Question

for many people, the womens figure skating competition is the highlight of the olympic winter games. scores in the short program x and scores in the free skate y were recorded for each of the 24 skaters who competed in both rounds during the 2010 winter olympics in vancouver, canada. here is a scatterplot with least - squares regression line y=-16.2 + 2.07x. for this model, s = 10.2 and r² = 0.736. interpret the value of r². about 73.6% of the variability in short program score is accounted for by the least - squares regression line with x = free skate score. about 73.6% of the variability in free skate score is accounted for by the least - squares regression line with x = short program score. for each one point increase in short program score, the model predicts about 73.6% increase in free skate score. about 73.6% of the free skate score is accounted for by the least - squares regression line with x = short program score. there is a strong, positive, linear association between free skate score and short program score.

Explanation:

The coefficient of determination $r^{2}$ represents the proportion of the variance in the dependent - variable that is predictable from the independent variable. In a regression model where $y$ (free - skate score) is regressed on $x$ (short - program score), $r^{2}=0.736$ means that about 73.6% of the variability in the free - skate score can be accounted for by the least - squares regression line using the short - program score as the predictor.

Answer:

B. About 73.6% of the variability in free skate score is accounted for by the least - squares regression line with $x =$ short program score.