XII. Course Descriptions
Statistics
Department of Mathematics and Statistics
Suggested initial course sequences:

For students interested in applied statistics a minimal course sequence is: (STAT*2040 or STAT*2100), STAT*2050, STAT*3210, STAT*3240, STAT*3320.

Credit may be obtained in only 1 of STAT*2050 or STAT*2090 and only 1 of STAT*2040, STAT*2060, STAT*2080, STAT*2100, STAT*2120.

Graduate students may be admitted to later parts of a sequence by permission of the department.

Students who major or minor in Statistics may not receive credit for the following courses unless taken to satisfy the requirements
of another program: ECON*2740, PSYC*2010, PSYC*3320.
STAT*2040 Statistics I S,F,W (31) [0.50] 
This course focuses on the practical methods of Statistics and the topics include: descriptive statistics; univariate models
such as binomial, Poisson, uniform and normal; central limit theorem; expected value; the t, F and chisquare models; point
and interval estimation; hypothesis testing methods up to twosample data; simple regression and correlation; ANOVA for CRD
and RCBD. Assignments will deal with real data from the natural sciences. Laboratory sessions involve statistical computing
and visualization using appropriate statistical software. (Also offered through Distance Education format.)

Prerequisite(s): 
1 of 4U Calculus and Vectors, Advanced Functions and Calculus, OAC Calculus, MATH*1080 
Restriction(s): 
STAT*1000, STAT*2060, STAT*2080, STAT*2100, STAT*2120 
STAT*2050 Statistics II F,W (31) [0.50] 
In this course, students will learn how to implement good study design and analyze data from complex studies. This course
follows naturally from STAT*2040 and features both previously unseen statistical techniques, as well as studying in greater depth some topics covered in STAT*2040. These topics will include: experiments and observational studies; a review of ttests and confidence intervals; confounding
variables; association and causality; Analysis of Variance (ANOVA); simple and multiple linear regression; binary responses
(logistic regression); odds ratios and relative risk; and an introduction to experimental design (including blocked designs
and factorial treatment designs). Assignments carried out using modern statistical software will form the basis for mastering
the material.

Prerequisite(s): 
STAT*2040 
Restriction(s): 
BIOL*2250, STAT*2090, STAT*2250 
STAT*2060 Statistics for Business Decisions F (32) [0.50] 
This course is designed for students interested in the application of statistics in a business setting. Topics covered will
include the role of statistics in business decisions, organization of data, frequency distributions, probability, normal and
sampling distributions, hypothesis tests, linear regression and an introduction to time series, quality control and operations
research. (Also offered through Distance Education format.)

Prerequisite(s): 
(4U mathematics or equivalent) or 0.50 credit in mathematics 
Restriction(s): 
STAT*2040, STAT*2080, STAT*2120 Not available to B.Sc. students.

STAT*2080 Introductory Applied Statistics I F (30) [0.50] 
The topics covered in this course include: Frequency distributions, graphing and tabulation of data; measures of central tendency,
variability and association; elementary probability; hypothesis testing and confidence intervals; basic concepts of experimental
design; treatment designs; simple linear regression and correlation. Examples come from a variety of disciplines, including
family studies, education, marketing, medicine, psychology and sociology.

Prerequisite(s): 
(4U mathematics or equivalent) or 0.50 credit in mathematics 
Restriction(s): 
STAT*2040, STAT*2060, STAT*2100, STAT*2120 BSC students cannot take this course for credit.

STAT*2090 Introductory Applied Statistics II W (30) [0.50] 
The topics covered in this course include: analysis of qualitative data; analysis of variance for designed experiments; multiple
regression; exposure to nonparametric methods; power and sample size calculations; special topics such as logistic regression.
Examples come from a variety of disciplines, including nutrition, family studies, education, marketing, medicine, psychology
and sociology.

Prerequisite(s): 
STAT*2080 
Restriction(s): 
BIOL*2250, STAT*2050, STAT*2250 
STAT*2120 Probability and Statistics for Engineers F,W (30) [0.50] 
The topics covered in this course include: Sample spaces; probability, conditional probability and independence; Bayes' theorem;
probability distributions; probability densities; algebra of expected values; descriptive statistics; inferences concerning
means, variances, and proportions; curve fitting, the method of least squares and correlation. An introduction to quality
control and reliability is provided. This course is recommended for students in the B.Sc.(Eng.) program.

Prerequisite(s): 
1 of IPS*1510, MATH*1210, MATH*2080 
Restriction(s): 
STAT*2040, STAT*2060, STAT*2080, STAT*2100 
STAT*2230 Biostatistics for Integrative Biology W (32) [0.50] 
This course introduces students to the design, completion and interpretation of research projects, including identifying categories
of research questions, types of data, data gathering methods, efficient graphic and numeric methods to summarize data, standard
statistical analyses involving parameter estimation and hypothesis tests and interpreting results in the context of research
goals. Statistical concepts underlying practical aspects of biological research will be emphasized. Computerintensive laboratory
sessions will focus on practical data organization, visualization, statistical analysis using software, and interpretation
and communication of statistical results. Department of Mathematics and Statistics and Department of Integrative Biology.

