search for: theorems

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

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

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?

Hello all, I have posted this before and received no response so I am providing more info this time. I am on Linux kernel 2.2.5-15 and running Samba 2.0.6 as the Samba server. The client is a Windows NT 4.0 machine. When browsing from the NT machine (as user administrator), any share I access appears to contain the contents I put in my own share on the server. This is causing a problem because

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 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

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 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

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,

Dear R : Sorry for the off topic question, but does anyone know the reference for the -2 Ln Lambda following a Chi Square distribution, please? Possibly one of Bartlett's? Thanks in advance! Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com

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

Full_Name: Jerry W. Lewis Version: 2.7.0 (2008-03-23 r44847) OS: Windows XP Professional Submission from: (NULL) (71.184.230.48) choose(n,k) = choose(n,n-k) is not satisfied if either 1. n is a negative integer with k a positive integer (due to automatically returning 0 for n-k<0) 2. n is not an integer with k a positive integer (due to rounding n-k to an integer, compounded by

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

some times ago i was playing with coding, shannon theoremes and other stuff, i have tried without success to compress audio wave, and i have notice that simply oversampling audio material enacnhe a lot compression ratio i only take awav file, oversampled it by 20 tiimes and then compressi it using pkzip or rar. i don0't remember if i also do a CONSTANT PREDICTION, iony know that pkzip and

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

If my sample size is small is there a particular switch option that I need to use with t.test so that it calculates the t ratio correctly? Here is a dummy example? á =0.05 Mean pain reduction for A =27; B =31 and SD are SDA=9 SDB=12 drgA.p<-rnorm(5,27,9); drgB.p<-rnorm(5,31,12) t.test(drgA.p,drgB.p) # what do I need to give as additional parameter here? I can do it manually but

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