Answer:
A.
The model is not a good fit.
Explanation:
The correlation coefficient is a measure of the degree of association between two quantitative variables , such as weight and height.
On the other hand, the quantity R-squared is an indicator of the predictive power of a model. It is an indicator of how well the model fits the data. R-squared is the coefficient of determination.
R-squared = the square of the correlation coefficient
= 0.6 * 0.6
= 0.36
Therefore, only 36% of the variations in the dependent variable can be explained by the model. More than 50% can thus not be explained by the model. The model is thus not a good fit.