similar to: Using R to illustrate the Central Limit Theorem

Hi, Is there a way to simulate a population with R and pull out m samples, each with n values for calculating m means? I need that kind of data to plot a graphic, demonstrating the central limit theorem and I don't know how to begin. So, perhaps someone can give me some tips and hints how to start and which functions to use. thanks for any help, joerg

Hello guys, I am stuck here: How do I make 1000 samples of n = 10 observations from an Exponential distribution and then compute the mean for all those 1000 samples? Basically I need to prove the Central Limit theorem, which states: http://www.nabble.com/file/p22664113/d175f06cbf200bd52a2c27a2e56dc594.png Where the Sn is sum of random variables, n we have from the question, mu is mean and

Dear all, I would like to know which is the right way to check the normality assumption for performing ANOVA. How do you check normality for the following example? I did an experiment where people had to evaluate on a 7 point scale, the degree of realism of some stimuli presented in 2 conditions. The problem is that if I check normality with the Shapiro test I get that the data are not

Hello, I have written a function that demonstrates the CLT by generating samples following the exponential distribution, calculating the means, plotting the histogram, and drawing the limiting normal curve as an overlay. I have the title of each histogram state the sample size and rate (1/theta) for the exponential (the output is actually 4 histograms), but I can't get the greek letter theta

----- Original Message ----- From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz> To: <paul at datavore.com> Sent: Thursday, September 04, 2003 2:56 AM Subject: Re: [R] Overlaying graphs > I do not know how to overlay the curve graphic on top of hist graphic. > > Do you know about the "add=TRUE" option for plot()? > > I am hoping to show visually

my data looks like this: PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 2 6.381684 2.250446 nein Sommer Sonntag nein ja Süd 3 62.586512 66.304869 ja Sommer Sonntag nein nein Ost 4 5.590101 8.526152 ja Sommer Donnerstag nein nein Nord 5 30.925054 16.073091 nein Winter Sonntag nein nein Ost 6

In complex analysis, Cauchy's integral theorem states (loosely speaking) that the path integral of any entire differentiable function, around any closed curve, is zero. I would like to see this numerically, using R (and indeed I would like to use the residue theorem as well). Has anyone coded up path integration? -- Robin Hankin Uncertainty Analyst Southampton Oceanography Centre

I want to understand ANOVA better. But a few textbook that I have do not describe Cochran's Theorem in details. Could somebody recommend a book for me?

Hi all, ########################################## dof=c(1,2,4,8,16,32) Q5=matrix(rt(100,dof),100,6,T,dimnames=list(NULL,dof)) par(mfrow=c(2,6)) apply(Q5,2,hist) myf=function(x){ qqnorm(x);qqline(x) } apply(Q5,2,myf) ########################################## These looks ok. However, I would like to achieve more. Apart from using a loop, is there are fast way to 'add' the titles to be

Dear experts, I tried to learn about Box-Cox-transformation but found the following thing: When I had to add a constant to make all values of the original variable positive, I found that the lambda estimates (box.cox.powers-function) differed dramatically depending on the specific constant chosen. In addition, the correlation between the transformed variable and the original were not 1 (as I

Dear Siegfried, I believe your boss is wrong saying that: >He also tried to explain me that the monthly means >(based on the daily measurements) must follow a >log-normal distribution too then over the course of a year. every statistician know that increasing the sample size the sample distribution of the mean is proxy to a gaussian distribution (Central Limit Theorem) independently

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Hi, Try either: set.seed(28) stats1<- as.data.frame(matrix(rnorm(5*10000),ncol=5)) pdf(paste("test",1,".pdf",sep="")) par(mfrow=c(2,1)) lst1<- lapply(names(stats1),function(i) {hist(stats1[,i],100,col="lightblue",main=paste0("Histogram of ",i),xlab=i );qqnorm(stats1[,i])}) dev.off() #or

Hy! I would like to know how run a montecarlo simulation with R. Thank you!!!! Francesca Matalucci __________________________________________________________________ Accesso Internet Gratis per utenti Excite! Attivalo subito! http://www.excite.it/hitech/accesso Il Mio Excite. Personalizza la tua Home page Excite come vuoi tu! http://www.excite.it AAA/Relazioni. Sfoglia gli annunci e trova la

Hi, r-gurus: I happened to have a question in my work: I have a dataset, which has only one dimention, like 0.99037297527605 0.991179836732708 0.995635340631367 0.997186769599305 0.991632565640424 0.984047197106486 0.99225943762649 1.00555642128421 0.993725402926564 .... the data is saved in a file called f392.txt. I used the following codes to play around :)

The following code was adapted from an example Vincent Zoonekynd gave on his web site http://zoonek2.free.fr/UNIX/48_R/03.html: n <- 1000 x <- rnorm(n) qqnorm(x) qqline(x, col="red") op <- par(fig=c(.02,.5,.5,.98), new=TRUE) hist(x, probability=T, col="light blue", xlab="", ylab="", main="", axes=F) lines(density(x),

Hello, I got two sets of data x=(124738, 128233, 85901, 33806, ...) y=(25292, 21877, 45498, 63973, ....) When I did a t test, I got two tail p-value = 0.117, which is not significantly different. If I changed x, y to log scale, and re-do the t test, I got two tail p-value = 0.042, which is significantly different. Now I got confused which one is correct. Any help would be very appreciated.

Hello! I am struggling for quite some time with proper printing of local characters in pdf plot, via Sweave snippets in Rnw file. When I am working directly within R, all is fine and I can get local character properly, like: pdf('figs/fig-relativeEntropy0.pdf', h=6, w=6, encoding='CP1250') matplot(par[, c(3)], type='b', ylim=c(0,0.5), xaxt='n', pch=c(21),

Tu fichero tiene los decimales como puntos y no como comas como tu le indicas. Te dejo un ejemplo #--------------------------------------------------------------------------------------------------------------------- setwd(dir="c:/Users/usuario/Desktop/") library(outliers) filename<-"timediff.csv" time<-read.csv(filename, sep=";",header=TRUE,dec=".")

Sorry for the contents not relating to R. Assume there are N i.i.d zero-mean complex gaussian random variables(RVs),as w(i),0<=i<N} with known variance,from which one can generate another N RVs,as R(0)=sum over i {w(i)*w'(i)} R(1)=sum over i {w(i+1)*w'(i)} ... up to R(N-1)= w(N-1)w'(i) where w'(i) is the complex conjugate of w(i). (from viewpoint of signal