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Course Criteria
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3.00 Credits
Studies linear algebra and related numerical algorithms important to statistics, including linear least-squares, eigenvalues and eigenvectors, QR decomposition, singular value decomposition, and generalized matrix inverses. (SI)
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3.00 Credits
This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Basic knowledge of R is required. Prerequisites: Concurrent enrollment in STAT 5120 or consent of instructor.
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3.00 Credits
The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Prerequisites: Introductory statistics course.
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3.00 Credits
This introductory, graduate level course will provide an introduction Bayesian methods with emphasis on modeling and applications. The topics to be covered include methods for forming prior distributions such as conjugate and noninformative priors, derivation of posterior and predictive distributions and their moments, and development of Bayesian models including linear regression, generalized linear models and hierarchical models. Prerequisites: At least one semester of mathematical statistics (STAT 3120 or 5190) and one course in linear models (STAT 5120 or equivalent), or instructor permission.
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1.00 - 4.00 Credits
This course provides the opportunity to offer a new topic in the subject area of statistics.
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1.00 Credits
This course, the laboratory component of the department’s applied statistics program, deals with the use of computer packages in data analysis. Enrollment in STAT 598 is required for all students in the department’s 500-level applied statistics courses (STAT 501, 512, 513, 514, 516, 517, 520). STAT 598 may be repeated for credit provided that a student is enrolled in at least one of these 500-level applied courses; however, no more than one unit of STAT 598 may be taken in any semester. Corequisite: 500-level STAT applied statistics course.
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3.00 Credits
Studies topics in statistics that are not part of the regular course offerings.
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3.00 Credits
Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression.
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3.00 Credits
Introduction to the concepts of statistics via the establishment of fundamental principles which are then applied to practical problems. Such statistical principles as those of sufficiency, ancillarity, conditionality, and likelihood will be discussed.
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3.00 Credits
A rigorous mathematical development of the principles of statistics. Covers point and interval estimation, hypothesis testing, asymtotic theory, Bayesian statistics, and decision theory from a unified perspective.
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