We present two methods for reducing multidimensional information to one
dimension for ease of understand or analysis while maintaining statistical
power. While not new, dimensional reduction is not greatly used in high-energy
physics and has applications whenever there is a distinctive feature (for
instance, a mass peak) in one variable but when signal purity depends on
others; so in practice in most of the areas of physics analysis. While both
methods presented here assume knowledge of the background, they differ in the
fact that only one of the methods uses a model for the signal, trading some
increase in statistical power for this model dependence