Central tendency Investeringen volgens de BTW-aangiften

*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 04:26:04 -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/t12284764073n17lfe03o7tr2t.htm/, Retrieved Fri, 05 Dec 2008 11:26:47 +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/t12284764073n17lfe03o7tr2t.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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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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24.1 26.2 22.7 22.9 12.6 12.2 10.1 17.3 22.9 21.7 21 18.8 19.7 21.4 24.3 26 23.5 23.8 25 31.8 26.6 26.5 21 17.9 17.1 17.7 26.2 22.3 26.6 19.6 21 14.5 16.8 18.7 23.3 23.3 27 22.8 20.3 16 14.7 17.4 17.7 20.7 18.5 15.6 20.2 17.9 16 9.5 7.9 7.1 6.2 8 9.5 7.7 7.5 11.5 17.2 18.1 19.1 19.2 21 20.5 20.1 20.5 16 16.5 16.2 16.7 6.4 3.8 2.3 11.9 14 15.3 12.1 12.8 18.2 20.9 28.3 22.3 21.9 23.3 26.8 24.2 20.8 23.8 29.6 43.1 34.7 31.5 9.7 12.6 16.9 16.6 14.9 13.1 18.4 18.3 18.3 5.2 10.4 8.9 25.6 23.8 25.7 26.7 28.5 29.6 29 26.1 24.2 28 29.4 31.5 26.7 27.5 25.6 27.4

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 19.6208333333333 0.648335399066363 30.2633997180909 Geometric Mean 17.9919193482532 Harmonic Mean 15.6543697921414 Quadratic Mean 20.8565916838458 Winsorized Mean ( 1 / 40 ) 19.5633333333333 0.627812365291631 31.1611150319503 Winsorized Mean ( 2 / 40 ) 19.5383333333333 0.6140992621948 31.8162462262258 Winsorized Mean ( 3 / 40 ) 19.5558333333333 0.608090637862654 32.1594053841531 Winsorized Mean ( 4 / 40 ) 19.5625 0.606867862834387 32.2351885773503 Winsorized Mean ( 5 / 40 ) 19.5125 0.589379109397201 33.1068741475360 Winsorized Mean ( 6 / 40 ) 19.5325 0.585883376818709 33.3385461558231 Winsorized Mean ( 7 / 40 ) 19.5325 0.582207191286259 33.5490531417986 Winsorized Mean ( 8 / 40 ) 19.5191666666667 0.57619598652943 33.8759157005507 Winsorized Mean ( 9 / 40 ) 19.4891666666667 0.569840505270769 34.2010904567166 Winsorized Mean ( 10 / 40 ) 19.5475 0.555228474561225 35.2062275182259 Winsorized Mean ( 11 / 40 ) 19.575 0.542891741120668 36.0569124879155 Winsorized Mean ( 12 / 40 ) 19.525 0.53650786226719 36.3927565152367 Winsorized Mean ( 13 / 40 ) 19.5358333333333 0.531773097493239 36.7371599379973 Winsorized Mean ( 14 / 40 ) 19.5358333333333 0.518858361951095 37.6515726948517 Winsorized Mean ( 15 / 40 ) 19.5483333333333 0.510162800584193 38.317833662016 Winsorized Mean ( 16 / 40 ) 19.6816666666667 0.487142792111953 40.4022536828247 Winsorized Mean ( 17 / 40 ) 19.7383333333333 0.479248921206984 41.1859734261321 Winsorized Mean ( 18 / 40 ) 19.7533333333333 0.473321793365461 41.7334118357009 Winsorized Mean ( 19 / 40 ) 19.7691666666667 0.471177373622325 41.9569524629015 Winsorized Mean ( 20 / 40 ) 19.8191666666667 0.460250960395831 43.0616519509737 Winsorized Mean ( 21 / 40 ) 19.7666666666667 0.45392211312138 43.5463840497698 Winsorized Mean ( 22 / 40 ) 19.8033333333333 0.449087049844462 44.0968701729255 Winsorized Mean ( 23 / 40 ) 19.8416666666667 0.439316988555234 45.1648062414322 Winsorized Mean ( 24 / 40 ) 20.0016666666667 0.414323354644109 48.2754989369293 Winsorized Mean ( 25 / 40 ) 20.0433333333333 0.394254487998792 50.8385673301321 Winsorized Mean ( 26 / 40 ) 20.065 0.386557473957326 51.9068996250091 Winsorized Mean ( 27 / 40 ) 20.11 0.381349880152645 52.7337257637276 Winsorized Mean ( 28 / 40 ) 20.0633333333333 0.354032261529296 56.6709182001287 Winsorized Mean ( 29 / 40 ) 19.9666666666667 0.32649426651795 61.1547237248925 Winsorized Mean ( 30 / 40 ) 20.0416666666667 0.312647910840818 64.1029924452965 Winsorized Mean ( 31 / 40 ) 20.0416666666667 0.312647910840818 64.1029924452965 Winsorized Mean ( 32 / 40 ) 20.015 0.309679625723481 64.6313103525635 Winsorized Mean ( 33 / 40 ) 19.9875 0.294652533581876 67.834135878713 Winsorized Mean ( 34 / 40 ) 20.0725 0.285592319030249 70.2837529670186 Winsorized Mean ( 35 / 40 ) 20.1016666666667 0.282540454289276 71.1461539807881 Winsorized Mean ( 36 / 40 ) 20.0416666666667 0.269613228874142 74.334878708872 Winsorized Mean ( 37 / 40 ) 20.0108333333333 0.259801680001957 77.0234947409985 Winsorized Mean ( 38 / 40 ) 20.0425 0.256527288996707 78.1300893109165 Winsorized Mean ( 39 / 40 ) 20.1075 0.249893990128856 80.4641199639563 Winsorized Mean ( 40 / 40 ) 20.0075 0.23237949046987 86.0983900065576 Trimmed Mean ( 1 / 40 ) 19.5686440677966 0.610808175922865 32.0372988430132 Trimmed Mean ( 2 / 40 ) 19.5741379310345 0.591875428424847 33.0713812248077 Trimmed Mean ( 3 / 40 ) 19.5929824561404 0.57890076640337 33.8451485871557 Trimmed Mean ( 4 / 40 ) 19.60625 0.566941433380492 34.5824962608468 Trimmed Mean ( 5 / 40 ) 19.6181818181818 0.554009092668405 35.4112993411176 Trimmed Mean ( 6 / 40 ) 19.6416666666667 0.544302636394639 36.0859296893535 Trimmed Mean ( 7 / 40 ) 19.6622641509434 0.534271052600027 36.8020390684786 Trimmed Mean ( 8 / 40 ) 19.6836538461538 0.523802575874857 37.5783830640354 Trimmed Mean ( 9 / 40 ) 19.7078431372549 0.513204946556901 38.4015065900574 Trimmed Mean ( 10 / 40 ) 