Use the residual plots to help you determine whether the model is adequate and meets the assumptions of the analysis. If the assumptions are not met, the model may not fit the data well and you should use caution when you interpret the results.
For more information on how to handle patterns in the residual plots, go to Graphs for Binary Fitted Line Plot and click the name of the residual plot in the list at the top of the page.
Residuals versus fits plot
Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.
The residuals versus fits plot is only available when the data are in Event/Trial format.
The patterns in the following table may indicate that the model does not meet the model assumptions.
||What the pattern may indicate
|Fanning or uneven spreading of residuals across fitted values
||An inappropriate link function
||A missing higher-order term or an inappropriate link function
|A point that is far away from zero
|A point that is far away from the other points in the x-direction
||An influential point
In this residuals versus fits plot, the data appear to be randomly distributed about zero. There is no evidence that the value of the residual depends on the fitted value.
Residuals versus order plot
Use the residuals versus order plot to verify the assumption that the residuals are independent from one another. Independent residuals show no trends or patterns when displayed in time order. Patterns in the points may indicate that residuals near each other may be correlated, and thus, not independent. Ideally, the residuals on the plot should fall randomly around the center line:
In this residuals versus order plot, the residuals appear to fall randomly around the centerline. There is no evidence that the residuals are not independent.