Sensitivity Analysis, Validation, and Other Issues

  • Thomas J. Santner
  • Brian J. Williams
  • William I. Notz
Part of the Springer Series in Statistics book series (SSS)

Abstract

In this section we discuss sensitivity analysis. In general, sensitivity analysis is the study of how variation in an observed response can be apportioned to different possible sources or factors. In computer experiments, the “observed response” is the output of the computer code and the “factors” are inputs to the code. In other words, sensitivity analysis tries to determine how variable the output is to changes in the inputs. There are many applications of sensitivity analysis and Saltelli, Chan and Scott (2000) discuss these in detail. One application of sensitivity analysis is the following. Suppose we collect data to determine whether a response depends on any of several factors. We wish to identify which factors are responsible for most of the variation in the response and which produce little variation in the response over some specified range of values of these factors. Sensitivity analysis provides methods that can be used to accomplish this.

Keywords

Sensitivity Index Spot Weld Latin Hypercube Sample Estimate Regression Coefficient Main Effect Plot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Thomas J. Santner
    • 1
  • Brian J. Williams
    • 1
  • William I. Notz
    • 1
  1. 1.Department of StatisticsOhio State UniversityColumbusUSA

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