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, 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*2120.
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Graduate students may be admitted to later parts of a sequence by permission of the department.
| STAT*2040 Statistics I S,F,W (3-0) [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; the 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; introduction
to analysis of variance. Assignments will deal with real data from the natural sciences and involve the use of statistical
software for computing and visualization.
|
| Offering(s): |
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*2060, STAT*2080, STAT*2120, STAT*2230 This is a Priority Access Course. Enrollment may be restricted to particular programs or specializations. See department for more information.
|
| Department(s): |
Department of Mathematics and
Statistics
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| STAT*2050 Statistics II F,W (3-0) [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 t-tests 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): |
1 of STAT*2040, STAT*2060, STAT*2120, STAT*2230 |
| Restriction(s): |
STAT*2090 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*2060 Statistics for Business Decisions F,W (3-0) [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.
|
| 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.
|
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*2080 Introductory Applied Statistics I F (3-0) [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.
|
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*2090 Introductory Applied Statistics II W (3-0) [0.50] |
| The topics covered in this course include: analysis of qualitative data; analysis of variance for designed experiments; multiple
regression; exposure to non-parametric 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 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*2120 Probability and Statistics for Engineers F,W (3-0) [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 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*2230 Biostatistics for Integrative Biology W (3-2) [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. Computer-intensive laboratory
sessions will focus on practical data organization, visualization, statistical analysis using software, and interpretation
and communication of statistical results.
|
| Prerequisite(s): |
BIOL*1070 |
| Restriction(s): |
BIOL*2250, STAT*2040, STAT*2060, STAT*2080, STAT*2120, STAT*2250. Restricted to students in the BSC majors in BIOD, MFB, WBC, WLB, and ZOO, and BSES majors in ECOL and ECOL:C.
|
| Department(s): |
Department of Mathematics and
Statistics, Department of Integrative Biology
|
| 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.
|
| Prerequisite(s): |
STAT*3100 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*3210 Experimental Design F (3-0) [0.50] |
| This course presents the 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.
|
| Offering(s): |
Offered in even-numbered years. |
| Prerequisite(s): |
STAT*2050 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*3240 Applied Regression Analysis F (3-1) [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
Box-Cox 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), (1 of MATH*1160, MATH*2150, MATH*2160), STAT*2050 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*3320 Sampling Theory with Applications F (3-0) [0.50] |
| This course focuses on the design and analysis of survey samples for finite populations. Topics covered include: non-probability
and probability sampling, simple random sampling, stratified sampling, cluster sampling, systematic sampling, double sampling,
two-phase sampling and multi-stage cluster sampling. Expectation, variance estimation procedures and sample size calculations
for the above techniques are included.
|
| Offering(s): |
Offered in odd-numbered years. |
| Prerequisite(s): |
(1 of IPS*1510, MATH*1210, MATH*2080), (1 of STAT*2050, STAT*3240, STAT*3100)
|
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*3510 Environmental Risk Assessment W (3-0) [0.50] |
| Contemporary statistical methods for assessing risk are discussed. Topics covered include: dose-response 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): |
STAT*2050 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4000 Statistical Computing F (3-0) [0.50] |
| The topics in this course will include pseudorandom number generation, numerical optimization as used in statistics, simulation
study design, Monte Carlo integration and variance reduction, and bootstrapping. Other topics may include permutation tests,
visualization of multivariate data, and big data.
|
| Prerequisite(s): |
MATH*2130, STAT*2050, STAT*3110 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4050 Topics in Applied Statistics I W (3-0) [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.
|
| Offering(s): |
Offered in odd-numbered years. |
| Prerequisite(s): |
STAT*3110, STAT*3240 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4340 Statistical Inference W (3-0) [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 likelihood-based perspectives. Foundational issues concerning the frequentist and Bayesian paradigms will
also be discussed.
|
| Prerequisite(s): |
STAT*3110, STAT*3240 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4350 Applied Multivariate Statistical Methods F (3-0) [0.50] |
| This course introduces the multivariate normal, and Wishart and Hotelling's T-square 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): |
(1 of MATH*1160, MATH*2150, MATH*2160), STAT*3110, STAT*3240 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4360 Applied Time Series Analysis F (3-0) [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 |
| Department(s): |
Department of Mathematics and
Statistics
|
| STAT*4600 Advanced Research Project in Statistics F,W (0-6) [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.
|
| Prerequisite(s): |
1.00 credits in Statistics at the 3000 level or above. |
| Restriction(s): |
Approval of a supervisor and the course coordinator. |
| Department(s): |
Department of Mathematics and
Statistics
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