Experimental design factors

Lee s study clearly showed that experimental design factors can have an enormous impact at detecting subpopulations within a population. Increasing the number of subjects in a population from 100 to 200 did... 

A one-factor-at-a-time optimization is consistent with a commonly held belief that to determine the influence of one factor it is necessary to hold constant all other factors. This is an effective, although not necessarily an efficient, experimental design when the factors are independent. Two factors are considered independent when changing the level of one factor does not influence the effect of changing the other factor s level. Table 14.1 provides an example of two independent factors. When factor B is held at level Bi, changing factor A from level Ai to level A2 increases the response from 40 to 80 thus, the change in response, AR, is... 

The experimental design for ruggedness testing is balanced in that each factor level is paired an equal number of times with the upper case and lower case levels... 

Experimental Design for a Ruggedness Test Involving Seven Factors... 

Phase transfer catalysis has been used with success to prepare N- substituted pyrazoles (78MI40403, 79MI40408, 70JHC1237, 80JOC3172) and this procedure can be considered the simplest and most efficient way to obtain these compounds. Experimental design methodology has been used to study the influence of the factors on the reaction between pyrazole and -butyl bromide under phase transfer conditions (79MI40408). 

Space Velocity. Most of our experimental data were developed with operation at a wet outlet space velocity of approximately 10,000 vol/vol hour. However, we do have data at space velocities of up to 25,000/hr. The pilot plant will operate at a space velocity of 5,000/hr while processing 1 million scf raw syngas/day. With operation on a once-through basis without recycle and at the indicated space velocities, catalyst volumes are minimum compared with other processes when identical over-design factors are used. 

Experimental design requires the analyst to identify the variables (factors) that are likely to affect the result of the analysis and to carry out experiments that allow... 

When an analytical method is being developed, the ultimate requirement is to be able to determine the analyte(s) of interest with adequate accuracy and precision at appropriate levels. There are many examples in the literature of methodology that allows this to be achieved being developed without the need to use complex experimental design simply by varying individual factors that are thought to affect the experimental outcome until the best performance has been obtained. This simple approach assumes that the optimum value of any factor remains the same however other factors are varied, i.e. there is no interaction between factors, but the analyst must be aware that this fundamental assumption is not always valid. 

As an analytical method becomes more complex, the number of factors is likely to increase and the likelihood is that the simple approach to experimental design described above will not be successful. In particular, the possibility of interaction between factors that will have an effect on the experimental outcome must be considered and factorial design [2] allows such interactions to be probed. 

Factorial design One method of experimental design that allows interactions between factors to be investigated, i.e. whether changing one experimental variable changes the optimum value of another. 

Because of the emphasis on experimental design. It Is required that a statistician serve as a member of the design team The assigned tasks and responsibilities for the statistician differ from those for the scientists The primary mechanism for obtaining the experimental design Is to require each scientist on the team to make explicit, documented, numerical predictions for all combinations of the test conditions specified In a factorial table In effect, such predictions require each scientist to quantify the effects of the experimental factors (control variables) on the dependent variable These predictions are based on the scientist s knowledge and assessment of related literature, data, experience, etc Candidate team members who are unable or unwilling to make such predictions are excluded from the team ... 

Form a complete factorial table consisting of all combinations of low (L) and high (H) values for each factor If there are n factors being considered, then the complete factorial table will consist of the 2 possible combinations This list of combinations of factor levels provides a guide for obtaining the information necessary to generate the experimental design ... 

The experimental designs discussed in Chapters 24-26 for optimization can be used also for finding the product composition or processing condition that is optimal in terms of sensory properties. In particular, central composite designs and mixture designs are much used. The analysis of the sensory response is usually in the form of a fully quadratic function of the experimental factors. The sensory response itself may be the mean score of a panel of trained panellists. One may consider such a trained panel as a sensitive instrument to measure the perceived intensity useful in describing the sensory characteristics of a food product. 

Experimental design techniques (see Chapters 21 to 26) should be used to study the relevant factors and interactions. 

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