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An existing mapping method [ 26, 27] was used to highlight through a graph the most important links among variables, using a mathematical approach based on an artificial adaptive system called Auto Contractive Map-Auto-CM algorithm.
An existing mapping method (21) was used to graphically highlight the most important links among variables, using the Auto Contractive Map algorithm, which is a special kind of ANN able to find the consistent patterns and/or systematic relationships and hidden trends and associations among variables.
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Accordingly, this study devises a multilevel model for examining the links among contextual variables and collects data via a multi-informant questionnaire design.
In order to decipher potential causal links among these variables, the IAMB algorithm (efficient constraint-based algorithm based on the inductive causation algorithm) in conjunction with Fisher's Z test to assess for conditional independence (α = 0.05) were used to infer the functional relationships involving these 10 phenotypic and expression traits.
Structural equation modeling was used to test hypothesized relationships among variables linked to expertise in physics problem solving including motivation, metacognitive planning, strategy use, categorization skills, and free-body diagrams.
In the second equation of the SEM, the 'structural' part of the SEM, we modeled the links among the 28 latent variables.
Exploratory analysis, such as data mining; information visualisation and machine-learning of big databases solely yields possibly spurious associations among variables and not causal links.
Multicollinearity among variables that are geographically linked may be considerable and may interfere with the detection of the most relevant ones.
In this way, statistical analyses, which deduce the correlated variation among variables, and deterministic mathematical models, which can show how those variable are causally linked, can work together to elucidate underlying biological function.
Moreover, the existing power allocation schemes are derived under an ideal channel assumption that channel coefficients for multiple links among the communication nodes are independent random variables following identical distributions.
There were no significant interactions among variables.
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