In most instances, designers of digital simulation models ignore the statistical design and the statistical implications associated with these models. Most ignore the problem because of the added difficulties related to autocorrelated data. Fishman and Kiviat of the RAND Corporation suggested the use of spectral analysis as a technique for evaluating the significance of statistics emanating from a simulation model, comparing model output with 'real-world' data for model verification and comparing model outputs for two or more policy change evaluations. These authors have developed this concept into a synthesized model and verified the model through a spectral analysis on data derived from the logistics composite model (LCOM). LCOM was developed jointly by the RAND Corporation and the Air Force Logistics Command for the purpose of simulating aircraft flight and base support processes in response to mission requirements. Some forty statistics were evaluated through spectral analysis for confidence limits. Further, certain policy changes regarding inventory re-order were implemented and a spectral analysis was performed to judge whether or not the separate results were statistically different.


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    Title :

    Statistical evaluation and verification of digital simulation models


    Additional title:

    Statistische Auswertung und Verifizierung von digitalen Simulationsmodellen


    Contributors:
    Hunt, A.W. (author) / Phillips, D.T. (author)

    Published in:

    Publication date :

    1979


    Size :

    14 Seiten, 92 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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