To determine how the point (4, 43.6) relates to the trend line \( y = 6.6x + 13.4 \), we can substitute \( x = 4 \) into the equation of the trend line to find the corresponding \( y \)-value:
\[ y = 6.6(4) + 13.4 \]
Calculating this:
\[ y = 26.4 + 13.4 = 39.8 \]
So, the trend line predicts that when \( x = 4 \), \( y \) should be \( 39.8 \).
Now, we compare this predicted value with the actual \( y \)-value from the dataset:
- Predicted \( y \)-value: \( 39.8 \)
- Actual \( y \)-value: \( 43.6 \)
Since the actual value \( 43.6 \) is greater than the predicted value \( 39.8 \), we can draw the conclusion that the point (4, 43.6) lies above the trend line.
An accurate conclusion Jason can make is that the actual value at \( x = 4 \) is higher than what the trend line predicts. This indicates that there might be some influence or factor affecting the data at this point that is not captured by the trend line.
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