Abstract
Principal components analysis is a way of reducing the number of variables in the model. It may be that some of the variables are highly correlated with each other, so that not all are needed for a description of the subject of study; perhaps a few linear combinations of the variables would suffice. Other variables may be unrelated to any features of interest. The data on communities in Emilia-Romagna offer many such possibilities. In Chapter 4 we arbitrarily divided the variables into three groups. But do we need all the nine demographic variables in order to describe the variation in the communities or would a few variables suffice, or a few combinations of variables? Then the other variables would be contributing nothing but noise to the measurements.
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© 2004 Springer Science+Business Media New York
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Atkinson, A.C., Riani, M., Cerioli, A. (2004). Principal Components Analysis. In: Exploring Multivariate Data with the Forward Search. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21840-3_5
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DOI: https://doi.org/10.1007/978-0-387-21840-3_5
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2353-0
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