The scree, or eigenvalue, plot provides one method for determining the number of principal components. The scree plot displays the component number versus the corresponding eigenvalue. The eigenvalues of the correlation matrix equal the variances of the principal components; therefore, choose the number of components based on the size of the eigenvalues.

The ideal pattern is a steep curve, followed by a bend and then a straight line. Retain those components in the steep curve before the first point that starts the line trend. In practice, you may have difficulty interpreting a scree plot. Use your knowledge of the data and the results from the other methods of selecting components to help decide the number of components to retain.

For the loan applicant data you can conclude that the first three principal components account for most of the total variability in data (given by the eigenvalues). The remaining principal components account for a very small proportion of the variability (close to zero) and are probably unimportant.