Central tendency Inflatie op jaarbasis: indexcijfer der consumptieprijzen

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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 04:21:15 -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/t12284761260k9818ir7g441b4.htm/, Retrieved Fri, 05 Dec 2008 11:22:08 +0000

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@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/t12284761260k9818ir7g441b4.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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0.42 0.74 1.02 1.51 1.86 1.59 1.03 0.44 0.82 0.86 0.57 0.59 0.95 0.98 1.23 1.17 0.84 0.74 0.65 0.91 1.19 1.3 1.53 1.94 1.79 1.95 2.26 2.04 2.16 2.75 2.79 2.88 3.36 2.97 3.1 2.49 2.2 2.25 2.09 2.79 3.14 2.93 2.65 2.67 2.26 2.35 2.13 2.18 2.9 2.63 2.67 1.81 1.33 0.88 1.28 1.26 1.26 1.29 1.1 1.37 1.21 1.74 1.76 1.48 1.04 1.62 1.49 1.79 1.8 1.58 1.86 1.74 1.59 1.26 1.13 1.92 2.61 2.26 2.41 2.26 2.03 2.86 2.55 2.27 2.26 2.57 3.07 2.76 2.51 2.87 3.14 3.11 3.16 2.47 2.57 2.89 2.63 2.38 1.69 1.96 2.19 1.87 1.6 1.63 1.22 1.21 1.49 1.64 1.66 1.77 1.82 1.78 1.28 1.29 1.37 1.12 1.51 2.24 2.94 3.09

