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Appendix C: Experimental Design Strategies
Pages 109-114

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From page 109...
... The main goal of a factorial design is to study the main effects of factors involved in the design. An experimenter may also be interested in studying the two factor interaction effects or even higher-order interactions.
From page 110...
... SSE / 8(n − 1) As we see from the previous discussion, if the number of factors k in a 2k factorial design increases, the total number of runs in a complete factorial design outgrows the resources of most experimenters.
From page 111...
... 25 ½ fractional replicate designs can estimate unconfounded main effects and two-factor interactions, but three-factor interactions may be confounded
From page 112...
... Resolution IV 2k fractional factorial designs include 24-1, 26-2, 27-2, 27-3, 28-3, 28-4, 29-3, 29-4 designs, where for example, a 29-4 design reduces the total number of experimental conditions (i.e., factor level combinations) from 29 = 512 to a far more manageable 25 = 32 and still permits estimates and tests of main effects that are unconfounded by two-factor interactions.
From page 113...
... and are also provided online by the National Institute of Standards and Technology at handbook/pri/section3/pri3347.htm. Response Surface Methodology The previous discussion of fractional factorial designs is based on discrete levels of each factor (e.g., high or low, experimental or control)
From page 114...
... Once the parameters have been estimated, we can use the estimated response surface to evaluate the values of x1 and x2 for a specific targeted value of y0 -- for example, a carcinogenetic threshold. A contour plot may help in this regard to estimate levels of x1 and x2 corresponding to a particular level of carcinogenic risk.

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