To estimate the correlation coefficient based on the provided data, we can first analyze the relationship between Data 1 and Data 2.
The provided data points are:
- (0, 3)
- (2, 6)
- (4, 9)
- (6, 12)
- (8, 15)
- (10, 18)
If we observe the points, we can see that as Data 1 increases (0, 2, 4, 6, 8, 10), Data 2 also increases (3, 6, 9, 12, 15, 18). This indicates a positive linear relationship.
To determine the strength of the correlation, we can look at the numerical change:
- For each increase of 2 in Data 1, Data 2 increases by 3.
Given this linear relationship, the correlation coefficient (r) is close to 1.
Considering the options provided:
- 1 indicates a perfect positive correlation.
- 0.9 indicates a strong positive correlation.
- −1 indicates a perfect negative correlation.
- −0.9 indicates a strong negative correlation.
- 0 indicates no correlation.
Given the data, the correlation is strong and positive but not perfect. Therefore, the best estimate for the correlation coefficient based on this data would be 0.9.