Centrummaten_Jeroen_kinne

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Fri, 13 Aug 2010 11:22:04 +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/Aug/13/t1281698536r9ibynk2s0jrxdr.htm/, Retrieved Fri, 13 Aug 2010 13:22:16 +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/Aug/13/t1281698536r9ibynk2s0jrxdr.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:

Jeroen_Kinne

Dataseries X:

» Textbox « » Textfile « » CSV «

36 35 34 32 52 51 36 26 27 27 28 30 28 29 25 28 55 53 42 32 37 41 37 38 39 32 36 39 83 83 66 53 72 77 69 72 81 71 63 66 114 116 109 97 111 120 110 106 115 110 103 112 163 166 156 140 166 176 163 162 171 167 163 168 222 216 197 178 204 220 196 195 213 218 216 225 280 272 252 230 248 259 240 237 252 250 255 255 313 291 271 247 268 283 259 259 267 270 279 269 334 326 301 276 301 313 291 287 289 298 320 312 385 380 351 322 350 363 344 345

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 170.325 9.89272405081928 17.2171991379760 Geometric Mean 127.190485633427 Harmonic Mean 86.7201196875877 Quadratic Mean 201.634963072711 Winsorized Mean ( 1 / 40 ) 170.291666666667 9.88418505807998 17.2287007645065 Winsorized Mean ( 2 / 40 ) 170.025 9.83351941024152 17.2903507794901 Winsorized Mean ( 3 / 40 ) 169.725 9.78542878961564 17.3446666108401 Winsorized Mean ( 4 / 40 ) 169.725 9.77617840547554 17.3610784255879 Winsorized Mean ( 5 / 40 ) 169.516666666667 9.74427206115816 17.3965449243131 Winsorized Mean ( 6 / 40 ) 169.466666666667 9.73672286632093 17.4048978278767 Winsorized Mean ( 7 / 40 ) 168.941666666667 9.64371100155014 17.5183253251275 Winsorized Mean ( 8 / 40 ) 168.475 9.56030514804886 17.6223454577058 Winsorized Mean ( 9 / 40 ) 168.325 9.50097416868721 17.7166043198766 Winsorized Mean ( 10 / 40 ) 168.158333333333 9.47842896596962 17.7411608967131 Winsorized Mean ( 11 / 40 ) 167.516666666667 9.39347606712851 17.8332989267811 Winsorized Mean ( 12 / 40 ) 167.716666666667 9.36935961667164 17.9005474790652 Winsorized Mean ( 13 / 40 ) 167.716666666667 9.3423089844819 17.9523784693113 Winsorized Mean ( 14 / 40 ) 166.55 9.16595248045448 18.170506595485 Winsorized Mean ( 15 / 40 ) 166.55 9.16595248045448 18.170506595485 Winsorized Mean ( 16 / 40 ) 166.15 9.11699286078087 18.2242108266573 Winsorized Mean ( 17 / 40 ) 165.3 8.98140469259129 18.4046934369136 Winsorized Mean ( 18 / 40 ) 165.3 8.98140469259129 18.4046934369136 Winsorized Mean ( 19 / 40 ) 165.141666666667 8.9253384296115 18.5025663697836 Winsorized Mean ( 20 / 40 ) 164.975 8.86670243748181 18.6061279447711 Winsorized Mean ( 21 / 40 ) 164.275 8.78648027621173 18.6963374224778 Winsorized Mean ( 22 / 40 ) 164.091666666667 8.68058295420513 18.9033003350513 Winsorized Mean ( 23 / 40 ) 164.091666666667 8.63631019348946 19.0002052948918 Winsorized Mean ( 24 / 40 ) 165.291666666667 8.35899183600944 19.7741150977815 Winsorized Mean ( 25 / 40 ) 164.666666666667 8.24308838173499 19.9763315690676 Winsorized Mean ( 26 / 40 ) 164.666666666667 8.19457302594209 20.0945999438129 Winsorized Mean ( 27 / 40 ) 164.441666666667 8.1700912830455 20.1272740988726 Winsorized Mean ( 28 / 40 ) 164.675 8.09146040233635 20.3517031304326 Winsorized Mean ( 29 / 40 ) 166.366666666667 7.84807540621899 21.1984031823693 Winsorized Mean ( 30 / 40 ) 166.866666666667 7.73829588190637 21.5637485582366 Winsorized Mean ( 31 / 40 ) 164.8 7.5170636886087 21.9234540010266 Winsorized Mean ( 32 / 40 ) 165.6 7.42917445300763 22.2904982306558 Winsorized Mean ( 33 / 40 ) 166.15 7.36928718109309 22.5462783464703 Winsorized Mean ( 34 / 40 ) 165.3 7.21935811915859 22.8967724376119 Winsorized Mean ( 35 / 40 ) 165.3 7.21935811915859 22.8967724376119 Winsorized Mean ( 36 / 40 ) 165.9 6.96368531492242 23.8235923217975 Winsorized Mean ( 37 / 40 ) 167.133333333333 6.83219161057036 24.4626238343132 Winsorized Mean ( 38 / 40 ) 167.133333333333 6.69925090228209 24.9480629657265 Winsorized Mean ( 39 / 40 ) 166.483333333333 6.63189795570353 25.1034220437839 Winsorized Mean ( 40 / 40 ) 170.816666666667 6.11324024958993 27.9420830349543 Trimmed