When genomic information is combined with biochemical, phenotypic or genetic data, functional roles and interrelationships of components within metabolic or regulatory networks become better defined [ 1- 5].
Interrelationships between subjective scales/psychological data and sleep structure were tested by simple Pearson correlation coefficients, and interrelationships between subjective scales were tested by least squares regression analyses.
Even after almost 25 years of investigation and data correlation, Mr. Jahn is still stimulated.
In this way, pairwise correlations and higher order interrelationships among the parameters can be detected.
In this study we present an approach that is able to identify both pairwise parameter correlations and higher order interrelationships among parameters in nonlinear dynamic models.
We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models.
We verified that data reflected real interrelationships and rejected data that were mere co-incidences of textual referral.
Therefore, EFA was conducted with the purpose of exploring the empirical data to discover and detect characteristic features and interrelationships without imposing any definite model on the data.
Constraints, units of measurement and interrelationships for data values are specified in order to homogenise the data, eliminate ambiguities and allow for automated validation.
By comparing and contrasting the coded data, sub-themes, themes, interrelationships and patterns were revealed.
