Description: The non-additive model is a statistical approach used to analyze the interaction between multiple variables. Unlike additive models, where the total effect of the variables is assumed to be simply the sum of their individual effects, a non-additive model recognizes that variables can interact in complex ways, generating effects that are neither linear nor simply cumulative. This type of model allows for capturing more realistic relationships in the data, where the impact of one variable may depend on the level of another. For example, in a study on various phenomena, the effect of different influencing factors may not be merely the sum of their individual contributions, but their interactions can significantly influence the outcomes. Non-additive models are essential in fields such as biology, economics, and psychology, where interactions between variables are common and relevant for understanding complex phenomena. By using techniques such as polynomial regression, mixed-effects models, or machine learning models, researchers can gain deeper and more accurate insights from the data, enabling them to make more informed predictions and evidence-based decisions.
