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Course Criteria
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3.00 Credits
Credit Hours: 3.00. Stochastic interest rate models. American options from the probabilistic and PDE points of view. Numerical methods for European and American options, including binomial, trinomial, and Monte-Carlo methods. Typically offered Fall. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. This is an introductory/interdisciplinary (master's level) quantitative trait locus (QTL) mapping course. QTL mapping is associated with the statistical analysis of genetic/genomic data and is considered part of the general science known as bioinformatics. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Least squares analysis of linear models. Gauss Markov Theorem. Estimability and testability of parameters. Confidence regions and prediction regions. Introduction to design of experiments. Analysis of variance. Factorial and block designs. Analysis of random, fixed, and mixed models. Components of variance. Distribution of linear and quadratic forms in normal vectors. A firm background in matrix algebra and some previous exposure to linear models or analysis of variance is desirable. Typically offered Fall. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. A practical and data oriented course on response surface methodology, requiring previous background in linear models and experimental design, Emphasis is on steepest ascent in process improvement, fitting first and second order models, elucidation of ridge systems, design aspects of variance, bias and lack of fit, and response surface alternatives to Taguchi methodology. Permission of instructor required. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. Formulation of the general statistical decision problem; foundations, utility, and prior information. Bayesian analysis including inference, decision making, empirical and hierarchical Bayes, combination of evidence, and robustness. Introduction to game theory, minimax procedures, sufficiency, admissibility, and complete classes. Typically offered Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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3.00 Credits
Credit Hours: 3.00. The use of numerical methods to obtain answers in problems arising in probability and statistics. Topics will include the use of the likelihood function, Bayesian and classical methods of estimation and testing, evaluation of probabilities, linear and nonlinear regression. STAT 532, 554, and some knowledge of computing and complex analysis are desirable. Typically offered Fall. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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1.00 Credits
Credit Hours: 1.00. This is a weekly forum for presenting both applied and theoretical work in the broad area of bioinformatics. Bioinformatics is the science of generating, organizing, and analyzing biological data. This seminar series occurs both in the fall and spring semesters and attracts speakers from Purdue University, as well as throughout the world. Students are encouraged to register for this course, and everyone else is encouraged to attend this open seminar. Typically offered Fall Spring. 1.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: Upper Division
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3.00 Credits
Credit Hours: 3.00. This course is designed to emphasize and develop the skills needed by a statistical consultant/collaborator. Topics include: problem solving, consulting session management, written and oral communication, research ethics, design of experiments, collection of data, and application of statistical methods to real problems. Class activities include actively participating in consulting sessions held by the Statistical Consulting Service, small group projects, short papers, and oral presentations. Permission of instructor required. Typically offered Fall Spring Summer. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Lecture College of Science College Statistics Department Course Attributes: Upper Division
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3.00 Credits
Credit Hours: 3.00. Students complete an internship where they will use statistical methods. A detailed report describing the internship work is required. Permission of instructor required. Typically offered Summer Fall Spring. 3.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Experiential College of Science College Statistics Department Course Attributes: CH Technical Electives, Full-Time Privileges, Internship, Upper Division
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1.00 Credits
Credit Hours: 1.00 or 2.00. Active participation in weekly consulting meetings, directed reading in the statistical literature, application of statistical methods to real problems, report writing. Permission of instructor required. Typically offered Fall Spring Summer. 0.000 TO 2.000 Credit Hours Levels: Graduate, Professional, Undergraduate Schedule Types: Individual Study, Lecture College of Science College Statistics Department Course Attributes: CH Technical Electives, Upper Division
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