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Course Criteria
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3.00 Credits
Survey of topics in data analysis including data visualization, multivariate density estimation, and nonparametric regression. Advanced applications will include clustering, discrimination, dimension reduction, and bump-hunting using nonparametric density procedures.
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3.00 Credits
This course covers the theory of some of the most frequently used stochastic processes in application; discrete and continuous time, Markov chains, Poisson and renewal processes, and Brownian motion.
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3.00 Credits
Same as STAT 453 with advanced problem sets.
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3.00 Credits
Continuation of STAT 581.
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1.00 - 6.00 Credits
No course description available.
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1.00 Credits
Students participate in the process of researching professional literature (journal articles, book chapters, dissertations), preparing, delivering and critiquing talks. Literature topics change each semester.
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1.00 Credits
No course description available.
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3.00 Credits
This course will cover the following: (1) DATA step including arrays, merging, do-loop processing, if then else statements, set statements importing and exporting, space optimization, (2) PROC TABULATE and PROC REPORT, (3) Brief functions survey, e.g. random number generators, character and mathematical functions, time and data functions etc., (4) Formats, (5) Brief survey of statistical PROC's, (6) SAS ODS (Output Delivery System) from statistical procedures, (7) Output datasets from statistical procedures, (8) PROC GRAPH and Statistical Graphics Procedures (SGPLOT, SGPANEL, SGSCATTER), (9) PROC SQL (includes built-in short course on basic SQL), (10) PROC IML including functions, subroutines and optimization etc., (11) Macro programming facility. Priority registration is given to STAT majors.
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3.00 Credits
Estimation and inference in single equation regression models, multicollinearity, autocorrelated and heteroskedastic disturbances, distributed lags, asymptotic theory, and maximum likelihood techniques. Emphasis is placed on the ability to analyze critically the literature.
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3.00 Credits
This course will cover Bayesian methods for analyzing data. The emphasis will be on applied data analysis rather than theoretical development. We will consider a variety of models, including linear regression, hierarchical models, and models for categorical data.
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