Loterre: SAGE: least squares criterion

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least squares criterion

The rule that using the ∗mean to predict the scores in a distribution results in predictions that are most accurate, with “accurate” in this case indicating that using the mean yields the smallest possible sum of squared ∗errors (or squared ∗deviation scores). In ∗regression analysis, it is called ∗ordinary least squares (OLS), which is a method or criterion for calculating the ∗regression equation (or drawing the regression line) that best summarizes or fits a distribution. [Source: Dictionary of Statistics & Methodology; Least Squares Criterion]

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