

A nonlinear regression model predicts one variable Y from another variables X. The Y variable is called the dependent variable, the response variable or the outcome variable. The X variable is called independent variables, explanatory variables or predictor variables.
The X variable can be a value that the experimenter manipulated or assigned, or a value that the experimenter measures.
The nonlinear regression model defines the dependent variable as a function of the independent variable and a set of parameters, also called regression coefficients. Regression methods find the values of each parameter that make the model predictions come as close as possible to the data. This approach is analogous to linear regression, which determines the values of the slope and intercept (the two parameters or regression coefficients of the model) to make the model predict Y from X as closely as possible.