Consider the scatter plot of airfare versus crude oil price per barrel fitted with a linear regression line. Here, the residual is the difference between the y-value of the data point and the predicted y-value from the regression equation. If these residual values are plotted against the x-value- the crude oil price, the resulting graph is called the residual plot. This plot helps in deciding whether the regression equation is a good model or not. As there is no obvious pattern other than a linear pattern in this residual plot, the regression line is a good fit. Any other pattern, which is nonlinear, indicates that the regression equation does not qualify as a good model. For example, predominantly positive residuals in a certain range and negative in others indicate a nonlinear trend where a linear regression equation is not a good fit. Also, a thickening of the residual plot, as it is viewed from left to right, indicates that the regression line is not a good model.