The fundamental models underlying hormonal physiological regulation and homeostasis remain poorly understood. We aimed to derive quantitative evidence regarding these models from the study of population data of balance points of different parameters and their respective controlling hormones. We studied the slopes of correlations between concentrations of circulating free thyroxine and thyrotropin, calcium and parathyroid hormone, haemoglobin and erythropoietin, and glucose and insulin in such population data, as well as the slopes of the limbs of various feedback loops estimated empirically and by reverse engineering of the population data. We used computer simulations to model the factors that influence the slopes derived from the population data, and then matched these simulations with the empirically derived slopes. Our simulations showed that changes to the population distribution of feedback loop limbs may alter the slopes of correlations within population data in specific ways. Non-random (interdependent) associations of the limbs of feedback loops may also have this effect, as well as producing discrepancies between the slopes of feedback limb loops determined experimentally and the same slopes determined by derivation from population data. Our corresponding empirical findings were consistent with the presence of such interdependence in the free thyroxine/thyrotropin, haemoglobin/erythropoietin and glucose /insulin systems. The glucose/insulin data provided evidence consistent with increasing interdependence with age in childhood. Our findings therefore provide strong evidence that the interdependence of the limbs of feedback loops is a general feature of endocrine homeostatic regulation. This interdependence potentially bestows evolutionary homeostatic and regulatory advantages.