*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 25 May 2011 18:15:14 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/25/t1306347102kpcxiavpqpw7utd.htm/, Retrieved Wed, 25 May 2011 20:11:45 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

505,7 55,7 735,7 575,9 545,8 905,8 765,8 945,7 15,7 645,7 155,9 416 825,8 725,9 925,9 556 116,1 876,3 336,2 186,1 286,1 26 915,8 405,7 965,7 395,6 425,8 545,6 65,6 445,6 895,5 175,4 715,4 865,5 57,4 145,4 315,3 635,4 5,2 515,2 515,1 955 955 634,9 205 275 425 84,9 534,7 4,8 704,7 684,7 884,6 994,6 294,7 524,7 914,5 564,4 984,5 934,4 514,6 474,5 784,4 504,5 824,4 414,6 964,7 64,6 244,7 344,7 34,7 685 425 484,8 785,1 704,9 245,4 285,6 218,8 706,1 856,2 456,6 606,8 527,3 657,8 948,2 486,6 238,9 289,4 969,5 589,5 189,7 639,8 9710,1 969,9 939,9 859,7 679,9 879,9 329,8 349,6 39,5 849,5 449,6 749,6 249,7 649,8 619,4 939 778,9

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 'Herman Ole Andreas Wold' @ www.yougetit.org Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 626.43 87.8863845526794 7.1277252237463 Geometric Mean 411.061295838867 Harmonic Mean 129.61987255576 Quadratic Mean 1111.00510943101 Winsorized Mean ( 1 / 36 ) 547.201818181818 28.2111494051908 19.3966509596072 Winsorized Mean ( 2 / 36 ) 547.209090909091 28.1513458139222 19.438114771709 Winsorized Mean ( 3 / 36 ) 547.091818181818 28.0471671155163 19.5061346455612 Winsorized Mean ( 4 / 36 ) 547.393636363636 27.9916122393582 19.5556308683771 Winsorized Mean ( 5 / 36 ) 547.439090909091 27.9312673642444 19.5995077405576 Winsorized Mean ( 6 / 36 ) 548.268181818182 27.7781773009828 19.7373706661014 Winsorized Mean ( 7 / 36 ) 547.759090909091 27.6764716512355 19.7915073066996 Winsorized Mean ( 8 / 36 ) 548.282727272727 27.5918064453486 19.8712153319398 Winsorized Mean ( 9 / 36 ) 547.808181818182 27.5038737473886 19.9174918722202 Winsorized Mean ( 10 / 36 ) 549.335454545455 27.1949240525223 20.1999260407754 Winsorized Mean ( 11 / 36 ) 551.875454545455 26.6387420084063 20.7170238884141 Winsorized Mean ( 12 / 36 ) 554.973636363636 26.1560273028164 21.2178107148511 Winsorized Mean ( 13 / 36 ) 555.670909090909 25.9059753121389 21.4495266978246 Winsorized Mean ( 14 / 36 ) 557.070909090909 25.414709522752 21.919231797326 Winsorized Mean ( 15 / 36 ) 557.152727272727 25.0327652413427 22.256938931883 Winsorized Mean ( 16 / 36 ) 557.487272727273 24.9368890176124 22.3559270899242 Winsorized Mean ( 17 / 36 ) 558.507272727273 24.4440898949176 22.8483561927744 Winsorized Mean ( 18 / 36 ) 559.08 23.9283928556407 23.3647116784196 Winsorized Mean ( 19 / 36 ) 560.669090909091 23.2395059853071 24.1256888706484 Winsorized Mean ( 20 / 36 ) 560.869090909091 22.9970003390932 24.3887934356227 Winsorized Mean ( 21 / 36 ) 560.315454545454 22.8928890112922 24.4755240052521 Winsorized Mean ( 22 / 36 ) 559.015454545455 22.512753615971 24.8310563905819 Winsorized Mean ( 23 / 36 ) 563.092727272727 21.7053134825622 25.942621272211 Winsorized Mean ( 24 / 36 ) 564.641818181818 21.3299498656409 26.471783653433 Winsorized Mean ( 25 / 36 ) 563.232727272727 21.1261620378744 26.6604377199691 Winsorized Mean ( 26 / 36 ) 558.410909090909 20.3458244541648 27.4459710565625 Winsorized Mean ( 27 / 36 ) 559.368181818182 20.1470746528436 27.7642383053974 Winsorized Mean ( 28 / 36 ) 554.608181818182 18.3352811267793 30.2481417101458 Winsorized Mean ( 29 / 36 ) 558.246363636364 17.8607062706338 31.2555592806662 Winsorized Mean ( 30 / 36 ) 558.491818181818 17.4826101197789 31.9455627252117 Winsorized Mean ( 31 / 36 ) 557.195454545455 16.7796267997898 33.2066655113233 Winsorized Mean ( 32 / 36 ) 553.908181818182 16.0818198387953 34.4431281640123 Winsorized Mean ( 33 / 36 ) 563.538181818182 14.0507493727076 40.107339962437 Winsorized Mean ( 34 / 36 ) 563.630909090909 13.3714889537325 42.1516938795045 Winsorized Mean ( 35 / 