CT werkloze vrouwen

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Sun, 19 Oct 2008 09:15:41 -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/19/t1224429394ntey63h29fj2yvv.htm/, Retrieved Sun, 19 Oct 2008 15:16:36 +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/19/t1224429394ntey63h29fj2yvv.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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8.9 8.9 8.5 8.1 7.5 7.1 6.9 7.1 7 6.7 7 7.3 7.7 8.4 8.4 8.8 9.1 9 8.6 7.9 7.7 7.8 9.1 9.4 9.3 8.7 8.4 8.6 9 9.1 8.7 8.2 7.9 7.9 9.1 9.4 9.5 9.1 9 9.3 9.9 9.8 9.4 8.3 8 8.5 10.4 11.1 10.9 9.9 9.2 9.2 9.5 9.6 9.5 9.1 8.9 9 10.1 10.3 10.2 9.6 9.2 9.3 9.4 9.4 9.2 9 9 9 9.8 10 9.9 9.3 9 9 9.1 9.1 9.1 9.2 8.8 8.3 8.4 8.1 7.8 7.9 7.9 8 7.9 7.5 7.2 6.9 6.6 6.7

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 8.71808510638298 0.0996443316497163 87.492032532568 Geometric Mean 8.66340696999498 Harmonic Mean 8.60691894316125 Quadratic Mean 8.77088389247738 Winsorized Mean ( 1 / 31 ) 8.71702127659574 0.098880004623186 88.1575735136215 Winsorized Mean ( 2 / 31 ) 8.7063829787234 0.0966116471509593 90.1173226569604 Winsorized Mean ( 3 / 31 ) 8.7095744680851 0.0946530848523022 92.0157486855883 Winsorized Mean ( 4 / 31 ) 8.70531914893617 0.0939044406540596 92.704020047425 Winsorized Mean ( 5 / 31 ) 8.70531914893617 0.091934732367607 94.6902103780254 Winsorized Mean ( 6 / 31 ) 8.69893617021277 0.0909229057516587 95.6737589752412 Winsorized Mean ( 7 / 31 ) 8.69893617021277 0.0883320449980216 98.4799590047711 Winsorized Mean ( 8 / 31 ) 8.69893617021277 0.0883320449980216 98.4799590047711 Winsorized Mean ( 9 / 31 ) 8.70851063829787 0.0865022229712664 100.673836338178 Winsorized Mean ( 10 / 31 ) 8.70851063829787 0.0829928010162161 104.930916075435 Winsorized Mean ( 11 / 31 ) 8.73191489361702 0.0788880160167146 110.687469840374 Winsorized Mean ( 12 / 31 ) 8.7063829787234 0.0753973461383687 115.473334601798 Winsorized Mean ( 13 / 31 ) 8.73404255319149 0.0708408366675649 123.291070010618 Winsorized Mean ( 14 / 31 ) 8.71914893617021 0.0689542866908363 126.448250784805 Winsorized Mean ( 15 / 31 ) 8.73510638297872 0.0664783564445062 131.397748833795 Winsorized Mean ( 16 / 31 ) 8.73510638297872 0.0664783564445062 131.397748833795 Winsorized Mean ( 17 / 31 ) 8.73510638297872 0.0616205212991643 141.756450591683 Winsorized Mean ( 18 / 31 ) 8.73510638297872 0.0616205212991643 141.756450591683 Winsorized Mean ( 19 / 31 ) 8.73510638297872 0.0616205212991643 141.756450591683 Winsorized Mean ( 20 / 31 ) 8.73510638297872 0.0616205212991643 141.756450591683 Winsorized Mean ( 21 / 31 ) 8.73510638297872 0.0616205212991643 141.756450591683 Winsorized Mean ( 22 / 31 ) 8.71170212765957 0.059006034903767 147.640866597247 Winsorized Mean ( 23 / 31 ) 8.73617021276596 0.0554479081565982 157.556353399175 Winsorized Mean ( 24 / 31 ) 8.73617021276596 0.0554479081565982 157.556353399175 Winsorized Mean ( 25 / 31 ) 8.76276595744681 0.0517146855303596 169.444440541025 Winsorized Mean ( 26 / 31 ) 8.73510638297872 0.0487479345498315 179.189261322435 Winsorized Mean ( 27 / 31 ) 8.76382978723404 0.0447891183181853 195.668727501513 Winsorized Mean ( 28 / 31 ) 8.7936170212766 0.0408334725336235 215.353152099314 Winsorized Mean ( 29 / 31 ) 8.7936170212766 0.0408334725336235 215.353152099314 Winsorized Mean ( 30 / 31 ) 8.82553191489362 0.036768836156652 240.027502564749 Winsorized Mean ( 31 / 31 ) 8.79255319148936 0.0333186662435931 263.892711887292 Trimmed Mean ( 