English language proficiency requirements Students registering in post-secondary level courses (numbered 100 to 499) will be required to meet the English language entrance proficiency requirements. Students in ELS or the University Foundations programs can register in those courses identified in the University Foundations program with lower levels of language proficiency. |

Please note that not all courses are offered every semester.

4 credits

Prerequisite(s): One of the following: (C or better in one of Principles of Math 11, Applications of Math 11, MATH 085, Foundations of Mathematics 11, or Pre-calculus 11) or (B or better in Apprenticeship and Workplace Mathematics 12) or (one of Foundations of Mathematics 12, Pre-calculus 12, Principles of Math 12, or Applications of Math 12) or (any UFV MATH course numbered 092 or higher) or (a score of 17/25 or better on Part A of the MSAT) or (45 university-level credits with department permission).

A basic introduction to descriptive statistics, probability, sampling, estimation, hypothesis testing, correlation, and regression. Recommended for anyone who wishes to evaluate research involving statistical analysis, especially students in humanities and social science. Using statistical computer software is essential to this course.

Note: As a general rule, students with Mathematics 11 are expected to take STAT 104, those with Mathematics 12 are expected to take STAT 106, and those with a full year of calculus are expected to take STAT 270/MATH 270. Before registering, students should check the requirements for their program. Students with STAT 104 may subsequently take STAT 270 in order to satisfy the requirements for a math degree.

Note: Students with credit for MATH 104, MATH 106, STAT 106, or STAT 270/MATH 270 cannot take this course for further credit.

4 credits

Prerequisite(s): One of the following: (C or better in one of Pre-calculus 11, Applications of Mathematics 12, Principles of Mathematics 12, Pre-calculus 12, MATH 092, MATH 096, MATH 110, MATH 124, or MATH 140) or (C or better in both MATH 094 and MATH 095) or (B or better in Foundations of Mathematics 12) or (a score of 17/25 or better on Part B of the MSAT together with a score of 34/50 or better on Parts A and B combined).

An introduction to descriptive statistics, sampling, probability, estimation, hypothesis testing, correlation, regression, and analysis of variances, including multiple linear regression and one-way ANOVA. Facility with Grade 12 level algebra is expected, but no calculus is required.

Note: As a general rule, students with Math 11 are expected to take STAT 104, those with Math 12 are expected to take STAT 106, and those with a full year of calculus are expected to take STAT 270/MATH 270. Before registering, students should check the requirements of their program. UFV mathematics degrees require STAT 270. Students with credit for STAT 106 may subsequently take STAT 270 in order to satisfy the requirements for a math degree.

Note: Students with credit for MATH 106 or MATH/STAT 270 cannot take this course for further credit.

4 credits

Prerequisite(s): One of the following: MATH 112, MATH 118, or a B or better in MATH 141.

An introduction to the theory and practice of statistics for engineering and science students who have experience with calculus. Topics include descriptive statistics, probability, random variables, bionomial, hypergeometric, Poisson, uniform, normal and exponential distributions, sampling distributions, confidence intervals and hypothesis tests for means and proportions, Pearson's Chi-squared test, correlation, and linear regression.

Note: This course is offered as STAT 270 and MATH 270. Students may only take one of these for credit.

Note: Students with credit for STAT 104 or STAT 106 may subsequently take STAT 270/MATH 270, but students with credit for STAT 270/MATH 270 may not subsequently take STAT 104 or STAT 106.

3 credits

Prerequisite(s): One of the following: STAT 104 with a B, STAT 106, or STAT 270.

This is a practical course on the use and understanding of statistical data as it arises in many areas of study. Topics include graphical presentation and interpretation of different types of statistical data, linear and nonlinear regression, design and analysis of experiments, survival time analysis, and time series analysis. Emphasis in this course is on the application and analysis of statistical data by using statistical software. Students are expected to complete a project on a real data set. Students who complete this course will be able to perform basic statistical computing in SAS and will have sufficient knowledge of data analysis to take upper-level applied statistics courses.

Note: Students with credit for MATH 271 cannot take this course for further credit.

3 credits

Prerequisite(s): One of the following: STAT 104 with a B, STAT 106, or STAT 270

Introduces statistical graphics generated by powerful yet flexible statistical programming languages such as SAS and R. Students will learn the codes and procedures of these languages to write computer programs for producing these graphics, to manipulate data, to compute summary statistics, and to communicate results effectively in simple reports and presentations.

3 credits

Prerequisite(s): One of the following: STAT 104 with a B+ or better, STAT 106 with a B or better, STAT 270, or STAT 271.

This is a practical course on the use and understanding of linear regression analysis. Statistical software is used throughout the course. Topics include the method of least squares, the analysis of variance table, F tests, selection of predictor variables, diagnostics, remedial measures and validation, qualitative predictor variables, the comparison of regression models, the analysis of covariance, nonparametric regression, introduction to nonlinear regression analysis, and logistic regression. Students complete at least one group project using a real data set.

Note: Students with credit for MATH 315 cannot take this course for further credit.

3 credits

Prerequisite(s): One of the following: STAT 106 with a B or better, STAT 104 with a B+ or better, STAT 270, or STAT 271.

This course discusses the construction and analysis of standard experimental designs. The basic techniques of randomization and blocking and the use of covariates are reviewed, followed by consideration of the 2k factorial and fractional factorial designs. Repeated measures designs are next discussed, including the split-plot and cross-over varieties. Variance components analysis and response surface methods are covered as time allows. Emphasis is on the conduct, assumption, implications, and rationale of particular designs. The data analysis is implemented using statistical software. Students are expected to produce a report which analyzes data collected from an experiment which they have designed and conducted, and which illustrates at least one of the major designs discussed.

