similar to: Cauchy's theorem

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

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

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?

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

R users, I am having a problem with the modulus operator for large numbers as follows, a <- 2 n <- 561 ## n is the first Carmichael number, so by Fermat's Little Theorem the below should equal zero. (a^(n-1) - 1) %% n [1] 2.193172e+152 ## Seems that R and Fermat disagree ## Also, 1000000000000000000 %% 11 [1] -32 This seems like a bug. Should I be avoiding integer math for large

Dear All I am totally new to R and I would like to know whether R is able and appropriate to illustrate to my students the Central Limit Theorem, using for instance 100 independent variables with uniform distribution and showing that their sum is a variable with an approximated normal distribution. Thanks in advance, Paul

Hello to the R people, within ward clustering the distance calculated to decide the clustering of 2 subsets (h1 and h2) is the variation of inertia : d(h1,h2)=I(h1Uh2)-I(h1)-I(h2); i've been said that a way to calculate faster this d(h1,h2) is using the huygens theorem decomposing the inertia into "the inertia to the centroid + the distance to an axe" (that's my version ...). My

Dear list useRs, I have to generate a random set of coordinates (x,y) in [-1 ; 1]^2 for say, N points. At each of these points is drawn a circle (later on, an ellipse) of random size, as in: > N <- 100 > > positions <- matrix(rnorm(2 * N, mean = 0 , sd= 0.5), nrow=N) > sizes<-rnorm(N, mean = 0 , sd= 1) > plot(positions,type="p",cex=sizes) My problem is to

Dear list, I?m trying to register Fortran routines in randtoolbox (in srt/init.c file), see https://r-forge.r-project.org/scm/viewvc.php/pkg/randtoolbox/src/init.c?view=markup&root=rmetrics. Reading https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Registering-native-routines and looking at what is done in stats package, I first thought that the following code will do the job:

Thanks for your email. I try to change the name in lowercase but it conflicts with a C implementation also named halton. So I rename the C function halton2() and sobol2() while the Fortran function are HALTON() and SOBOL() (I also try lower case in the Fortran code). Unfortunately, it does not help since I get init.c:97:25: error: use of undeclared identifier 'halton_'; did you mean

Dear LLVMers, This email is intended for those interested in path-sensitive analysis, integer overflow analysis, static analysis, and (perhaps) loop invariant computation. Traditionally, such analyses have been considered too expensive to be practical, and were mostly an academic curiosity. The core of the problem is the lack of adequate automated decision procedures which could quickly

Hi all, I need to transform classic 32bit Fortran code to 64bit Fortran code, see the discussion [R-SIG-Mac] rnorm.halton. But I'm clearly a beginner in Fortran... Does someone already do this for his package? From here, http://techpubs.sgi.com/library/tpl/cgi-bin/getdoc.cgi?coll=linux&db=bks&fname=/SGI_Developer/Porting_Guide/ch03.html , I identify the following changes

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

In package fOptions, there are functions that generate Halton sequences. The van der Corput sequence for base 2 is a particular case of the Halton sequence generated by: n <- 8 # anything here... x <- runif.halton(n, 1) In fact, x <- runif.halton(n, dim) will generate the van der Corput sequences for the base b as the i-th prime number in x[,i]. (in other words, if I want the van der

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Have you tried using tools:::package_native_routine_registration_skeleton()? If you don't like its output, you can easily edit its results and still avoid most pitfalls. Cheers, Jari Oksanen ________________________________________ From: R-devel <r-devel-bounces at r-project.org> on behalf of Berend Hasselman <bhh at xs4all.nl> Sent: 10 May 2017 09:48 To: Christophe Dutang Cc:

On 4/9/07, Domagoj Babic <babic.domagoj at gmail.com> wrote: > > > Traditionally, such analyses have been considered too expensive to be > practical, and were mostly an academic curiosity. The core of the > problem is the lack of adequate automated decision procedures which > could quickly determine whether a set of constraints is satisfiable or > not, and if it is

Hi, I have a question to ask. if in a linear regression model, the independent variables are not statistically significant, is it necessary to test these variables in a non-linear model? Since most of non-linear form of a variable can be represented to a linear combination using Taylor's theorem, so I wonder whether the non-linear form is also not statistically significant in such a

IMO super optimizer would yield less benefits on LLVM compared to other compilers. If you check the patch of the instcombine pass, you'll find out people keep dragging "correct" optimization out, not because the optimization violates the semantic of LLVM IR, but it will generate wrong code sequences when lowering to machine code. An example: %3 = fcmp %1, %2 %6 = fcmp %4, %5 %7 =

Hello R list, I need to compute empirical variograms using data from a large geographic area (~10^6 km2). Although I could not find a specific reference, I assume that both geoR and gstat calculate distances among data points assuming points are on a flat surface (using the Pythagorean Theorem). Because the location of my data is large and located near the pole, assuming that latitude and