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Course Criteria
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3.00 Credits
Multivariate distributions; correlations, multiple and partial; estimation and testing in multivariate analysis; multivariate regression analysis including regression with two or more variables subject to error; discriminating between multivariate populations; classification problems; determining the structure of multivariate observations by principle components and factor analysis. Prerequisites/Corequisites: Prerequisite: DSES 4140 or DSES 6110. When Offered: Spring term annually. Credit Hours: 3
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3.00 Credits
Statistical methods for the analysis of life-test, failure, or other durational data. Engineering applications are emphasized, but the methods are applicable to biometric, actuarial, and social science durational data. Included are basic reliability concepts and definitions; statistical life and failure distributions such as the exponential, gamma, Weibull, normal, lognormal, and extreme value; probability and hazard plotting techniques; maximum likelihood and other estimation methods. Prerequisites/Corequisites: Prerequisite: DSES 4140, or DSES 4760 (MATP 4620), or DSES 6110. When Offered: Spring term odd-numbered years. Credit Hours: 3
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3.00 Credits
Study of time series data for both description and prediction. Main emphasis on the classical Box-Jenkins approach to model identification, estimation, and diagnosis. Includes an introduction to spectral analysis. Applications to real data series, including forecasting problems and empirical comparison of alternative approaches. Use of computer packages for time series analysis. Prerequisites/Corequisites: Prerequisite: DSES 4760 (MATP 4620) or equivalent. When Offered: Spring term odd-numbered years. Credit Hours: 3
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3.00 Credits
A graduate course in basic statistics. Stresses application to common tasks such as summarizing large databases, making quick estimates, establishing relationships among variables, forecasting, and evaluating alternatives. Topics include probability, common discrete and continuous distributions, sampling, confidence intervals, hypothesis tests, contingency tables, statistical process control, multiple regression analysis. Extensive use of computers to analyze data sets. Students cannot obtain credit for both this course and DSES 4140. When Offered: Spring term annually. Credit Hours: 3
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3.00 Credits
A course on modern computational and graphical statistics. It covers topics that are currently active in real world applications including biotechnology and information technology. The topics include stochastic simulation, importance sampling, Gibbs sampling, data visualization, dimensionality reduction, model selection, data smoothing techniques, and methods for pattern recognition. Prerequisites/Corequisites: Prerequisites: DSES 4140 or DSES 4760 (MATP 4620), or DSES 6110. When Offered: Fall term annually. Credit Hours: 3
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3.00 Credits
Exposition of the philosophy and tools of exploratory data analysis. Tools include graphical techniques, data transformation, robust and resistant summaries, residual analysis, and resampling methods. Applications to the analysis of real data sets, stressing alternative analysis using statistical software. Prerequisites/Corequisites: Prerequisites: DSES 4750 (MATP 4600) and DSES 4760 (MATP 4620) or equivalent; DSES 6100 recommended. When Offered: Spring term even-numbered years. Credit Hours: 3
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3.00 Credits
Data mining is the computationally intelligent extraction of information from large databases. It is the process of automated presentation of patterns, rules, and functions from large data bases to make crucial business decisions. This course takes a multi-disciplinary approach to data mining and knowledge discovery involving statistics, rule and tree induction, neural networks, genetic algorithms, visualization and fuzzy logic. The course is project driven and puts a special emphasis on the use of computational intelligence for scientific data mining related to drug design and bioinformatics. Prerequisites/Corequisites: Prerequisite: ENGR 2600 or equivalent introductory course in statistics. When Offered: Spring term annually. Credit Hours: 3
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3.00 Credits
Analytical and computational modeling of industrial engineering problems in the areas of industrial and manufacturing logistics. Specific applications include facilities planning/design, materials handling equipment/systems, material storage/distribution systems, flow line scheduling and modeling. Prerequisites/Corequisites: Prerequisites: DSES 4770 (MATP 4700) or DSES 4610 or equivalent, and DSES 6110 or equivalent. When Offered: Fall term even-numbered years. Credit Hours: 3
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3.00 Credits
Problems of scheduling several tasks over time. Topics include measures of performance, single machine sequencing, flowshop scheduling, the job shop problem, and priority dispatching. Integer programming, dynamic programming, and heuristic approaches to various problems are also presented. Prerequisites/Corequisites: Prerequisite: DSES 4770 (MATP 4700), or equivalent. When Offered: Fall term odd-numbered years. Credit Hours: 3
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3.00 Credits
This course examines issues in concurrent engineering (CE), a product design process using extensive information and knowledge about the product's manufacture and life cycle performance, including design for manufacturing and assembly. When Offered: Spring term annually. Credit Hours: 3
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