Simulation and Statistical Analysis

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Simulation and Statistical Analysis is concerned with the study of processes and systems. When modeling an process or system there is often an uncertainty factor present. Such uncertainty is often caused by the random nature of certain factors that affect the process/system. In order to properly analyze a model it is important to correctly model any uncertainty that is present. Once the right model is in place various scenarios can be simulated, using Monte Carlo simulation, to gain insight. The results of such analyses need to be properly interpreted and uncertainty has to be reduced. The modeling, implementation, analysis and technical aspects will all be discussed in this course. The emphasis will be on discrete even simulation. After completing this course the students will be familiar with the essentials of simulation, such as the model cycle, discrete event simulation, output analysis and experimental design. Students will be able to employ simulation as a tool for evaluation.