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Course Criteria
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3.00 Credits
Introduction to the structure and techniques that are the foundations of probability. Emphasis on applications to real world problems and case studies, possibly involving DNA matching, sports statistics, forecasting, lotteries and epidemics. Interface of probability and inference. Prereq: MA 113 or MA 123.
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3.00 Credits
Set theory; fundamental concepts of probability, including conditional and marginal probability; random variables and probability distributions (discrete and continuous); expected values and moments; moment-generating and characteristic functions; random experiments; distributions of random variables and functions of random variables; limit theorems. Prereq: MA 213 or equivalent. (Same as MA 320.)
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3.00 Credits
Simple random sampling; point and interval estimation; hypothesis testing. Prereq: STA/MA 320.
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4.00 Credits
Introduction to statistical methodology appropriate for data that fail to meet the assumptions of parametric inference. Familiarity with classical sampling techniques as well as modern sampling practice. Emphasis on applications to real-world problems and case studies, possibly involving questionnaire construction, random digit dialing, response bias, use of modern sampling software, categorical regression, and skewed data. Prereq: STA 291 and STA 295; or STA 321.
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1.00 Credits
An integrated lecture and demonstration computational laboratory course in the theory and techniques of data analysis and error propagation. An emphasis is given to applications common to physical sciences: curve fitting, statistical methods of data analysis, systematic uncertainties, and both independent and correlated errors in several variables. Prereq: PHY 242. (Same as PHY 335.)
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3.00 Credits
Probability; population and sample distributions; sampling; hypothesis testing; regression on one variable; quality control. Prereq: MA 213.
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3.00 Credits
A continuation of MA 416 with topics selected from stochastic models, decision making under uncertainty, inventory models with random demand, waiting time models and decision problems. Prereq: CS/MA 416G and MA/STA 320, or consent of instructor. (Same as MA 417G.)
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4.00 Credits
Theory of least squares; regression; analysis of variance and covariance; experimental design models; factorial experiments; variance component models. Lecture, three hours; laboratory, two hours per week. Prereq: STA 291 and STA 295; or STA 321.
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4.00 Credits
Summary statistics, graphical methods, point and interval estimation, hypothesis testing, experimental design, simple and multiple regression, covariance and ANOVA as a special case of regression, categorical data analysis. Lecture, three hours; laboratory, two hours per week. Prereq: Graduate standing in Statistics.
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3.00 Credits
Mathematical and computational aspects of linear programming and combinatorial optimization. Linear optimization is introduced by presenting solution techniques (primal and dual simplex) and studying geometric properties and duality for linear systems of inequalities. Asics of combinatorial optimization, including trees, paths, flows, matchings, and matroids, and the corresponding algorithms are presented. Prereq: A course in linear algebra or consent of instructor. (Same as MA 515.)
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