central tendency omzet industrie (Q9)

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 20 Oct 2008 12:57:04 -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/20/t12245292023h7enjk5lx5u0b6.htm/, Retrieved Mon, 20 Oct 2008 19:00:04 +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/20/t12245292023h7enjk5lx5u0b6.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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Original text written by user:

IsPrivate?

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User-defined keywords:

Dataseries X:

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83,1 89,6 105,7 110,7 110,4 109 106 100,9 114,3 101,2 109,2 111,6 91,7 93,7 105,7 109,5 105,3 102,8 100,6 97,6 110,3 107,2 107,2 108,1 97,1 92,2 112,2 111,6 115,7 111,3 104,2 103,2 112,7 106,4 102,6 110,6 95,2 89 112,5 116,8 107,2 113,6 101,8 102,6 122,7 110,3 110,5 121,6 100,3 100,7 123,4 127,1 124,1 131,2 111,6 114,2 130,1 125,9 119 133,8 107,5 113,5 134,4 126,8 135,6 139,9 129,8 131 153,1 134,1 144,1 155,9 123,3 128,1 144,3 153 149,9 150,9 141 138,9 157,4 142,9 151,7 161 138,5 135,9 151,5 164 159,1 157 142,1 144,8 152,1 154,6 148,7 157,7 146,7

