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Course Criteria
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4.00 Credits
Topics include basic probability, descriptive statistics, probability distributions, confidence intervals, significance testing, simple linear regression and correlation, association between categorized variables.
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3.00 Credits
Probability and the central concepts and methods of statistics including probability distributions, expectation, estimation, hypothesis testing, sampling distributions, linear models. Section 1 presents the general use in multiple disciplines; section 2 focuses on problem sets and examples in civil and environmental engineering.
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3.00 Credits
Practical applications and relevance of infrastructure risk are developed in the context of real engineering problems and phenomena, including unique systems and challenges of the gulf const area. The course starts with a survey of the roles of probability in engineering and focuses on computer-based methods, the Bayesian approach, risk analysis tools, and infrastructure safety.
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3.00 Credits
Elementary probability theory, conditional probability, independence, discrete and continuous random variables, expectation, standard discrete and continuous distributions, transformation techniques, central limit theorems, estimation, and correlation. Selected topics such as the Poisson process, Markov chains, and statistical techniques. Illustrations from engineering are emphasized.
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4.00 Credits
The three general topic areas covered in this methodology oriented course are statistical methods including regression, sampling, and experimental design; simulation based methods in statistics, queuing and inventory problems; and an introduction to optimization methods. Excel will serve as the basic computing software.
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3.00 Credits
Survey of estimation and forecasting models. Includes multiple regression time series analysis. A good understanding of linear algebra is highly desirable. Required for mathematical economic analysis majors.
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3.00 Credits
Programming techniques and tools useful in advanced statistical studies. Higher level graphical methods and exploratory data analysis.
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3.00 Credits
Advanced topics in statistical applications such as sampling, experimental design and statistical process control.
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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. Computational methods will be emphasized.
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1.00 Credits
Course covers application of statistics to bioengineering. Topics include descriptive statistics, estimation, hypothesis testing, ANOVA, and regression. Offered first five weeks of the semester.
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