Mean ( 1 / 40 ) 169.737288135593 9.81607201877355 17.2917729017233 Trimmed Mean ( 2 / 40 ) 169.163793103448 9.73937697508912 17.3690569259334 Trimmed Mean ( 3 / 40 ) 168.710526315789 9.68196908357123 17.4252287793466 Trimmed Mean ( 4 / 40 ) 168.348214285714 9.63557087887108 17.4715350446821 Trimmed Mean ( 5 / 40 ) 167.972727272727 9.58491707726084 17.5246927979402 Trimmed Mean ( 6 / 40 ) 167.629629629630 9.53480618603727 17.5808114353814 Trimmed Mean ( 7 / 40 ) 167.283018867925 9.47863493150657 17.6484293441752 Trimmed Mean ( 8 / 40 ) 167.009615384615 9.43238283802374 17.7059835518304 Trimmed Mean ( 9 / 40 ) 166.794117647059 9.39331619271645 17.7566808382742 Trimmed Mean ( 10 / 40 ) 166.59 9.35672496230396 17.8043065999216 Trimmed Mean ( 11 / 40 ) 166.397959183673 9.31639589335968 17.8607651594405 Trimmed Mean ( 12 / 40 ) 166.270833333333 9.28070389192179 17.9157567431993 Trimmed Mean ( 13 / 40 ) 166.117021276596 9.24074730467152 17.9765787116176 Trimmed Mean ( 14 / 40 ) 165.956521739130 9.19616749879563 18.0462700098563 Trimmed Mean ( 15 / 40 ) 165.9 9.16549260441838 18.1005001215129 Trimmed Mean ( 16 / 40 ) 165.840909090909 9.12701912737232 18.1703255768957 Trimmed Mean ( 17 / 40 ) 165.813953488372 9.08569193749224 18.2500083239823 Trimmed Mean ( 18 / 40 ) 165.857142857143 9.05118901697985 18.3243486072380 Trimmed Mean ( 19 / 40 ) 165.902439024390 9.00758504281544 18.4180818982904 Trimmed Mean ( 20 / 40 ) 165.9625 8.96015165286866 18.5222869466574 Trimmed Mean ( 21 / 40 ) 166.038461538462 8.90833630073075 18.6385488752643 Trimmed Mean ( 22 / 40 ) 166.171052631579 8.85365200014165 18.7686451454067 Trimmed Mean ( 23 / 40 ) 166.324324324324 8.79841517712516 18.9038958694229 Trimmed Mean ( 24 / 40 ) 166.486111111111 8.73476749360138 19.0601651655948 Trimmed Mean ( 25 / 40 ) 166.571428571429 8.68943270629173 19.1694250017978 Trimmed Mean ( 26 / 40 ) 166.705882352941 8.64442602647935 19.2847832629133 Trimmed Mean ( 27 / 40 ) 166.848484848485 8.59126901150727 19.4207031144067 Trimmed Mean ( 28 / 40 ) 167.015625 8.52518712810437 19.5908456307559 Trimmed Mean ( 29 / 40 ) 167.177419354839 8.45073207814308 19.7825960885951 Trimmed Mean ( 30 / 40 ) 167.233333333333 8.38805872596319 19.9370723068144 Trimmed Mean ( 31 / 40 ) 167.258620689655 8.32039747384118 20.1022392518514 Trimmed Mean ( 32 / 40 ) 167.428571428571 8.2611537016554 20.2669720810329 Trimmed Mean ( 33 / 40 ) 167.555555555556 8.19327444299894 20.4503775286973 Trimmed Mean ( 34 / 40 ) 167.653846153846 8.11028268498116 20.6717635704995 Trimmed Mean ( 35 / 40 ) 167.82 8.0218930027107 20.9202491161739 Trimmed Mean ( 36 / 40 ) 168 7.90221712312392 21.2598562381169 Trimmed Mean ( 37 / 40 ) 168.152173913043 7.78757634348506 21.5923628220629 Trimmed Mean ( 38 / 40 ) 168.227272727273 7.65646265153971 21.971931475881 Trimmed Mean ( 39 / 40 ) 168.309523809524 7.5038115049942 22.4298709659091 Trimmed Mean ( 40 / 40 ) 168.45 7.30875397146358 23.0477042540629 Median 166.5 Midrange 205 Midmean - Weighted Average at Xnp 165.524590163934 Midmean - Weighted Average at X(n+1)p 167.233333333333 Midmean - Empirical Distribution Function 165.524590163934 Midmean - Empirical Distribution Function - Averaging 167.233333333333 Midmean - Empirical Distribution Function - Interpolation 167.233333333333 Midmean - Closest Observation 165.524590163934 Midmean - True Basic - Statistics Graphics Toolkit 167.233333333333 Midmean - MS Excel (old versions) 167.177419354839 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281698536r9ibynk2s0jrxdr/1yl1l1281698520.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281698536r9ibynk2s0jrxdr/1yl1l1281698520.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281698536r9ibynk2s0jrxdr/2rc061281698520.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/13/t1281698536r9ibynk2s0jrxdr/2rc061281698520.ps (open in new window)

Parameters (Session):

par1 = grey ;

Parameters (R input):

par1 = grey ;

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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