Regressions by Leaps and Bounds

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Abstract
This paper describes several algorithms for computing the residual sums of squares for all possible regressions with what appears to be a minimum of arithmetic (less than six floating-point operations per regression) and shows how two of these algorithms can be combined to form a simple leap and bolmd technique for finding the best subsets without examining all possible subsets. The resldt is a reduction of several orders of magnitude in the nllmber of operations reqllired to find the best subsets.

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