Experimental factors, effect

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 ... 

Also, a chromatographic profile or fingerprint of trace unknowns can be established and monitored, so that if product performance unexpectedly changes, there will be a starting point for troubleshooting. The effects of experimental variables on sample recoveries should be measured directly by controlled variation of an experimental factor, using the reference standard, or suitable external standards, or spiked addition of an external standard to the reference standard. A detailed example of the use of internal and external standards is presented in Chapter 4. 

The target for optimization in FTA with CL detection is to adjust all experimental factors in such a way so that the detector views as much radiation as possible while the chemiluminescent solution flows through the cell. Hence the kinetics of the flow and detector system should be monitored to match the kinetics of the reaction and generate maximum intensity inside the cell. The effect of experimental variables on the CL signal cannot be exactly predicted in advance and there is not enough theoretical background to support any suggestion. 

The two-factor interaction effects and the dummy factor effects in FF and PB designs, respectively, are often considered negligible in robustness testing. Since the estimates for those effects are then caused by method variability and thus by experimental error, they can be used in the statistical analysis of the effects. Requirement is that enough two-factor interaction or dummy factor effects (>3) can be estimated to allow a proper error estimate (see Section VII.B.2.(b)). 

Another way to estimate (SE)e is using effects that are a priori considered negligible, such as two-factor interaction effects and dummy factor effects " in EE and PB designs, respectively (Equation (8)). Such effects are considered solely due to the experimental error of the method. ... 

As already mentioned, an experimental design approach is preferred to evaluate method robusmess. It is a multivariate approach, evaluating the factor effects on the responses by varying the factors simultaneously, according to the experimental conditions defined by the design. 

Full second-order polynomial models used with central composite experimental designs are very powerful tools for approximating the true behavior of many systems. However, the interpretation of the large number of estimated parameters in multifactor systems is not always straightforward. As an example, the parameter estimates of the coded and uncoded models in the previous section are quite different, even though the two models describe essentially the same response surface (see Equations 12.63 and 12.64). It is difficult to see this similarity by simple inspection of the two equations. Fortunately, canonical analysis is a mathematical technique that can be applied to full second-order polynomial models to reveal the essential features of the response surface and allow a simpler understanding of the factor effects and their interactions. 

The saturated fractional factorial designs are satisfactory for exactly 3, or 7, or 15, or 31, or 63, or 127 factors, but if the number of factors is different from these, so-called dummy factors can be added to bring the number of factors up to the next largest saturated fractional factorial design. A dummy factor doesn t really exist, but the experimental design and data treatment are allowed to think it exists. At the end of the data treatment, dummy factors should have very small factor effects that express the noise in the data. If the dummy factors have big effects, it usually indicates that the assumption of first-order behavior without interactions or curvature was wrong that is, there is significant lack of fit. 

Analysis of binding experiments required a careful comparison of (i) the MYKO 63 bands, either in the presence or absence of DNA bands and (ii) the DNA Raman bands, either in the presence or absence of MYKO 63 bands. This comparison was achieved by computer-subtracting variable amounts of one spectrum from another. Previously, the various spectra were normalized to the same relative Raman intensity, with the 934 cm band (CIO symmetric stretch) as an internal standard. The intensity of the CIO. scattering measures the combined effect of such experimental factors as counting time, optical alignment and laser power. 

When an unreplicated experiment is run, the error or residual sum of squares is composed of both experimental error and lack-of-fit of the model. Thus, formal statistical significance testing of the factor effects can lead to erroneous conclusions if there is lack-of-fit of the model. Therefore, it is recommended that the experiment be replicated so that an independent estimate of the experimental error can be calculated and both lack-of-fit and the statistical significance of the factor effects can be formally tested. 

Computation of the effect of the factors on the response(s) of the method, to derive which factors might have experimentally relevant effects ... 

Any experimenter is usually interested in obtaining an optimal response (a nominal value, a maximum or a minimum response) which is related directly to changes in the levels of the factors, but he/she needs also to guarantee a constant quality and, as a consequence, a minimum variability in the response. The main objective is then to control, although partially, the noise factors and obtain a system which is insensitive robust to them. In such a way, we can discard the noise factors in future studies. In short, a robustness design identifies the levels of the experimental factors that reduce the effect of the noise factors and consequently minimise the variability in the response in a simple and economical way. 

As discussed earlier, the two most important experimental factors in determining heterogeneous rate constants by CV are the precision in the measurement of AFp and the effectiveness with which the l u problem is dealt with. A detailed study of the kinetics of the reduction of benzo-nitrile in DMF—Bu4NBF4 (0.1 M) was carried out using derivative techniques and extensions of the correlations described by Tables 4—6 [42]. The study resulted in k° equal to 0.37 0.02 at 23.5°C for the reaction... 

Designing an isolation procedure for DNA requires extensive knowledge of the chemical stability of DNA as well as its condition in the cellular environment. Figures El3.1 and El3.2 illustrate several chemical bonds in DNA that may be susceptible to cleavage during the extraction process. The experimental factors that must be considered and their effects on various structural aspects of intact DNA are outlined below. 

Unfortunately, other experimental factors, such as contact capacitance at the junction of the cell leads and the measurement system, lead capacitance, and capacitance due to the dielectric properties of the thermostatting medium, may contribute substantially to the parallel capacitance. These effects may be minimized by proper choice of cell design and use of oil rather than water in the thermostatting bath. The art of making ac conductance measurements has been refined to a high degree of precision and accuracy, and detailed discussions of the rather elaborate procedures that are often necessary are available [9,10]. 

Subjective-effect studies require consideration of several experimental factors and control procedures. Participants must be able to comprehend and respond appropriately to questionnaires. Drugs should be administered under double-blind conditions to avoid the introduction of bias into participants reports. The participants prior drug exposure could influence responding most studies assessing abuse potential have used participants with histories of illicit drug use, though a number of studies have been conducted in healthy volunteers without histories of drug abuse.40,44-47... 

In this example, orthogonality of all factor effects has been achieved by including additional center points in the coded rotatable design of Equation 11.81. Orthogonality of some experimental designs may be achieved simply by appropriate coding (compare Equation 11.26 with Equation 11.20, for example). Because orthogonality is almost always achieved only in coded factor spaces, transformation of... 

---