A Multi-disciplinary Investigation of Linearization Deviations in Different Regression Models

Abstract

Aim: This study's objective is to examine linearization deviations in various regression models using a multidisciplinary approach. Methods: Curve estimate models' accuracy for each type of data was tested using social, financial, and medical data sets. Results: Although the power of a given model reduces with non-normal distributions, linearization in the social sciences is more efficient and has less variation from regression points in parametric equations. However, single-center distributions in the social sciences typically lead to nonparametric distributions. When compared to social sciences and health sciences, the effectiveness of linearization in financial sciences is higher. The original essence of financial techniques and models is frequently present, and they test their presumptions. Financial models and assumptions are more linear than those seen in real life since they correspond to artificial systems that individuals have developed, making them better suited for predetermined formulas. The rise in linearization issues in the field of finance, which has been increasingly common in recent years, is a symptom of this together with behavioral finance. Studies in the realm of health and well-known models were discovered to have the highest linearization deviations. Exponential or growth functions exhibit the highest linearization deviations in processes like growth, proliferation, and the spread of disease or pandemics. The data display considerable departures from normality and linearization, particularly in animal trials with very small statistical units or research conducted on a particular population. Conclusion: Despite research on R2's explanatory capacity in regression, there aren't enough studies in various fields that concentrate on R2's departures from linearization. Additionally, no study was located in which the subject's mathematical foundation was examined and cross-compared across various data sets. As a result of this feature, the research represents a field first. The research's ability to pragmatically assess the distinctions between disciplines, made possible by its multi-disciplinary nature, is another unique aspect of the work.