Potential Application of the Objective Regression Regressive Methodology

The possibility of having a methodology that allows the modeling and prediction, in the short, medium and long term, of biological, social and natural disaster processes and/or phenomena is something great. The objective of the research consisted in demonstrating the potentialities and real capacity of application of the methodology of the Regressive Objective Regression (ROR) in the different fields and branches of scientific research. In the ORR methodology, in a first step, dichotomous variables DS, DI and NoC are created. Then, the module corresponding to the Regression analysis of the SPSS statistical package (ENTER method) is executed, where the predicted variable and the ERROR are obtained; subsequently, the autocorrelograms of the ERROR variable are obtained, paying attention to the maximum of the significant partial autocorrelations, and the new variables are calculated according to the significant Lag of the PACF. Finally, these regressed variables are included in the new regression in a process of successive approximations until a white noise is obtained. Wide possibilities of modeling and forecasting in the short, medium and long term, which go beyond the modeling of infectious entities of parasitic and viral etiology, Acute Respiratory Infections, Acute Bronchial Asthma crises, forecasting of extreme meteorological disturbances, prediction of latitude and longitude of earthquakes, modeling of climatic variables, and even the own electric consumption of a municipality, province and nation. The ROR methodology has demonstrated potential and real capabilities of application in dissimilar fields and branches of science, so it is a novel contribution to the science of modeling and forecasting of variables to know the future, as well as the impact that different variables contribute to an event or phenomenon, and being universal, it can be applied anywhere in the universe.