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Course Criteria
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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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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Includes moving averages, smoothing, Box-Jenkins (ARIMA) models, testing for nonstationarity, model fitting and checking, prediction and model selection, seasonal adjustment, ARCH, GARCH, cointegration, state-space models. Statistical packages used throughout the course. Letter grade only (A-F). (Lecture 3 hrs.) Not open for credit to students with credit in MATH 582.
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3.00 Credits
Prerequisites: STAT 381 or consent of instructor. Lifetime distributions, hazard and survival functions, censoring and truncation, Kaplan Meier and Nelson-Aelen estimators, Cox proportional hazard models, m-sample tests, goodness-of-fit tests, Bayesian survival analysis, analysis of multivariate survival data, exploring longitudinal data designs and models, clinical trials. Letter grade only (A-F). (Lecture 3 hrs.)
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3.00 Credits
Prerequisites: Consent of instructor.Topics of current interest from statistics literature. Letter grade only (A-F). Course may be repeated to a maximum of 6 units with different topics. (Lecture 3 hrs)
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3.00 Credits
Prerequisites: Consent of instructor. Presentation and discussion of advanced work in applied statistics. May be repeated to a maximum of six units. Letter grade only (A-F).
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1.00 - 3.00 Credits
Prerequisites: Consent of instructor. Research on a specific area in applied statistics. Topic for study to be approved and directed by a statistics faculty member.Letter grade only (A-F).
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1.00 - 6.00 Credits
Prerequisites: Advancement to candidacy. Formal report of research or project in mathematics. May be repeated to a maximum of 6 units. Letter grade only (A-F).
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