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.