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

Reducing multi-dimensional information into a 1-d histogram