Non-Linear Regression
Non-linear regression is a statistical modeling technique used to describe relationships between variables that do not follow a straight-line pattern, instead fitting curves or more complex functions to data. It extends linear regression by allowing models with parameters that appear non-linearly, such as exponential, polynomial, or logistic functions, to capture trends like growth, decay, or saturation. This method is essential in fields like biology, economics, and engineering where data often exhibits non-linear behavior.
Developers should learn non-linear regression when working on predictive modeling tasks where relationships between variables are curved or complex, such as in machine learning for time-series forecasting, dose-response analysis in pharmacology, or population growth modeling. It is particularly useful in data science and analytics to improve model accuracy over linear approaches when underlying patterns are non-linear, enabling better insights and predictions in real-world applications.