The criterion for deriving the values of the slope and intercept in a linear regression equation is formally known as the:

Question

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The criterion for deriving the values of the slope and intercept in a linear regression equation is formally known as the least squares criterion, which aims to minimize the sum of the squared differences between the predicted values and the actual values of the dependent variable. The other options (A) maximum likelihood criterion, (B) oblique squares criterion, and (D) sum of squares criterion, are not typically used for linear regression calculations.

The Criterion for Deriving the Values of the Slope and Intercept