What is Non-Linear Regression?

Non-Linear Regression is a statistical method that is used to model the relationship between a dependent variable and one of the independent variable(s). Unlike linear regression, where the relationship between the dependent and independent variables is linear, in non-linear regression, the relationship is modeled using a non-linear equation. This means that the model can capture more complex and non-linear relationships between the variables, but also requires more computational resources and a more sophisticated approach to estimate the model’s parameters. Kindly go through the link for types of regression 

Non-Linear Regression is a form of regression analysis in which function models observational data is a nonlinear combination with non-linear parameters To perform non-linear regression in R, you can use various functions and packages, including ‘nls’, ‘nlme‘, and ‘mgcv‘. You need to provide the equation of the model and the data, and the function will estimate the parameters of the equation that best fit the data. An example of non-linear regression that can be used in predicting population growth over time, the GDP of a country, etc.