Find definitions and interpretation guidance for every residual plot.

You can plot marginal and conditional residuals. The marginal fits are the fitted values for the overall population. Use the conditional residuals to check the normality of the error term in the model.

The histogram of the residuals shows the distribution of the residuals for all observations. Use this graph to identify rows of data with much larger residuals than other rows. Further investigate those rows to see whether they are collected correctly.

The normal probability plot of the residuals displays the residuals versus their expected values when the distribution is normal. Use this graph to identify rows of data with much larger residuals than other rows. Further investigate those rows to see whether they are collected correctly.

The residuals versus fits graph plots the residuals on the y-axis and the fitted values on the x-axis. Use this graph to identify rows of data with much larger residuals than other rows. Further investigate those rows to see whether they are collected correctly. In addition, you can also use this plot to look for specific patterns in the residuals that may indicate additional variables to consider.

The residuals versus order plot displays the residuals in the order that the data were collected. Use this graph to identify rows of data with much larger residuals than other rows. Further investigate those rows to see whether they are collected correctly. If the plot shows a pattern in time order, you can try to include a time-dependent term in the model to remove the pattern.

The residual versus variables plot displays the residuals versus another variable. The variable could already be included in your model. Or, the variable may not be in the model, but you suspect it affects the response.

If you see a non-random pattern in the residuals, it indicates that the variable affects the response in a systematic way. Consider including this variable in an analysis.