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Course Criteria
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3.00 - 4.00 Credits
Statistics instruction in existing courses at appropriate level for graduate students enrolled in a degree program who need knowledge of Statistics for their field of study. May b repeated for credit. May not be taken by undergraduate students in any field nor by graduate students in Statistics. \DFI Prerequisites & Corequisites: Prerequisites: \FS Approval of department of student’s graduate program and approval of Department of Statistics. Credits: 3 to 4 hours
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3.00 Credits
A course in statistical computation using Excel software. Topics will include: data management and manipulation, numerous types of graphical presentations, descriptive statistics for one and several variables, categorical variables and tables, multiple analyses, macro programming, and simulations. Excel results to be organized in high quality reports and presented on the web. Prerequisites & Corequisites: Prerequisite: MATH 1100 or satisfactory score on the Mathematics Department placement exam. Students cannot receive credit for both STAT 3030 and STAT 5030. Credits: 3 hours
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3.00 Credits
A first course in probability for upper division and graduate students interested in applications. Topics will include: probability spaces, expectation, moment generating functions, central limit theorem, special discrete and continuous distributions. Applications will include reliability and production problems, and Markov chain methods. Prerequisites & Corequisites: Prerequisite: MATH 2720. Credits: 3 hours
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3.00 Credits
An applied treatment of multivariate procedures is presented. Classical procedures such as Hotelling's T-squared methods are discussed for the one and two sample problems and MANOVA for standard designs. Topics that will be accentuated are principal components, discriminant analysis, cluster analysis, and factor analysis. Emphasis will be on graphical methods and applications. Prerequisites & Corequisites: Prerequisites: an introductory course in statistics and a course in linear algebra. Credits: 3 hours
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4.00 Credits
A first course in statistical theory. Topics include: random variables, distributions of statistics, limiting distributions, elementary theory of estimation, and hypothesis testing. Prerequisites & Corequisites: Prerequisites: MATH 2300; STAT 3640 and (5600 or 4600). Credits: 4 hours
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3.00 Credits
This course consists of a broad overview of the techniques of survey data collection and analysis and contains a minimum of theory. Topics may include: simple random, stratified, systematic, single-stage cluster, and two-stage cluster sampling; ratio and regression estimation; subpopulation analyses; problems of nonresponse; surveys of sensitive issues; minimization of survey costs; sample size determination. Real surveys are discussed and actual survey data are analyzed. Prerequisites & Corequisites: Prerequisite: An introductory statistics course and consent of instructor. Credits: 3 hours
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3.00 Credits
This course covers statistical methods useful for improving the quality of products and systems in an industrial setting. It provides a comprehensive set of tools to use in building better products and in reducing manufacturing and other costs. The focus will be on solving real engineering problems through case studies. Taguchi methods will be discussed along with modifications from standard statistical practice. Topics will include planning and experiment, experimental strategy, Analysis of Variance concepts, factorial designs, orthogonal arrays, loss functions, signal-to-noise ratios, identifying significant factor effects, graphical methods, parameter design and tolerance design. Prerequisites & Corequisites: Prerequisite: An introductory course in statistics. Credits: 3 hours
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3.00 Credits
This course presents a broad overview of statistical methods commonly referred to as nonparametric or distribution-free methods. Topics include: inferences for proportions, contingency tables, goodness of fit problems, estimation and hypothesis testing based on ranking methods, measures of rank correlation, efficiency. Emphasis will be on the application of nonparametric statistical methods to data from many different applied fields. Prerequisites & Corequisites: Prerequisite: An introductory statistics course. Credits: 3 hours
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4.00 Credits
A course in experimental design and the analysis of variance with particular emphasis on industrial experiments. Topics include: complete randomized, randomized complete block; Latin square, and split-plot designs; orthogonal contrasts and polynomials; multiple comparisons; factorial arrangement of treatments; confounding; fractional replication. The course is molded around the complete analysis of good applied problems. Prerequisites & Corequisites: Prerequisite: An introductory statistics course. Credits: 4 hours
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3.00 Credits
An applied course in regression analysis; simple and multiple linear regression; resolution of fit of a model, including residual analysis, precision of estimation, and tests of general hypotheses; model building; step-wise regression; use of indicator variables; non-linear regression. Prerequisites & Corequisites: Prerequisite: An introductory statistics course. Credits: 3 hours
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