Regression analysis
In order to use Peirce's criterion, one must first understand the input and return values. Regression analysis (or the fitting of curves to data) results in residual errors (or the difference between the fitted curve and the observation points). Therefore, each observation point has a residual error associated with a fitted curve. By taking the square (i.e., residual error raised to the power of two), residual errors are expressed as positive values. If the squared error is too large (i.e., due to a poor observation) it can cause problems with the regression parameters (e.g., slope and intercept for a linear curve) retrieved from the curve fitting.