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 Advanced Functions and Calculus, OAC calculus, equivalent 
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]

A course 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): 
(1 of 4U mathematics, OAC mathematics, equivalent) or 0.50 credit in mathematics 
Restriction(s): 
STAT*1000, STAT*2040, STAT*2080, STAT*2100, STAT*2120

STAT*2080 Introductory Applied Statistics I F (32) [0.50]

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. Illustrated with examples from a variety of disciplines, including family studies, education, marketing, medicine, psychology and sociology. 
Prerequisite(s): 
(1 of 4U mathematics, OAC mathematics, equivalent) or 0.50 credit in mathematics 
Restriction(s): 
STAT*1000, STAT*2040, STAT*2060, STAT*2100, STAT*2120

STAT*2120 Probability and Statistics for Engineers W (31) [0.50]

Sample spaces. Probability, conditional probability, 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, correlation. Introduction to quality control and reliability. Recommended especially for students in the B.Sc.(Eng.) program. 
Prerequisite(s): 
1 of MATH*1010, MATH*1210, MATH*2080, IPS*1210

Restriction(s): 
STAT*1000, 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]

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): 
(IPS*1210 or MATH*1210 ), (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 W (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*4080 Data Analysis F (32) [0.50]

Principles of statistical modelling; the likelihood function; model fitting; model choice; analysis of nonnormal data; generalized linear models; binomial regression models; regression models for counts; Poisson and multinomial data; overdispersion. Statistical modelling and analysis using appropriate software (eg. Splus and/or SAS) in the computing lab. 
Prerequisite(s): 
(MATH*2150 or MATH*2160), STAT*3110, STAT*3240

STAT*4100 Survival Analysis W (31) [0.50]

Theory and methodology of survival analysis. A set of techniques for modelling the time of a welldefined event (typically failure or death), and for dealing with censored data. The emphasis will be on regression, including parametric, proportional hazards and accelerated life regression models. Areas of application include environmental sciences, medicine, industrial reliability, and economics, where the events of interest may be respectively early death, organ failure, component failure, or strikes. Students will learn specialized techniques for modelling censored data and understand why they are necessary. The interpretation of real data will be emphasized throughout the course. Statistical computing packages (SPlus or SAS) will be used extensively. 
Prerequisite(s): 
STAT*3110 and 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 
