Stepwise regression is an alternative to splines for fitting noisy data.

In this study, we compared numerical methods that are used to fit noisy data. Comparisons included polynominal regression, stepwise polynomial regression and quintic spline approximation. The advantages and limitations of each method are discussed in terms of curve fit quality, computational speed and ease, and solution compactness. Overall, the spline approximation and stepwise polynomial regression provide the best fits to the data. Stepwise regression provides the added utility of providing a simple, unconstrained function which can be easily implemented in simulation studies.