3 ms·

Yeah, I think this is completely off-base in blaming the axes. The axes are perfectly fine in showing the actual correlation that's present here (almost certain

by mjn 13y ago

Yeah, I think this is completely off-base in blaming the axes. The axes are perfectly fine in showing the actual correlation that's present here (almost certainly due to a third common causal factor, of course). There's no really magically "unbiased" way of choosing axes. There seems to be a popular view recently that you should always start your axes at zero, I assume as a backlash to some graphs magnifying very small differences by choosing zoomed-in axis values that visually exaggerate variation. That isn't really an absolute truth either, since interesting data regions are not always near zero. For example, if you graph temperature variation in different cities staring at 0 K, you can make it look like essentially all habitable cities have around the same temperature, somewhere in the range of 250-300 K give or take. Of course 250 K versus 300 K is a huge difference to human perception of temperature, while the entire range 0-200 K is more or less irrelevant when discussing weather, so starting your axis at 0 would be a poor choice. In this case that would be the biased choice, intended to visually minimize actually important variation by choosing an unreasonably low starting point for the Y axis.

scotty79 13y ago
> (almost certainly due to a third common causal factor, of course). ..or absolutely certainly associated so remotely that correlation is purely accidental and found only by carefully cherry picking data sources, ranges, functions to massage the data (log with the right base) and axis ranges. To sum up ... not meaningful. You could find similar correlation between ocean temperatures and lottery numbers but you'd have to precisely adjust so many inputs that the correlation would be not so much found as constructed.
- squidfood 13y ago
  Actually, in this case I'd argue that a general/generic measure of "human scientific and technical advancement" is a decent underlying causal factor. Maybe meaningful, if kind of obvious...
- Someone 13y ago
  "log with the right base" doesn't make any difference. That's a linear mapping. For instance: 2log x = log x / log 2 = ln x / ln 2 (With 2log the logarithm in base 2, log and ln the logarithm in bases you pick, but you can think of 10 and e) See, for example, http://www.purplemath.com/modules/logrules5.htm http://www.purplemath.com/modules/logrules5.htm, or derive it from the axioms.
  - scotty79 13y ago
    I think this ln2 factor matters when you are drawing a graph. But I guess that's included in what I called axis range.

by mjn 13y ago

scotty79 13y ago

> (almost certainly due to a third common causal factor, of course). ..or absolutely certainly associated so remotely that correlation is purely accidental and found only by carefully cherry picking data sources, ranges, functions to massage the data (log with the right base) and axis ranges. To sum up ... not meaningful. You could find similar correlation between ocean temperatures and lottery numbers but you'd have to precisely adjust so many inputs that the correlation would be not so much found as constructed.

squidfood 13y ago
Actually, in this case I'd argue that a general/generic measure of "human scientific and technical advancement" is a decent underlying causal factor. Maybe meaningful, if kind of obvious...
Someone 13y ago
"log with the right base" doesn't make any difference. That's a linear mapping. For instance: 2log x = log x / log 2 = ln x / ln 2 (With 2log the logarithm in base 2, log and ln the logarithm in bases you pick, but you can think of 10 and e) See, for example, http://www.purplemath.com/modules/logrules5.htm http://www.purplemath.com/modules/logrules5.htm, or derive it from the axioms.
- scotty79 13y ago
  I think this ln2 factor matters when you are drawing a graph. But I guess that's included in what I called axis range.