Answer:
The linear model is not appropriate.
Step-by-step explanation:
In regression, the difference amid the observed-value of the dependent-variable (y) and the predicted-value (
) is known as the residual (e).

A residual plot is a graphical representation of the residuals on the y-axis and the independent variable on the x-axis. If the data-points on the residual plot are randomly spread around the x-axis, a linear regression model is appropriate for the data. And if the residual plots shows a non-random or a U or inverted U pattern, a nonlinear model is more appropriate for the data.
The residual provided shown an inverted U pattern. That is the points are not scattered around he graph.
Thus, the linear model is not appropriate.