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Factorial Experiments

  • Scott A. Pardo
Chapter

Abstract

We have emphasized the need of the EAS to construct an approximating function to relate product design features to performance measures. The EAS needs a method for choosing the different combinations of input feature/characteristic values in the most efficient manner possible. Also, sometimes the EAS is faced with the problem of deciding which smaller subset of too many input variables are most important, that is, have the greatest influence on the response. Attempting to optimize a response over many inputs may be at best difficult, if not completely impractical. The EAS will need a plan that involves the fewest number of input variable points to determine whether or not each potential input variable should or should not be investigated further. This chapter will be largely concerned with making such plans, which are termed “factorial experiments”. In this context, the input variables will often be referred to as “factors”.

Keywords

Apply Scientist Volume Yield Discrete Factor High Average Volume Product Design Feature 
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.

References

  1. Adler, J. (2010). R in a nutshell. Sebastopol: O’Reilly Media.Google Scholar
  2. Draper, N. R., & Smith, H. (1998). Applied regression analysis (3rd ed.). New York: Wiley.Google Scholar
  3. Montgomery, D. C. (2001). Design and analysis of experiments. New York: Wiley.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Scott A. Pardo
    • 1
  1. 1.Ascensia Diabetes CareParsippanyUSA

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