  We present an algorithm to compute best least-squares approximations of
discrete real-valued functions by first-degree splines (broken lines) with free
knots. We demonstrate that the algorithm delivers after a finite number of
steps a (global) best approximation. The analysis is complemented by remarks on
programming and by a number of numerical examples including applications from
medicine (MBC, MIC).