1 / 31 ) 8.71521739130435 0.095677417074773 91.0895972922565 Trimmed Mean ( 2 / 31 ) 8.71333333333333 0.0920049936222796 94.7050044816627 Trimmed Mean ( 3 / 31 ) 8.71333333333333 0.0891915036489967 97.6924143764152 Trimmed Mean ( 4 / 31 ) 8.71976744186047 0.086816626211275 100.438911558717 Trimmed Mean ( 5 / 31 ) 8.72380952380952 0.0843390666476225 103.437349624208 Trimmed Mean ( 6 / 31 ) 8.72380952380952 0.0820593565739864 106.310966695723 Trimmed Mean ( 7 / 31 ) 8.73375 0.0796738555581043 109.618769404609 Trimmed Mean ( 8 / 31 ) 8.7397435897436 0.0775142396893061 112.750168546765 Trimmed Mean ( 9 / 31 ) 8.74605263157895 0.0749839933673259 116.638928374146 Trimmed Mean ( 10 / 31 ) 8.75135135135135 0.0724113481721393 120.856075356411 Trimmed Mean ( 11 / 31 ) 8.75694444444444 0.0700945702805886 124.930424844469 Trimmed Mean ( 12 / 31 ) 8.75694444444444 0.0681734482597077 128.450953677519 Trimmed Mean ( 13 / 31 ) 8.76617647058823 0.0665073185130254 131.807696755529 Trimmed Mean ( 14 / 31 ) 8.76969696969697 0.0653471881378818 134.201596420538 Trimmed Mean ( 15 / 31 ) 8.775 0.0642416074439621 136.593717827724 Trimmed Mean ( 16 / 31 ) 8.77903225806452 0.0633181342731934 138.649572651436 Trimmed Mean ( 17 / 31 ) 8.78333333333333 0.0621719261342827 141.274911032394 Trimmed Mean ( 18 / 31 ) 8.78793103448276 0.0615428566735672 142.793680850652 Trimmed Mean ( 19 / 31 ) 8.79285714285714 0.0607238342376937 144.80075662612 Trimmed Mean ( 20 / 31 ) 8.79814814814815 0.0596689411241384 147.449376214738 Trimmed Mean ( 21 / 31 ) 8.80384615384615 0.0583173780244758 150.964368633843 Trimmed Mean ( 22 / 31 ) 8.81 0.0565865780581096 155.690630222469 Trimmed Mean ( 23 / 31 ) 8.81875 0.0547929896962938 160.946684035321 Trimmed Mean ( 24 / 31 ) 8.81875 0.0532458324429977 165.623290976640 Trimmed Mean ( 25 / 31 ) 8.83409090909091 0.0511953500525175 172.556509527304 Trimmed Mean ( 26 / 31 ) 8.84047619047619 0.0493934785542368 178.980635687945 Trimmed Mean ( 27 / 31 ) 8.85 0.0475016868796283 186.309173028452 Trimmed Mean ( 28 / 31 ) 8.8578947368421 0.0459809440007501 192.642733404899 Trimmed Mean ( 29 / 31 ) 8.86388888888889 0.0449254722842701 197.302074707235 Trimmed Mean ( 30 / 31 ) 8.87058823529412 0.0433715884406237 204.525325316089 Trimmed Mean ( 31 / 31 ) 8.87058823529412 0.042359825763448 209.410404207764 Median 9 Midrange 8.85 Midmean - Weighted Average at Xnp 8.82857142857143 Midmean - Weighted Average at X(n+1)p 8.82857142857143 Midmean - Empirical Distribution Function 8.82857142857143 Midmean - Empirical Distribution Function - Averaging 8.82857142857143 Midmean - Empirical Distribution Function - Interpolation 8.82857142857143 Midmean - Closest Observation 8.82857142857143 Midmean - True Basic - Statistics Graphics Toolkit 8.82857142857143 Midmean - MS Excel (old versions) 8.82857142857143 Number of observations 94

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/19/t1224429394ntey63h29fj2yvv/11ow91224429339.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/19/t1224429394ntey63h29fj2yvv/11ow91224429339.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/19/t1224429394ntey63h29fj2yvv/2vlq71224429339.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/19/t1224429394ntey63h29fj2yvv/2vlq71224429339.ps (open in new window)

Parameters (Session):

Parameters (R input):

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 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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