Catalog Entries |
2024 September Semester
Apr 25, 2024 |
|
 |
||
 | Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. |
| STAT 100 - Statistical Reasoning for Everyday Life |
|
This course is an introduction to the role random chance plays in our life, and how to evaluate statistical evidence in support of the assessment of risk, decision-making or discovering new knowledge. Students gain a working knowledge of the framework of statistical reasoning and apply graphical techniques to assess variability. Students learn to assess the strength and validity of a statistical argument and learn to develop a statistical reasoning framework in simple situations. Example situations include lotteries, political polls, risk, incorporating prior knowledge and meeting your long-lost relative in an airport. This course requires no mathematical background and is accessible to students in any discipline.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam |
| STAT 240 - Basic Statistics |
|
This course is an introduction to the basic principles of statistics and procedures for data analysis. Topics include gathering data, displaying and summarizing data, examining relationships between variables, probability models, sampling distributions, estimation and significance tests, inference for means and proportions in one and two sample situations, contingency tables, and simple linear regression. Students register in a computer lab corresponding to their area of interest. Please note: You must register separately in lecture and lab/tutorial components if applicable. Credits: 0.000 OR 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam, Lec/Lab/Tut Combination, Laboratory, World Wide Web |
| STAT 271 - Statistical Reasoning for Engineers |
|
This course is an introduction to statistical reasoning for engineers. Students gain a working knowledge of statistical reasoning, the probability and statistical theory underlying many common statistical techniques, and the application of these statistical techniques to real engineering problems. Students learn to critically assess the strength and validity of a statistical argument for many common engineering problems. Topics covered include basic probability, common statistical distributions used in engineering, fitting basic statistical models and assessing the fit of these models, and
statistical inference including classical parametric and Monte Carlo techniques.
Credits: 0.000 OR 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam, Laboratory, Tutorial |
| STAT 371 - Probability and Statistics for Scientists and Engineers |
|
This course is a calculus-based introduction to the theory and application of probability and statistics. The topics covered include concepts of probability, events, populations, probability theorems, the concept of a random variable, continuous and discrete random variables, joint probability distributions, distributions of functions of a random variable, moments, Chebyshev’s inequality, the de Moivre-Laplace theorem, the central limit theorem, sampling and statistical estimation theory, hypothesis testing, simple regression analysis, and an introduction to the design of experiments.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Self-Directed, Final Exam |
| STAT 372 - Mathematical Statistics |
|
This course introduces the theory of statistical inference. Topics covered from likelihood theory are maximum likelihood estimation, sufficiency, and the likelihood ratio test. Topics covered from frequentist theory are point estimation, unbiasedness, consistency, efficiency, confidence intervals, and small sample and large sample hypothesis tests. Topics covered from Bayesian theory are risk, point estimation, and credible intervals.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Self-Directed, Final Exam |
| STAT 471 - Linear Models |
|
This course discusses the estimation of parameters in the multiple linear regression model by the least-squares method. Topics covered include the statistical properties of the least-spares estimators, the Gauss-Markov theorem, estimates of residual and regression sums of squares, distribution theory under normality of the observations, assessment of normality, variance stabilizing transformations, examination of multicollinearity, variable selection methods, logistic regression for a binary response, log-linear models for count data, and generalized linear models.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam |
| STAT 472 - Survey Sampling Design and Analysis |
|
This course discusses the planning and practice of sample surveys. Topics covered include simple random sampling, unequal probability sampling, stratified sampling, cluster sampling, multistage sampling, cost-effective design, analysis and control of sources of sampling and non-sampling error, ratio estimation, model-based regression estimation, resampling, and replication methods.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam |
| STAT 473 - Experimental Design and Analysis |
|
This course discusses experimental designs and analyses. Topics covered include basic principles and guidelines for designing experiements, simple comparative designs, single factor, analysis of variance, block designs, factorial designs, response surface methods and designs, nested and split plot designs, and the analysis of covariance.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam |
| STAT 475 - Methods for Multivariate Data |
|
This course discusses practical techniques for the analysis of multivariate data. Topics covered include estimation and hypothesis testing for multivariate means and variances; partial, multiple and canonical correlations; principal components analysis and factor analysis for data reduction; multivariate analysis of variance; discriminant analysis for classification; and cluster analysis.
Credits: 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam |
| STAT 499 - Special Topics in Statistics |
|
The topic for this course varies, depending on student interest and faculty availability. The course may be taken any number of times provided that topics are distinct.
