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The plot shown is a residual plot for a linear regression of \( y \) versus \( x \). In a well-fitting linear model, we expect the residuals to show no clear pattern. They should be randomly scattered around zero, without systematic structure, indicating that the model captures the relationship between \( y \) and \( x \) well.

Here, the residual plot displays a distinct curved pattern, indicating that the residuals are not randomly scattered. This suggests that the relationship between \( y \) and \( x \) might not be linear and that a linear model might not be appropriate.

Thus, the correct answer to the question, "What is indicated by the plot?" is:

**B: A linear model is not appropriate.**

Would you like further details on residual plots or linear regression models?

Here are 5 related questions to expand your understanding:
1. What other types of regression models might be more appropriate for data showing a curved residual pattern?
2. How can we statistically test whether a linear model fits the data well?
3. What does heteroscedasticity mean, and how can it be detected in a residual plot?
4. How would adding polynomial terms to the regression model help fit this data better?
5. How do influential points differ from outliers in regression analysis?

**Tip:** Always check the residual plot after fitting a regression model to assess the goodness of fit and detect any non-linear patterns or outliers.

The following is a residual plot for a linear regression of y versus x. What is indicated by the plot?

This problem involves interpreting a residual plot to determine if a linear model is appropriate. Residuals should be randomly scattered if a linear model fits well, but a pattern suggests the model is not suitable.

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