3 credits

Prerequisite(s): CIS 230 and one of the following: STAT 106 (formerly MATH 106) with a B, MATH 270/STAT 270, or STAT 271.

Data quality issues pertaining to data acquisition, storage, integrity, and use. Identifying and analyzing data quality problems, and assessing strategies and tools to correct them. Also covers privacy and security, and data quality needs of data warehousing and mining applications.

Note: This course is offered as COMP 331 and STAT 331 (formerly MATH 331). Students may take only one of these for credit.

Note: This course is offered as COMP 331 and STAT 331 (formerly MATH 331). Students may take only one of these for credit.

3 credits

Prerequisite(s): One of the following: STAT 106 with a B, STAT 104 with a B+, STAT 270, or STAT 271.

This course introduces the theory and practice of survey sampling. The basic theories of simple random sampling, stratified random sampling, ratio estimation, cluster sampling, and systematic sampling are covered, together with the more specialized topics of questionnaire design, estimation of population size, and the random response method for sensitive questions. Students are expected to produce a report resulting from analyzing data collected in a survey which they have designed and conducted, and which illustrates at least one of the sample designs discussed during the course.

Note: Students with credit for MATH 350 cannot take this course for further credit.

3 credits

Prerequisite(s): MATH 211

This course covers the theory of probability and stochastic processes for science and mathematics students who have experience with third semester calculus. Topics include probability space, conditional probability and independence, continuous and discrete random variables, jointly distributed random variables, expectation, conditional expectation and properties, limit theorems, Markov chains and Poisson processes, and simulation.

Note: This course is offered as STAT 370 and MATH 370. Students may only take one of these for credit.

3 credits

Prerequisite(s): One of the following: STAT 271, MATH 302, or STAT 315

The course covers the application of the methods of the linear model analysis to non-normal data. This includes analysis of contingency tables using log-linear models, analysis of incidence data using Poisson models, analysis of binomial data using various link functions such as logit and probit, analysis of case-control data using logistic models, analysis of matched case-control data using logistic models, analysis of matched case-control data using conditional logistic regression, and analysis of survival data by adjusting for covariates or using Cox’s proportional hazard model.

Note: Students with credit for MATH 402 cannot take this course for further credit.

3 credits

Prerequisite(s): One of the following: STAT 270, STAT 271, STAT 315, or STAT 330

When the normality assumption of the underlying distribution of data does not hold, the traditional parametric approach for constructing confidence intervals and testing hypotheses fails. In this case, the non-parametric approach can be used. This course introduces various non-parametric techniques to test parameters for location and dispersion. It deals with problems in single sample, two or more independent samples, and two or more related samples. Goodness-of-fit tests and tests of association are also discussed.

Note: Students with credit for MATH 420 cannot take this course for further credit.

3 credits

Prerequisite(s): STAT 315 or STAT 271

This course introduces the basic ideas of time series analysis and forecasting methods. Topics include stationarity, autocovariance, autocorrelation and partial autocorrelation functions, and the Box-Jenkins classical time series models such as MA(q), AR(p), ARMA(p,q), ARIMA(p,q), and SARIMA models. The emphasis of this course is on the practical implementation of the methods and the analysis of time series data. Students are expected to complete a group project, analyzing some real-life data.

Note: Students with credit for MATH 390 or MATH 430 cannot take this course for further credit.

3 credits

Prerequisite(s): COMP 230 (formerly CIS 230), STAT 271, and STAT 331/COMP 331.

Data mining provides the techniques of extracting useful information and hidden patterns from a massive amount of data. Main topics include data exploration, classification, decision trees, Bayesian classifiers, frequent item sets, association rules, clustering, K-means, EM algorithm, and anomaly detection. Students will complete a group project based on a large real-life data set.

Note: This course is offered as STAT 431 and COMP 431. Students may take only one of these for credit.

3 credits

Prerequisite(s): MATH 370/STAT 370 or (MATH 270/STAT 270 and MATH 211).

A course in mathematical statistics. Distributions of functions of random variables; transformations; beta, t, F, multivariate normal distributions; order statistics; convergence in distribution and probability; Law of Large Numbers; Central Limit Theorem; method of maximum likelihood; inference.

Note: This course is offered as STAT 450 and MATH 450. Students may only take one of these for credit.

3 credits

Prerequisite(s): One of the following: STAT 271, STAT 315, STAT 302, or STAT 330.

This course is the extension of the linear model methods to the multivariate situation. The emphasis of the course is on examination of a range of widely-used multivariate statistical techniques, their relationship with familiar univariate methods, and the solution to practical problems. Topics include multivariate regression, principal components, factor analysis, canonical correlations, and discrimination and classification analysis. The emphasis is on applications by using statistical software.

Note: Students with credit for MATH 470 cannot take this course for further credit.

3 credits

Prerequisite(s): At least three upper-level STAT courses, and at least one additional upper-level course labeled MATH or STAT. Certain programs of study may require more particular prerequisites. The written permission of the instructor is required.

This course is designed for students who wish to examine in greater depth a particular statistical technique or application. It will be offered either as an individual reading course or as a seminar, depending upon student and faculty interest. May not be repeated for additional credit.

Note: Students with credit for MATH 488 cannot take this course for further credit.

Last extracted: October 31, 2019 02:57:24 PM