To determine which scatterplot shows a non-linear association, we need to analyze the relationship between the x and y values provided in the tables.
Each pair of x and y values provided can be plotted on a scatterplot. We can summarize the data to help visualize the trends:
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First dataset:
- x: 50, 46, 80, 59, 78, 64, 68, 53, 70, 68
- y: 35, 30, 75, 45, 70, 50, 55, 36, 62, 67
- This dataset appears to have a generally positive association, as y increases with x, but check for linearity.
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Second dataset:
- x: 50, 46, 80, 59, 78, 64, 68, 53, 70, 68
- y: 90, 100, 57, 80, 60, 75, 72, 82, 70, 67
- This dataset has more variation in y values. Here, it seems like there isn’t a consistent trend related to x, indicating a possible non-linear association.
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Third dataset:
- x: 50, 46, 90, 59, 78, 64, 68, 53, 70, 72
- y: 75, 71, 100, 80, 90, 82, 85, 77, 87, 89
- This dataset also shows a positive trend, suggesting a linear relationship.
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Fourth dataset:
- x: 50, 46, 98, 59, 78, 64, 68, 53, 70, 72
- y: 75, 71, 90, 55, 70, 100, 90, 25, 40, 76
- This dataset appears to have significant variability in y values as well. The lack of consistent change as x values increase suggests a non-linear relationship.
Based on these analyses, the second and fourth datasets show signs of non-linear associations due to the variability and lack of a consistent trend in y values relative to x. You should plot each of them to confirm visually, but likely candidates for non-linear relationships are the second and fourth datasets.