Regression Observation for Mathematical Model Development in Dynamic System

Abstract

Developing mathematical models for dynamic systems requires careful observation and analysis of data to establish the relationships between variables. In this study, we focus on the critical stage of regression observation to refine the model's accuracy and predictive capabilities. We employ a systematic approach to gather relevant data and apply regression techniques to uncover underlying patterns and dependencies within the dynamic system. The study emphasizes the importance of accurate parameter estimation and the identification of significant factors influencing system behavior. Through the process of regression observation, we improve our understanding of the dynamic system, enabling the development of more robust and effective mathematical models. This research contributes to the fields of system dynamics and modeling, providing valuable insights for a wide range of applications, from engineering to economics.