Modeling non-linearity often requires more sophisticated statistical techniques compared to linear models. Some common approaches include:
Polynomial Regression Polynomial regression can capture curved relationships by including polynomial terms (e.g., squares, cubes) of the predictor variables in the model.
Generalized Additive Models (GAMs) GAMs extend linear models by allowing for non-linear functions of the predictor variables. These models are flexible and can fit a wide range of non-linear relationships.
Machine Learning Methods Machine learning methods such as random forests, neural networks, and support vector machines are powerful tools for modeling complex, non-linear relationships. These methods can handle large datasets and identify intricate patterns that traditional statistical models might miss.
