Nonlinear Regression Definition
Nonlinear regression is a type of regression analysis that uses a curve to represent the difference between two variables instead of a straight line used by linear regression. Nonlinear regression generates a curve to reflect two variables. The core principles or functions used by nonlinear regression are logarithms, trigonometry, and exponents.
Rather than relate two variables; X and Y using a straight line where y = mx + b, nonlinear regression typically generates a curve. In this form of regression, the model parameters are not linearly combined, it entails one or more independent variables.
A Little More on What is Nonlinear Regression
There are two forms of regression, linear regression, and nonlinear regression. While the two share some similarities, they have their differences. Both linear and nonlinear regressions seek to plot and relate a set of variables on a graph, while linear regression is straightforward, nonlinear regression uses complex processes and methods.
References for “Nonlinear Regression”