The problem involves interpreting a residual plot to determine if a linear function is a good fit for the data. Residual plots display the differences between observed and predicted values from a model, with the x-axis representing the input values and the y-axis showing the residuals.

Analysis of the Plot:

1. The residuals do not appear to be scattered randomly around the horizontal axis. Instead, they show a clear pattern, suggesting the relationship between the variables is not linear.
2. Patterns such as curves or systematic clustering in a residual plot indicate that a linear model does not adequately capture the trend in the data.

Correct Answer:

A) A linear function is not a good fit for the data.

Would you like me to go into more detail about residual analysis or linear model fitting?

Five Related Questions:

1. What does a residual plot look like when a linear model is a good fit?
2. How can residual analysis be used to decide between different models (e.g., quadratic vs. exponential)?
3. What are the limitations of using residual plots for model evaluation?
4. How do you calculate residuals for a given set of data points?
5. Can patterns in residuals suggest which non-linear model might be more appropriate?

Tip:

Always check the residual plot after fitting a model—if there's a pattern, the chosen model might not be the best fit!