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A perspective on statistical experimental design

The utility of statistical experiment designs are many fold. First of aU, a priori design of experiments requires that the researcher carefully consider the dependent and independent parameters. Second, crosscorrelations between the independent parameters can be explored. Last but most important of aU, the designed experiments minimize experimental effort while maximizing the obtained information. Several experimental design protocols and procedures exist in the literature, and the interested reader is directed to the textbooks by Montgomery and Runger (1994), Box et al. (2005), and Lazic (2004). [Pg.218]

Classical experimental designs require the investigation of one parameter at a time. In contrast, full factorial designs include aU design points and when combined with the statistical methods of data analysis provide maximum amount of information with the minimum amount of experimentation. [Pg.218]

Suppose that you are looking for the composition of a catalyst in terms of support, active material, and promoter. A catalyst screening test for a combination of all parameters may require a large number of expensive experimentation and a large number of samples. Instead, factoring out these three independent parameters in 2 full factorial design (Table E7.1.1) will enable you to determine the focal point of the optimum composition. The measured variable is the reaction rate. Careful measures should be taken to determine the reaction rate free from artifacts which will be explained in the later sections of this chapter. [Pg.219]

In this scheme, the experimentalist has to choose two levels of each variable labeled as (-) and (+). The first experiment, which is conducted with all (-) levels of all independent variables, is called the reference trial. The basic effects and mutual interactions are determined from simple algebraic relationships given below  [Pg.219]

Experiment Number Variable Xi Variable Xz Variable X3 Response [Pg.219]


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