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Course Criteria
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Properties of a random sample, convergence in probability, law of large numbers, sampling from the normal distribution, the central limit theorem, principles of data reduction, likelihood principle, point estimation, Bayesian estimation, methods of evaluating estimators, hypothesis testing, decision theory, confidence intervals. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 580.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Design of experiments to permit efficient analysis of sources of variation with application to quality assurance. Factorial and fractional factorial designs; block designs; confounding. Fixed and random effect models. Effects of departure from assumptions; transformations. Response surface techniques. Taguchi methods. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 581.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Introduction to methods of statistical quality control. Includes control charts, acceptance sampling, process capability analysis, and aspects of experimental design. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 584.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Theory and practice of sampling from finite populations. Simple random sampling, stratified random sampling, systematic sampling, cluster sampling, properties of various estimators including ratio, regression, and difference estimators. Error estimation for complex samples. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 583.
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3.00 Credits
Prerequisites: STAT 381, prerequisite or corequisite STAT 410. (Undergraduates register in STAT 450; graduates enroll in STAT 550.) Discriminate analysis, principal components, factor analysis, cluster analysis, logistic regression, canonical correlation, multidimensional scaling, and some nonlinear techniques. Statistical software used. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 483 or 593.
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3.00 Credits
Prerequisites: STAT 410, or 510, or consent of instructor. Alternatives to normal-theory statistical methods, analysis of categorical and ordinal data, methods based on ranks, measures of association, goodness of fit tests, order statistics. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 585.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Simulation modeling techniques; generation of discrete and continuous random numbers from given distributions; Monte Carlo methods; discrete event simulations, statistical analysis of simulated data; variance reduction; statistical validation; introduction to simulation languages; industry applications. Statistical packages used. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 587 or 487.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Random number generation, sampling and subsampling, exploratory data analysis, Markov chain Monte Carlo methods, density estimation and EM algorithm. Topics of current interest. Letter grade only (A-F). (Lecture 3 hrs.)
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3.00 Credits
Prerequisites: STAT 410, or 510, or consent of instructor. Basics of data mining algorithms with emphasis on industrial applications. Prediction and classification techniques such as decision trees, neural networks, Multivariate Adaptive Regression Splines, and other methods. Several software packages utilized. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 586.
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3.00 Credits
Prerequisites: STAT 410/510 or consent of instructor. Genetic algorithms, fuzzy logic, discrete choice analysis, online analytical processing, structured query language, statistical database management, and text and web mining. Topics of current interest. Letter grade only (A-F). (Lecture 3 hrs).
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