19.737 0.502405637152316 39.2849891411873 Trimmed Mean ( 11 / 40 ) 19.7602040816327 0.492747185864303 40.1021145295275 Trimmed Mean ( 12 / 40 ) 19.78125 0.483822300685232 40.8853621918296 Trimmed Mean ( 13 / 40 ) 19.8085106382979 0.474648202816222 41.7330362166514 Trimmed Mean ( 14 / 40 ) 19.8358695652174 0.46491983989634 42.6651389401667 Trimmed Mean ( 15 / 40 ) 19.8644444444444 0.455706804769126 43.5904055777888 Trimmed Mean ( 16 / 40 ) 19.8931818181818 0.446406163179829 44.5629640874114 Trimmed Mean ( 17 / 40 ) 19.9116279069767 0.4390121313234 45.3555300327425 Trimmed Mean ( 18 / 40 ) 19.9261904761905 0.431574785976947 46.1708865384339 Trimmed Mean ( 19 / 40 ) 19.9402439024390 0.423770019085675 47.0543998026619 Trimmed Mean ( 20 / 40 ) 19.95375 0.415007029641546 48.0805108704657 Trimmed Mean ( 21 / 40 ) 19.9641025641026 0.406318618463644 49.1341072176068 Trimmed Mean ( 22 / 40 ) 19.9789473684211 0.397012155850284 50.323263592854 Trimmed Mean ( 23 / 40 ) 19.9918918918919 0.386790191435719 51.6866568350256 Trimmed Mean ( 24 / 40 ) 20.0027777777778 0.376167833208913 53.175141550896 Trimmed Mean ( 25 / 40 ) 20.0028571428571 0.367228844930447 54.4697330261344 Trimmed Mean ( 26 / 40 ) 20 0.359439374220637 55.6422068210126 Trimmed Mean ( 27 / 40 ) 19.9954545454545 0.351211236274832 56.9328440557282 Trimmed Mean ( 28 / 40 ) 19.9875 0.342069303987012 58.4311417804356 Trimmed Mean ( 29 / 40 ) 19.9822580645161 0.335100829309722 59.6305837430418 Trimmed Mean ( 30 / 40 ) 19.9833333333333 0.330571895021004 60.4507934108059 Trimmed Mean ( 31 / 40 ) 19.9793103448276 0.326855618594007 61.125797472199 Trimmed Mean ( 32 / 40 ) 19.975 0.322099948189031 62.0149121796109 Trimmed Mean ( 33 / 40 ) 19.9722222222222 0.316519514156451 63.0994972788637 Trimmed Mean ( 34 / 40 ) 19.9711538461538 0.311812883065650 64.0485205415616 Trimmed Mean ( 35 / 40 ) 19.964 0.307149634476722 64.9976355466347 Trimmed Mean ( 36 / 40 ) 19.9541666666667 0.301517414392657 66.1791515652923 Trimmed Mean ( 37 / 40 ) 19.9478260869565 0.296464499270777 67.2857159492042 Trimmed Mean ( 38 / 40 ) 19.9431818181818 0.291518545990062 68.4113655632108 Trimmed Mean ( 39 / 40 ) 19.9357142857143 0.285393984225239 69.8533094165733 Trimmed Mean ( 40 / 40 ) 19.9225 0.27837641690197 71.5667664011053 Median 20.15 Midrange 22.7 Midmean - Weighted Average at Xnp 19.9114754098361 Midmean - Weighted Average at X(n+1)p 19.9833333333333 Midmean - Empirical Distribution Function 19.9114754098361 Midmean - Empirical Distribution Function - Averaging 19.9833333333333 Midmean - Empirical Distribution Function - Interpolation 19.9833333333333 Midmean - Closest Observation 19.9114754098361 Midmean - True Basic - Statistics Graphics Toolkit 19.9833333333333 Midmean - MS Excel (old versions) 19.9822580645161 Number of observations 120

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284764073n17lfe03o7tr2t/16cdd1228476357.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284764073n17lfe03o7tr2t/16cdd1228476357.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284764073n17lfe03o7tr2t/26h7z1228476357.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284764073n17lfe03o7tr2t/26h7z1228476357.ps (open in new window)

Parameters (Session):

par1 = Inflatie indexcijfer der consumptieprijzen ; par2 = http://ecodata.mineco.fgov.be/mdn/ts_structur.jsp?table=EI0_ ; par3 = Economische indicator voor België. Maandelijks, volledige tijdreeks van januari 1998 tot december 2007 - Inflatie op jaarbasis: indexcijfer der consumptieprijzen ;

Parameters (R input):

par1 = Inflatie indexcijfer der consumptieprijzen ; par2 = http://ecodata.mineco.fgov.be/mdn/ts_structur.jsp?table=EI0_ ; par3 = Economische indicator voor België. Maandelijks, volledige tijdreeks van januari 1998 tot december 2007 - Inflatie op jaarbasis: indexcijfer der consumptieprijzen ;

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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