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The results showed that board density and some interactions between the experimental variables were significant factors that influenced board mechanical properties.
In order to visualize the relationship between the experimental variables and responses, the response surface and contour plots were generated from the models.
Thus, in the present study, a two-level three-factor (23) full factorial central composite design (CCD) in RSM and ANN-based models were developed to predict the relationship between the experimental variables (pH, biosorbent dose and contact time) on the biosorption efficiency (response variable).
To examine which computation is changed by the leaning, we need to make a link between the experimental variables and the computations.
Two-tailed post-hoc Student's t-tests were performed to further examine the main effects and the interactions between the experimental variables.
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The fitted polynomial equation was expressed in the form of three-dimensional surface plots in order to illustrate the relationship between responses and the experimental variables used.
Another advantage of response surface models is that it is possible to make different projections, which provide graphic illustrations of the shape of the surfaces, thus, allowing a visual interpretation of functional relations between the response and the experimental variables.
The fitted polynomial equation was then expressed in the form of three-dimensional surface plots, in order to illustrate the relationship between gelatinolytic enzyme production and the experimental variables used.
However, since the latter model is expressed in terms of dimensionless parameters, the experiment should elucidate how the experimental variables determine the transition between the various morphological states.
The experimental variables were optimized using an experimental design, taking into account all the possible interactions between them.
The gold standard of medical research is the randomized controlled trial (RCT), which assures that treatment assignment is independent of patient characteristics, eliminating selection bias and confounding and allowing one to infer a causal relationship between the outcome and the experimental variable.
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