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The negative coefficient of this variable (pH) indicated that level of the pollutant removal decreased as the pH increased from 3 to 5. The negative coefficient of the interaction between variable x1 and x2 indicated that a simultaneous increase in H2O2-to-Fe(II) ratio with decrease in the pH of the reaction led to an increase in the COD, TP and color removal.
Similarly, consomic strains generated with Y chromosomes in various genetic backgrounds revealed that fitness was dependent on the interaction between variable Y chromosomes and the genetic background [ 95].
For example, three of the benchmark problems include an additional constraint of type { g i, j ∈[−3, 3], g i, j ≠0} (there is an interaction between variable i and j but the direction is unknown).
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The methodology captured the interaction between variables.
The methodology also captured the interaction between variables which enabled exploration of the retention mechanism involved.
Two key areas; interaction between variables and concept transference were identified.
In addition, significant interaction between variables X1 and X3 was shown.
Besides confounding effect of other variables, we also tested for interaction between variables.
There was no interaction between variables included in the logistic regression model.
There was no significant interaction between variables.
2) Interaction between variables was not significant (P > 0.05).
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CEO of Professional Science Editing for Scientists @ prosciediting.com