Robuustheid boekenverkoop Noorwegen

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Sun, 17 Oct 2010 18:27:08 +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/17/t1287339972tpgdcaijuondbdu.htm/, Retrieved Sun, 17 Oct 2010 20:26:12 +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/17/t1287339972tpgdcaijuondbdu.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:

Centrummaten van de boekenverkoop (indexcijfer) in Noorwegen

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

90 69,3 87,3 57,4 56,2 61,6 77,7 177,2 97,6 81,6 96,8 191,3 106 75,1 72 63,5 57,4 62,3 79,4 178,1 109,3 85,2 102,7 193,7 108,4 73,4 85,9 58,5 58,6 62,7 77,5 180,5 102,2 82,6 97,8 197,8 93,8 72,4 77,7 58,7 53,1 64,3 76,4 188,4 105,5 79,8 96,1 202,5 97,3 89,5 64,7 61,2 57,8 62 76,3 195 110,9 81,4 101,7 202,2 97,4 68,5 86,8 59,1 62,4 66,2 68 198,5 120,4 90,2 103,2 207,3 106,4 75,5 97,3 60 67,5 71,2 73,7 213,3 114,6 96,1 117 229,2 105,6 99,9 79,3 72,5 67,4 78,3 85,7 177,4 113,6 94,1 105,7 228,3 100,3 70,3 94,2 66,5 64,4 73,7 87,9 152,2 97,3 89,3 107,6 228,4

