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 (32) [0.50] 
A course stressing the practical methods of Statistics. 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.

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 S,F,W (32) [0.50] 
The methods of STAT*2040 are extended to the multisample cases. Methods include: simple and multiple regression analysis including ANOVA and lackoffit;
experimental design including analysis for CRD, RCBD, LSD, SPD and factorial experiments with interaction; ANCOVA; Bioassay.
Assignments employing data from the natural sciences will be processed in the microcomputer laboratory.

Prerequisite(s): 
STAT*2040 or STAT*2100 (or equivalent)

Restriction(s): 
STAT*2090, STAT*2250 
STAT*2060 Statistics for Business Decisions W (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*2100, STAT*2120 B.Sc. students cannot take this course for credit.

STAT*2080 Introductory Applied Statistics I F (32) [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 B.Sc. students cannot take this course for credit.

STAT*2120 Probability and Statistics for Engineers F,W (31) [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): 
MATH*1210 or MATH*2080 
Restriction(s): 
STAT*2040, STAT*2060, STAT*2080, STAT*2100 
STAT*2250 Biostatistics and the Life Sciences W (32) [0.50] 
This course in biostatistical methods will emphasize the design of research projects, data gathering, analysis and the interpretation
of results. Statistical concepts underlying practical aspects of biological research will be acquired while working through
the process of scientific enquiry. Weekly computer laboratory sessions will focus on practical data visualization and statistical
analysis using computer statistical packages. Simple parametric and nonparametric methods are reviewed, followed by more advanced
topics that will include some or all of the following: two factor ANOVA and multiple regression, and introductions to discriminant
analysis, cluster analysis, principal components analysis, logistic regression, and resampling methods. (Also listed as BIOL*2250.) Departments of Mathematics and Statistics and Zoology.

Prerequisite(s): 
STAT*2040 or STAT*2100 
Equate(s): 
BIOL*2250 
Restriction(s): 
STAT*2050 
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 (32) [0.50] 
The topics covered in this course include: Theory and applications of regression techniques; linear, nonlinear and multiple
regression and correlation; analysis of residuals; other statistical techniques including: response surfaces and covariance
analysis, prediction and timeseries analysis. The computer lab involves interactive data analysis and investigation of the
methodology using SAS and/or SPLUS statistical software.

Prerequisite(s): 
(MATH*1210 or MATH*2080 ), (MATH*2150 or MATH*2160, may be taken concurrently or with instructor consent), (STAT*2050 or STAT*2100)

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

Prerequisite(s): 
(1 of IPS*1110, MATH*1000, MATH*1080, MATH*1200), (STAT*2050 or 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. Intended for statistics students and interested students in other disciplines with 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 on methods of statistical inference reviews and extends the theory of estimation introduced in STAT*3110: interval estimation tests for simple and composite hypotheses, likelihood ratio tests. Recent likelihood concepts as well
as classical large sample theory, asymptotics and approximations and their applications are covered. This material is directly
relevant to current research and applications in areas as diverse as survival analysis, nonparametric regression and environmetrics.

Prerequisite(s): 
STAT*3110, STAT*3240 
STAT*4360 Applied Time Series Analysis W (32) [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. 