XII. Course Descriptions
Statistics
Department of Mathematics and Statistics
Suggested initial course sequences:
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For students interested in applied statistics a minimal course sequence is: (STAT*2040 or STAT*2100), STAT*2050, STAT*3210, STAT*3240, STAT*3320.
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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.
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Graduate students may be admitted to later parts of a sequence by permission of the department.
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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 (3-2) [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 chi-square models; point and interval
estimation; hypothesis testing methods up to two-sample 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.
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| 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 (3-2) [0.50] |
| The methods of STAT*2040 are extended to the multi-sample cases. Methods include: simple and multiple regression analysis including ANOVA and lack-of-fit;
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 (3-2) [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.)
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| 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 (3-2) [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 (3-1) [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 (3-2) [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 (3-0) [0.50] |
| Estimation, unbiasedness, Cramer-Rao inequality, consistency, sufficiency, method of moments, maximum likelihood estimation;
hypothesis testing, Neyman-Pearson lemma, likelihood ratio test, uniformly most powerful test; linear regression and correlation;
non-parametric methods.
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| Prerequisite(s): |
STAT*3100 |
| STAT*3210 Experimental Design W (3-0) [0.50] |
| Basic principles of design: randomization, replication, and local control (blocking); RCBD, Latin square and crossover designs,
incomplete block designs, factorial and split-plot 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 (3-2) [0.50] |
| Theory and applications of regression techniques; linear, non-linear and multiple regression and correlation; analysis of
residuals; other statistical techniques including: response surfaces and covariance analysis, prediction and time-series analysis.
The computer lab involves interactive data analysis and investigation of the methodology using SAS and/or S-PLUS statistical
software.
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| 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 (3-0) [0.50] |
| Contemporary statistical methods for assessing risk, including dose-response 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.
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| 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 (3-0) [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 odd-numbered
years.)
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| Prerequisite(s): |
STAT*3110, STAT*3240 |
| STAT*4080 Data Analysis F (3-2) [0.50] |
| Principles of statistical modelling; the likelihood function; model fitting; model choice; analysis of non-normal 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 (3-1) [0.50] |
| Theory and methodology of survival analysis. A set of techniques for modelling the time of a well-defined 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 (S-Plus or SAS) will
be used extensively.
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| Prerequisite(s): |
STAT*3110 and STAT*3240 |
| STAT*4340 Statistical Inference W (3-0) [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 (3-2) [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.
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| Prerequisite(s): |
STAT*3240 or instructor consent
|
| STAT*4600 Advanced Research Project in Statistics F,W (0-6) [0.50] |
| 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. |
