To determine which table has a non-linear association by graphing the data on a scatterplot, we need to analyze the provided (x, y) data pairs from each table.
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Table 1:
- x: 2, 5, 7, 9, 3, 1, 4, 8, 6, 10
- y: 60, 75, 80, 95, 65, 50, 70, 82, 78, 100
This data appears to increase as x increases, but we need to plot the points to confirm whether the relationship is linear or not.
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Table 2:
- x: 2, 5, 7, 9, 3, 1, 4, 8, 6, 10
- y: 60, 75, 90, 97, 65, 50, 70, 60, 78, 100
Again, this data needs to be plotted to verify the relationship's nature.
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Table 3:
- x: 2, 5, 7, 9, 3, 1, 4, 8, 6, 10
- y: 60, 75, 80, 65, 22, 59, 86, 100, 25, 97
This dataset shows a more irregular pattern; plotting it would show whether it follows a non-linear pattern.
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Table 4:
- x: 60, 75, 80, 95, 65, 50, 70, 82, 78, 100
- y: 2, 5, 7, 9, 3, 1, 4, 8, 6, 10
This table has x and y switched, suggesting that it will not have the same characteristics as the previous tables.
To analyze which table has a non-linear association, we would typically check the scatterplots visually. However, I can describe what trends would indicate non-linearity.
- If the points follow a curve, show clusters, or deviate from a straight line, then the table likely exhibits a non-linear association.
Conclusion:
Without direct graphs to visualize, I can deduce that Table 3, which has values that deviate more irregularly than the others, likely represents a non-linear association. The presence of lower y-values and inconsistencies suggests a potential curve or non-linear behavior.
So, the table with a non-linear association is Table 3.