Central Tendency Werkloosheidsgraad in procenten van de beroepsbevolking

*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, 05 Dec 2008 04:16:53 -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/05/t1228475934zyqsrzz2t43uhpz.htm/, Retrieved Fri, 05 Dec 2008 11:18:54 +0000

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/05/t1228475934zyqsrzz2t43uhpz.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}, }

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2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

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Dataseries X:

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9.4 9.4 9.5 9.5 9.4 9.4 9.3 9.4 9.4 9.2 9.1 9.1 9.1 9.1 9 8.9 8.8 8.7 8.4 8.3 8.2 7.9 7.8 7.7 7.3 7.2 7.1 6.9 6.8 6.7 6.8 6.9 6.7 6.8 6.8 6.7 6.3 6.2 6.2 6.5 6.5 6.4 6.2 6.2 6.3 7.5 7.4 7.4 7.4 7.4 7.4 7.2 7.2 7.1 7.5 7.4 7.5 8 8.1 8.1 8.1 8.1 8.1 7.9 7.9 8 8.1 8.1 8.1 8.6 8.6 8.6 8.4 8.4 8.4 7.7 7.8 7.9 8.7 8.8 8.8 8.5 8.5 8.5 8.4 8.5 8.5 8.3 8.4 8.4 8.5 8.4 8.4 8.5 8.5 8.5 8.5 8.5 8.5 8.3 8.3 8.3 8.3 8.2 8.2 8.1 8 7.8 7.9 7.8 7.7 7.8 7.7 7.6 7.3 7.3 7.1 7.1 7.1 7

