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campermon wrote:

Sityl wrote:I was reading the following, and didn't even get past the beggining since the example he used involves math which I haven't learned yet. I was wondering if anyone could, assuming no background in calculus or statistics, explain the relationship between gaussian distribution and pi.

http://www.dartmouth.edu/~matc/MathDrama/reading/Wigner.html wrote:THERE IS A story about two friends, who were classmates in high school, talking about their jobs. One of them became a statistician and was working on population trends. He showed a reprint to his former classmate. The reprint started, as usual, with the Gaussian distribution and the statistician explained to his former classmate the meaning of the symbols for the actual population, for the average population, and so on. His classmate was a bit incredulous and was not quite sure whether the statistician was pulling his leg. "How can you know that?" was his query. "And what is this symbol here?" "Oh," said the statistician, "this is pi." "What is that?" "The ratio of the circumference of the circle to its diameter." "Well, now you are pushing your joke too far," said the classmate, "surely the population has nothing to do with the circumference of the circle."

A cursory google search of explanations seems to involve funky "f" from infinity to negative infinity, but I don't know what that means.

Thanks!

I'll take your questions in reverse order and try to answer them in 'pub notation'

The funky 'f' is an elongated 'S' and basically means 'sum'. If you have a straight line graph and you want to know the area under the graph, you are basically working out the area of a triangle which is easy. If you have a curvy graph, it's a little trickier. What you do is break down the curve graph into a sequence of rectangles;



Finding the area of rectangle is easy, so you do that and add (sum) them all up to find the total area.

Now then. If you have the maths expression for the curve, then you bugger about with some calculus to come up with an expression to calculate the area. In simple terms, the calculus bit slices the curve into an infinite number of infinitesimally thin rectangles that then add (sum) to make the area. Hence the big 's'.

Finally. For the gaussian normal distribution, we want to make the total area under the graph to equal 1. For example, the curve might represent the probability that a molecule might have a given speed. The molecule has to have some speed, so the probability that is has a speed somewhere on the graph is 1. The Pi comes in to the sums because it makes the area under the graph equal 1.