Nonlinear Least Squares Regression - CurveFitter

CurveFitter is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting). CurveFitter determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. CurveFitter can handle linear, polynomial, exponential, and general nonlinear functions. Unlike many "nonlinear" regression programs that can only handle a limited set of function forms, CurveFitter can handle essentially any function whose form you can specify algebraically.

CurveFitter performs true nonlinear regression analysis, it does not transform the function into a linear form. As a result, it can handle functions that are impossible to linearize such as ("Newton's Law of Cooling"):

y = (a - c) * exp(-b * x) + c

Another advantage of handing the function in true nonlinear form is that the minimization of the sum of squared residual values (i.e., "least squares") is based on the true nonlinear value.