To determine which scatter plot shows a linear association, let's analyze the provided data pairs for each set of X and Y values. A linear association indicates that as X increases, Y either consistently increases or decreases in a straight-line manner.
1. First Set:
- X: 5, 8, 2, 4, 6, 12, 10, 13, 7
- Y: 550, 800, 250, 500, 500, 400, 650, 150, 400, 750
Here, the Y values fluctuate significantly with little correlation to the X values. Thus, this set likely does not form a linear association.
2. Second Set:
- X: 5, 8, 2, 4, 6, 12, 10, 13, 7
- Y: 550, 800, 350, 450, 700, 1000, 900, 250, 400, 750
Again, the Y values vary widely, and it doesn't seem that Y consistently increases or decreases with X.
3. Third Set:
- X: 5, 8, 2, 4, 6, 12, 10, 13, 7
- Y: 800, 700, 350, 450, 500, 1000, 900, 250, 400, 750
Similar to the previous datasets, the Y values here also do not show a clear linear pattern as they fluctuate across different X values.
4. Fourth Set:
- X: 5, 8, 2, 4, 6, 12, 10, 13, 7
- Y: 550, 800, 250, 300, 700, 1000, 650, 300, 100, 700
In this case, while there is variability, the Y values also fluctuate without a clear linear relationship.
Conclusion:
From the analysis, none of the above datasets show a clear linear association based on the relationships between X and Y values. However, one might investigate visually by plotting these values on an actual scatter plot to observe trends.
You would likely find that seasonally adjusted data points can vary, and visually analyzing them is often necessary to confirm whether there is a discernible pattern. None specifically appear linear here when examined just numerically. If additional data were provided via a visual scatterplot, it could better illuminate trends that might exist.