Principal Component Analysis
from all variants
discussed in the textual commentary TCG

Wieland Willker, 2009

What is a Principal Component Analysis?
Principal Component Analysis (PCA) is a multivariate analysis technique which finds major patterns in data variability. In mathematical terms, it is finding eigenvalues and corresponding eigenvectors (=principal components, PC). Most important are the first few principal components that explain most of the observed variance; the rest of them are mostly random fluctuations. Thus, by plotting data versus first 2 or 3 PC we can reduce dimensionality of the data without much loss of information.

Other analyses I have done: This time I used all variants discussed in the textual commentary TCG.

Note that this analysis is not based on any subjective rating, but is just the result of entering the manuscripts for each variant reading!

Gospel of Matthew:

Gospel of Mark:

Gospel of Luke:

Gospel of John: