Correlations between variables have been analyzed using the Spearman correlation tests, where values of P < 0.05 were considered statistically significant.
Correlations between variables have been analysed using the Spearman correlation tests, where values of P < 0.05 were considered statistically significant [ 23].
A full factorial design taking into account the two-level interactions between these variables has been conducted.
The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables, has been widely used in modeling and fault diagnosis of multi-energies systems.
Lepp et al. (2015: 7) argue that more research is needed in these topics because relationship between these variables has been proved but relationship does not mean, necessarily, causality: "[f]uture research should examine the many potential underlying reasons for the negative relationship identified here, including time spent studying and multitasking".
Mutual information (MI) [26], [27], a generalization of linear correlation between random variables, has been at the heart of many algorithms for correlated substitution analysis for a number of reasons.
In [12], [13] and [14] the weaker problem of finding the causal (static) relationships between the variables in the network (as opposed to reverse engineering the dynamical properties of the network, which automatically provides the dependencies between the variables) has been studied in the context of the LS-algorithm.
Although one study investigating the relationship between these variables has been previously conducted in osteoarthritic individuals, the strength of this relationship has not been explored in healthy individuals.
The correlation between input random variables has been taken into consideration.