Credits: 1.000 TO 3.000 Levels: Undergraduate Schedule Types: Lecture, Final Exam, Laboratory, Seminar |
| STAT 530 - Undergraduate Thesis |
|
This undergraduate thesis allows students to examine and research a topic in the field of statistics. Students must have completed at least 90 credit hours and be a Mathematics major. This thesis may be taken in one or two semesters. STAT 530 is normally taken over two semesters and requires that a student find an Undergraduate Thesis research supervisor. Therefore, students are encouraged to apply to potential supervisors well in advance of completing 90 credit hours. This course is taken for a total of 6 credit hours.
Credits: 3.000 OR 6.000 Levels: Undergraduate Schedule Types: Undergrad Thesis |
| STAT 671 - Linear Models |
|
This course discusses the estimation of parameters in the multiple linear regression model by the least-squares method . Topics covered include the statistical properties of the least-squares estimators, the Gauss-Markov theorem, estimates of residual and regression sums of squares, distribution theory under normality of the observations, assessment of normality, variance stabilizing transformations, examination of multicollinearity, variable selection methods, logistic regression for a binary response, log-linear models for count data, and generalized linear models.
Credits: 3.000 Levels: Graduate Schedule Types: Lecture, Final Exam |
| STAT 672 - Survey Sampling Design and Analysis |
|
This course discusses the planning and practice of sample surveys. Topics covered include simple random sampling, unequal probability sampling, stratified sampling, cluster sampling, multistage sampling, cost-effective design, analysis and control of sources of sampling and non-sampling error, ratio estimation, model-based regression estimation, resampling, and replication methods.
Credits: 3.000 Levels: Graduate Schedule Types: Lecture, Final Exam |
| STAT 673 - Experimental Design and Analysis |
|
This course discusses experimental designs and analyses. Topics covered include basic principles and guidelines for designing experiments, simple comparative designs, single factor analysis of variance, block designs, factorial designs, response surface methods and designs, nested and split plot designs, and the analysis of covariance.
Credits: 3.000 Levels: Graduate Schedule Types: Lecture, Final Exam |
| STAT 675 - Methods for Multivariate Data |
|
This course discusses practical techniques for the analysis of multivariate data. Topics covered include estimation and hypothesis testing for multivariate means and variances; partial, multiple and canonical correlations; principal components analysis and factor analysis for data reduction; multivariate analysis of variance; discriminant analysis for classification; and cluster analysis.
Credits: 3.000 Levels: Graduate Schedule Types: Lecture, Final Exam |
| STAT 699 - Special Topics in Statistics |
|
The topic for this course varies, depending on student interest and faculty availability. This course may be taken any number of times provided all topics are distinct.
Credits: 1.000 TO 3.000 Levels: Graduate Schedule Types: Lecture, Self-Directed, Final Exam, Laboratory, Seminar |
| STAT 704 - Seminar in Statistics |
|
This course comprises seminar sessions relating to applications or the theory of statistics, or both. Students investigate and present ideas and results pertaining to current research. The offerings may include presentations of current literature, statistical methodology, and topics related to the student’s own research or project work or that of others. Students participate in discussions and critiques of their and others’ presentations. This is a PASS/FAIL course. This course may be repeated to a maximum of 3 credit hours. Student must attend and participate in all seminar session to obtain credit for the course.
Credits: 1.500 Levels: Graduate Schedule Types: Seminar |
| STAT 731 - Advanced Topics in Statistics |
|
This course is intended to fulfill requirements for specialized instruction in the discipline of Statistics. Topics are chosen depending upon student interest and instructor availability, and topic headings vary from year to year and from section to section. This course may be taken any number of times provided all topics are distinct.
Credits: 1.000 TO 6.000 Levels: Graduate Schedule Types: Lecture, Self-Directed, Final Exam, Laboratory, Seminar |
| STAT 793 - Master of Science (Mathematics) Project |
|
The MSc project requires the completion of an extended position paper, report, plan or program making a contribution to, or addressing a major issue in, a scientific field. The development of the project requires the application of original thought to the problem or issue under investigation. The non thesis project does not require the development of a research design or research methodology, and need not involve the collection or generation of an original data. This is a PASS/FAIL course.
Credits: 6.000 Levels: Graduate Schedule Types: Self-Directed, Masters Project |
| STAT 794 - Master of Science (Mathematics) Thesis |
|
The MSc thesis documents a scientific contribution to the field of Statistics. Students are expected to conduct original research involving a literature review, development of a research design and methodology, testing and analysis of data, and development of conclusions. Successful defence of the thesis is required for graduation in the Master of Science (Mathematics) thesis stream. This is a PASS/FAIL course.
Credits: 12.000 Levels: Graduate Schedule Types: Self-Directed, Masters Thesis |
|