Spearman's g, of course, is IQ. When various tests of mental abilities (verbal, mathematical, and geometrical, for example) are given, it is found that scores tend to be positively correlated, so that better performance on one type of test is correlated with better performance on others. Factor analysis is a tool for analyzing such correlations. If we measure a couple of parameters that are strongly correlated, like human height and weight, for example, and display them on a graph, they will tend to cluster in a roughly elliptical region along a line. Factor analysis finds the line of best bit. For poorly correlated variables, like perhaps time of day and height, clustering will be less evident. Factor analysis works in higher dimensions too. The essential idea is to transform the original measurement variables into linear combinations that resolve the highest amount of variance. If one measures a large number of variables, or simulates a large number of random variables, c...