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Material Detail

ESD.86 Models, Data and Inference for Socio-Technical Systems | Engineering Systems Division

ESD.86 Models, Data and Inference for Socio-Technical Systems | Engineering Systems Division

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.
Keywords:
risk assessment, Computational Modeling and Simulation, hypothesis testing, Weibull analysis, inference, probability, Probability and Statistics, probabilistic model, system design, poisson, Data Mining, modelling, statistical model, distributions, queuing theory, system analysis, regression, operations research, systems engineering, congestion, Systems Engineering, statistics, traffic, markov
Disciplines:
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More about this material

Material Type:
Online Course
Date Added to MERLOT:
May 12, 2016
Date Modified in MERLOT:
May 12, 2016
Author:
Unknown (Know the author?)
Submitter:
Barbra Bied Sperling
Hispanic Serving Institution
Primary Audience:
Graduate School
Technical Format:
Website
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States

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