STEPWISE REGRESSION IS AN ALTERNATIVE TO SPLINES FOR FITTING NOISY DATA

In this study, we compared numerical methods that are used to fit nois y data. Comparisons included polynominal regression, stepwise polynomi al regression and quintic spline approximation. The advantages and lim itations of each method are discussed in terms of curve fit quality, c omputational speed and ease, and solution compactness, Overall, the sp line approximation and stepwise polynomial regression provide the best fits to the data. Stepwise regression provides the added utility of p roviding a simple, unconstrained function which can be easily implemen ted in simulation studies.