|principal components analysis|
A mathematical technique invented in 1901 by Karl Pearson that is used to summarize data found in a large number of noisy records. The goal is to make patterns in the data more obvious and easily seen.
In a nutshell, the procedure transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible; each succeeding component accounts for as much of the remaining variability as possible.
An in-depth 132 second introduction
1. Click here to read Real Climate's explanation of how it was used to create the hockey stick.
2. For a more detailed tutorial, click here.