Least squares regression finds the estimated values for the parameters in a regression model to minimize ESS 5 gn i51 1Yi 2 Y^ i 2 2 . Why is it necessary to square the estimation errors? What problem might be encountered if we attempt to minimize just the sum of the estimation errors?
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