Use the plot on the Variances Plot page to determine what proportion and cumulative proportion of the variance between samples is contributed by each principal component.
In this plot, squares represent the proportion of the variance as data points. Circles represent the cumulative proportion of the variance as data points. The x axis is the number of principal components, and the y axis is the proportion and the cumulative proportion of the variance contributed by each principal component.
Point to a data point square to view the number of the principal component and the sum of the proportion of variance of that principal component and that of its preceding principal components. Point to a data point circle to view the number of the principal component and the proportion of variance of that principal component.
In general, as the proportion of variance increases for the first two or three principal components, the dissimilarity between the sample groups increases.
The following figure gives an example of a PCA plot on the Variances Plot page. In general, this plot shows that the principal component analysis is able to distinguish between the samples.