# Posts Tagged ‘ distribution ’

## Where hiding if you don’t want to get wet ?

April 5, 2012
By

Following the previous post, two additional remarks. Following a comment by @cosi, I have investigated quickly a binomial fit to the distribution of the number of people not getting wet, with a fixed number of players on the field. It looks like it...

## Diagram for a Bernoulli process (using R)

November 10, 2011
By

A Bernoulli process is a sequence of Bernoulli trials (the realization of n binary random variables), taking two values (0/1, Heads/Tails, Boy/Girl, etc…). It is often used in teaching introductory probability/statistics classes about the binomial distribution. When visualizing a Bernoulli process, it is common to use a binary tree diagram in order to show the Read more...

## Tumblr Likes

April 11, 2011
By

Look at just the first digit and the number of digits. science: 32914, 11566, 4989, 3743, 968, 814, 673, 482, 286, 2811 black and white: 1694, 1167, 1108, 988, 919, 639, 596, 591, 580, 544 lol: 22627, 18100, 17688, 14374, 13459, 12045, 4711, 3779, 36...

## A von Mises variate…

March 25, 2010
By

Inspired from a mail that came along the previous random generation post the following question rised : How to draw random variates from the Von Mises distribution? First of all let’s check the pdf of the probability rule, it is , for . Ok, I admit that Bessels functions can be a bit frightening, but

## The distribution of rho…

March 21, 2010
By

There was a post here about obtaining non-standard p-values for testing the correlation coefficient. The R-library SuppDists deals with this problem efficiently. library(SuppDists) plot(function(x)dPearson(x,N=23,rho=0.7),-1,1,ylim=c(0,10),ylab="density") plot(function(x)dPearson(x,N=23,rho=0),-1,1,add=TRUE,col="steelblue") plot(function(x)dPearson(x,N=23,rho=-.2),-1,1,add=TRUE,col="green") plot(function(x)dPearson(x,N=23,rho=.9),-1,1,add=TRUE,col="red");grid() legend("topleft", col=c("black","steelblue","red","green"),lty=1, legend=c("rho=0.7","rho=0","rho=-.2","rho=.9"))</pre> This is how it looks like, Now, let’s construct a table of critical values for some arbitrary or not significance levels. q=c(.025,.05,.075,.1,.15,.2) xtabs(qPearson(p=q, N=23, rho

## In search of a random gamma variate…

March 16, 2010
By

One of the most common exersices given to Statistical Computing,Simulation or relevant classes is the generation of random numbers from a gamma distribution. At first this might seem straightforward in terms of the lifesaving relation that exponential and gamma random variables share. So, it’s easy to get a gamma random variate using the fact that