The residual is calculated as the difference between the actual value and the predicted value:

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

Given:

• Actual value $y = 73.7$
• Predicted value $\hat{y} = 72.0$

Substitute these values into the formula:

$\text{Residual} = 73.7 - 72.0 = 1.7$

Thus, the residual is $\boxed{1.7}$.

Would you like more details or have any questions?

Here are 5 related questions:

1. What does a positive residual indicate about the predicted model?
2. How is the residual used in evaluating the accuracy of a regression model?
3. What is the formula for calculating the residual sum of squares (RSS)?
4. How can you interpret the residual if it equals zero?
5. What are outliers in terms of residuals?

Tip: Residuals help assess the fit of a regression model. Large residuals can indicate that the model is not capturing some aspect of the data properly.