Nonlinear Regression

Introduction Multiple regression deals with models that are linear in the parameters. That is, the multiple regression model may be thought of as a weighted average of the independent variables. A linear model is usually a good first approximation, but occasionally, you will require the ability to use more complex, nonlinear, models. Nonlinear regression models are those that are not linear in the parameters. Examples of nonlinear equations are: Y = A + B EXP(-CX) Y = (A +BX)/(1+CX) Y = A + B/(C+X) This program estimates the parameters in nonlinear models using the Levenberg-Marquardt nonlinear leastsquares algorithm as presented in Nash (1987). We have implemented Nash’s MRT algorithm with numerical derivatives. This has been a popular algorithm for solving nonlinear least squares problems, since the use of numerical derivatives means you do not have to supply program code for the derivatives.