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Course Criteria
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3.00 Credits
Foster. Prerequisite(s): STAT 621 or equivalent. This is a course in modern methods in statistics. It will focus on regression, time series, data mining and machine learning. The regression module will extend your knowledge of building multiple regressions. The time series module will introduce you to some ideas in finance and forecasting. The last two modules will show how these ideas can be applied to large data sets that are more frequently found in the modern age. Throughout the class data based on finance, credit card data, global warming, and the "wikipedia" will be discussed.
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3.00 Credits
Shaman. Prerequisite(s): STAT 621 or equivalent. This course provides an introduction to the wide range of techniques available for statistical forecasting. Qualitative techniques, smoothing and decomposition of time series, regression, adaptive methods, autoregressive-moving average modeling, and ARCH and GARCH formulations will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.
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3.00 Credits
Stine. Prerequisite(s): STAT 511 or STAT 621 or equivalent. Fundamentals of modern decision analysis with emphasis on managerial decision making under uncertainty and risk. The basic topics of decision analysis are examined. These include payoffs and losses, utility and subjective probability, the value of information, Bayesian analysis, inference and decision making. Examples are presented to illustrate the ideas and methods. Some of these involve: choices among investment alternatives; marketing a new product; health care decisions; and costs, benefits, and sample size in surveys.
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3.00 Credits
Staff. Prerequisite(s): STAT 531 or equivalent. The topics covered will change from year to year. Typical topics include the theory of large deviations, percolation theory, particle systems, and probabilistic learning theory.
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3.00 Credits
Staff. Prerequisite(s): OPIM 930 or equivalent. Martingales, optimal stopping, Wald's lemma, age-dependent branching processes, stochastic integration, Ito's lemma.
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3.00 Credits
Stine. Prerequisite(s): STAT 511 or 541 or equivalent. Fourier analysis of data, stationary time series, properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Discussion of applications to problems in economics, engineering, physical science, and life science.
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3.00 Credits
Staff. Prerequisite(s): STAT 511 or equivalent. Statistical inference when the functional form of the distribution is not specified. Nonparametric function estimation, density estimation, survival analysis, contingency tables, association, and efficiency.
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3.00 Credits
Staff. Prerequisite(s): STAT 511 or equivalent with permission of instructor. This course will cover the design and analysis of sample surveys. The focus of attention will be on the latter, specifically, classical analyses of random sampling, stratified sampling, cluster sampling, large sample results, and other topics as time permits and students' interests dictate.
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3.00 Credits
Small. Prerequisite(s): STAT 541 or 550 or permission of instructor. This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching, propensity score and regression methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.
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3.00 Credits
Staff. Prerequisite(s): STAT 552. Factorial designs, confounding, incomplete blocks, fractional factorials, random and mixed models, response surfaces.
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