voorspelling investeringen transportmiddelensector

*Unverified author*

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Tue, 21 Oct 2008 01:56:22 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2.htm/, Retrieved Tue, 21 Oct 2008 08:01:06 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Dataseries X:

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37,6 52,8 32,1 33,3 43,6 37,5 31,9 102 55 81,1 36,2 39,7 45,6 43 33,9 34,1 62,1 43,2 70,7 74,9 68,4 64,1 70,4 51,6 52,4 73,9 85,9 52,9 68,6 71,4 71,7 94,8 94,6 59,4 94,3 49,7 59,7 58 40,8 66,4 54 31,8 55,1 72,3 69,7 56,3 57 44,1 43,8 51 42 52,6 73,7 40,5 49 74,3 59,4 125,8 77,8 102,8 70,3 103,2 42,1 59 45 57,1 57,2 147,5 98,7 70,5 41 54,2 39,8 50,9 42,3 37,6 44,6 46,4 48 152,9 64,5 50,5 60,3 53,8 52,8 50,2 70,8 62 73,7 43,4 49,6 141 122,3 86,4 134 107,1 80,9

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 63.7515463917526 2.66304338525301 23.9393570321783 Geometric Mean 59.4094482582304 Harmonic Mean 55.849587438264 Quadratic Mean 68.884486455419 Winsorized Mean ( 1 / 32 ) 63.6969072164948 2.64401615369118 24.0909674956300 Winsorized Mean ( 2 / 32 ) 63.5670103092784 2.60057889143033 24.4434077807638 Winsorized Mean ( 3 / 32 ) 63.3876288659794 2.53087899586139 25.0456971548754 Winsorized Mean ( 4 / 32 ) 63.0742268041237 2.43318987523906 25.9224433925144 Winsorized Mean ( 5 / 32 ) 62.9041237113402 2.38425755765084 26.3831076091118 Winsorized Mean ( 6 / 32 ) 62.0938144329897 2.14200333697301 28.9886637248372 Winsorized Mean ( 7 / 32 ) 61.9061855670103 2.07026994439917 29.9024703201092 Winsorized Mean ( 8 / 32 ) 61.8814432989691 2.06242167918403 30.0042633975083 Winsorized Mean ( 9 / 32 ) 61.8072164948454 2.04716101687205 30.1916732418456 Winsorized Mean ( 10 / 32 ) 61.6835051546392 1.95271238378995 31.5886280369256 Winsorized Mean ( 11 / 32 ) 61.2525773195876 1.86624478970066 32.8212985014750 Winsorized Mean ( 12 / 32 ) 61.3144329896907 1.85135549260229 33.1186707440538 Winsorized Mean ( 13 / 32 ) 61.3144329896907 1.83914456392068 33.3385608682011 Winsorized Mean ( 14 / 32 ) 60.2030927835052 1.63327621069741 36.8603255157916 Winsorized Mean ( 15 / 32 ) 60.280412371134 1.60171101542157 37.6350114288676 Winsorized Mean ( 16 / 32 ) 59.5051546391753 1.47307476073599 40.3952034379061 Winsorized Mean ( 17 / 32 ) 59.5051546391753 1.46342608880123 40.6615373981197 Winsorized Mean ( 18 / 32 ) 59.059793814433 1.36238958046991 43.3501508386921 Winsorized Mean ( 19 / 32 ) 58.5309278350515 1.27888646999877 45.7671022472446 Winsorized Mean ( 20 / 32 ) 58.4484536082474 1.25737012251939 46.4846846298004 Winsorized Mean ( 21 / 32 ) 58.4051546391753 1.24066044511159 47.075857757295 Winsorized Mean ( 22 / 32 ) 58.4051546391753 1.22914332886053 47.516960201313 Winsorized Mean ( 23 / 32 ) 58.4762886597938 1.22037644641043 47.9165988755305 Winsorized Mean ( 24 / 32 ) 58.2536082474227 1.16085481251452 50.1816485743294 Winsorized Mean ( 25 / 32 ) 58.2020618556701 1.12888217087354 51.55725137428 Winsorized Mean ( 26 / 32 ) 58.2824742268041 1.09941702990588 53.0121624837787 Winsorized Mean ( 27 / 32 ) 58.3381443298969 1.05219195533205 55.4443930446955 Winsorized Mean ( 28 / 32 ) 58.7711340206186 0.995248547289816 59.0517154540537 Winsorized Mean ( 29 / 32 ) 59.0103092783505 0.9545755751342 61.8183733331481 Winsorized Mean ( 30 / 32 ) 59.1649484536082 0.930620568133054 63.5758014378522 Winsorized Mean ( 31 / 32 ) 59.1649484536082 0.923186018816058 64.0877864782706 Winsorized Mean ( 32 / 32 ) 59.1319587628866 0.880859792594196 67.1298193651667 Trimmed Mean ( 1 / 32 ) 63.1494736842105 2.52725473872547 24.9873796719272 Trimmed