Residual plots are the plots of some type of residual which might be helpful in assessing the assumption made by the fitted model. In regression analysis there are various methods of plotting the residual values which can be helpful in assessing particular components of the regression model. The most useful plots when diagnosing the linear regression models are given below;
* A boxplot or probability plot of the residuals can be useful in checking for symmetry and specifically for normality of the error terms in the regression model.
* Plotting the residuals against the corresponding values of the explanatory variable.
Any sign of curvature in the plot might suggest that say a quadratic term in the explanatory variable should be included in the model.
* Plotting residuals against fitted values of the response variable. If the variability of the residuals appears to increase with the increase in the size of the fitted values, a transformation of the response variable former to fitting is indicated.
Figure shows some idealized residual plots that indicate particular points about models.
* Figure (a) is what is looked for to confirm that the fitted model is appropriate,
* Figure (b) suggests that the assumption of constant variance is not justified so that some transformation of the response variable before fitting might be sensible,
* Figure (c) implies that the model requires a quadratic term in the explanantory variable.