To determine which scatterplot shows a linear association based on the provided data tables, we need to analyze the patterns of the (x, y) coordinates from each table.
Data Analysis
Table 1:
- (5, 550), (8, 800), (2, 250), (4, 300), (6, 700), (12, 1000), (10, 650), (1, 300), (3, 100), (7, 700)
The first table seems to have a nonlinear pattern because there's no clear single line that fits the data points.
Table 2:
- (5, 550), (8, 800), (2, 250), (4, 300), (6, 700), (12, 1000), (10, 650), (1, 300), (3, 100), (7, 700)
This table contains the same data as the first table, and thus exhibits similar patterns, which seem nonlinear.
Table 3:
- (5, 550), (8, 800), (2, 250), (4, 500), (6, 500), (12, 400), (10, 650), (1, 150), (3, 400), (7, 750)
In this table, the y-values fluctuates at certain x-values. For example, at x = 6, the y-value is 500, and at x = 12, it's lower (400). This inconsistency suggests a nonlinear trend.
Table 4:
- (5, 550), (8, 800), (2, 250), (4, 500), (6, 500), (12, 400), (10, 650), (1, 150), (3, 400), (7, 750)
This table is similar to the third, and thus portrays a similar pattern of no linear correlation.
Table 5:
- (5, 800), (8, 700), (2, 350), (4, 450), (6, 500), (12, 1000), (10, 900), (1, 250), (3, 400), (7, 750)
This last table shows more variability in the y-values compared to their corresponding x-values, indicating potential nonlinear association.
Conclusion
None of the tables appear to exhibit a clear linear association based on the standard analysis of trends in the scatterplots. Points do not line up to form a straight line in any of these datasets. For linear relationships, you would typically expect a more uniform distribution of points along a straight line (regardless of slope).
If you're using visual plotting tools, it might be easier to identify linearity more clearly, but based on the given coordinates, it seems none show a linear relationship.