Large data tables usually contain a large amount of information, which is partly hidden because the data are too complex to be easily interpreted.
Principal Component Analysis (PCA) is a projection method that helps you visualize all the information contained in a data table.
PCA helps you find out in what respect one sample is different from another, which variables contribute most to this difference, and whether those variables contribute in the same way (i.e. are correlated) or independently from each other. It also enables you to detect sample patterns, like any particular grouping.
Finally, it quantifies the amount of useful information - as opposed to noise or meaningless variation - contained in the data.
It is important that you understand PCA, since it is a very useful method in itself, and forms the basis for several classification (SIMCA) and regression (PLS/PCR) methods.
|All-In-One Multivariate Data Analysis (MVA) and Design of Experiments (DoE) Package with Principal Component Analysis|