Principal Components Analysis, or PCA, is a data analysis tool that is usually used to reduce the dimensionality (number of variables) of a large number of interrelated variables, while retaining as much of the information (variation) as possible. PCA calculates an uncorrelated set . In principal component analysis (PCA), we get eigenvectors (unit vectors) and eigenvalues. Now, let us define loadings as Loadings=Eigenvectorsâ‹…âˆšEigenvalues. I know that eigenvectors are just directions and loadings (as defined above) also include variance along these directions. Question: In Principal Component Analysis, can loadings be both positive and negative? Answer: Yes. Recall that in PCA, we are creating one index variable (or a few) from a set of variables. You can think of this index variable as a weighted average of the original variables. The loadings are the weights.

# Principal components analysis loadings definition

6 - Correlation and PCA, time: 10:34

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