To analyze the data provided and create a scatter plot, we can use the pairs from the table:
| Data 1 | Data 2 | |--------|--------| | 0 | 3 | | 2 | 6 | | 4 | 9 | | 6 | 12 | | 8 | 15 | | 10 | 18 |
If we plot these points on a scatter plot with Data 1 on the x-axis and Data 2 on the y-axis, we would see that:
- (0, 3)
- (2, 6)
- (4, 9)
- (6, 12)
- (8, 15)
- (10, 18)
When plotting these points, we can notice that as the values of Data 1 increase, the values of Data 2 also increase, showing a positive correlation.
To estimate the correlation coefficient, we can observe the pattern of the data. The relationship appears linear and increases steadily.
The correlation coefficient (r) ranges from -1 to 1:
- If r is close to 1, it indicates a strong positive linear relationship.
- If r is close to -1, it indicates a strong negative linear relationship.
- If r is close to 0, it indicates little to no linear relationship.
Based on the provided data, we can say that the correlation coefficient is likely close to 1, given the linear trend observed in the scatter plot.
Thus, the best estimate for the correlation coefficient based on the data is 1 (indicating a strong positive correlation).