Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations, 2. Robustness of Techniques

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Procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses are described and illustrated. These procedures attempt to detect increasingly complex patterns in scatterplots and involve the identification of (i) linear relationships with correlation coefficients, (ii) monotonic relationships with rank correlation coefficients, (iii) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (iv) trends in variability as defined by variances and interquartile ranges, and (v) deviations from randomness as defined by the chi-square statistic. A sequence of example analyses with a large model for two-phase fluid flow illustrates how the individual procedures can differ ... continued below

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Medium: P; Size: 75 pages

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Helton, J.C. & Kleijnen, J.P.C. March 24, 1999.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)
    Place of Publication: Albuquerque, New Mexico

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Procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses are described and illustrated. These procedures attempt to detect increasingly complex patterns in scatterplots and involve the identification of (i) linear relationships with correlation coefficients, (ii) monotonic relationships with rank correlation coefficients, (iii) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (iv) trends in variability as defined by variances and interquartile ranges, and (v) deviations from randomness as defined by the chi-square statistic. A sequence of example analyses with a large model for two-phase fluid flow illustrates how the individual procedures can differ in the variables that they identify as having effects on particular model outcomes. The example analyses indicate that the use of a sequence of procedures is a good analysis strategy and provides some assurance that an important effect is not overlooked.

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Medium: P; Size: 75 pages

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OSTI as DE00005004

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  • Journal Name: Reliability Engineering an System Saftey; Other Information: Submitted to Reliability Engineering and System Safety

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  • Report No.: SAND99-0064J
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 5004
  • Archival Resource Key: ark:/67531/metadc687612

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  • March 24, 1999

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  • July 25, 2015, 2:21 a.m.

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  • April 12, 2016, 6:43 p.m.

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Helton, J.C. & Kleijnen, J.P.C. Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations, 2. Robustness of Techniques, article, March 24, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc687612/: accessed November 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.