The results of the orthogonal rotation yielded an interpretable three - factor solution that collectively explained 74.624 % of the variance for the set of six variables (34.238 % explained by Factor 1, 23.574 % by Factor 2, and 16.812 % by Factor 3) with the rotated factors
obtaining eigenvalues ranging from 1.01 to 2.054.
Not exact matches
Mann et al. are very clear that better results are
obtained when the data set is first reduced by taking the first M
eigenvalues.
We found that a good description of the shower shape is
obtained when only the two most significant parameters, corresponding to the largest
eigenvalues, are kept.