After the recovery, eight experimental conditions in a cross factor design (with manganese, iron and temperature as factors) were tested in duplicate.
Complexity factors and cross factors are introduced, and the methodologies used in their measurement are reviewed.
Cross factors for the sample data and OGJ factors indicate a slightly smaller range of variation (Table 5).
However, it is obvious that complexity factors can be defined with respect to any two process technologies, for example, hydrotreating or catalytic cracking, in what we refer to as complexity cross factors, or more simply cross factors.
When using the DE algorithm for optimization, the selection of the scaling factor (F) and the cross factor (CR) is very important.
Cross factors can be inferred from known complexity factors, if available, since the distillation normalization cancels out in the numerator and denominator terms: {text{CF}}left( {U_{i},U_{j} } right) = frac{{{text{CF}}(U_{i} )}}{{{text{CF}}(U_{j} )}}.
Items with factor loading of less than 0.6, or with cross factor loadings greater than 0.35 were removed from further analysis.
Conclusions: The ICCs assuming random crossed factors understate reliability compared with previously published ICC results assuming nested factors.
The two most common building blocks used for statistically designed experiments are nested factors and crossed factors.
A nested and crossed factor design and the discriminant analysis were used for analyzing the agronomic trait variations.
We add some tricks and tips for some special data kind (haploid, single locus), some other procedure (bootstrap over loci) and how to handle crossed factors.
