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Which of the Following Statements Best Describes Why a Linear

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Which of the following statements best describes why a linear regression is also called a least squares regression model? AA linear regression is al so called a least squares regression model because the regression  line is calculated by minimizing the square of the difference between each actual x data value and the predicted x value. B A linear regression is al so called a least squares regression model because the regression line is  calculated by minimizing the sum of the difference betweeneach actual y data value and the predicted y value. C A linear regression is al so called a least squares regression model because the regression line iscalculated by minimizing the square of each actual y  data value and the predicted y value.D A. linear regression is also called a least squares regression model because the regression line is calculatedby minimizing the sum of the square of the differences between  each actual y data value and the predicted y value.\begin{array}{|l|l|}\hline A&\text {A linear regression is al so called a least squares regression model because the regression }\\&\text { line is calculated by minimizing the square of the difference}\\&\text { between each actual \( x \) data value and the predicted \( \mathrm{x} \) value. }\\\hline B&\text { A linear regression is al so called a least squares regression model because the regression line is }\\&\text { calculated by minimizing the sum of the difference between}\\&\text {each actual y data value and the predicted y value. }\\\hline C&\text { A linear regression is al so called a least squares regression model because the regression line is}\\&\text {calculated by minimizing the square of each actual \( y \) }\\&\text { data value and the predicted \( y \) value.}\\\hline D&\text { A. linear regression is also called a least squares regression model because the regression line is calculated}\\&\text {by minimizing the sum of the square of the differences between }\\&\text { each actual y data value and the predicted y value.}\\\hline \end{array}


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