Central Tendency: Nieuwe wagens

*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: Fri, 12 Dec 2008 04:36:57 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t1229081877gzciu0huqqa1y64.htm/, Retrieved Fri, 12 Dec 2008 12:38:02 +0100

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/Dec/12/t1229081877gzciu0huqqa1y64.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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103,1 103,1 103,3 103,5 103,3 103,5 103,8 103,9 103,9 104,2 104,6 104,9 105,2 105,2 105,6 105,6 106,2 106,3 106,4 106,9 107,2 107,3 107,3 107,4 107,55 107,87 108,37 108,38 107,92 108,03 108,14 108,3 108,64 108,66 109,04 109,03 109,03 109,54 109,75 109,83 109,65 109,82 109,95 110,12 110,15 110,2 109,99 110,14 110,14 110,81 110,97 110,99 109,73 109,81 110,02 110,18 110,21 110,25 110,36 110,51 110,64 110,95 111,18 111,19 111,69 111,7 111,83 111,77 111,73 112,01 111,86 112,04 101,81 101,72 101,78 102,04 102,36 102,56 102,69 102,77 102,85 102,9 102,72 102,79 102,9 102,91 103,29 103,35 102,97 103,05 103,18 103,21 103,32 103,31 103,6 103,68 103,77 103,82 103,86 103,9 103,63 103,65 103,7 103,77