36 ) 563.121818181818 12.6962213144733 44.3534973307277 Winsorized Mean ( 36 / 36 ) 560.536363636364 12.3144406051718 45.5186217229349 Trimmed Mean ( 1 / 36 ) 548.077777777778 27.9823022165265 19.5865863193372 Trimmed Mean ( 2 / 36 ) 548.98679245283 27.7232322863256 19.802409285573 Trimmed Mean ( 3 / 36 ) 549.926923076923 27.4646053445898 20.0231139744104 Trimmed Mean ( 4 / 36 ) 550.946078431373 27.2130152631391 20.2456829242898 Trimmed Mean ( 5 / 36 ) 551.923 26.9442648028792 20.4838767744378 Trimmed Mean ( 6 / 36 ) 552.929591836735 26.6540883437203 20.7446446753826 Trimmed Mean ( 7 / 36 ) 552.929591836735 26.3590643061554 20.9768292764328 Trimmed Mean ( 8 / 36 ) 554.832978723404 26.0428869662733 21.3045880605302 Trimmed Mean ( 9 / 36 ) 555.811956521739 25.6975164695966 21.629014507271 Trimmed Mean ( 10 / 36 ) 556.898888888889 25.3173889058459 21.9966952737491 Trimmed Mean ( 11 / 36 ) 557.844318181818 24.9349813793722 22.3719564773087 Trimmed Mean ( 12 / 36 ) 558.538372093023 24.5868660883291 22.7169404220308 Trimmed Mean ( 13 / 36 ) 558.927380952381 24.2605802194665 23.0385001469957 Trimmed Mean ( 14 / 36 ) 558.927380952381 23.9193734095716 23.3671414121877 Trimmed Mean ( 15 / 36 ) 559.263414634146 23.5945207712142 23.703105481865 Trimmed Mean ( 16 / 36 ) 559.697435897436 23.2691639140431 24.0531820552287 Trimmed Mean ( 17 / 36 ) 559.897368421053 22.9001902216633 24.4494636508039 Trimmed Mean ( 18 / 36 ) 560.018918918919 22.5362630705827 24.8496796991126 Trimmed Mean ( 19 / 36 ) 560.098611111111 22.1788788320839 25.2536936312972 Trimmed Mean ( 20 / 36 ) 560.051428571429 21.8508807278622 25.6306112118082 Trimmed Mean ( 21 / 36 ) 559.985294117647 21.4917069847036 26.0558779493974 Trimmed Mean ( 22 / 36 ) 559.959090909091 21.0746685598066 26.5702442399042 Trimmed Mean ( 23 / 36 ) 560.0328125 20.6284653239274 27.1485446787167 Trimmed Mean ( 24 / 36 ) 559.796774193548 20.2097921938074 27.6992840314844 Trimmed Mean ( 25 / 36 ) 559.426666666667 19.7559749764294 28.3168341392471 Trimmed Mean ( 26 / 36 ) 559.137931034483 19.2320973433727 29.0731645671064 Trimmed Mean ( 27 / 36 ) 559.192857142857 18.7163953158136 29.8771663938082 Trimmed Mean ( 28 / 36 ) 559.192857142857 18.109876302833 30.8777844636841 Trimmed Mean ( 29 / 36 ) 559.17962962963 17.6709474042679 31.6440096185547 Trimmed Mean ( 30 / 36 ) 559.525 17.1996324806488 32.531218363504 Trimmed Mean ( 31 / 36 ) 559.622 16.6681263101967 33.5743796024423 Trimmed Mean ( 32 / 36 ) 559.902173913043 16.1226031777847 34.7277773780683 Trimmed Mean ( 33 / 36 ) 560.370454545455 15.5561186848269 36.0225108781169 Trimmed Mean ( 34 / 36 ) 560.119047619048 15.2401988603141 36.7527387767632 Trimmed Mean ( 35 / 36 ) 559.835 14.9491923930736 37.4491802152061 Trimmed Mean ( 36 / 36 ) 559.563157894737 14.6880736695825 38.0964291494219 Median 545.7 Midrange 4857.45 Midmean - Weighted Average at Xnp 554.370909090909 Midmean - Weighted Average at X(n+1)p 559.192857142857 Midmean - Empirical Distribution Function 559.192857142857 Midmean - Empirical Distribution Function - Averaging 559.192857142857 Midmean - Empirical Distribution Function - Interpolation 559.17962962963 Midmean - Closest Observation 559.192857142857 Midmean - True Basic - Statistics Graphics Toolkit 559.192857142857 Midmean - MS Excel (old versions) 559.192857142857 Number of observations 110

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/25/t1306347102kpcxiavpqpw7utd/1btif1306347312.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/25/t1306347102kpcxiavpqpw7utd/1btif1306347312.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/25/t1306347102kpcxiavpqpw7utd/2sw661306347312.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/25/t1306347102kpcxiavpqpw7utd/2sw661306347312.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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