The outline of the expert system for the design of experiment

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The design of experiment (DoE) is a methodology originated from early 1920s when Fisher’s papers created the analysis of variance and first known experimental designs: latin squares. It is focused on a construction of empirical models based on measurements obtained from specifically structured and driven experiments. Its development resulted in the constitution of four distinctive branches recognized by the industry: factorials (full or fractional), Taguchi’s robust design, Shainin’s Red-X®and a response surface methodology (RSM). On one hand, the well-known success stories of this methodology implementations promise great benefits, while on other hand, the mathematical complexity of mathematical and statistical assumptions very often lead to improper use and wrong inferences. The possible solution to avoid such mistakes is the expert system supporting the design of experiments and subsequently the analysis of obtained data. The authors propose the outline of such system and provides the general analysis of the ontology and related inference rules.

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  • Agresti A. 2002. Categorical data analysis. John Wiley & Sons Hoboken.

  • Bhote K. 1991. World Class Quality. AMACOM New York.

  • Box G.E.P. Wilson K.B. 1951. On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society B 13 1-45.

  • Everitt B.S. Landau S. Leese M. Stahl D. 2011. Cluster analysis 5th edition. John Wiley & Sons Hoboken.

  • Fisher R. 1918. Studies in Crop Variation. I. An examination of the yield of dressed grain from Broadbalk. Journal of Agricultural Science 11 107-135.

  • Fisher R.A. 1925. Statistical Methods for Research Workers. Oliver & Boyd Press Edinburgh.

  • Fisher R.A. 1935. The Design of Experiments. Oliver & Boyd Press Edinburgh.

  • Gentle J.E. Hardle W.K. 2012. Handbook of Computational Statistics. Springer-Verlag Berlin-Heidelberg.

  • Hilbe J.M. 2009. Logistic regression models. CRC Press Boca Raton.

  • Hosmer D.W. Lemeshow S. 2000. Applied Logistic Regression. John Wiley & Sons Hoboken.

  • Izenman A.J. 2008. Modern multivariate statistical techniques: regression classification and manifold learning. Springer New York.

  • Jackson P. 1999. Introduction to expert systems. Addison-Wesley Harlow England.

  • John P.W.M. 1998. Statistical design and analysis of experiments. SIAM Philadelphia.

  • Jolliffe I.T. 2010. Principal component analysis. Springer New York.

  • Liebowitz J. (ed.) 1998. The Handbook of applied expert systems. CRC Press Boca Raton.

  • Montgomery D.C. 1997. Introduction to Statistical Quality Control. John Wiley & Sons Hoboken.

  • Montgomery D.C. 2008. Design and Analysis of Experiments. John Wiley & Sons Hoboken.

  • Niewiadomski A. 2008. Methods for the linguistic summarization of data: applications of fuzzy sets and their extensions. AOW Exit Warszawa Poland.

  • Phadke M.S. 1989. Quality Engineering Using Robust Design. Prentice Hall International Inc. London.

  • Plackett R.L. Burman J.P. 1946. The design of optimum multifactorial experiments. Biometrika 33 305-325.

  • Scheffé H. 1958. Experiments with Mixtures. Journal of the Royal Statistical Society B 20 344-360.

  • Siegel S. Tukey J.W. 1960. A nonparametric sum of ranks procedure for relative spread in unpaired samples. Journal of the American Statistical Association 55 (291) 429-445.

  • Siler W. Buckley J.J. 2005. Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons Hoboken.

  • Stevens S.S. 1946. On the theory of scales of measurements. Science 103 677-680.

  • Yates F. 1935. Complex experiments. Journal of the Royal Statistical Society Suppl. 2 181-247.

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