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Course Criteria
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3.00 Credits
Shaman, Staff. Prerequisite(s): STAT 101.Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.
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3.00 Credits
May be counted as a General Requirement Course in Formal Reasoning & Analysis. Class of 2009 & prior only. Staff. Prerequisite(s): High school algebra. Basic ideas of probability and statistics. Statistical methods for the behavioral sciences, especially psychology. Topics include probability, estimation, hypothesis testing, regression.
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3.00 Credits
May be counted as a General Requirement Course in Formal Reasoning & Analysis. Class of 2009 & prior only. Staff. Prerequisite(s): STAT 111. Basic ideas of probability and statistics. Statistical methods for the behavioral sciences, especially psychology. Continuation of STAT 111. Topics are: regression, analysis of variance, experimental design, analysis of covariance.
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3.00 Credits
Staff. Prerequisite(s): MATH 114 or equivalent. Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.
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3.00 Credits
Staff. Prerequisite(s): STAT 430. Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 101 and 102.
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3.00 Credits
Staff. Prerequisite(s): STAT 430 or 510 or equivalent. An introductory course in the mathematical theory of statistics. Topics include estimation, confidence intervals, hypothesis testing, decision theory models for discrete data, and nonparametric statistics.
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3.00 Credits
Foster. Prerequisite(s): STAT 430, or permission of instructor. This course is to be a basic introduction to stochastic processes. The primary focus will be on Markov chains both in discrete time and in continuous time. By focusing attention on Markov chain, we can discuss many interesting models (from physics to economics). Topics covered include: stable distributions, birth-death processes, Poisson processes, time reversibility, random walks, Brownian motion and Black-Scholes.
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3.00 Credits
Steele. Prerequisite(s): STAT 101 - 102 or 431. Familiarity with linear algebra. This course will introduce students to the time series methods and practices which are most relevant to the analysis of financial and economic data. After an introduction to the statistical programming language S-Plus the course develops an autoregressive models, moving average models, and their generalizations. The course then develops models that are closely focused on particular features of financial series such as the challenges of time dependent volatility.
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3.00 Credits
Shaman. Prerequisite(s): STAT 102 or 112 or 431. 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
Foster. Prerequisite(s): STAT 102 or 112 or 431. 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 databased on finance, credit card data, global warming, and the "wikipedia" will be discussed.
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