Stat 201 Elementary Statistics: This course teaches statistics for students in business and arts. Topics include descriptive statistics; data collection, tabulation, and presentation; measures of central tendency and variability; binomial, normal, and chi-square distributions; estimation of parameters and tests of hypothesis; simple linear regression and correlation; introduction to a statistical computer package. Acceptable for credit in the Faculties of Arts and Business, and the Departments of Human Kinetics and Human Nutrition. Cross-listed as HKIN 301. Credit will be granted for only one of STAT 201 and STAT 224, 231, PSYC 291, 292. Three credits.
COURSE WEBSITES:
Stat 224 Probability and Statistics for Engineers: This course covers probability laws and the interpretation of numerical data, probability distributions and probability densities, functions of random variables, joint distributions, characteristic functions, inferences concerning mean and variance, tests of hypotheses, linear regression, and time series analysis. Engineering applications are emphasized and statistical computer packages are used extensively. Cross-listed as ENGR 224. Prerequisite: ENGR 122. Credit will be granted for only one of STAT 224 and STAT 201, 231. Three credits and two-hour problem session.
Stat 231 Statistics for Students in the Sciences: Topics include descriptive statistics; data collection, tabulation, and presentation; measures of central tendency and variability; elementary probability; binomial, normal and chi-square distributions; parameter estimation and tests of hypotheses; linear regression and correlation. Students will learn about statistical significance and the communication of statistical evidence, and be introduced to a statistics computer package. Prerequisite: MATH 112 or 122. Credit will be granted for only one of STAT 231 and STAT 201, 224, PSYC 292. Three credits and a one-hour lab.
Stat 311 Survey Sampling Design: Topics include simple random sampling, stratified sampling, systematic sampling, cluster sampling, multi-stage sampling, bootstrap samples. Prerequisite: STAT 201 or 224 or 231. Three credits and a one-hour lab. Not offered 2013/14
Stat 331 Statistical Methods: An investigation of statistics and experimental design in the context of biological and health science issues. Topics include analysis of variance, categorical data; distribution-free tests; linear and multiple regression. Students will learn to analyze data and interpret conclusions using a statistical software package. Recommended strongly for all major, advanced major, and honours students. Prerequisite: STAT 231. Cross-listed as BIOL 331. Credit will be granted for only one of STAT 331 and PSYC 390. Three credits and a one-hour lab.
Stat 333 Introductory Probability Theory: Material will include: combinational analysis; axioms of probability; the law of total probability and Bayes’ Theorem; discrete and continuous random variables; mathematical expectation and variance; joint distributions; introduction to moment-generating functions and their applications; limit theorems. Prerequisites: MATH 222 or 267 and MATH 223 or 253. Three credits.
Stat 334 Mathematical Statistics: Topics include distribution theory; order statistics; point and interval estimation; MVUEs and the Rao-Blackwell theorem; consistency and sufficiency; the method of maximum likelihood; the method of moments; uniformly most powerful tests and the Neymann-Pearson fundamental lemma; likelihood ratio tests; least squares theory; statistical models and estimation in ANOVA. Prerequisite: STAT 333. Three credits.
Stat 435 Regression Analysis: Topics include straight-line regression, multiple regression, variable selection, residual analysis, multicolinearity, multiple and partial correlations, analysis of co-variance, logistic regression. Prerequisite: STAT 231 or 333. Three credits and a one-hour lab.
Stat 472 Topics in Statistics: 2012/2013 - Statistical Learning: data mining and prediction - The course covers the most current techniques used in data mining and machine learning and their background theoretical results. Two basic groups of methods are covered in this course: supervised learning (classification or regression) and unsupervised learning (clustering). The supervised learning methods includes Recursive Partitioning Tree, Random Forest, Linear Discriminant and Quadratic Discriminant Analysis, Neural Network, Support Vector Machine. The unsupervised learning methods include Hierarchical Clustering, K-means, K-nearest-neigbour, model-based clustering methods. Furthermore, the course also covers the dimensional reduction techniques such as LASSO and Ridge Regression, and model checking criteria. Prerequisite: STAT 231 or 333. Three credits. Not offered 2013/14
Stat 491 Senior Seminar: Cross-listed as CSCI 491 and MATH 491. The purpose of this non-credit course is to assist students in carrying out senior paper research, composition, and oral presentation. Students will present their research topic in the fall term and their completed research in the spring. Attendance at Departmental seminars is mandatory. No credit.
Stat 493 Senior Thesis: Students will prepare and present a thesis based on original research conducted under the supervision of a faculty member. Three credits.