Mean ( 2 / 32 ) 62.5784946236559 2.39101376401049 26.1723690451250 Trimmed Mean ( 3 / 32 ) 62.0516483516484 2.25942787372705 27.4634340282306 Trimmed Mean ( 4 / 32 ) 61.5662921348315 2.13756585943508 28.8020562562233 Trimmed Mean ( 5 / 32 ) 61.1459770114943 2.03153158190836 30.0984624388932 Trimmed Mean ( 6 / 32 ) 60.7447058823529 1.92266920691581 31.5939453671256 Trimmed Mean ( 7 / 32 ) 60.7447058823529 1.86359491998551 32.5954450889065 Trimmed Mean ( 8 / 32 ) 60.2382716049383 1.81213891073337 33.2415309048024 Trimmed Mean ( 9 / 32 ) 59.9860759493671 1.75366795477629 34.2060626619703 Trimmed Mean ( 10 / 32 ) 59.7311688311688 1.68823505267995 35.3808367717221 Trimmed Mean ( 11 / 32 ) 59.4786666666667 1.63047585308937 36.4793299784039 Trimmed Mean ( 12 / 32 ) 59.2643835616438 1.57970669717458 37.5160678040059 Trimmed Mean ( 13 / 32 ) 59.030985915493 1.52191448788947 38.7873210914463 Trimmed Mean ( 14 / 32 ) 59.030985915493 1.45485872495717 40.5750640270799 Trimmed Mean ( 15 / 32 ) 58.6373134328358 1.41475687775709 41.4469187990784 Trimmed Mean ( 16 / 32 ) 58.4738461538462 1.37185002285494 42.6240807520322 Trimmed Mean ( 17 / 32 ) 58.3746031746032 1.34331320429886 43.4556907412160 Trimmed Mean ( 18 / 32 ) 58.2688524590164 1.31009852585651 44.4766949271404 Trimmed Mean ( 19 / 32 ) 58.1966101694915 1.28687109179842 45.2233409705093 Trimmed Mean ( 20 / 32 ) 58.1666666666667 1.27180316541603 45.735588846124 Trimmed Mean ( 21 / 32 ) 58.1418181818182 1.25591312752897 46.2944585157837 Trimmed Mean ( 22 / 32 ) 58.1188679245283 1.23812518213857 46.9410272587636 Trimmed Mean ( 23 / 32 ) 58.0941176470588 1.21703404101039 47.7341764399857 Trimmed Mean ( 24 / 32 ) 58.0612244897959 1.19123005771164 48.74056368367 Trimmed Mean ( 25 / 32 ) 58.0612244897959 1.16882021255414 49.6750688139786 Trimmed Mean ( 26 / 32 ) 58.0311111111111 1.14524851620110 50.6711951949134 Trimmed Mean ( 27 / 32 ) 58.0093023255814 1.11956235493811 51.8142665924029 Trimmed Mean ( 28 / 32 ) 58.0093023255814 1.09468349090989 52.9918490662213 Trimmed Mean ( 29 / 32 ) 57.9102564102564 1.07218416242821 54.0114827653352 Trimmed Mean ( 30 / 32 ) 57.8108108108108 1.04888603702949 55.1163889782864 Trimmed Mean ( 31 / 32 ) 57.8108108108108 1.02036525720119 56.6569769039208 Trimmed Mean ( 32 / 32 ) 57.5454545454545 0.980598639332145 58.6840040739266 Median 57 Midrange 92.35 Midmean - Weighted Average at Xnp 57.7645833333333 Midmean - Weighted Average at X(n+1)p 58.0612244897959 Midmean - Empirical Distribution Function 58.0612244897959 Midmean - Empirical Distribution Function - Averaging 58.0612244897959 Midmean - Empirical Distribution Function - Interpolation 58.0612244897959 Midmean - Closest Observation 57.782 Midmean - True Basic - Statistics Graphics Toolkit 58.0612244897959 Midmean - MS Excel (old versions) 58.0612244897959 Number of observations 97

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2/1hpei1224575776.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2/1hpei1224575776.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2/2no9s1224575776.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/21/t1224576066veo3g7ezif5qxh2/2no9s1224575776.ps (open in new window)

Parameters (Session):

par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = investeringen wegtransporteurs ; par5 = investeringen transportmiddelenindustrie ;

Parameters (R input):

par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = investeringen wegtransporteurs ; par5 = investeringen transportmiddelenindustrie ;

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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