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Five main factor interactions were studied: four soil textures, two field slopes, three ISOS, six filter vegetation types, and ten filter lengths.
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The resolution of this factorial design allows us to estimate all the main effects, factor interactions and pure quadratic effects of the four design factors selected to perform this study.
In all post hoc analyses, all main effects and factor interactions that were unchanged from the main GLM were not reported, and any non-significant main effects and interactions were removed and the model rerun without them.
Several significant main factors and interactions were revealed.
The effect size of the main factors or interactions was estimated by partial η2-values.
Main factors and interactions of the experimental factors were included in the model.
In order to examine the presence of any non-linearity in the behavior of the significant main factors and interactions, we conducted 3-level fractional factorial experiments.
After identifying the significant main factors and interactions in the 2-level experiment, with the low transmissibility scenario, we developed a regression model for the total number of infected.
Two-way ANOVA was performed with the NOD agonists (VEH, MDP, FK565) and LPS (VEH, LPS) as the between subject variables in order to reveal significant main factor effects or interactions denoted as NOD × LPS interactions.
This result indicated that temperature was the main factor, and the interactions of other parameters were discussed in detail.
Response surface methodology coupled with central composite design (CCD) was used to investigate the exact effect of main factor and their interactions.
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