# Statistics Courses

### 100 Level

101 Introductory Statistics
This course will give an introduction to descriptive and inferential statistics. Topics
include descriptive statistics; graphical display of data, random variables and
probability distributions, parameter estimations, hypothesis testing and simple
linear regression. Students will learn to use statistical software tools; to identify
bias in data collection; to organize and summarize data; to make inferences from
data and to be able to test the significance of the results. Acceptable for credit
in the Faculties of Arts and Business, and the Departments of Human Kinetics,
Human Nutrition and B.Sc. Nursing. STAT 101.H will focus on applications to health
sciences and STAT 101.B will focus on applications to business and economics.
Credit will be granted for only one of STAT 101, STAT 201, STAT 224, STAT 231,
PSYC 290(292), HKIN 301. Three credits.

### 200 Level

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. Credit will be granted for only one of STAT 224, STAT 101(201),
STAT 231, PSYC 290(292). Cross-listed as ENGR 224. Prerequisite: ENGR 122
or MATH 122. Three credits and two-hour problem session.

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. Credit will be granted for only one of STAT 231, STAT 101(201), STAT
224, PSYC 290(292). Prerequisite: MATH 107 or 127(112) or 122. Three credits
and a one-hour lab.

### 300 Level

311 Survey Sampling Design
Topics include simple random sampling, stratified sampling, systematic sampling,
cluster sampling, multi-stage sampling, bootstrap samples. Prerequisite: STAT
101(201) or 224 or 231. Three credits and a one-hour lab. Not offered 2017-2018;
next offered 2018-2019.

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. Credit will be
granted for only one of STAT 331, PSYC 394, PSYC 390. Cross-listed as BIOL
331. Prerequisite: STAT 101(201) or 224 or 231. Three credits and a one-hour lab.

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 momentgenerating
functions and their applications; limit theorems. Prerequisites: MATH
222 or 267 and MATH 223 or 253. Three credits.

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. Offered 2017-2018 and in alternate years.

### 400 Level

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. Offered 2017-2018 and in alternate years.

445 Statistical Learning and Data Mining
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 include
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-neighbour,
model-based clustering methods. Furthermore, the course also covers the
dimensional reduction techniques such as LASSO and Ridge Regression, and
model checking criteria. Prerequisites: CSCI 161, STAT 224 or 231 or permission
of instructor. Three credits. Offered 2017-2018 and in alternate years.

472 Topics in Statistics
This course will cover a selection of current statistical topics, such as sampling
theory, time-series analysis, stochastic processes, design and analysis of
experiments, bootstrap methods, multivariate analysis, and bioinfomatics. Three