The question asks for the residual for the point $(2, 5)$ given the line of best fit:

$\hat{y} = 2.5x - 1.5$

Step 1: Calculate the predicted $y$-value $\hat{y}$ using the line of best fit for $x = 2$.

$\hat{y} = 2.5(2) - 1.5 = 5 - 1.5 = 3.5$

Step 2: Find the residual. The residual is the difference between the actual $y$-value and the predicted $y$-value:

$\text{Residual} = y_{\text{actual}} - \hat{y}$

Here, the actual $y$-value is $5$, and the predicted $y$-value is $3.5$:

$\text{Residual} = 5 - 3.5 = 1.5$

Conclusion: The residual for the point $(2, 5)$ is 1.5, so the correct answer is:

$\boxed{A. 1.5}$

Do you need more details or have any questions?

Here are 5 related questions to expand on this:

1. What does a positive residual indicate about a data point in relation to the line of best fit?
2. How do you interpret a residual of zero for a particular data point?
3. What is the meaning of the slope in the equation $\hat{y} = 2.5x - 1.5$?
4. How would you calculate the residual for a different point, such as $(3, 6)$?
5. Can the residual be negative, and what would that mean for the data point?

Tip: Residuals help us measure the accuracy of predictions from the line of best fit. A smaller residual indicates a better fit for the data point.