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A set of data points has a line of best fit of [tex]\( y = 2.5x - 1.5 \)[/tex]. What is the residual for the point [tex]\( (2, 5) \)[/tex]?

A. -1.5
B. 5
C. 1.5
D. 3.5


Sagot :

To find the residual for the point [tex]\((2,5)\)[/tex] given the line of best fit [tex]\(y = 2.5x - 1.5\)[/tex], we need to follow these steps:

1. Substitute [tex]\(x = 2\)[/tex] into the line of best fit equation to find the predicted [tex]\(y\)[/tex]-value.

[tex]\[ y_{\text{predicted}} = 2.5 \cdot 2 - 1.5 \][/tex]

2. Calculate the predicted value:

[tex]\[ y_{\text{predicted}} = 5 - 1.5 = 3.5 \][/tex]

3. Compare the actual [tex]\(y\)[/tex]-value of the point [tex]\((2, 5)\)[/tex] to this predicted [tex]\(y\)[/tex]-value. The actual [tex]\(y\)[/tex]-value is 5.

4. Calculate the residual: The residual is the difference between the actual [tex]\(y\)[/tex]-value and the predicted [tex]\(y\)[/tex]-value.

[tex]\[ \text{Residual} = y_{\text{actual}} - y_{\text{predicted}} \][/tex]

Substitute the values:

[tex]\[ \text{Residual} = 5 - 3.5 = 1.5 \][/tex]

Therefore, the residual for the point [tex]\((2, 5)\)[/tex] is [tex]\(1.5\)[/tex].

The correct answer is [tex]\( \boxed{1.5} \)[/tex].