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The pitprops data is a correlation matrix that was calculated from 180 observations. There are 13 explanatory variables.
pitprops
data(pitprops)
Jeffers (1967) tried to interpret the first six PCs. This is a classical example showing the difficulty of interpreting principal components.
Jeffers, J. (1967) "Two case studies in the application of principal component", Applied Statistics, 16, 225-236.