Literature Review-Based Mathematical Modelling Using Matlab

A review of literature on modeling using linear and nonlinear regression with multiple decision variables reveals its significance across diverse fields. Linear regression is widely regarded as a foundational method, offering a straightforward approach to understanding relationships between dependent and independent variables. Studies emphasize its utility in identifying trends and making predictions, especially in scenarios with linear associations. Nonlinear regression, by contrast, addresses complex, non-linear patterns, proving valuable in contexts where linear models fall short. The inclusion of multiple decision variables further enhances model accuracy, allowing researchers to analyze interactions and their combined effects on outcomes. Researchers frequently highlight the need for appropriate model selection, balancing simplicity with precision to suit specific datasets. This dual approach provides a robust framework for exploring and predicting multifaceted relationships in empirical research.