Use the outlier plot to detect outliers; any point above the reference line is an outlier.

The outlier plot displays Mahalanobis distances, the distance between each data point and the mulitivariate space's centroid. Examining Mahalanobis distances is a more powerful method for detecting outliers than looking at one variable at a time. This multivariate approach takes into account the different scales between variables and the correlations among them.

It is important to identify outliers because they can significantly influence your results. If you identify an outlier in your data, you should examine the observation to understand why it is unusual and identify an appropriate remedy if necessary.

From the graph, you can flag outliers for easy identification in your worksheet by using the Editor > Brush command.

For the loan applicant data, you conclude there are no outliers because there are no points above the reference line.