MIE360 17 IID Concepts

# MIE360 17 IID Concepts - MIE360 Computer Modeling and...

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MIE360 Computer Modeling and Simulation Lecture Notes Daniel Frances © 2010 1 Lecture 17 – Checking if data is IID. In the lab you have learned how to build a DES simulation model and we dealt with deriving input distributions from the collected data. But before we move on, we need to warn you about the IID pitfalls There are generally three types of problems that we need to take care off. Suppose for example that we are dealing with a hospital ward and that we are trying to capture the random times between patient discharges. Since each doctor discharges patients independently you suspect these times are exponential, so you request how many patients are discharged per week, and rush to use this as the µ in your distribution. What is wrong with this? You are implicitly assuming that this rate applies both during the day and night! OK, so you only use a distribution from 9-5 each day. Still… You are implicitly assuming that discharges occur on weekends! OK so you only use the distribution 9-5 Mon-Fri. Still… Suppose that after you finally ask, you find out that 80% of the discharges occur on Friday! Your models would have totally been ignorant of the Friday rush to discharge patients, and your results would be totally misleading. While you may catch this in the validation stage it will save much

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## This note was uploaded on 09/20/2011 for the course MIE 360 taught by Professor D.frances during the Fall '10 term at University of Toronto.

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MIE360 17 IID Concepts - MIE360 Computer Modeling and...

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