Amazingly bad article, riddled with inaccuracies. Is it because the stat/math has to be toned down so much becaudse otherwise the average Forbes reader can't gr

Amazingly bad article, riddled with inaccuracies. Is it because the stat/math has to be toned down so much becaudse otherwise the average Forbes reader can't grok the content ? First he says, "mean and standard deviation define a normal distribution". They do no such thing. You don't even know what probability distribution attendance at sports events is when you compute mu and sigma. Then he says, ok its not normal, its power law. But the reason he attributes for power law is all wrong. Its not normal because there are no "negative people" to lower the mean ?! Ha ha ha! His second example on the height weight regression line has little to do with whether there is a physical relationship between height and weight. If you really went to know, your weight might be a function of some two dozen variables like body surface area, bone structure/density, genetics, food-intake, income levels, nature of work ( sedentary vs active ) etc. Even if you gathered data on all those, 24 dimensions is a lot. So you do a principal components, and what does that do ? The first PC from the PCA gives you a conflated component that explains say 90% of your weight. That conflated component is going to be a linear weighted sum of many of the 24 dimensions. That conflated component is just a math object, doesn't exist in reality. If you want, I can easily connect your body weight to the attendance at your local sports event and a residual, and that regression might even be a good fit :) In any case, none of this has anything to do with R, so his title is pure linkbait.