Talk:Bean machine

From Wikipedia, the free encyclopedia
Jump to: navigation, search
WikiProject Statistics (Rated Start-class, Low-importance)
WikiProject icon

This article is within the scope of the WikiProject Statistics, a collaborative effort to improve the coverage of statistics on Wikipedia. If you would like to participate, please visit the project page or join the discussion.

Start-Class article Start  This article has been rated as Start-Class on the quality scale.
 Low  This article has been rated as Low-importance on the importance scale.
 

Three dimensional bean machine[edit]

I am preparing a paper in which I use the bivariate binomial distribution proposed by Aitken and Gonin (1935), which is based on a fourfold sampling procedure in contrast to the twofold sampling procedure in the ordinary bean machine. For a demonstration one actually needs a three dimensional bean machine. In the traditional bean machine, after the ball has fallen on the first pin, the ball can fall on one of two pins with probabilities p1 and p2 with p1+p2 = 1. In the case of a fourfold sampling procedure according to Aitken and Gonin after the ball has fallen on the first pin, the ball can fall on one of four pins, arranged as the corner points of a square, with probabilities p1, p2, p3, p4 with p1+p2+p3+p4 = 1 and so on. Does one know of such three dimensional bean machine.

Reference

Aitken, A.C. and Gonin, H.T. On fourfold sampling with and without replacement, Proc. Roy. Soc. Edinburgh 55, 114–125, 1935. — Preceding unsigned comment added by Ad van der Ven (talkcontribs) 18:51, 30 January 2012 (UTC)

Pic[edit]

A picture of a bean machine would make this article much clearer. Michael Hardy 02:10, 16 May 2004 (UTC)

You are SO stupid!!! Dumbass!!! — Preceding unsigned comment added by 187.57.178.2 (talk) 13:45, 23 January 2013 (UTC)

But how does it work?[edit]

Can anyone explain why it aproximates a bell curve or normal distribution? The article doesnt define why it just isnt random. —The preceding unsigned comment was added by 159.153.156.60 (talk) 10:30, 3 May 2007 (UTC).

Give Binomial distribution a read and see if it answers your question. Essentally, each peg in the bean machine/Galton board presents a left/right binary decision and the resulting bin that the the beans/balls eventually fall into is a result of a series of these left/right binary decisions.
Atlant 12:07, 3 May 2007 (UTC)


The statement

"According to the central limit theorem the binomial distribution approximates normal distribution provided that n, the number of rows of pins in the machine, is large."

is incorrect. The central limit theorem is freqently misapplied in this fashion. It has to do with the distribution of the SUM of the random variables. This does not mean that if you "sum" enough graphs of the binomial distribution that it will bcome a normal distribution. —Preceding unsigned comment added by 192.25.240.225 (talk) 15:13, 31 March 2008 (UTC)

does that statement really wrong? I do not think so. As I knew, when a ball is falling down from the top of the machine, it will bounce left and right as they hit each pin. And each hit is an binomial experiment. The final position a ball get depends on the SUM of the results of the experiments(hits). Then, the height curve of the balls is the Probability density function curve of binomial distribution. according to CLT, it will be a bell curve.Chaosconst (talk) 06:35, 16 September 2008 (UTC)

Clarity of machine description[edit]

I had not come across Galton's box before. It would have been helpful to point out that balls are fed to the machine centrally at the top.

Tony.payton (talk) 10:41, 15 September 2008 (UTC)

Distribution of the balls[edit]

The article states "If a ball bounces to the right k times on its way down (and to the left on the remaining pins) it ends up in the kth bin counting from the left." Surely this cannot be correct? If the ball bounces right k times and left l times it will end up (k - l) bins to the right of the central bin?

Tony.payton (talk) 10:47, 15 September 2008 (UTC)