Let's start by interpreting the question from the image.

We have a point $(5, 9.6)$ that represents actual data on a scatterplot. The equation of the line of best fit predicts that when $x = 5$, $y = 10.1$.

Now, the residual is the difference between the actual value and the predicted value from the line of best fit. We can calculate it as follows:

$\text{Residual} = \text{Actual } y - \text{Estimated } y$ Given:

• Actual $y = 9.6$
• Estimated $y = 10.1$

So: $\text{Residual} = 9.6 - 10.1 = -0.5$

The residual for the point $(5, 9.6)$ is $-0.5$.

The estimated value from the line of best fit for $x = 5$ is $10.1$, as given in the problem.

Would you like a further explanation of residuals or other related topics?

Here are some related questions:

1. What does the residual tell us about the accuracy of the line of best fit?
2. How do you calculate the residual for different points on the scatterplot?
3. What is the significance of residuals in regression analysis?
4. What other methods can be used to measure the fit of a regression line?
5. How does the size of the residual affect the overall regression model?

Tip: Residuals help assess the quality of a regression model. Smaller residuals indicate a better fit.