Tijdreeks A - Stap 14

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 09 Aug 2010 16:56:21 +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/09/t1281373039y4igqgql27x6vnj.htm/, Retrieved Mon, 09 Aug 2010 18:57:21 +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/09/t1281373039y4igqgql27x6vnj.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:

Platini Olivier

Dataseries X:

» Textbox « » Textfile « » CSV «

94 93 92 90 110 109 94 84 85 85 86 88 93 94 90 91 104 103 88 79 82 88 93 89 94 96 94 92 113 122 107 98 103 110 113 110 123 124 118 117 139 146 134 121 123 122 127 122 139 136 127 123 140 146 138 120 122 115 115 102 119 114 108 102 121 109 102 95 98 92 94 90 113 111 103 90 108 99 95 91 85 72 90 90 114 115 104 93 101 90 79 75 71 61 84 87 107 99 93 74 87 71 67 61 63 52 80 84 102 93 87 72 83 72 66 64 64 47 77 79

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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 98.3666666666667 1.86411390073693 52.7685924276302 Geometric Mean 96.1830484502639 Harmonic Mean 93.8834907328604 Quadratic Mean 100.446586137443 Winsorized Mean ( 1 / 40 ) 98.4083333333333 1.85490853977846 53.0529302243047 Winsorized Mean ( 2 / 40 ) 98.4583333333333 1.80556623928463 54.5304465663596 Winsorized Mean ( 3 / 40 ) 98.4333333333333 1.80078310592489 54.6614042576646 Winsorized Mean ( 4 / 40 ) 98.5 1.78940235394774 55.0463118497029 Winsorized Mean ( 5 / 40 ) 98.5 1.77466610265435 55.5033985563114 Winsorized Mean ( 6 / 40 ) 98.4 1.75631712966195 56.0263282400143 Winsorized Mean ( 7 / 40 ) 98.4 1.71679787918764 57.3160074303919 Winsorized Mean ( 8 / 40 ) 98 1.63050403782602 60.1041136522822 Winsorized Mean ( 9 / 40 ) 98.3 1.58479292888723 62.02703091881 Winsorized Mean ( 10 / 40 ) 98.05 1.54814653798703 63.333797928127 Winsorized Mean ( 11 / 40 ) 98.05 1.52205310625592 64.4195656491854 Winsorized Mean ( 12 / 40 ) 98.05 1.52205310625592 64.4195656491854 Winsorized Mean ( 13 / 40 ) 98.05 1.52205310625592 64.4195656491854 Winsorized Mean ( 14 / 40 ) 98.1666666666667 1.47321241122592 66.6344282186558 Winsorized Mean ( 15 / 40 ) 98.2916666666667 1.45619491075677 67.4989769162053 Winsorized Mean ( 16 / 40 ) 98.5583333333333 1.42126683505710 69.3454113627955 Winsorized Mean ( 17 / 40 ) 98.8416666666667 1.38615583101678 71.3063166889137 Winsorized Mean ( 18 / 40 ) 98.6916666666667 1.36532672408185 72.2842854577792 Winsorized Mean ( 19 / 40 ) 98.6916666666667 1.36532672408185 72.2842854577792 Winsorized Mean ( 20 / 40 ) 98.6916666666667 1.3226001380299 74.619428676058 Winsorized Mean ( 21 / 40 ) 98.8666666666667 1.25846897630907 78.561067875213 Winsorized Mean ( 22 / 40 ) 98.8666666666667 1.21359787186057 81.4657548097823 Winsorized Mean ( 23 / 40 ) 98.8666666666667 1.16760266574751 84.674923727903 Winsorized Mean ( 24 / 40 ) 98.4666666666667 1.11658827435023 88.1852952682729 Winsorized Mean ( 25 / 40 ) 98.4666666666667 1.11658827435023 88.1852952682729 Winsorized Mean ( 26 / 40 ) 98.6833333333333 1.09339648481961 90.2539332286353 Winsorized Mean ( 27 / 40 ) 98.4583333333333 1.06549496877182 92.4061926325423 Winsorized Mean ( 28 / 40 ) 98.4583333333333 1.06549496877182 92.4061926325423 Winsorized Mean ( 29 / 40 ) 98.4583333333333 1.01071956460829 97.4140966307415 Winsorized Mean ( 30 / 40 ) 98.7083333333333 0.985283578549206 100.182663633426 Winsorized Mean ( 31 / 40 ) 98.7083333333333 0.985283578549206 100.182663633426 Winsorized Mean ( 32 / 40 ) 98.175 0.921548941174798 106.532594866688 Winsorized Mean ( 33 / 40 ) 98.175 0.861945433291452 113.899321474569 Winsorized Mean ( 34 / 40 ) 98.175 0.861945433291452 113.899321474569 Winsorized Mean ( 35 / 40 ) 98.175 0.861945433291452 113.899321474569 Winsorized Mean ( 36 / 40 ) 98.175 0.79816513143009 123.000863022039 Winsorized Mean ( 37 / 40 ) 98.4833333333333 0.768969565290853 128.071822057201 Winsorized Mean ( 38 / 40 ) 98.1666666666667 0.732913044828088 133.940400378182 Winsorized Mean ( 39 / 40 ) 98.1666666666667 0.732913044828088 133.940400378182 Winsorized Mean ( 40 / 40 ) 97.8333333333333 