The correct answer is: B. Standard error of the estimate.
The standard error of the estimate (SE) is a measure of how closely the data points fit the regression line. It is calculated by taking the square root of the sum of the squared residuals divided by the number of data points minus 2. The residuals are the differences between the actual values and the predicted values from the regression line.
The correlation coefficient (r) is a measure of the strength of the linear relationship between two variables. It is calculated by taking the covariance of the two variables and dividing it by the product of their standard deviations.
The square root of two standard error of the estimate gives us the value of correlation coefficient. This is because the correlation coefficient is equal to the covariance of the two variables divided by the product of their standard deviations. The covariance is a measure of how much the two variables vary together. The standard deviation is a measure of how spread out the data points are.
The other options are incorrect because they do not measure the strength of the linear relationship between two variables.