4 ms·
It's at least tangentially relevant. I naively took some data and threw some curve fits at it until I had a function that reasonably matched reality for the ob
by gnarbarian 9y ago
It's at least tangentially relevant. I naively took some data and threw some curve fits at it until I had a function that reasonably matched reality for the observed data.
When I set the axi to log/log I get a straight line, which is his first point.
- deleted 9y ago[deleted]
- wenc 9y agoThe article is talking about power law probability distributions [0]. What you seem to be doing is fitting a curve with a term that has an exponent, which is a power-law relationship. That is not what the article is talking about, unless you're claiming the residuals of your fit follow a power law distribution. I'm not blaming you for latching on the the term "power-law" -- it's a simple enough mistake -- but HN upvoters should recognize that this is not what the article is talking about at all. [0] https://en.wikipedia.org/wiki/Power_law#Power-law_probability_distributions https://en.wikipedia.org/wiki/Power_law#Power-law_probabilit...
- titzer 9y agoIf you are thinking of using a log-log plot to demonstrate something, please don't. Log-log plots are extremely deceptive. All polynomials (even x^99) are straight lines on a log-log plot. http://www.cs.utsa.edu/~cs1173/experiments/Experiment2LogarithmicScales/Experiment2LogarithmicScales.html http://www.cs.utsa.edu/~cs1173/experiments/Experiment2Logari...
- gnarbarian 9y agoIt's the r^2 that matters right? I could give a crap about the picture.