Effect of factor

Quantify effects of factors and interactions Account for probabilities of recovery trnm errors... 

Little is known about the cumulative effects of factors such as prolonged working hours and extra mural demands on workers and how such demands interact with workers performance reserves and productivity. 

Parameter in Equation 18.1 accounts for the lumped effects of factors... 

ANOVA was developed by Fisher [1925,1935] as a statistical procedure that investigates influences (effects) of factors on a target quantity y according to a linear model which holds in the simplest case... 

Methods of variance analysis are helpful tools to evaluate effects of factors on the results of experiments afterwards. On the other hand, it may be advantageous to plan experiments in a comparative way (comparative experiments). 

In vivo the corresponding parameter to a K is A iiv, a parameter that is determined directly from in vivo experiments. Theoretically, K and K should be equal, but for the reasons outlined above they may not be. K is a direct measure of the molecular interaction of the drug with the enzyme. A iiv on the other hand is a measure of the actual in vivo effectiveness of the inhibitor. That is /(" iv, unlike Kj, automatically incorporates into its value the effects of factors such as differences in active site inhibitor concentration,... 

Effects can be estimated from the measured or corrected design responses, depending on the absence or presence of drift in the considered response. When drift is absent, both effect estimates are similar, while in the presence of drift, they are different for the factors mostly affected by the drift. The effect of factor X, Ex, on a response Y is calculated with the following equation -i" i" " ... 

For example, the effect of factor A on response CR t at ot = 0.05 was found significant when using the variance from duplicated design experiments to estimate the critical effect (see Table 11). However, since this factor represents different CE equipments, i.e., is discrete, calculating a non-significance interval is irrelevant. 

FIGURE 7 Experimental domains when examining (a) two and (b) three factors with either the OVAT or an experimental design approach. ( ) Nominal level Ex, effect of factor X. 

FIGURE 8 Standardized Pareto chart, tx, standardized effect of factor X tcriticai> critical t-value. 

The classical main effects of factors B and C can also be calculated in this manner ... 

The effects of factors 1-8 on the polycondensation of monomers of the type XSiYj may be summarized as follows. 

When using predefined values [4] no statistical test is performed to identify relevant factors. So-called chemically relevant effects of factors are identified by comparing them with predefined critical values for the responses. 

In this method the effect values Ex(%) are compared with predefined values to identify relevant factors. These predefined values do not represent the limit of statistical significance but the limit of chemical relevance. These limits represent acceptable variations that are allowed to occur in practice. A list of these predefined values for the effect of factors on responses measured in HPLC is shown in Table 3.20. 

Examination of the main effect of factor D reveals that it is identical to this main effect. This can be done for all two factor interaction effects to reveal the confounding pattern shown in Table 5.13. 

The effect of factors which affect the a factor, such as growth temperature or solute-solvent interaction, may be observed by comparing the surface micro-... 

Interactions can involve more than two factors. If we explain an interaction of A x 8 by saying that the effect of factor A is not the same at all levels of factor 6, then an A x 8 x C interaction simply means that the effect of A and B jointly is not the same at all levels of C. You can build up step-by-step to higher order interactions. 

The net effect of each factor can be calculated as the mean of the responses obtained when the factor is in its high level (-I-) minus the mean of the responses obtained when the factor is in its low level (—). Thus, denoting as Yi the response of run i, the effect of factor A in a 2 factorial design is given by... 

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