0.69565813641743 140.634211276770 Trimmed Mean ( 1 / 40 ) 98.3983050847458 1.79965469954477 54.6762137812526 Trimmed Mean ( 2 / 40 ) 98.3879310344828 1.73800328108324 56.609749881002 Trimmed Mean ( 3 / 40 ) 98.3508771929825 1.69856065061824 57.9024818202311 Trimmed Mean ( 4 / 40 ) 98.3214285714286 1.65662423096432 59.3504711169145 Trimmed Mean ( 5 / 40 ) 98.2727272727273 1.61353269410549 60.9053213682838 Trimmed Mean ( 6 / 40 ) 98.2222222222222 1.56940136773606 62.5857885952482 Trimmed Mean ( 7 / 40 ) 98.188679245283 1.52441131804553 64.4108831277719 Trimmed Mean ( 8 / 40 ) 98.1538461538462 1.48258305251742 66.2046190175863 Trimmed Mean ( 9 / 40 ) 98.1764705882353 1.45277111974494 67.5787598293337 Trimmed Mean ( 10 / 40 ) 98.16 1.42748772748532 68.7641638593416 Trimmed Mean ( 11 / 40 ) 98.1734693877551 1.40511115859958 69.8688276631443 Trimmed Mean ( 12 / 40 ) 98.1875 1.38390755097627 70.9494647462069 Trimmed Mean ( 13 / 40 ) 98.2021276595745 1.35982993365476 72.2164773911399 Trimmed Mean ( 14 / 40 ) 98.2173913043478 1.33244151954210 73.7123467438189 Trimmed Mean ( 15 / 40 ) 98.2222222222222 1.30835954102216 75.0728061687737 Trimmed Mean ( 16 / 40 ) 98.215909090909 1.28322600587003 76.5382782468768 Trimmed Mean ( 17 / 40 ) 98.1860465116279 1.25922170259291 77.9735977464887 Trimmed Mean ( 18 / 40 ) 98.1309523809524 1.23625892662106 79.3773458519438 Trimmed Mean ( 19 / 40 ) 98.0853658536585 1.21260086136987 80.8884184222435 Trimmed Mean ( 20 / 40 ) 98.0375 1.18514590112864 82.7218825181241 Trimmed Mean ( 21 / 40 ) 97.9871794871795 1.15908917209692 84.5380854605948 Trimmed Mean ( 22 / 40 ) 97.921052631579 1.13721160337039 86.1062728707367 Trimmed Mean ( 23 / 40 ) 97.8513513513514 1.11744313854252 87.5671861737668 Trimmed Mean ( 24 / 40 ) 97.7777777777778 1.10008219607223 88.8822472783274 Trimmed Mean ( 25 / 40 ) 97.7285714285714 1.08632025151401 89.9629471993797 Trimmed Mean ( 26 / 40 ) 97.6764705882353 1.06966332129624 91.3151536970237 Trimmed Mean ( 27 / 40 ) 97.6060606060606 1.05244277685055 92.7423920359342 Trimmed Mean ( 28 / 40 ) 97.546875 1.03556060068375 94.1971671533202 Trimmed Mean ( 29 / 40 ) 97.483870967742 1.01489226607128 96.0534179111535 Trimmed Mean ( 30 / 40 ) 97.4166666666667 0.997536985612455 97.6571977497717 Trimmed Mean ( 31 / 40 ) 97.3275862068966 0.979335160681807 99.3812844819517 Trimmed Mean ( 32 / 40 ) 97.2321428571429 0.95651935012388 101.652039600192 Trimmed Mean ( 33 / 40 ) 97.1666666666667 0.939002697380753 103.478580985659 Trimmed Mean ( 34 / 40 ) 97.0961538461538 0.926100346465186 104.844096232939 Trimmed Mean ( 35 / 40 ) 97.02 0.90913280051904 106.717082415913 Trimmed Mean ( 36 / 40 ) 96.9375 0.886965426857054 109.291182119123 Trimmed Mean ( 37 / 40 ) 96.8478260869565 0.86976446992522 111.349485332832 Trimmed Mean ( 38 / 40 ) 96.7272727272727 0.8511673592981 113.640721381794 Trimmed Mean ( 39 / 40 ) 96.6190476190476 0.834054773163556 115.842569010873 Trimmed Mean ( 40 / 40 ) 96.5 0.810191493666933 119.107644000606 Median 94 Midrange 96.5 Midmean - Weighted Average at Xnp 97.483870967742 Midmean - Weighted Average at X(n+1)p 97.672131147541 Midmean - Empirical Distribution Function 97.483870967742 Midmean - Empirical Distribution Function - Averaging 97.672131147541 Midmean - Empirical Distribution Function - Interpolation 97.672131147541 Midmean - Closest Observation 97.483870967742 Midmean - True Basic - Statistics Graphics Toolkit 97.672131147541 Midmean - MS Excel (old versions) 97.483870967742 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/09/t1281373039y4igqgql27x6vnj/1p2d51281372979.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/09/t1281373039y4igqgql27x6vnj/1p2d51281372979.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/09/t1281373039y4igqgql27x6vnj/2huc81281372979.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/09/t1281373039y4igqgql27x6vnj/2huc81281372979.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 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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