Prerequisite(s): 
BIOL*1070, BIOL*1080 
Restriction(s): 
BIOL*2250, STAT*2040, STAT*2060, STAT*2080, STAT*2120, STAT*2250, Enrollment restricted to the BSC majors in BIOD, ECOL, MFB, WBC, WLB, ZOO

STAT*3110 Introductory Mathematical Statistics II W (30) [0.50] 
Estimation, unbiasedness, CramerRao inequality, consistency, sufficiency, method of moments, maximum likelihood estimation;
hypothesis testing, NeymanPearson lemma, likelihood ratio test, uniformly most powerful test; linear regression and correlation;
nonparametric methods.

Prerequisite(s): 
STAT*3100 
STAT*3210 Experimental Design W (30) [0.50] 
Basic principles of design: randomization, replication, and local control (blocking); RCBD, Latin square and crossover designs,
incomplete block designs, factorial and splitplot experiments, confounding and fractional factorial designs, response surface
methodology; linear mixed model computer analysis of the designs; nonparametric methods; Taguchi philosophy.

Prerequisite(s): 
STAT*2050, STAT*3240 
Restriction(s): 
STAT*4220 
STAT*3240 Applied Regression Analysis F (31) [0.50] 
This course reviews simple linear regression and introduces multiple regression with emphasis on theory of least squares estimation,
residual analysis, and model interpretation. Within the multiple regression context, transformations of variables, interactions,
model selection techniques, ANOVA, influence diagnostics and multicollinearity will be discussed. Topics may also include
BoxCox transformations, weighted regression, and logistic and Poisson regression. This course is supplemented with computer
labs involving interactive data analysis using statistical software.

Prerequisite(s): 
(1 of IPS*1510, MATH*1210, MATH*2080), (MATH*2150 or MATH*2160, may be taken concurrently), STAT*2050 
STAT*3320 Sampling Theory with Applications F (30) [0.50] 
This course focuses on the design and analysis of survey samples for finite populations. Topics covered include: nonprobability
and probability sampling, simple random sampling, stratified sampling, cluster sampling, systematic sampling, double sampling,
twophase sampling and multistage cluster sampling. Expectation, variance estimation procedures and sample size calculations
for the above techniques are included.

Prerequisite(s): 
(1 of IPS*1510, MATH*1210, MATH*2080), (1 of STAT*2050, STAT*3240, STAT*3100)

STAT*3510 Environmental Risk Assessment W (30) [0.50] 
Contemporary statistical methods for assessing risk are discussed. Topics covered include: doseresponse models, survival
analysis, relative risk analysis, bioassay, estimating methods for zero risk, trend analysis, survey of models for assessing
risk. Case studies are used to illustrate the methods.

Prerequisite(s): 
(1 of IPS*1500, MATH*1000, MATH*1080, MATH*1200), (1 of BIOL*2250, STAT*2050, STAT*2250)

STAT*4050 Topics in Applied Statistics I F (30) [0.50] 
Topics such as statistical computing procedures, quality control, bioassay, survival analysis and introductory stochastic
processes will be covered. This course is intended for statistics students and interested students from other disciplines
who have appropriate previous courses in statistics. Information on particular offerings will be available at the beginning
of each academic year. (Offered in oddnumbered years.)

Prerequisite(s): 
STAT*3110, STAT*3240 
STAT*4340 Statistical Inference W (30) [0.50] 
This course reviews and extends the theory of estimation introduced in STAT*3110. Topics including point estimation, interval estimation, hypothesis testing and decision theory will be presented from both
the frequentist and likelihoodbased perspectives. Foundational issues concerning the frequentist and Bayesian paradigms will
also be discussed.

Prerequisite(s): 
STAT*3110, STAT*3240 
STAT*4350 Applied Multivariate Statistical Methods W (30) [0.50] 
This course incroduces the multivariate normal, and Wishart and Hotelling's Tsquare distributions. Topics covered include:
statistical inference on the mean vector, canonical correlation, multivariate analysis of variance and covariance, multivariate
regression, principal components analysis, and factor analysis. Topics will be illustrated using examples from various disciplines.

Prerequisite(s): 
(MATH*2150 or MATH*2160), STAT*3110, STAT*3240 
STAT*4360 Applied Time Series Analysis F (30) [0.50] 
This course will investigate the nature of stationary stochastic processes from the spectral and time domain points of view.
Aspects of parameter estimation and prediction in a computationally intensive environment will be the presentation style.
The methods developed in this course will have applicability in many sciences such as engineering, environmental sciences,
geography, soil sciences, and life sciences.

Prerequisite(s): 
STAT*3240 or instructor consent

STAT*4600 Advanced Research Project in Statistics F,W (06) [1.00] 
Each student in this course will undertake an individual research project in some area of statistics, under the supervision
of a faculty member. A written report and a public presentation of the project will be required.

Restriction(s): 
Approval of a supervisor and the course coordinator. 