Timeline for Can averaging all the variables be seen as a crude form of PCA?

7 events

when toggle format	what		by	license	comment
Feb 24, 2015 at 18:39	vote	accept	Bryan
Jan 28, 2015 at 14:59	history	edited	amoeba	CC BY-SA 3.0	simplified the title
Dec 29, 2014 at 1:09	answer	added	amoeba		timeline score: 11
Dec 29, 2014 at 1:09	history	edited	amoeba	CC BY-SA 3.0	clarified the title
Apr 15, 2014 at 20:37	comment	added	whuber♦		Arguably this is not a question about data analysis at all, so PCA is practically irrelevant. It is a question about valuation. Because the scores are intended to be used to compare objects, the weights given to the variables and how these weighted values are combined determine how much of one variable can be traded off for how much of another variable. This is the subject of multi-attribute valuation theory, as discussed in a concrete version of the same question at stats.stackexchange.com/questions/9358. (One comment thread there also discusses the inapplicability of PCA.)
Apr 15, 2014 at 17:58	comment	added	Nick Cox		I don't think this helps much. One way to think about it is that PCA is usually based on a correlation or covariance matrix, so means are subtracted out at the first step. So, the means of the variables are extra information, not something produced by PCA. There is a loose sense in which most statistics is a kind of averaging, which may be the direction of your thinking. Furthermore, a key feature of PCA is that the PCs are new variables, but if you reduce those to their means, you get zeros everywhere.
Apr 15, 2014 at 17:47	history	asked	Bryan	CC BY-SA 3.0