To identify the outlier from the points given in the scatterplot, we need to look for the point that stands out from the others in terms of its distribution and relationship between the two variables.
- (2.5, 87)
- (4.5, 97)
- (0.5, 45)
- (1, 78)
Let's analyze the y-values against the x-values:
- The first point (2.5, 87) has a relatively high score given its x-value.
- The second point (4.5, 97) has the highest score and is also aligned with a higher x-value.
- The point (0.5, 45) has a lower score, which may suggest it’s not following the trend of the other points, but let's check further.
- The point (1, 78) has a score that seems to fit with the lower scores somewhat.
When examining these points, (0.5, 45) appears to be significantly lower than the others in terms of both the x-value and the corresponding y-value (score). It doesn't fit in with the trend of higher scores associated with higher x-values.
Therefore, the outlier is (0.5, 45).