampling Theory**

Simple random sampling; systematic sampling; stratified and cluster sampling; ratio and regression estimators.
***Prerequisite: STAT 252 and CS 110.***

**
STAT 362 - Bayesian Statistics**

An introduction to Bayesian methods; Bayesian inference for discrete random variables, binomial proportions, and normal means; comparisons between Bayesian and frequentist inferences; robust Bayesian methods.
***Prerequisite: STAT 252 and CS 110.***

**
STAT 384 - Categorical Data Analysis**

Odds ratio; two-way and higher-way contingency tables; Chi-squared tests of independence; loglinear and logit models; multinomial response models; models for matched pairs.
***Prerequisite: STAT 252 and CS 110.***
*Note: It is suggested that students register for STAT 354 concurrently if possible.*

**
STAT 386 - Nonparametric Statistical Methods**

Nonparametric statistics for data analysis including rank-based methods, bootstrap methods, and permutation tests; one-sample and two-sample methods; paired comparisons and blocked designs; tests for trends and association; smoothing methods and robust model fitting.
***Prerequisite: STAT 252 and CS 110.***

**
STAT 418 - Time Series Analysis and Forecasting**

This course aims to introduce various statistical models for time series and cover the main methods for analysis and forecasting. Topics include: Deterministic time series: Trends and Seasonality; Random walk models; Stationary time series: White noise processes, Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) models; Estimation, Diagnosis and Forecasting with various time series models; computer programming for Time Series Analysis.
***Prerequisite: STAT 354***
*Note: Students may receive credit for only one of ACSC 418 or STAT 418*

**
STAT 426 - Survival Analysis**

Life tables; survival distributions; types of censoring; estimation of and interface for basic survival quantities; proportional hazards regression model; planning and design of clinical trials.
***Prerequisite: STAT 351.***

**
STAT 441 - Stochastic Calculus with Applications to Finance**

Processes derived from Brownian motion; the Itô integral and Itô's formula; applications of Itô's formula in financial modelling, especially within the context of the Black-Scholes option pricing model.
***Prerequisite: STAT 351.***

**
STAT 451 - Advanced Probability**

Probability measures; distribution functions; sequences of random variables; characteristic functions; modes of convergence; convergence theorems; weak and strong laws of large numbers; Central Limit Theorem.
***Prerequisite: STAT 351.***

**
STAT 452 - Advanced Statstical Inference**

Detailed theoretical development of statistical inference; statistical models; exponential families; sufficiency; completeness; properties of point estimation; testing hypotheses and confidence regions; asymptotic properties of estimators. ***Prerequisite: STAT 351 and STAT 252.***

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STAT 454 - Applied Multivariate Analysis**

Review of multivariate normal distribution; inferences about a mean vector; multivariate linear regression analysis; principal components; factor analysis; canonical correlation analysis.
***Prerequisite: STAT 351 and STAT 354.***

**
STAT 456 - Applied Stochastic Processes**

An introduction to stochastic processes; Markov chains; Poisson processes; renewal processes; Brownian motion; simulation.
***Prerequisite: STAT 351.***
*Note: Credit can be earned for only one of STAT 456, ACSC 456, or STAT 856.*

**
STAT 470 - Bootstrap Methods**

A first course in Bootstrap techniques. Topics include bootstrap and jackknife procedures, confidence intervals, hypothesis testing, standard errors, regression models. Additional topics may vary.
***Prerequisite: STAT 351 and STAT 354***
*Note: Credit cannot be received for both STAT 470 and STAT 870*

**
STAT 472 -
Large Sample Methods**

Asymptotic behavior of estimators and test statistics, asymptotic relative efficiency, large sample theory for regression models.
***Prerequisite: STAT 351.***
*Note: Students may receive credit for one of STAT 472 or STAT 495AE.*

**
STAT 485 - Design and Analysis of Experiments**

Theory and application of analysis of variance for standard experimental designs including blocked, nested, factorial, Latin square, and split-plot designs; fixed and random effects; multiple comparisons; analysis of covariance.
***Prerequisite: STAT 354***

**
STAT 489 - Statistical Consulting and Communications**

This course aims to provide students with an understanding of the nature of applied statistical consulting and skills for communicating technical statistical contents with non-statisticians. Topics include the general principles for solving statistical problems, oral and written communication skills, ethics, and collaborative project.
***Prerequisite: STAT 301 and STAT 354.***

**
STAT 495AC - Readings in Mathematical Finance**

This course presents a selection of readings in the theory of mathematical finance, as chosen by the instructor.
*** Prerequisite: STAT 351 with a minimum grade of 80% ***

**
STAT 495AD - Topics in Probability Theory**

In depth study of selected topics in probability theory.
***Prerequisite: STAT 451 ***

**
STAT 496 - Data Science Capstone**

This is a capstone course for data science majors. This course aims to enhance students’ competencies by applying data scientific methodologies to the challenges imposed by real data and skills to effectively communicate project requirements and findings. This course also covers ethical issues and responsible practices in data science.
***Prerequisite: STAT 300, STAT 301, STAT 354, CS 280, and one of CS 412 or CS 465.***

**
STAT 497 - Honours Seminar**

This is the first of two honours seminars. This course must be taken by all honours students in their fourth year. Students are required to attend the seminars and to work in consultation with an assigned supervisor on an independent research project. To receive credit for STAT 497, students must present a seminar on their preliminary work.
*Note: This seminar is restricted to honours standing students in statistics.*

**
STAT 498 - Honours Seminar**

This is the second of two honours seminars. This course must be taken by all honours students in their fourth year. Students are required to attend the seminars and to work in consultation with an assigned supervisor on an independent research project. To receive credit for STAT 498, students must present their project in both written form and as a seminar.
*Note: This seminar is restricted to honours standing students in statistics.*