You can use any model you want, as long as it’s linear and has a positive slope

4 thoughts on “You can use any model you want, as long as it’s linear and has a positive slope”

  1. Brilliant.

    I have always regarded principal components analysis (and its close cousin, factor analysis) with a great deal of scepticism. Factor analysis with poor graphing goes the extra mile.

    The projections of vehicle miles reminded me of a counter-example of the limits of linear regression from a stats text used in my freshman year in college. The weights of the starting defensive line at the University of Texas were plotted and projected (I’m old enough that in those days, UT was a prestige football programme).

    Recall the old maxim of Box – all models are wrong; some models are useful.

    These sort of things are dangerously close to escaping the latter.

    1. Were the defensive linemen expected to have an infinite mass, or zero? In any case, this reminds me of the widely criticized (and quite wrong) prediction in Nature long ago of women’s and men’s running speeds becoming equal around now, again a product of linear extrapolation. I think the article can be accessed here:

      http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCcQFjAA&url=http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F31960868_How_predictable_is_chaos%2Ffile%2F9fcfd4fba7a7ce126a.pdf&ei=6W_0UpWSEMf0oAT36oGACg&usg=AFQjCNG-wzl5TiXUz2mOFgHLf-Z2P3c03A&sig2=nYzIYO_AIl-M_0ZQK04AkA&bvm=bv.60799247,d.cGU

      Pubmed http://www.ncbi.nlm.nih.gov/pubmed/1731197

      and better-looking things on the topic (that I haven’t read): http://www.isds.duke.edu/~dalene/chance/chanceweb/131.wainer.pdf

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