From the image provided, the question is about residual plots and whether a linear function is a good fit for the given data. Let’s analyze it:

Key Observations

A residual plot displays the difference between observed and predicted values. If a linear function is a good fit:

1. The residuals should be randomly scattered around the horizontal axis (y = 0), showing no clear pattern.

If the residuals show a pattern, such as a curve or systematic trend, then a linear function is not a good fit.

Answer Analysis

In the residual plot shown:

• The residuals do not appear to be randomly scattered. Instead, there seems to be a noticeable pattern, possibly curved.
• This indicates that the linear function does not adequately capture the relationship in the data.

The correct answer is: A) A linear function is not a good fit for the data.

Would you like further clarification or an explanation of residual plots?

Related Questions

1. How do you determine if a quadratic function is a better fit based on residuals?
2. Why is it important to analyze residuals when fitting models?
3. Can an exponential function fit data better than a linear one? Why?
4. How does the residual sum of squares (RSS) help assess model accuracy?
5. What steps follow after determining a linear function is a poor fit?

Tip

When analyzing residual plots, always look for randomness. A pattern in residuals suggests the model isn't capturing the data's complexity.