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 121.826804123711 2.08064406262663 58.5524484038445 Geometric Mean 120.158959166765 Harmonic Mean 118.540153089419 Quadratic Mean 123.520694038166 Winsorized Mean ( 1 / 32 ) 121.856701030928 2.06337774575692 59.0569037984009 Winsorized Mean ( 2 / 32 ) 121.829896907217 2.05376674150146 59.3202209605115 Winsorized Mean ( 3 / 32 ) 121.851546391753 2.03538083847974 59.8667060670404 Winsorized Mean ( 4 / 32 ) 121.859793814433 2.02995800692193 60.0306968907263 Winsorized Mean ( 5 / 32 ) 121.916494845361 2.01468283064659 60.5139890958582 Winsorized Mean ( 6 / 32 ) 121.941237113402 1.98919479846925 61.3018077501709 Winsorized Mean ( 7 / 32 ) 121.984536082474 1.95399644453459 62.4282282722007 Winsorized Mean ( 8 / 32 ) 121.90206185567 1.92736609807039 63.2480056475592 Winsorized Mean ( 9 / 32 ) 122.143298969072 1.89428734435691 64.479815764455 Winsorized Mean ( 10 / 32 ) 122.081443298969 1.87497498831938 65.1109716446916 Winsorized Mean ( 11 / 32 ) 122.047422680412 1.86608596575284 65.4028940361141 Winsorized Mean ( 12 / 32 ) 122.047422680412 1.85905655227844 65.6501936591615 Winsorized Mean ( 13 / 32 ) 122.007216494845 1.84109975224840 66.2686616224063 Winsorized Mean ( 14 / 32 ) 121.949484536082 1.80755153003844 67.466671079352 Winsorized Mean ( 15 / 32 ) 121.887628865979 1.76367951059453 69.1098513838782 Winsorized Mean ( 16 / 32 ) 121.557731958763 1.71231630756067 70.9902320161464 Winsorized Mean ( 17 / 32 ) 121.259793814433 1.65816257806525 73.1290136555364 Winsorized Mean ( 18 / 32 ) 121.241237113402 1.63592268316535 74.1118381455607 Winsorized Mean ( 19 / 32 ) 121.39793814433 1.60800121611976 75.4961730920038 Winsorized Mean ( 20 / 32 ) 121.377319587629 1.54695584642744 78.4620452276896 Winsorized Mean ( 21 / 32 ) 121.290721649485 1.51265784901962 80.1838444352074 Winsorized Mean ( 22 / 32 ) 121.041237113402 1.47722235791053 81.938400448129 Winsorized Mean ( 23 / 32 ) 120.851546391753 1.43312037402576 84.3275614401254 Winsorized Mean ( 24 / 32 ) 120.703092783505 1.38844678666457 86.9338990466228 Winsorized Mean ( 25 / 32 ) 120.806185567010 1.35251596279827 89.3196005739336 Winsorized Mean ( 26 / 32 ) 120.109278350515 1.25945230493530 95.366277769991 Winsorized Mean ( 27 / 32 ) 120.025773195876 1.24857416820226 96.130271034434 Winsorized Mean ( 28 / 32 ) 119.765979381443 1.19469841788774 100.247876441648 Winsorized Mean ( 29 / 32 ) 119.855670103093 1.16423282804778 102.948196628393 Winsorized Mean ( 30 / 32 ) 120.041237113402 1.12350965867232 106.844864382615 Winsorized Mean ( 31 / 32 ) 119.274226804124 1.01286853030763 117.758843556822 Winsorized Mean ( 32 / 32 ) 119.307216494845 0.994572089580105 119.958339616403 Trimmed Mean ( 1 / 32 ) 121.790526315789 2.03645173361573 59.8052604465857 Trimmed Mean ( 2 / 32 ) 121.721505376344 2.00574435080852 60.6864505575089 Trimmed Mean ( 3 / 32 ) 121.663736263736 1.97640632700736 61.5580584828209 Trimmed Mean ( 4 / 32 ) 121.595505617978 1.95044513797286 62.3424382725034 Trimmed Mean ( 5 / 32 ) 121.521839080460 1.9223167945373 63.2163436462667 Trimmed Mean ( 6 / 32 ) 121.431764705882 1.89407169768717 64.111493167952 Trimmed Mean ( 7 / 32 ) 121.431764705882 1.86755957051922 65.0216285588799 Trimmed Mean ( 8 / 32 ) 121.220987654321 1.84436388382415 65.7250929263366 Trimmed Mean ( 9 / 32 ) 121.116455696203 1.82252970058353 66.4551341234243 Trimmed Mean ( 10 / 32 ) 120.972727272727 1.80264827244088 67.1083367300065 Trimmed Mean ( 11 / 32 ) 120.829333333333 1.78233653037453 67.7926593963392 Trimmed Mean ( 12 / 32 ) 120.682191780822 1.75946272074722 68.5903658871327 Trimmed Mean ( 13 / 32 ) 120.526760563380 1.73311103920992 69.5435882852174 Trimmed Mean ( 14 / 32 ) 120.526760563380 1.70441689216962 70.7143663719249 Trimmed Mean ( 15 / 32 ) 120.202985074627 1.67535272098360 71.7478675201336 Trimmed Mean ( 16 / 32 ) 120.035384615385 1.64725358663996 72.8700095655766 Trimmed Mean ( 17 / 32 ) 119.888888888889 1.62163292679424 73.9309660700428 Trimmed Mean ( 18 / 32 ) 119.760655737705 1.59900802358842 74.8968447756402 Trimmed Mean ( 19 / 32 ) 119.625423728814 1.57423660350426 75.989481798687 Trimmed Mean ( 20 / 32 ) 119.466666666667 1.54730004423854 77.2097610360109 Trimmed Mean ( 21 / 32 ) 119.298181818182 1.52318886415511 78.3213327155951 Trimmed Mean ( 22 / 32 ) 119.124528301887 1.49797615200046 79.523648051942 Trimmed Mean ( 23 / 32 ) 118.958823529412 1.47184338593157 80.823017358005 Trimmed Mean ( 24 / 32 ) 118.795918367347 1.44578309872839 82.1671787917786 Trimmed Mean ( 25 / 32 ) 118.795918367347 1.41964252116832 83.6801635594725 Trimmed Mean ( 26 / 32 ) 118.444444444444 1.39085234950980 85.159610569871 Trimmed Mean ( 27 / 32 ) 118.3 1.37172442822046 86.2418118145427 Trimmed Mean ( 28 / 32 ) 118.3 1.34680797855984 87.8373174819621 Trimmed Mean ( 29 / 32 ) 118.005128205128 1.32363518544796 89.1523053349414 Trimmed Mean ( 30 / 32 ) 117.837837837838 1.29630076214442 90.9031617345524 Trimmed Mean ( 31 / 32 ) 117.837837837838 1.26489075395774 93.1604863654295 Trimmed Mean ( 32 / 32 ) 117.478787878788 1.24923277648479 94.040750523182 Median 114.2 Midrange 123.55 Midmean - Weighted Average at Xnp 118.377083333333 Midmean - Weighted Average at X(n+1)p 118.795918367347 Midmean - Empirical Distribution Function 118.795918367347 Midmean - Empirical Distribution Function - Averaging 118.795918367347 Midmean - Empirical Distribution Function - Interpolation 118.795918367347 Midmean - Closest Observation 118.54 Midmean - True Basic - Statistics Graphics Toolkit 118.795918367347 Midmean - MS Excel (old versions) 118.795918367347 Number of observations 97

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/20/t12245292023h7enjk5lx5u0b6/16cfk1224529021.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/20/t12245292023h7enjk5lx5u0b6/16cfk1224529021.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/20/t12245292023h7enjk5lx5u0b6/2ja0q1224529021.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/20/t12245292023h7enjk5lx5u0b6/2ja0q1224529021.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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