centrummaten eigen reeks

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 20 Oct 2010 18:48:24 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Oct/20/t1287600459xzd35961q7o269u.htm/, Retrieved Wed, 20 Oct 2010 20:47:41 +0200

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/2010/Oct/20/t1287600459xzd35961q7o269u.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

57,7 63,6 78 77,4 74,1 85,9 82 78,4 68,1 70,9 85,2 149,6 57,9 63,7 85 66,1 80,2 83,4 85,7 81,8 69,4 76,4 90,3 157,3 65,3 68,4 72,7 86,6 82,6 84,8 93,4 82,2 75,2 83,9 85,4 166,3 70,4 73,9 82,4 92,3 82,7 95,8 105,8 84,2 82,7 88,4 90,2 176,6 69,5 77,3 98,6 86,4 90,8 101,5 112,2 93,6 93,8 90,8 98,1 187,6 75 83,7 99,7 104,9 98,9 117,3 115,7 102,2 101,9 96,6 110 203,7 82,3 93,3 121,9 100,9 107,7 130 123,2 116,1 105,3 107,7 123,9 205,2 90,3 106,9 122,4 111,3 122,6 124,8 139,5 118,8 111 121,2 120,6 219,1 101,3 105 113,4 133,6 123,9 136,2 151,7 121,9 120,2 132,2 125,2 233,8