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 101.601851851852 4.46328528212794 22.7639161356523 Geometric Mean 93.550429700932 Harmonic Mean 87.5534886213357 Quadratic Mean 111.599615856235 Winsorized Mean ( 1 / 36 ) 101.623148148148 4.45848734344127 22.7931897794086 Winsorized Mean ( 2 / 36 ) 101.643518518519 4.45590628867942 22.8109641301820 Winsorized Mean ( 3 / 36 ) 101.226851851852 4.35034117505268 23.2687156658756 Winsorized Mean ( 4 / 36 ) 101.019444444444 4.29650204636626 23.5120205586498 Winsorized Mean ( 5 / 36 ) 100.829629629630 4.24289262962101 23.7643604096189 Winsorized Mean ( 6 / 36 ) 100.818518518519 4.23864601270557 23.7855480774543 Winsorized Mean ( 7 / 36 ) 100.585185185185 4.18501501383209 24.0346055755442 Winsorized Mean ( 8 / 36 ) 100.562962962963 4.17093521594605 24.1104111563510 Winsorized Mean ( 9 / 36 ) 100.404629629630 4.11351317330613 24.4084862256396 Winsorized Mean ( 10 / 36 ) 100.395370370370 4.07761608021709 24.6210943834186 Winsorized Mean ( 11 / 36 ) 100.191666666667 4.02194463387265 24.9112496037008 Winsorized Mean ( 12 / 36 ) 99.9138888888889 3.95011719494667 25.2939049546954 Winsorized Mean ( 13 / 36 ) 98.999074074074 3.75067237432892 26.3950204639741 Winsorized Mean ( 14 / 36 ) 98.700925925926 3.68657967092491 26.7730348280154 Winsorized Mean ( 15 / 36 ) 98.6453703703704 3.66320009458278 26.9287420352083 Winsorized Mean ( 16 / 36 ) 98.7342592592592 3.64645808461973 27.0767569427733 Winsorized Mean ( 17 / 36 ) 94.925 2.86826645361693 33.09490297887 Winsorized Mean ( 18 / 36 ) 89.6416666666667 1.96410816426379 45.6398829237938 Winsorized Mean ( 19 / 36 ) 89.0962962962963 1.8722462995481 47.5879142171632 Winsorized Mean ( 20 / 36 ) 88.9296296296296 1.77776119179987 50.0233833654532 Winsorized Mean ( 21 / 36 ) 88.7935185185185 1.74475244144995 50.8917577124741 Winsorized Mean ( 22 / 36 ) 88.4268518518519 1.65133582734673 53.5486788256334 Winsorized Mean ( 23 / 36 ) 88.1074074074074 1.60610406665889 54.8578446667491 Winsorized Mean ( 24 / 36 ) 88.0185185185185 1.56833506761129 56.1222664316103 Winsorized Mean ( 25 / 36 ) 87.949074074074 1.53222029186211 57.3997580773387 Winsorized Mean ( 26 / 36 ) 87.8527777777778 1.47518381459231 59.5537836768208 Winsorized Mean ( 27 / 36 ) 88.0027777777778 1.43433063081223 61.35459697179 Winsorized Mean ( 28 / 36 ) 88.1583333333333 1.39853006263807 63.0364235195907 Winsorized Mean ( 29 / 36 ) 88.3462962962963 1.37123721332193 64.4281641702753 Winsorized Mean ( 30 / 36 ) 88.4296296296296 1.35564040934601 65.2308894157928 Winsorized Mean ( 31 / 36 ) 87.7981481481482 1.27632472902370 68.789819825326 Winsorized Mean ( 32 / 36 ) 87.9166666666667 1.23000272972313 71.4768061420943 Winsorized Mean ( 33 / 36 ) 87.8555555555556 1.20281078258420 73.0418756030775 Winsorized Mean ( 34 / 36 ) 87.6981481481482 1.18534957701624 73.9850503586477 Winsorized Mean ( 35 / 36 ) 87.6981481481481 1.08603583474014 80.7506947219033 Winsorized Mean ( 36 / 36 ) 87.6981481481481 1.05719770183119 82.9534040759308 Trimmed Mean ( 1 / 36 ) 100.855660377358 4.35934581345337 23.1355035120425 Trimmed Mean ( 2 / 36 ) 100.058653846154 4.24670513465484 23.5614789992445 Trimmed Mean ( 3 / 36 ) 99.2196078431373 4.11974002701006 24.0839487910956 Trimmed Mean ( 4 / 36 ) 98.497 4.02056291726882 24.4983108153694 Trimmed Mean ( 5 / 36 ) 97.8020408163265 3.92560605169642 24.9138705026354 Trimmed Mean ( 6 / 36 ) 97.1208333333333 3.83228778170597 25.3427818748255 Trimmed Mean ( 7 / 36 ) 96.4127659574468 3.72602723573425 25.8754861029478 Trimmed Mean ( 8 / 36 ) 95.7130434782609 3.61617848239139 26.4680086849489 Trimmed Mean ( 9 / 36 ) 94.9855555555555 3.49189593648475 27.2017142787984 Trimmed Mean ( 10 / 36 ) 94.246590909091 3.35923256713236 28.0559886895671 Trimmed Mean ( 11 / 36 ) 93.4744186046512 3.21077537121415 29.1127244349404 Trimmed Mean ( 12 / 36 ) 92.6892857142857 3.04681770999479 30.421670915929 Trimmed Mean ( 13 / 36 ) 91.8963414634146 2.86628190371895 32.0611665391952 Trimmed Mean ( 14 / 36 ) 91.15875 2.69192868470037 33.8637314272487 Trimmed Mean ( 15 / 36 ) 90.4128205128205 2.49436618547624 36.2468113299725 Trimmed Mean ( 16 / 36 ) 89.6328947368421 2.25511145572608 39.7465475638688 Trimmed Mean ( 17 / 36 ) 88.8027027027027 1.95210987440920 45.4906272781282 Trimmed Mean ( 18 / 36 ) 88.2625 1.76610456721333 49.9758064378183 Trimmed Mean ( 19 / 36 ) 88.1442857142857 1.72316771678887 51.1524704504927 Trimmed Mean ( 20 / 36 ) 88.064705882353 1.68653158367401 52.2164581646964 Trimmed Mean ( 21 / 36 ) 87.9939393939394 1.65683704449716 53.1095919699484 Trimmed Mean ( 22 / 36 ) 87.9296875 1.62588449628973 54.0811402659019 Trimmed Mean ( 23 / 36 ) 87.8903225806452 1.60226853602668 54.8536781472326 Trimmed Mean ( 24 / 36 ) 87.8733333333333 1.57981953247164 55.6223869417888 Trimmed Mean ( 25 / 36 ) 87.8620689655172 1.55742436246665 56.4149830213016 Trimmed Mean ( 26 / 36 ) 87.8553571428571 1.5347128832601 57.2454679315855 Trimmed Mean ( 27 / 36 ) 87.8555555555556 1.51469085176703 58.0023015607862 Trimmed Mean ( 28 / 36 ) 87.8442307692308 1.49519444767613 58.7510413149009 Trimmed Mean ( 29 / 36 ) 87.82 1.47536781992886 59.5241395493055 Trimmed Mean ( 30 / 36 ) 87.7791666666667 1.45354469618452 60.3897265058876 Trimmed Mean ( 31 / 36 ) 87.7282608695652 1.42710024240393 61.4730894599164 Trimmed Mean ( 32 / 36 ) 87.7227272727273 1.40681617523859 62.35550089396 Trimmed Mean ( 33 / 36 ) 87.7071428571429 1.38748509888156 63.213034091568 Trimmed Mean ( 34 / 36 ) 87.695 1.36550896869619 64.2214749301371 Trimmed Mean ( 35 / 36 ) 87.6947368421053 1.33792448425989 65.5453561645641 Trimmed Mean ( 36 / 36 ) 87.6944444444444 1.32115836886654 66.376935960887 Median 87.6 Midrange 141.15 Midmean - Weighted Average at Xnp 87.5181818181818 Midmean - Weighted Average at X(n+1)p 87.8555555555556 Midmean - Empirical Distribution Function 87.5181818181818 Midmean - Empirical Distribution Function - Averaging 87.8555555555556 Midmean - Empirical Distribution Function - Interpolation 87.8555555555556 Midmean - Closest Observation 87.5181818181818 Midmean - True Basic - Statistics Graphics Toolkit 87.8555555555556 Midmean - MS Excel (old versions) 87.8553571428572 Number of observations 108

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Oct/17/t1287339972tpgdcaijuondbdu/1uf2t1287340024.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/17/t1287339972tpgdcaijuondbdu/1uf2t1287340024.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/17/t1287339972tpgdcaijuondbdu/2n71e1287340024.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/17/t1287339972tpgdcaijuondbdu/2n71e1287340024.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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• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

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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