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STA 295: The Art And Practice of Probability
3.00 Credits
University of Kentucky
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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STA 295 - The Art And Practice of Probability
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STA 320: Introductory Probability
3.00 Credits
University of Kentucky
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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STA 320 - Introductory Probability
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STA 321: Basic Statistical Theory I
3.00 Credits
University of Kentucky
Simple random sampling; point and interval estimation; hypothesis testing. Prereq: STA/MA 320.
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STA 321 - Basic Statistical Theory I
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STA 322: Statistical Methods in Nonparametric Inference And Survey Sampling
4.00 Credits
University of Kentucky
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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STA 322 - Statistical Methods in Nonparametric Inference And Survey Sampling
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STA 335: Data Analysis For Physicists
1.00 Credits
University of Kentucky
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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STA 335 - Data Analysis For Physicists
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STA 381: Introduction to Engineering Statistics
3.00 Credits
University of Kentucky
Probability; population and sample distributions; sampling; hypothesis testing; regression on one variable; quality control. Prereq: MA 213.
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STA 381 - Introduction to Engineering Statistics
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STA 417G: Principles of Operations Research II
3.00 Credits
University of Kentucky
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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STA 417G - Principles of Operations Research II
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STA 422G: Basic Statistical Theory II
4.00 Credits
University of Kentucky
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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STA 422G - Basic Statistical Theory II
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STA 503: Introduction to Statistical Methods
4.00 Credits
University of Kentucky
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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STA 503 - Introduction to Statistical Methods
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STA 515: Linear And Combinatorial Optimization
3.00 Credits
University of Kentucky
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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STA 515 - Linear And Combinatorial Optimization
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