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 103.467592592593 3.21828235930957 32.1499424353772 Geometric Mean 99.096449660065 Harmonic Mean 95.4397206073653 Quadratic Mean 108.691205903425 Winsorized Mean ( 1 / 36 ) 103.333333333333 3.16902328007719 32.607312790336 Winsorized Mean ( 2 / 36 ) 103.181481481481 3.07163159315661 33.5917502969311 Winsorized Mean ( 3 / 36 ) 103.142592592593 3.05842842487543 33.7240498269282 Winsorized Mean ( 4 / 36 ) 102.605555555556 2.87705134225714 35.663442653428 Winsorized Mean ( 5 / 36 ) 102.133333333333 2.73735173048067 37.3110010657633 Winsorized Mean ( 6 / 36 ) 101.672222222222 2.58398053505852 39.3471316222278 Winsorized Mean ( 7 / 36 ) 101.108333333333 2.45076267083198 41.2558647708671 Winsorized Mean ( 8 / 36 ) 100.767592592593 2.35511853130066 42.7866331368646 Winsorized Mean ( 9 / 36 ) 100.600925925926 2.31911164202737 43.3790784810949 Winsorized Mean ( 10 / 36 ) 99.749074074074 2.13436643248961 46.7347464595023 Winsorized Mean ( 11 / 36 ) 99.4638888888889 2.07067471288609 48.0345310974783 Winsorized Mean ( 12 / 36 ) 99.375 1.99821317074419 49.7319312348392 Winsorized Mean ( 13 / 36 ) 99.350925925926 1.95367627269412 50.8533206419714 Winsorized Mean ( 14 / 36 ) 99.0916666666667 1.90648822272035 51.976018254796 Winsorized Mean ( 15 / 36 ) 98.55 1.79426054116485 54.9251336352861 Winsorized Mean ( 16 / 36 ) 98.5203703703704 1.78243601771574 55.2728790212777 Winsorized Mean ( 17 / 36 ) 98.5675925925926 1.74024245490131 56.6401493740033 Winsorized Mean ( 18 / 36 ) 98.7175925925926 1.72259776934310 57.3073960441953 Winsorized Mean ( 19 / 36 ) 98.612037037037 1.70383463714596 57.8765303199957 Winsorized Mean ( 20 / 36 ) 98.612037037037 1.67606060695113 58.835603335621 Winsorized Mean ( 21 / 36 ) 98.650925925926 1.66196454842207 59.3580206145724 Winsorized Mean ( 22 / 36 ) 98.9157407407408 1.60751693591949 61.5332495294438 Winsorized Mean ( 23 / 36 ) 99.2564814814815 1.57127989685959 63.1691919942834 Winsorized Mean ( 24 / 36 ) 99.1453703703704 1.54582219503717 64.1376289515537 Winsorized Mean ( 25 / 36 ) 99.0527777777778 1.52258323462379 65.0557391709704 Winsorized Mean ( 26 / 36 ) 98.9805555555556 1.50739061854128 65.6635077451523 Winsorized Mean ( 27 / 36 ) 98.6555555555556 1.45918767933127 67.6099154022247 Winsorized Mean ( 28 / 36 ) 98.3185185185185 1.40411250555615 70.021823842083 Winsorized Mean ( 29 / 36 ) 98.0231481481482 1.36093484764655 72.0263341905445 Winsorized Mean ( 30 / 36 ) 97.912037037037 1.34718256416301 72.6791153935908 Winsorized Mean ( 31 / 36 ) 97.4527777777778 1.24571363270749 78.2304818852868 Winsorized Mean ( 32 / 36 ) 97.1861111111111 1.19417028779116 81.3837960169608 Winsorized Mean ( 33 / 36 ) 96.9722222222222 1.15562257673682 83.9134023290257 Winsorized Mean ( 34 / 36 ) 96.9722222222222 1.13473497204859 85.458036114947 Winsorized Mean ( 35 / 36 ) 96.8425925925926 1.07712230528682 89.9086316542346 Winsorized Mean ( 36 / 36 ) 96.1425925925926 0.984206543053987 97.6853824749657 Trimmed Mean ( 1 / 36 ) 102.669811320755 3.0055468845341 34.1601097121697 Trimmed Mean ( 2 / 36 ) 101.980769230769 2.81577529457965 36.2176518229567 Trimmed Mean ( 3 / 36 ) 101.345098039216 2.65762187731986 38.1337536780889 Trimmed Mean ( 4 / 36 ) 100.698 2.47873785901966 40.6247073015724 Trimmed Mean ( 5 / 36 ) 100.172448979592 2.33873869335791 42.831826088175 Trimmed Mean ( 6 / 36 ) 99.73125 2.22067059202608 44.910420464031 Trimmed Mean ( 7 / 36 ) 99.3595744680851 2.12644358983874 46.7257043369865 Trimmed Mean ( 8 / 36 ) 99.066304347826 2.05065293062650 48.3096397582795 Trimmed Mean ( 9 / 36 ) 98.8111111111111 1.98463161765323 49.7881371193473 Trimmed Mean ( 10 / 36 ) 98.5670454545455 1.91599964877443 51.4441876425155 Trimmed Mean ( 11 / 36 ) 98.4186046511628 1.87264892206256 52.5558226593606 Trimmed Mean ( 12 / 36 ) 98.2964285714286 1.83392712943804 53.5988736921892 Trimmed Mean ( 13 / 36 ) 98.1780487804878 1.80109122832184 54.5103142121041 Trimmed Mean ( 14 / 36 ) 98.05625 1.76991725808714 55.4016011494095 Trimmed Mean ( 15 / 36 ) 97.9538461538462 1.74091781718135 56.2656348203958 Trimmed Mean ( 16 / 36 ) 97.8973684210526 1.72359678364059 56.7983007106067 Trimmed Mean ( 17 / 36 ) 97.8405405405405 1.70448796590423 57.4017197526158 Trimmed Mean ( 18 / 36 ) 97.7763888888889 1.68738471820398 57.9455223423858 Trimmed Mean ( 19 / 36 ) 97.6957142857143 1.66892489130361 58.5381132457095 Trimmed Mean ( 20 / 36 ) 97.6191176470588 1.64910550866896 59.1951922626528 Trimmed Mean ( 21 / 36 ) 97.5378787878788 1.62876784288647 59.884457575626 Trimmed Mean ( 22 / 36 ) 97.4484375 1.60572974587976 60.6879443754772 Trimmed Mean ( 23 / 36 ) 97.3322580645161 1.58487659261565 61.4131463093167 Trimmed Mean ( 24 / 36 ) 97.1816666666667 1.56349328503644 62.1567534678612 Trimmed Mean ( 25 / 36 ) 97.0293103448276 1.54042720133656 62.988572430193 Trimmed Mean ( 26 / 36 ) 96.8732142857143 1.51486249659192 63.9485197525557 Trimmed Mean ( 27 / 36 ) 96.7111111111111 1.48484933405394 65.1319355392577 Trimmed Mean ( 28 / 36 ) 96.5615384615385 1.45523746940288 66.3544888664528 Trimmed Mean ( 29 / 36 ) 96.426 1.42717877832671 67.5640651783333 Trimmed Mean ( 30 / 36 ) 96.3020833333333 1.39877887975983 68.8472529338362 Trimmed Mean ( 31 / 36 ) 96.1760869565217 1.36414112627409 70.5030330836879 Trimmed Mean ( 32 / 36 ) 96.075 1.33889657086877 71.7568497002424 Trimmed Mean ( 33 / 36 ) 95.9857142857143 1.31510288689490 72.9872280277226 Trimmed Mean ( 34 / 36 ) 95.905 1.29033363803161 74.3257380674829 Trimmed Mean ( 35 / 36 ) 95.8157894736842 1.26000139038844 76.0441934466009 Trimmed Mean ( 36 / 36 ) 95.7277777777778 1.23099914968515 77.7642923654834 Median 94.8 Midrange 145.75 Midmean - Weighted Average at Xnp 96.449090909091 Midmean - Weighted Average at X(n+1)p 96.7111111111111 Midmean - Empirical Distribution Function 96.449090909091 Midmean - Empirical Distribution Function - Averaging 96.7111111111111 Midmean - Empirical Distribution Function - Interpolation 96.7111111111111 Midmean - Closest Observation 96.449090909091 Midmean - True Basic - Statistics Graphics Toolkit 96.7111111111111 Midmean - MS Excel (old versions) 96.8732142857143 Number of observations 108

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Oct/20/t1287600459xzd35961q7o269u/1w8rv1287600502.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/20/t1287600459xzd35961q7o269u/1w8rv1287600502.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/20/t1287600459xzd35961q7o269u/2oz9y1287600502.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/20/t1287600459xzd35961q7o269u/2oz9y1287600502.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 do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

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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