Interaction of factors

Figure 2.9 Interaction of factor B and humidity in data from Table 2.1...

The discussion thus far has revealed the complex interaction of factors that contribute to the effectiveness of naps in well-controlled, laboratory-based research. 

In two-way analysis of variance with no replications, interaction of factors was part of the experimental error or more precisely of the residual variance. To separate the interaction from the residual variance or from experimental error variance, it is necessary to replicate design point, i.e. all combinations of rows and columns k... 

One way of achieving a smaller number of experiments is the use of fractional factorial designs. Such designs are possible if interactions of factors can be neglected. Then... 

From the core design we are now able to construct a general computation scheme in which we omit all the 1 and introduce a common factor, z0, as well as the interaction of pairs of factors, e.g. ziz2. The factor z0 is assumed to be present independently of the variation of the planned factors z and z2. It may, e.g., be considered as a blank value or a basic level of contamination. The interaction of factors is expected to be important in antagonistic or synergistic effects. 

When interaction of factors z and z2 is negligible it is even possible to create an incomplete or fractional factorial design 2 "1. Obviously this saves 22 = 4 measurements or experiments, the drawback is, as already mentioned, that the estimate of the major effect of z3 is confounded with the (minor) effect of interaction of z, and z2. 

The interaction of factors regulating levels of prolactin receptors [26,56] in a variety of tissues is complex and not fully understood. The topic will not be considered in detail here. The actions of prolactin itself on the process will be discussed briefly, however. In the short term, prolactin often leads to down-regulation of its own receptors, presumably as a consequence of internalization following binding of... 

The potential importance for dispersion of main effects for E and F and an EF interaction suggests that this would be a good test case for the McGrath-Lin statistic. The appropriate location model includes main effects and all interactions of factors A, E, and F. That model has zero residuals for all four observations with E and F at their low values. (As in the full factorial, our software actually computes these as machine-zero.) Thus, the McGrath-Lin statistic will indicate strong effects for all three terms. However, the presence of the zero residuals casts questions on the validity of any distributional results for the statistic. 

Considering the formal system (5.112), we observe that the triple interaction is indirectly considered here. It is doubtful that the actual results could confirm this class of interaction but if we can prove that they are present, then the plan from Table 5.21 can be suggested. Table 5.22 shows the FFE plan from the case when the first relation from the assembly (5.111) is the production relation. It is important to observe that all the binary interactions are indirectly considered in the formal system of the correlations of the obtainable and theoretical coefficients (5.113). Therefore, if the interest is to keep all the binary interactions of factors in the process model relationship, this FFE plan can be used successfully. 

Analysis of Variances and Interaction of Factors 427 Table S.SO Terminology used forthe interaction analysis using a CFE 2 plan. 

The inltinsic pathway refers to the system for coagulation that occurs from the interaction of factors circulating in the blood. It is activated when blood comes into contact with a... 

Factor C is aliased with the interaction of factors A and B (refer to text). 

---