The residual is the difference between the actual value and the predicted value. Mathematically, it is given by:

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

Given: $y_{\text{actual}} = 51.8$ $\hat{y}_{\text{predicted}} = 49.5$

Now, calculate the residual:

$\text{Residual} = 51.8 - 49.5 = 2.3$

So, the residual equals 2.3.

Would you like more details or have any other questions?

Here are 5 related questions:

1. How is the residual used in regression analysis?
2. What does a positive residual indicate about the prediction?
3. How are residuals related to the goodness of fit of a model?
4. What is the importance of calculating residuals in predictive modeling?
5. How can the sum of residuals be used to evaluate a model?

Tip: Residuals are essential for checking how well a regression model fits the data—large residuals may indicate that the model is underperforming.