
English Language Requirements Students registering in postsecondary level courses (numbered 100 to 499) will be required to meet the English language entrance proficiency requirements. Students in ESL or the University Foundations programs can register in those courses identified in the University Foundations program with lower levels of language proficiency.

STAT 1044 credits
Introductory Statistics 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 Precalculus 11; or B or better in Apprenticeship and Workplace Mathematics 12; or one of Foundations of Mathematics 12, Precalculus 12, Principles of Math 12, or Applications of Math 12; or 45 universitylevel credits with department permission.
This course is an introduction to descriptive statistics, probability, sampling, estimation, hypothesis testing, correlation, and regression. It provides an intuitive approach to why and when the procedures may be used, without involving mathematical proofs. This course is recommended for anyone who wishes to develop the ability to intelligently evaluate published statistical data, and for students of arts, criminal justice, education, and social science in particular. 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. Students should check program requirements. Students with credit for STAT 106 or STAT 270 are not allowed to take STAT 104. 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 cannot take this course for further credit.
STAT 1064 credits
Statistics I Prerequisite(s): One of the following: C or better in one of Principles of Mathematics 12, Applications of Mathematics 12, Foundations of Mathematics 12, Precalculus 11, MATH 110, MATH 124, or MATH 140; or C or better in both MATH 094 and MATH 095; or Precalculus 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.
This course is an introduction to descriptive statistics, sampling, probability, estimation, hypothesis testing, correlation, regression, and analysis of variances. This course is similar to STAT 104, but includes multiple regression, oneway ANOVA, and a more detailed discussion of probability results. Facility with Grade 12 level algebra is expected, but no calculus is required. 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. While STAT 106 is not equivalent to STAT 270, students with credit for STAT 270 are not allowed to take STAT 106. Those 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 cannot take this course for further credit.
STAT 2704 credits
Introduction to Probability and Statistics Prerequisite(s): MATH 112 or MATH 118
An introduction to the theory and practice of statistics for engineering, science, and mathematics students who have experience with calculus. Topics include descriptive statistics, elementary probability theory, expectation and variance of random variables, bionomial, hypergeometric, Poisson, uniform, normal and exponential distributions, sampling distributions, confidence intervals and hypothesis tests for means and proportions, tests of goodnessoffit and independence, correlation, and linear regression. Note: This course is offered as STAT 270 and MATH 270. Students may only take one of these for credit.
STAT 2713 credits
Introduction to Data Analysis and Statistical Modeling 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 upperlevel applied statistics courses.
Note: Students with credit for MATH 271 cannot take this course for further credit.
STAT 2723 credits
Statistical Graphics and Languages Prerequisite(s): One of the following: STAT 104 with a B, STAT 106, or STAT 270
Statistical graphics are important for analyzing patterns and relationships of data sets in many disciplines. This course 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. They will also learn how to manipulate data, compute summary statistics, and present results in simple reports.
Note: Students with credit for MATH 272 cannot take this course for further credit.
STAT 3153 credits
Applied Regression Analysis 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.
STAT 3303 credits
Design of Experiments 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 splitplot and crossover 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.
STAT 3313 credits
Data Quality Prerequisite(s): CIS 230 and one of the following: STAT 104 with a B+, STAT 106 with a B, STAT 270, or STAT 271.
This course will focus on issues relating to data quality as they pertain to data acquisition, storage, integrity, and use. Students will learn to identify and analyze data quality problems, assess strategies and costs to solve quality problems, and use simple statistical and other tools to find and correct problems. Privacy and security issues will be introduced. The course will also focus on the data quality needs of data warehousing and data 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.
STAT 3503 credits
Survey Sampling 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.
STAT 3703 credits
Probability and Stochastic Processes 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.
STAT 4023 credits
Applied Generalized Linear Models and Survival Analysis 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 nonnormal data. This includes analysis of contingency tables using loglinear models, analysis of incidence data using Poisson models, analysis of binomial data using various link functions such as logit and probit, analysis of casecontrol data using logistic models, analysis of matched casecontrol data using logistic models, analysis of matched casecontrol data using conditional logistic regression, and analysis of survival data by adjusting for covariates or using Coxâ€™s proportional hazard model.
Note: Students with credit for MATH 402 cannot take this course for further credit.
STAT 4203 credits
Empirical and NonParametric Statistics 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 nonparametric approach can be used. This course introduces various nonparametric 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. Goodnessoffit tests and tests of association are also discussed.
Note: Students with credit for MATH 420 cannot take this course for further credit.
STAT 4303 credits
Time Series and Forecasting 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 BoxJenkins 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 reallife data.
Note: Students with credit for MATH 390 or MATH 430 cannot take this course for further credit.
STAT 4313 credits
Data Mining Prerequisite(s): STAT 271, STAT 331/COMP 331, and CIS 230
Advances in data collection and computer storage technology have generated a very large volume of data sets in business, internet, medicine, and a variety of scientific fields. Traditional methods of statistical data analysis have been challenged. New methodologies and algorithms in Computer Science, Statistics, and Business Intelligence are then developed. Data mining provides the techniques of extracting useful information and hidden patterns from this massive amount of data. The main topics in this course are data exploration, classification, decision trees, Bayesian classifiers, frequent item sets, association rules, clustering, Kmeans, EM algorithm, and anomaly detection. Statistical software such as SAS will be used to implement the algorithms. Students are expected to complete a group project based on a large data set.
Note: This course is offered as STAT 431 (formerly MATH 431) and COMP 431. Students may take only one of these for credit.
STAT 4503 credits
Statistical Distribution Theory Prerequisite(s): STAT 370/MATH 370
This is a course in mathematical statistics. It is the continuation of Math 370 in the stream of theoretical statistics, which is designed for students specializing in either mathematics or statistics. Topics include distributions of functions of random variables; transformations of discrete and continuous random variables; beta, t, and F distributions; order statistics; multivariate normal distribution; convergence in distribution and probability; the Law of Large Numbers; the Central Limit Theorem; method of maximum likelihood; confidence intervals; and tests of statistical hypotheses. Note: This course is offered as STAT 450 and MATH 450. Students may only take one of these for credit.
STAT 4703 credits
Applied Multivariate Statistical Analysis Prerequisite(s): 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 widelyused 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.
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 widelyused 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.
STAT 4883 credits
Selected Topics in Statistics Prerequisite(s): At least three upperlevel STAT courses, and at least one additional upperlevel 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: March 22, 2013 09:17:13 PM