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 106.732980769231 0.327910358380272 325.49438601587 Geometric Mean 106.681159498590 Harmonic Mean 106.629426185607 Quadratic Mean 106.784850422743 Winsorized Mean ( 1 / 34 ) 106.733269230769 0.327780016780791 325.624698781284 Winsorized Mean ( 2 / 34 ) 106.730961538462 0.327250680978243 326.14435276166 Winsorized Mean ( 3 / 34 ) 106.736730769231 0.326171137513339 327.241495317364 Winsorized Mean ( 4 / 34 ) 106.746730769231 0.324153589687072 329.309111993117 Winsorized Mean ( 5 / 34 ) 106.754423076923 0.322626387891029 330.891790267884 Winsorized Mean ( 6 / 34 ) 106.760192307692 0.32143332088534 332.137912813884 Winsorized Mean ( 7 / 34 ) 106.761538461538 0.321085455101592 332.501945401915 Winsorized Mean ( 8 / 34 ) 106.726923076923 0.315103592533773 338.704240782288 Winsorized Mean ( 9 / 34 ) 106.727788461538 0.314774062099274 339.061572448999 Winsorized Mean ( 10 / 34 ) 106.715288461538 0.311605102723811 342.469643560762 Winsorized Mean ( 11 / 34 ) 106.718461538462 0.310689738629867 343.488851640505 Winsorized Mean ( 12 / 34 ) 106.716153846154 0.310383634422038 343.820169658329 Winsorized Mean ( 13 / 34 ) 106.699903846154 0.307941607924002 346.493949179114 Winsorized Mean ( 14 / 34 ) 106.685096153846 0.304052016500007 350.877778683779 Winsorized Mean ( 15 / 34 ) 106.677884615385 0.300345633391479 355.183737518626 Winsorized Mean ( 16 / 34 ) 106.6625 0.296620142857188 359.592908871851 Winsorized Mean ( 17 / 34 ) 106.644519230769 0.294463280556525 362.165764876404 Winsorized Mean ( 18 / 34 ) 106.651442307692 0.292034160759633 365.201940862922 Winsorized Mean ( 19 / 34 ) 106.655096153846 0.291187083778417 366.276878664739 Winsorized Mean ( 20 / 34 ) 106.666634615385 0.288976510546816 369.118702463202 Winsorized Mean ( 21 / 34 ) 106.662596153846 0.288033979718708 370.312545269874 Winsorized Mean ( 22 / 34 ) 106.660480769231 0.287785490937295 370.624941590509 Winsorized Mean ( 23 / 34 ) 106.662692307692 0.287534951954354 370.955571080016 Winsorized Mean ( 24 / 34 ) 106.660384615385 0.286732542999619 371.985626394442 Winsorized Mean ( 25 / 34 ) 106.643557692308 0.283117741727544 376.675643997384 Winsorized Mean ( 26 / 34 ) 106.673557692308 0.278053551341357 383.643931817106 Winsorized Mean ( 27 / 34 ) 106.663173076923 0.276853798688186 385.268952719177 Winsorized Mean ( 28 / 34 ) 106.657788461538 0.270177622428546 394.769142991278 Winsorized Mean ( 29 / 34 ) 106.663365384615 0.268942192878134 396.603315542042 Winsorized Mean ( 30 / 34 ) 106.66625 0.267982469480454 398.034431904435 Winsorized Mean ( 31 / 34 ) 106.657307692308 0.264974290704404 402.519457298183 Winsorized Mean ( 32 / 34 ) 106.657307692308 0.263606614439819 404.607858262439 Winsorized Mean ( 33 / 34 ) 106.654134615385 0.258327605577016 412.863868641355 Winsorized Mean ( 34 / 34 ) 106.618173076923 0.254297152765888 419.266090545175 Trimmed Mean ( 1 / 34 ) 106.730098039216 0.326544342222236 326.847181956619 Trimmed Mean ( 2 / 34 ) 106.7268 0.325069188594886 328.320258408148 Trimmed Mean ( 3 / 34 ) 106.724591836735 0.323627340225146 329.776191846112 Trimmed Mean ( 4 / 34 ) 106.720208333333 0.322339854810523 331.079780364316 Trimmed Mean ( 5 / 34 ) 106.712872340426 0.321408463159906 332.016373468467 Trimmed Mean ( 6 / 34 ) 106.703478260870 0.320626381496857 332.796938800607 Trimmed Mean ( 7 / 34 ) 106.692555555556 0.319872070289184 333.547581878902 Trimmed Mean ( 8 / 34 ) 106.680909090909 0.318942366149215 334.483343743048 Trimmed Mean ( 9 / 34 ) 106.673953488372 0.318849130839654 334.559335970143 Trimmed Mean ( 10 / 34 ) 106.666547619048 0.318617049559292 334.779785848207 Trimmed Mean ( 11 / 34 ) 106.660365853659 0.318673545843224 334.701035730564 Trimmed Mean ( 12 / 34 ) 106.6535 0.318669310958610 334.683938278112 Trimmed Mean ( 13 / 34 ) 106.646538461538 0.318495350477565 334.844883297757 Trimmed Mean ( 14 / 34 ) 106.640921052632 0.318424255094814 334.902003683351 Trimmed Mean ( 15 / 34 ) 106.640921052632 0.318640928296958 334.674273084114 Trimmed Mean ( 16 / 34 ) 106.6325 0.319124373569220 334.140883090119 Trimmed Mean ( 17 / 34 ) 106.629714285714 0.319884358726251 333.338318604585 Trimmed Mean ( 18 / 34 ) 106.628382352941 0.320731276636276 332.453957940193 Trimmed Mean ( 19 / 34 ) 106.626363636364 0.321690438608317 331.456427793272 Trimmed Mean ( 20 / 34 ) 106.62390625 0.322559027542836 330.556261476329 Trimmed Mean ( 21 / 34 ) 106.620322580645 0.323479722746465 329.604346372621 Trimmed Mean ( 22 / 34 ) 106.616833333333 0.324281604244250 328.778542901957 Trimmed Mean ( 23 / 34 ) 106.613275862069 0.324837191100066 328.205263384471 Trimmed Mean ( 24 / 34 ) 106.609285714286 0.325085488970435 327.942308504522 Trimmed Mean ( 25 / 34 ) 106.605185185185 0.325027950283450 327.987747183641 Trimmed Mean ( 26 / 34 ) 106.602115384615 0.324985825951326 328.020814669565 Trimmed Mean ( 27 / 34 ) 106.5964 0.325131403262981 327.856364934949 Trimmed Mean ( 28 / 34 ) 106.591041666667 0.324907821022583 328.065484330886 Trimmed Mean ( 29 / 34 ) 106.585652173913 0.325053938619381 327.901432687203 Trimmed Mean ( 30 / 34 ) 106.585652173913 0.324735767969367 328.222705002325 Trimmed Mean ( 31 / 34 ) 106.572142857143 0.323756718810508 329.173532671977 Trimmed Mean ( 32 / 34 ) 106.565 0.322283519073915 330.656064282206 Trimmed Mean ( 33 / 34 ) 106.557105263158 0.319810778846925 333.187973361463 Trimmed Mean ( 34 / 34 ) 106.548611111111 0.316814039085316 336.312782788070 Median 107.05 Midrange 106.88 Midmean - Weighted Average at Xnp 106.540754716981 Midmean - Weighted Average at X(n+1)p 106.602115384615 Midmean - Empirical Distribution Function 106.540754716981 Midmean - Empirical Distribution Function - Averaging 106.602115384615 Midmean - Empirical Distribution Function - Interpolation 106.602115384615 Midmean - Closest Observation 106.540754716981 Midmean - True Basic - Statistics Graphics Toolkit 106.602115384615 Midmean - MS Excel (old versions) 106.605185185185 Number of observations 104

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229081877gzciu0huqqa1y64/16tkm1229081815.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229081877gzciu0huqqa1y64/16tkm1229081815.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229081877gzciu0huqqa1y64/2do7r1229081815.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229081877gzciu0huqqa1y64/2do7r1229081815.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')

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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 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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