Description: Non-linear effects in a statistical model refer to relationships between variables that are not additive or proportional. This means that the impact of one variable on another cannot be simply described as a sum of individual effects, but rather the interaction between the variables can generate complex and unpredictable outcomes. In statistical terms, a linear model assumes that a change in an independent variable produces a constant change in the dependent variable. However, in many real-world phenomena, this assumption is overly simplistic. Non-linear effects can manifest in various ways, such as curves, interactions, or threshold effects, where the effect of one variable may change depending on the level of another. This complexity is crucial for a more accurate understanding of the data and for building more robust predictive models. Identifying and analyzing these effects requires advanced statistical techniques, such as polynomial regression, generalized additive models, or neural networks, which allow capturing the intrinsic nature of relationships between variables. In summary, non-linear effects are fundamental for a more faithful representation of reality in statistical analysis, as they reflect the complexity of interactions in the data.
