# Total Variance Explained

The variance explained by the initial solution, extracted components, and rotated components is displayed. This first section of the table shows the Initial Eigenvalues.

The Total column gives the eigenvalue, or amount
of variance in the original variables accounted for by each component.
The % of Variance column gives the ratio, expressed
as a percentage, of the variance accounted for by each component to
the total variance in all of the variables.
The Cumulative % column gives the percentage
of variance accounted for by the first *n* components. For example, the cumulative percentage for the second
component is the sum of the percentage of variance for the first
and second components.

For the initial solution, there are as many components as variables, and in a correlations analysis, the sum of the eigenvalues equals the number of components. You have requested that eigenvalues greater than 1 be extracted, so the first three principal components form the extracted solution.

The second section of the table shows the extracted components. They explain nearly 88% of the variability in the original ten variables, so you can considerably reduce the complexity of the data set by using these components, with only a 12% loss of information.

The rotation maintains the cumulative percentage of variation explained by the extracted components, but that variation is now spread more evenly over the components. The large changes in the individual totals suggest that the rotated component matrix will be easier to interpret than the unrotated matrix.