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 1.88216666666667 0.0666589824002776 28.2357545658967 Geometric Mean 1.72148144780478 Harmonic Mean 1.53707703462586 Quadratic Mean 2.01775081051485 Winsorized Mean ( 1 / 40 ) 1.88066666666667 0.0663380626947419 28.3497375454067 Winsorized Mean ( 2 / 40 ) 1.8825 0.0659052721863796 28.5637239260055 Winsorized Mean ( 3 / 40 ) 1.883 0.0658221651343162 28.6073847032769 Winsorized Mean ( 4 / 40 ) 1.884 0.0653392782980911 28.8341109524474 Winsorized Mean ( 5 / 40 ) 1.88733333333333 0.0646965263586525 29.1720968583489 Winsorized Mean ( 6 / 40 ) 1.88683333333333 0.0646180312501993 29.1997960449703 Winsorized Mean ( 7 / 40 ) 1.89033333333333 0.0637587730365188 29.6482075062928 Winsorized Mean ( 8 / 40 ) 1.885 0.062566827018295 30.1277863978752 Winsorized Mean ( 9 / 40 ) 1.88425 0.0620326405563858 30.3751377194281 Winsorized Mean ( 10 / 40 ) 1.88508333333333 0.0616840004217453 30.5603287796618 Winsorized Mean ( 11 / 40 ) 1.88508333333333 0.0609225291948066 30.9423025972144 Winsorized Mean ( 12 / 40 ) 1.88808333333333 0.0602519564908149 31.3364651257617 Winsorized Mean ( 13 / 40 ) 1.89025 0.0596798722339445 31.6731576198796 Winsorized Mean ( 14 / 40 ) 1.89375 0.058927513571695 32.1369405429934 Winsorized Mean ( 15 / 40 ) 1.89375 0.0585986040239648 32.3173227680564 Winsorized Mean ( 16 / 40 ) 1.88575 0.0571658678721825 32.9873414012774 Winsorized Mean ( 17 / 40 ) 1.89425 0.0561319302397216 33.7463898339191 Winsorized Mean ( 18 / 40 ) 1.89275 0.055178087349322 34.3025663071168 Winsorized Mean ( 19 / 40 ) 1.89275 0.05478362869863 34.5495551310082 Winsorized Mean ( 20 / 40 ) 1.88608333333333 0.0522925802312868 36.0678957701323 Winsorized Mean ( 21 / 40 ) 1.88958333333333 0.0518929348981497 36.4131135971018 Winsorized Mean ( 22 / 40 ) 1.88958333333333 0.051018670146801 37.0370950065192 Winsorized Mean ( 23 / 40 ) 1.88575 0.0505414191433087 37.3109823974870 Winsorized Mean ( 24 / 40 ) 1.88775 0.0503175438300285 37.5167358402226 Winsorized Mean ( 25 / 40 ) 1.88566666666667 0.049571516022278 38.0393180999190 Winsorized Mean ( 26 / 40 ) 1.8835 0.0477986923042294 39.4048437143822 Winsorized Mean ( 27 / 40 ) 1.8835 0.0477986923042294 39.4048437143822 Winsorized Mean ( 28 / 40 ) 1.87883333333333 0.0472384711430105 39.7733730129691 Winsorized Mean ( 29 / 40 ) 1.874 0.0455634503873936 41.1294575820468 Winsorized Mean ( 30 / 40 ) 1.869 0.0449801387854024 41.551672593028 Winsorized Mean ( 31 / 40 ) 1.86641666666667 0.0440992634823138 42.3230802350066 Winsorized Mean ( 32 / 40 ) 1.85041666666667 0.0422889215140301 43.7565348185283 Winsorized Mean ( 33 / 40 ) 1.84491666666667 0.0410721159565748 44.9189583662376 Winsorized Mean ( 34 / 40 ) 1.84491666666667 0.0392035548024707 47.0599331096984 Winsorized Mean ( 35 / 40 ) 1.83325 0.0354449829133192 51.7210010929676 Winsorized Mean ( 36 / 40 ) 1.83025 0.0351354845691697 52.09121269971 Winsorized Mean ( 37 / 40 ) 1.86416666666667 0.031525096341588 59.1327825446628 Winsorized Mean ( 38 / 40 ) 1.86733333333333 0.0312020463069999 59.8465022120814 Winsorized Mean ( 39 / 40 ) 1.86733333333333 0.0312020463069999 59.8465022120814 Winsorized Mean ( 40 / 40 ) 1.874 0.0305292714385035 61.383711818177 Trimmed Mean ( 1 / 40 ) 1.88203389830508 0.0654440873720057 28.7578904967684 Trimmed Mean ( 2 / 40 ) 1.88344827586207 0.0644531957664 29.2219532866658 Trimmed Mean ( 3 / 40 ) 1.88394736842105 0.0636036304340762 29.6201231842218 Trimmed Mean ( 4 / 40 ) 1.88428571428571 0.06269188232015 30.056295082403 Trimmed Mean ( 5 / 40 ) 1.88436363636364 0.0618287509975771 30.4771422026216 Trimmed Mean ( 6 / 40 ) 1.88370370370370 0.0610340672554463 30.8631521445200 Trimmed Mean ( 7 / 40 ) 1.88311320754717 0.0601620099654475 31.3007030288497 Trimmed Mean ( 8 / 40 ) 1.88192307692308 0.059360557781008 31.7032579758741 Trimmed Mean ( 9 / 40 ) 1.88147058823529 0.0586822877215186 32.0619843105634 Trimmed Mean ( 10 / 40 ) 1.8811 0.0580074699125601 32.4285820918505 Trimmed Mean ( 11 / 40 ) 1.88061224489796 0.0572989062790276 32.8210845027298 Trimmed Mean ( 12 / 40 ) 1.88010416666667 0.0566116531444461 33.2105505180979 Trimmed Mean ( 13 / 40 ) 1.87925531914894 0.0559269630591456 33.6019554139124 Trimmed Mean ( 14 / 40 ) 1.87815217391304 0.055225275808266 34.0089233856199 Trimmed Mean ( 15 / 40 ) 1.87666666666667 0.05452415882094 34.4189934746125 Trimmed Mean ( 16 / 40 ) 1.87511363636364 0.0537623146492181 34.8778442408619 Trimmed Mean ( 17 / 40 ) 1.87418604651163 0.053078025089178 35.3100184749291 Trimmed Mean ( 18 / 40 ) 1.8725 0.0524171545345687 35.7230379372291 Trimmed Mean ( 19 / 40 ) 1.87085365853659 0.0517705780319235 36.1373917320945 Trimmed Mean ( 20 / 40 ) 1.869125 0.0510643654858097 36.603313919947 Trimmed Mean ( 21 / 40 ) 1.86782051282051 0.0505539238369743 36.947092748801 Trimmed Mean ( 22 / 40 ) 1.86618421052632 0.0499936722681115 37.3284082937165 Trimmed Mean ( 23 / 40 ) 1.86445945945946 0.0494344699890527 37.7157772678122 Trimmed Mean ( 24 / 40 ) 1.86291666666667 0.0488255804305678 38.1545216716024 Trimmed Mean ( 25 / 40 ) 1.86114285714286 0.0481214572978592 38.6759454441056 Trimmed Mean ( 26 / 40 ) 1.85941176470588 0.0473748442900609 39.2489261457263 Trimmed Mean ( 27 / 40 ) 1.85772727272727 0.0467123297014038 39.7695273304136 Trimmed Mean ( 28 / 40 ) 1.8559375 0.0459040140883627 40.4308323979559 Trimmed Mean ( 29 / 40 ) 1.85435483870968 0.0450016868875562 41.2063406276986 Trimmed Mean ( 30 / 40 ) 1.853 0.0441468402360221 41.9735589250174 Trimmed Mean ( 31 / 40 ) 1.85189655172414 0.0431835492868638 42.8843062301846 Trimmed Mean ( 32 / 40 ) 1.85089285714286 0.0421317327932672 43.9310879100286 Trimmed Mean ( 33 / 40 ) 1.85092592592593 0.0411235194474929 45.0089377269671 Trimmed Mean ( 34 / 40 ) 1.85134615384615 0.0400631372432725 46.2107134197793 Trimmed Mean ( 35 / 40 ) 1.8518 0.0390506630636957 47.4204496087435 Trimmed Mean ( 36 / 40 ) 1.853125 0.0384139872875264 48.2408916869126 Trimmed Mean ( 37 / 40 ) 1.85478260869565 0.0376222504963374 49.3001504223204 Trimmed Mean ( 38 / 40 ) 1.85409090909091 0.037249967268482 49.7742963296426 Trimmed Mean ( 39 / 40 ) 1.85309523809524 0.0367676069608894 50.4002134288049 Trimmed Mean ( 40 / 40 ) 1.852 0.0360771964343698 51.3343658332511 Median 1.805 Midrange 1.89 Midmean - Weighted Average at Xnp 1.84360655737705 Midmean - Weighted Average at X(n+1)p 1.84360655737705 Midmean - Empirical Distribution Function 1.84360655737705 Midmean - Empirical Distribution Function - Averaging 1.84360655737705 Midmean - Empirical Distribution Function - Interpolation 1.84360655737705 Midmean - Closest Observation 1.84360655737705 Midmean - True Basic - Statistics Graphics Toolkit 1.84360655737705 Midmean - MS Excel (old versions) 1.85435483870968 Number of observations 120

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284761260k9818ir7g441b4/1q40h1228476073.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284761260k9818ir7g441b4/1q40h1228476073.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284761260k9818ir7g441b4/2i4n21228476073.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12284761260k9818ir7g441b4/2i4n21228476073.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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