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 3 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 7.95333333333333 0.0776577037662611 102.415252416834 Geometric Mean 7.9069195954496 Harmonic Mean 7.85922058628615 Quadratic Mean 7.99832274084177 Winsorized Mean ( 1 / 40 ) 7.95333333333333 0.0776577037662611 102.415252416834 Winsorized Mean ( 2 / 40 ) 7.95166666666667 0.077387157691292 102.751760161382 Winsorized Mean ( 3 / 40 ) 7.95166666666667 0.077387157691292 102.751760161382 Winsorized Mean ( 4 / 40 ) 7.955 0.0767681401294546 103.623716643199 Winsorized Mean ( 5 / 40 ) 7.955 0.0767681401294546 103.623716643199 Winsorized Mean ( 6 / 40 ) 7.96 0.0758832215006857 104.898024129459 Winsorized Mean ( 7 / 40 ) 7.96583333333333 0.0748995203050289 106.353596136429 Winsorized Mean ( 8 / 40 ) 7.95916666666667 0.073854424085829 107.768312666239 Winsorized Mean ( 9 / 40 ) 7.96666666666667 0.07034661416953 113.248757750694 Winsorized Mean ( 10 / 40 ) 7.95833333333333 0.0691543895823401 115.080667784040 Winsorized Mean ( 11 / 40 ) 7.95833333333333 0.0691543895823401 115.080667784040 Winsorized Mean ( 12 / 40 ) 7.96833333333333 0.0676639344841742 117.763375631032 Winsorized Mean ( 13 / 40 ) 7.96833333333333 0.0676639344841742 117.763375631032 Winsorized Mean ( 14 / 40 ) 7.95666666666667 0.0660694538781801 120.428824511510 Winsorized Mean ( 15 / 40 ) 7.94416666666667 0.0644606565058217 123.240548534426 Winsorized Mean ( 16 / 40 ) 7.94416666666667 0.0608851128392881 130.477982156919 Winsorized Mean ( 17 / 40 ) 7.94416666666667 0.0608851128392881 130.477982156919 Winsorized Mean ( 18 / 40 ) 7.95916666666667 0.0587747901489192 135.418036312853 Winsorized Mean ( 19 / 40 ) 7.95916666666667 0.0547913771014065 145.263125107004 Winsorized Mean ( 20 / 40 ) 7.95916666666667 0.0547913771014065 145.263125107004 Winsorized Mean ( 21 / 40 ) 7.94166666666667 0.0528803857847781 150.181708185509 Winsorized Mean ( 22 / 40 ) 7.94166666666667 0.0528803857847781 150.181708185509 Winsorized Mean ( 23 / 40 ) 7.94166666666667 0.0528803857847781 150.181708185509 Winsorized Mean ( 24 / 40 ) 7.94166666666667 0.0482259988538635 164.676043117984 Winsorized Mean ( 25 / 40 ) 7.94166666666667 0.0482259988538635 164.676043117984 Winsorized Mean ( 26 / 40 ) 7.94166666666667 0.0482259988538635 164.676043117984 Winsorized Mean ( 27 / 40 ) 7.96416666666667 0.0453865414183923 175.474191638654 Winsorized Mean ( 28 / 40 ) 7.96416666666667 0.0453865414183923 175.474191638654 Winsorized Mean ( 29 / 40 ) 7.96416666666667 0.0453865414183923 175.474191638654 Winsorized Mean ( 30 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 31 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 32 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 33 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 34 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 35 / 40 ) 7.98916666666667 0.042386705696416 188.482839970793 Winsorized Mean ( 36 / 40 ) 7.98916666666667 0.0359503983166017 222.227486780788 Winsorized Mean ( 37 / 40 ) 7.98916666666667 0.0359503983166017 222.227486780788 Winsorized Mean ( 38 / 40 ) 7.98916666666667 0.0359503983166017 222.227486780788 Winsorized Mean ( 39 / 40 ) 8.02166666666667 0.0323059458354216 248.303105178594 Winsorized Mean ( 40 / 40 ) 8.055 0.0287423111140923 280.24886266194 Trimmed Mean ( 1 / 40 ) 7.95508474576271 0.0764316964786694 104.080965257429 Trimmed Mean ( 2 / 40 ) 7.95689655172414 0.075073418716598 105.988200454297 Trimmed Mean ( 3 / 40 ) 7.95964912280702 0.0737228294268992 107.967222428698 Trimmed Mean ( 4 / 40 ) 7.9625 0.0722203143826154 110.252912467475 Trimmed Mean ( 5 / 40 ) 7.96454545454545 0.0707470722333106 112.577739306015 Trimmed Mean ( 6 / 40 ) 7.96666666666667 0.0691014374200228 115.289449309751 Trimmed Mean ( 7 / 40 ) 7.96792452830189 0.0674732940389454 118.090047948494 Trimmed Mean ( 8 / 40 ) 7.96826923076923 0.0658611086252313 120.985956615322 Trimmed Mean ( 9 / 40 ) 7.96960784313726 0.0642564088009765 124.0282174471 Trimmed Mean ( 10 / 40 ) 7.97 0.0630936453282585 126.320169940005 Trimmed Mean ( 11 / 40 ) 7.97142857142857 0.0619821275557313 128.608501930836 Trimmed Mean ( 12 / 40 ) 7.97291666666667 0.0607244128243781 131.296727227734 Trimmed Mean ( 13 / 40 ) 7.97340425531915 0.0595332937754424 133.931851400572 Trimmed Mean ( 14 / 40 ) 7.97391304347826 0.0581788756340028 137.058562177126 Trimmed Mean ( 15 / 40 ) 7.97555555555556 0.0568703348332654 140.241051489122 Trimmed Mean ( 16 / 40 ) 7.97840909090909 0.055601791382128 143.491943201556 Trimmed Mean ( 17 / 40 ) 7.98139534883721 0.054647524400819 146.052276591647 Trimmed Mean ( 18 / 40 ) 7.98452380952381 0.0535459236794202 149.115437009308 Trimmed Mean ( 19 / 40 ) 7.98658536585366 0.0525691123969695 151.925436852422 Trimmed Mean ( 20 / 40 ) 7.98875 0.0519552433362272 153.762151556119 Trimmed Mean ( 21 / 40 ) 7.99102564102564 0.0512314518935702 155.978902523131 Trimmed Mean ( 22 / 40 ) 7.99473684210526 0.050606661264682 157.977954725986 Trimmed Mean ( 23 / 40 ) 7.99864864864865 0.0498607183811025 160.419843683604 Trimmed Mean ( 24 / 40 ) 8.00277777777778 0.0489711127451534 163.418336426709 Trimmed Mean ( 25 / 40 ) 8.00714285714286 0.048510801280493 165.058969256042 Trimmed Mean ( 26 / 40 ) 8.01176470588235 0.047939520161847 167.122338288621 Trimmed Mean ( 27 / 40 ) 8.01666666666667 0.0472351323821341 169.718306319362 Trimmed Mean ( 28 / 40 ) 8.0203125 0.0467802448373632 171.446569548396 Trimmed Mean ( 29 / 40 ) 8.0241935483871 0.0462035985805492 173.670315622669 Trimmed Mean ( 30 / 40 ) 8.02833333333333 0.0454787258060472 176.529425375102 Trimmed Mean ( 31 / 40 ) 8.03103448275862 0.0450318766274915 178.341101553289 Trimmed Mean ( 32 / 40 ) 8.03392857142857 0.0444502337885753 180.739849640419 Trimmed Mean ( 33 / 40 ) 8.03703703703704 0.0437015716828267 183.907276730629 Trimmed Mean ( 34 / 40 ) 8.04038461538462 0.042743368972694 188.108350105044 Trimmed Mean ( 35 / 40 ) 8.044 0.0415181297994119 193.746684613764 Trimmed Mean ( 36 / 40 ) 8.04791666666667 0.0399456172128479 201.471831660125 Trimmed Mean ( 37 / 40 ) 8.05217391304348 0.0391626276591214 205.608622157100 Trimmed Mean ( 38 / 40 ) 8.05681818181818 0.0381104613595716 211.406996777138 Trimmed Mean ( 39 / 40 ) 8.06190476190476 0.0366976504535119 219.684493755737 Trimmed Mean ( 40 / 40 ) 8.065 0.0357250899408532 225.75170596778 Median 8.1 Midrange 7.85 Midmean - Weighted Average at Xnp 8.03768115942029 Midmean - Weighted Average at X(n+1)p 8.07121212121212 Midmean - Empirical Distribution Function 8.03768115942029 Midmean - Empirical Distribution Function - Averaging 8.07121212121212 Midmean - Empirical Distribution Function - Interpolation 8.07121212121212 Midmean - Closest Observation 8.03768115942029 Midmean - True Basic - Statistics Graphics Toolkit 8.07121212121212 Midmean - MS Excel (old versions) 8.03768115942029 Number of observations 120

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228475934zyqsrzz2t43uhpz/1oufv1228475806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228475934zyqsrzz2t43uhpz/1oufv1228475806.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228475934zyqsrzz2t43uhpz/2evli1228475806.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228475934zyqsrzz2t43uhpz/2evli1228475806.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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