https://manuals.pqstat.pl/ 2026-05-07T17:44:10+00:00 https://manuals.pqstat.pl/ https://manuals.pqstat.pl/lib/tpl/dokuwiki/images/favicon.ico text/html 2022-02-16T09:21:04+00:00 Anonymous (anonymous@undisclosed.example.com) https://manuals.pqstat.pl/en:statpqpl:redpl:pcapl:grafpl Graphical interpretation A lot of information carried by the coefficients returned in the tables can be presented on one chart. The ability to read charts allows a quick interpretation of many aspects of the conducted analysis. The charts gather in one place the information concerning the mutual relationships among the components, the original variables, and the cases. They give a general picture of the principal components analysis which makes them a very good summary of it. text/html 2022-02-16T09:16:52+00:00 Anonymous (anonymous@undisclosed.example.com) https://manuals.pqstat.pl/en:statpqpl:redpl:pcapl:interpl Interpretation of coefficients related to the analysis Every principal component is described by: * Eigenvalue An eigenvalue informs about which part of the total variability is explained by a given principal component. The first principal component explains the greatest part of variance, the second principal component explains the greatest part of that variance which has not been explained by the previous component, and the subsequent component explains the greatest part of that variance … text/html 2022-02-16T09:22:37+00:00 Anonymous (anonymous@undisclosed.example.com) https://manuals.pqstat.pl/en:statpqpl:redpl:pcapl:kryterpl The criteria of dimension reduction There is not one universal criterion for the selection of the number of principal components. For that reason it is recommended to make the selection with the help of several methods. * The percentage of explained variance text/html 2022-02-16T09:23:27+00:00 Anonymous (anonymous@undisclosed.example.com) https://manuals.pqstat.pl/en:statpqpl:redpl:pcapl:sklpl Defining principal components When we have decided how many principal components we need we can start generating them. In the case of principal components created on the basis of a correlation matrix they are computed as a linear combination of standardized original values. If, however, principal components have been created on the basis of a covariance matrix, they are computed as a linear combination of eigenvalues which have been centralized with respect to the mean of the original values. text/html 2022-02-16T09:31:39+00:00 Anonymous (anonymous@undisclosed.example.com) https://manuals.pqstat.pl/en:statpqpl:redpl:pcapl:zasadnpl The advisability of using the Principal Component Analysis If the variables are not correlated (the Pearson's correlation coefficient is near 0), then there is no use to conduct a principal component analysis, as in such a situation every variable is already a separate component.