Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.825 and the least squares regression line is y = 0.157x+13.091. Complete parts (a) and (b) below. Height, x 27.25 Head Circumference, y 17.2 25.00 17.0 26.50 25.00 27.50 17.1 17.0 17.5 26.25 17.3 26.00 17.2 27.00 17.4 (a) Compute the coefficient of determination, R. R2 = % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. OA. R2 of the variation in head circumference is not explained by the least-squares regression equation. The linear model appears to be appropriate. OC. R2 of the variation in head circumference is explained by the least-squares regression equation. The linear model appears to be appropriate. OB. R2 of the variation in head circumference is explained by the least-squares regression equation. The linear model appears to be not appropriate. OD. R2 of the variation in head circumference is not explained by the least-squares regression equation. The linear model appears to be not appropriate. In linear regression, which value for the correlation coefficient would indicate the strongest relationship between the independent (predictor) and dependent variables?