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The Decomposition of Effects in Full Factorial Experimental Design into Individual Treatment Combinations

Summary: [This abstract is based on the author's abstract.]Traditional experimental design provides information on main effects and interactions that may be difficult to interpret because average effects, or contrasts, are being calculated. Experimenters use a number of techniques to interpret the data, but higher order interactions may be difficult to identify. The decomposition of all effects into their individual treatment combinations is examined and resulting linear equations are discussed. It is shown how significant effects that could otherwise be missed can be identified, and how higher-order effects can be identified without the need to transform the data. Simple models can be derived through the use of decomposed effects, even when analyzing complex systems. The method enables main effects to be evaluated without interference from interactions and provides a solution to a critical mix.

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  • Topics: Design of Experiments
  • Keywords: Contrasts,Critical values,Design of experiments (DOE),Factorial designs,Interactions,Main effects
  • Author: Orzsulik, Stefan T.
  • Journal: Quality Engineering