To determine which scatterplot shows a linear association based on the provided data, we need to identify the relationships between the sets of x and y values. Here's a summary of the supplied data for each scatterplot:
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First Set:
- x: 5, 8, 2, 4, 6, 12, 10, 1, 3, 7
- y: 800, 700, 350, 450, 500, 1000, 900, 250, 400, 750
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Second Set:
- x: 5, 8, 2, 4, 6, 12, 10, 1, 3, 7
- y: 550, 800, 350, 450, 700, 1000, 900, 250, 400, 750
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Third Set:
- x: 5, 8, 2, 4, 6, 12, 10, 1, 3, 7
- y: 550, 800, 250, 500, 500, 400, 650, 150, 400, 750
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Fourth Set:
- x: 5, 8, 2, 4, 6, 12, 10, 1, 3, 7
- y: 550, 800, 250, 300, 700, 1000, 650, 300, 100, 700
To determine which scatterplot shows a linear association, you could graph the points for each dataset and visually inspect the distributions, but I can summarize based on potential trends.
Analysis:
- First Set: Appears to have a scattered arrangement without a clear linear trend.
- Second Set: Similar to the first set, but some points at higher y-values seem to form a slight upward trend; however, it is not explicitly linear.
- Third Set: This scatterplot shows more variation and does not suggest linearity.
- Fourth Set: The points seem to be more spread out with some high variability in y-values, but there could be some increasing trend; still, it lacks a clear linear association.
None of the datasets clearly show perfect linearity, but if we had to choose, the second scatterplot might indicate a slight upward trend that could suggest a quasi-linear relationship.
Conclusion:
To accurately identify linearity, graphing each set of data would provide the best visual representation, but based on this analysis, the second scatterplot may give the appearance of a linear association if we consider slight trends.