Description: Non-linear relationships refer to interactions between variables that cannot be described by a straight line on a graph. In this context, the behavior of a dependent variable does not follow a direct proportional relationship with an independent variable. Instead, these relationships can take various forms, such as curves, parabolas, or exponential functions. This type of relationship is fundamental in statistical analysis, as many real-world situations do not fit simple linear patterns. For example, the relationship between temperature and the amount of ice that melts is not linear; at higher temperatures, ice melts more quickly, but not at a constant rate. Non-linear relationships are essential for modeling complex phenomena in various fields, including economics, biology, and engineering, where interactions between variables can be influenced by multiple factors and do not behave predictably. To analyze these relationships, advanced statistical techniques are used, such as polynomial regression or machine learning models, which allow capturing the complexity of the data and providing more accurate predictions.
