Principal Components Analysis (PCA) and Canonical Correlation Analysis (CCA) are among the methods used in Multivariate Data Analysis. PCA is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Its general objectives are data reduction and interpretation. CCA seeks to identify and quantify the associations between two sets of variables i.e Pulp fibres and Paper variables.PCA shows that the first PC already exceeds 90% of the total variability. According to the proportion of variability explained by each canonical variable , the results suggest that the first two canonical correlations seem to be sufficient to explain the structure between
Pulp and Paper characteristics with 98.86%. Despite the fact that the first the two canonical
variables keep 98% of common variability, 78% was kept in the pulp fiber set and about
94% of the paper set as a whole. In the proportion of opposite canonical variable,there were
approximately 64% for the paper set of variables and 78% for the pulp fiber set of variables
kept for the two respectively.
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