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Select all the statements that are true of a least-squares regression line. The slope of the regression line is resistant to outliers. In the equation of the least-squares regression line, \( \hat{Y} \) is an observed value when \( X \) is known. The regression line maximizes the residuals between the observed values and the predicted values. The sum of the squares of the residuals is the smallest sum possible. \( R^{2} \) measures how much of the variation in \( Y \) is explained by \( X \) in the estimated linear regression. In the equation of the least-squares regression line, \( \widehat{Y} \) is a predicted value when \( X \) is known.

          Select all the statements that are true of a least-squares regression line.
The slope of the regression line is resistant to outliers.
In the equation of the least-squares regression line, \( \hat{Y} \) is an observed value when \( X \) is known.
The regression line maximizes the residuals between the observed values and the predicted values.
The sum of the squares of the residuals is the smallest sum possible.
\( R^{2} \) measures how much of the variation in \( Y \) is explained by \( X \) in the estimated linear regression.
In the equation of the least-squares regression line, \( \widehat{Y} \) is a predicted value when \( X \) is known.

Select all the statements that are true of a least-squares regression line.
The slope of the regression line is resistant to outliers.
In the equation of the least-squares regression line, Ŷ is an observed value when X is known.
The regression line maximizes the residuals between the observed values and the predicted values.
The sum of the squares of the residuals is the smallest sum possible.
R^2 measures how much of the variation in Y is explained by X in the estimated linear regression.
In the equation of the least-squares regression line, Y is a predicted value when X is known.

Added by Margaret P.

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