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All correlation coefficients between variables were significant during the course with instruction, while the coefficients were not significant for the course without instruction.
No interactions between variables were significant.
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If the interaction between variables was significant by two-way ANOVA, Bonferroni post tests were used to determine each variable's effect on colonization outcome.
Some two-way interactions between predictor variables were significant (Table 2).
No correlations between these variables were significant when split by both year and method (all r < 0.48, N = 17 18, all P > 0.05).
A series of 4 × 2 × 4 × 2 (PAINTING × HEMISPHERE × REGION × GROUP) ANOVAs with repeated measures were computed to determine whether the differences in ApEn between the variables were significant.
By keeping another variable at its zero level, these types of contour plots visualized whether the interactions between the two variables were significant or not.
The results showed that board density and some interactions between the experimental variables were significant factors that influenced board mechanical properties.
As displayed in the illustrated model in Figure 2, the paths between all three variables were significant and the direct path became non-significant under the influence of the mediator.
The 66 correlation coefficients between all movement variables were significant at 0.05% level using the Bonferroni adjustment.
The models suggested that species richness/composition and temperature were more influential on MPB than CO2, but the interactions between the explanatory variables were significant.
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CEO of Professional Science Editing for Scientists @ prosciediting.com