US DOllar per euro- robuust- Raf Deceulaer

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

Title produced by software: Central Tendency

Date of computation: Mon, 21 Apr 2008 15:55:27 -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/Apr/21/t1208814979gfjruznjy12dn6l.htm/, Retrieved Mon, 21 Apr 2008 23:56:22 +0200

User-defined keywords:

Dataseries X:

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1,0137 0,9834 0,9643 0,947 0,906 0,9492 0,9397 0,9041 0,8721 0,8552 0,8564 0,8973 0,9383 0,9217 0,9095 0,892 0,8742 0,8532 0,8607 0,9005 0,9111 0,9059 0,8883 0,8924 0,8833 0,87 0,8758 0,8858 0,917 0,9554 0,9922 0,9778 0,9808 0,9811 1,0014 1,0183 1,0622 1,0773 1,0807 1,0848 1,1582 1,1663 1,1372 1,1139 1,1222 1,1692 1,1702 1,2286 1,2613 1,2646 1,2262 1,1985 1,2007 1,2138 1,2266 1,2176 1,2218 1,249 1,2991 1,3408 1,3119 1,3014 1,3201 1,2938 1,2694 1,2165 1,2037 1,2292 1,2256 1,2015 1,1786 1,1856 1,2103 1,1938 1,202 1,2271 1,277 1,265 1,2684 1,2811 1,2727 1,2611 1,2881 1,3213 1,2999 1,3074 1,3242 1,3516 1,3511 1,3419 1,3716 1,3622 1,3896 1,4227 1,4684 1,457 1,4718

Text written by user:

Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.12979896907217 0.0182046066409809 62.061157999912 Geometric Mean 1.11534535970301 Harmonic Mean 1.10067745466107 Quadratic Mean 1.14379231069281 Winsorized Mean ( 1 / 32 ) 1.12978453608247 0.0181945270544227 62.0947460026364 Winsorized Mean ( 2 / 32 ) 1.12957422680412 0.0181457672399097 62.2500119102016 Winsorized Mean ( 3 / 32 ) 1.12864639175258 0.0179346519891338 62.9310450203551 Winsorized Mean ( 4 / 32 ) 1.12766494845361 0.0176535220487105 63.8776186044972 Winsorized Mean ( 5 / 32 ) 1.12684536082474 0.0174977692503485 64.3993725544357 Winsorized Mean ( 6 / 32 ) 1.12639381443299 0.0173946940115681 64.7550232090254 Winsorized Mean ( 7 / 32 ) 1.12574432989691 0.0172711555348059 65.180602862862 Winsorized Mean ( 8 / 32 ) 1.12632164948454 0.0171733198597715 65.5855512318817 Winsorized Mean ( 9 / 32 ) 1.1257 0.0170245408575073 66.1221943911399 Winsorized Mean ( 10 / 32 ) 1.12584432989691 0.0169718555540436 66.3359599256472 Winsorized Mean ( 11 / 32 ) 1.12438144329897 0.0166690318336763 67.453314296718 Winsorized Mean ( 12 / 32 ) 1.12407216494845 0.0166172684876127 67.6448217579434 Winsorized Mean ( 13 / 32 ) 1.12456804123711 0.0165026117730375 68.1448522635954 Winsorized Mean ( 14 / 32 ) 1.12384639175258 0.0162923104386206 68.9801729464057 Winsorized Mean ( 15 / 32 ) 1.12370721649485 0.0161299728105219 69.6657849145182 Winsorized Mean ( 16 / 32 ) 1.12301443298969 0.0159716176655387 70.3131302355663 Winsorized Mean ( 17 / 32 ) 1.12276907216495 0.0159386264753051 70.4432765210062 Winsorized Mean ( 18 / 32 ) 1.12327010309278 0.0158297568192201 70.9594036042889 Winsorized Mean ( 19 / 32 ) 1.12254536082474 0.0156665141987777 71.6525288639078 Winsorized Mean ( 20 / 32 ) 1.12258659793814 0.0153633429972675 73.0691619745655 Winsorized Mean ( 21 / 32 ) 1.12208865979381 0.0150534484378748 74.5403064569988 Winsorized Mean ( 22 / 32 ) 1.12492371134021 0.0144365871628825 77.9217206011451 Winsorized Mean ( 23 / 32 ) 1.12423608247423 0.0142807445655683 78.723912280094 Winsorized Mean ( 24 / 32 ) 1.12522577319588 0.0139508270024196 80.6565641592945 Winsorized Mean ( 25 / 32 ) 1.12553505154639 0.0138477721949094 81.2791426450637 Winsorized Mean ( 26 / 32 ) 1.12628556701031 0.0135305946268905 83.239916505365 Winsorized Mean ( 27 / 32 ) 1.12865154639175 0.0131949803594723 85.5364324647535 Winsorized Mean ( 28 / 32 ) 1.13159587628866 0.0125919431228785 89.866660391171 Winsorized Mean ( 29 / 32 ) 1.13243298969072 0.0124716698481316 90.8004303738346 Winsorized Mean ( 30 / 32 ) 1.12878350515464 0.0120644641164327 93.562672511674 Winsorized Mean ( 31 / 32 ) 1.12319072164948 0.0113330713106278 99.1073550023631 Winsorized Mean ( 32 / 32 ) 1.12589587628866 0.0109424761058921 102.892239872693 Trimmed Mean ( 1 / 32 ) 1.12911052631579 0.0179976102976152 62.7366915742922 Trimmed Mean ( 2 / 32 ) 1.12840752688172 0.0177720013074918 63.4935541224634 Trimmed Mean ( 3 / 32 ) 1.12778571428571 0.0175424314875199 64.28901917548 Trimmed Mean ( 4 / 32 ) 1.12747303370787 0.0173663263095391 64.922944185873 Trimmed Mean ( 5 / 32 ) 1.12741954022988 0.0172526400738861 65.3476531940388 Trimmed Mean ( 6 / 32 ) 1.12755058823529 0.017158964029466 65.7120433552412 Trimmed Mean ( 7 / 32 ) 1.12755058823529 0.0170691987239863 66.0576167908115 Trimmed Mean ( 8 / 32 ) 1.12812345679012 0.0169848361265481 66.4194489946719 Trimmed Mean ( 9 / 32 ) 1.1284 0.0168989852563380 66.7732401019042 Trimmed Mean ( 10 / 32 ) 1.12877792207792 0.0168183038352259 67.1160381651389 Trimmed Mean ( 11 / 32 ) 1.12915733333333 0.0167249917301137 67.5131773787523 Trimmed Mean ( 12 / 32 ) 1.12973424657534 0.0166561961653021 67.8266655461698 Trimmed Mean ( 13 / 32 ) 1.13037887323944 0.0165731817640633 68.2053023572403 Trimmed Mean ( 14 / 32 ) 1.13037887323944 0.0164829896712689 68.5785100751336 Trimmed Mean ( 15 / 32 ) 1.13174776119403 0.0163967654859079 69.0226229171056 Trimmed Mean ( 16 / 32 ) 1.13254769230769 0.0163065084203794 69.4537213676145 Trimmed Mean ( 17 / 32 ) 1.13346507936508 0.0162089208461285 69.928472729621 Trimmed Mean ( 18 / 32 ) 1.13446557377049 0.0160830997551338 70.5377440321084 Trimmed Mean ( 19 / 32 ) 1.13548813559322 0.0159351240032912 71.2569375273578 Trimmed Mean ( 20 / 32 ) 1.13664736842105 0.0157662767676990 72.0935820909696 Trimmed Mean ( 21 / 32 ) 1.13788727272727 0.0155935918925505 72.97146677738 Trimmed Mean ( 22 / 32 ) 1.13926415094340 0.0154144276502489 73.908949251515 Trimmed Mean ( 23 / 32 ) 1.14050392156863 0.0152804126729985 74.6382932173014 Trimmed Mean ( 24 / 32 ) 1.14190408163265 0.0151172852251352 75.5363191622547 Trimmed Mean ( 25 / 32 ) 1.14190408163265 0.0149493209722661 76.3850133227526 Trimmed Mean ( 26 / 32 ) 1.14487333333333 0.0147321603811169 77.7125217018944 Trimmed Mean ( 27 / 32 ) 1.14648604651163 0.0144919493369862 79.1119275848958 Trimmed Mean ( 28 / 32 ) 1.14648604651163 0.014229117828247 80.5732344302944 Trimmed Mean ( 29 / 32 ) 1.14951025641026 0.0139923199461223 82.1529425310788 Trimmed Mean ( 30 / 32 ) 1.15105405405405 0.0136766002102946 84.1622944558717 Trimmed Mean ( 31 / 32 ) 1.15105405405405 0.0133007820402092 86.5403290253411 Trimmed Mean ( 32 / 32 ) 1.15594848484848 0.0128977183509069 89.6242617026294 Median 1.1856 Midrange 1.1625 Midmean - Weighted Average at Xnp 1.13924791666667 Midmean - Weighted Average at X(n+1)p 1.14190408163265 Midmean - Empirical Distribution Function 1.14190408163265 Midmean - Empirical Distribution Function - Averaging 1.14190408163265 Midmean - Empirical Distribution Function - Interpolation 1.14190408163265 Midmean - Closest Observation 1.13786 Midmean - True Basic - Statistics Graphics Toolkit 1.14190408163265 Midmean - MS Excel (old versions) 1.14190408163265 Number of observations 97

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/21/t1208814979gfjruznjy12dn6l/1kq861208814924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/21/t1208814979gfjruznjy12dn6l/1kq861208814924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/21/t1208814979gfjruznjy12dn6l/24zzc1208814924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/21/t1208814979gfjruznjy12dn6l/24zzc1208814924.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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