Maatstaven voor centrale tendentie

*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, 01 Oct 2010 10:31:24 +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/Oct/01/t12859290425qikmjpxhdqnasr.htm/, Retrieved Fri, 01 Oct 2010 12:30:42 +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/Oct/01/t12859290425qikmjpxhdqnasr.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:

Dataseries X:

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426,113 383,703 232,444 70,939 226,731 611,281 158,047 33,999 37,028 388,3 506,652 392,25 180,818 198,296 217,465 275,562 57,47 136,452 556,277 213,361 274,482 220,553 236,71 260,642 213,923 169,861 403,064 449,594 406,167 206,893 156,187 257,102 62,156 251,422 171,328 350,089 221,588 4,813 183,186 190,379 223,166 232,669 356,725 109,215 475,834 315,955 8,95 278,741 308,16 207,533 192,797 601,162 289,714 293,671 386,688 85,094 131,812 197,549 308,174 86,58 242,205 238,502 187,881 140,321 440,31 421,403 218,761 137,55 262,517 348,821 150,034 64,016 261,596 259,7 171,26 203,077 249,148 211,655 252,64 438,555 239,89 401,915 216,886 184,641 380,155 313,906 366,936 236,302 229,641 235,577 103,898 263,906 241,171 216,548 295,281 193,299 204,386 257,567 136,813 240,755 59,609 213,511 380,531 242,344 250,407 183,613 191,835 266,793 246,542 330,563 403,556 208,108 324,04 308,532 199,297 200,156 262,875 etc...

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 249.7775390625 10.4261760273841 23.956773644188 Geometric Mean 212.153175105741 Harmonic Mean 128.239309446963 Quadratic Mean 276.033281747829 Winsorized Mean ( 1 / 42 ) 249.7308046875 10.3989298906582 24.0150484053022 Winsorized Mean ( 2 / 42 ) 249.4208671875 10.1536481470609 24.5646553411149 Winsorized Mean ( 3 / 42 ) 248.3287734375 9.88342218912961 25.1257882831948 Winsorized Mean ( 4 / 42 ) 247.4288046875 9.68414122914855 25.5498963545428 Winsorized Mean ( 5 / 42 ) 247.123453125 9.38347349931044 26.3360314432774 Winsorized Mean ( 6 / 42 ) 246.841359375 9.30357874713545 26.5318718832795 Winsorized Mean ( 7 / 42 ) 246.919015625 9.27147761884985 26.63211041172 Winsorized Mean ( 8 / 42 ) 246.925578125 9.23532412462929 26.7370776372087 Winsorized Mean ( 9 / 42 ) 247.229609375 9.13056117714572 27.0771538110745 Winsorized Mean ( 10 / 42 ) 246.609453125 8.98483694910215 27.4472930919068 Winsorized Mean ( 11 / 42 ) 247.257421875 8.76444929877392 28.211404213336 Winsorized Mean ( 12 / 42 ) 245.968359375 8.52657378083665 28.847268046611 Winsorized Mean ( 13 / 42 ) 247.4620390625 8.23689005435425 30.0431397565741 Winsorized Mean ( 14 / 42 ) 247.9897734375 8.1499478015258 30.4283879451439 Winsorized Mean ( 15 / 42 ) 250.5032109375 7.792834144545 32.1453281682958 Winsorized Mean ( 16 / 42 ) 251.0215859375 7.71469560401537 32.5381063391338 Winsorized Mean ( 17 / 42 ) 249.851375 7.52476822313384 33.203863240846 Winsorized Mean ( 18 / 42 ) 249.399546875 7.43022715765824 33.5655346173296 Winsorized Mean ( 19 / 42 ) 249.5715859375 7.34657366508876 33.9711540800952 Winsorized Mean ( 20 / 42 ) 250.6228359375 7.10438307840148 35.2772131192412 Winsorized Mean ( 21 / 42 ) 251.11190625 6.91683105758966 36.3044729818088 Winsorized Mean ( 22 / 42 ) 251.36696875 6.87294016084773 36.5734260545338 Winsorized Mean ( 23 / 42 ) 251.1145078125 6.30431055395069 39.8321919048127 Winsorized Mean ( 24 / 42 ) 249.4622578125 6.0043253164337 41.5470922486041 Winsorized Mean ( 25 / 42 ) 248.1794453125 5.82245716493273 42.6245205902459 Winsorized Mean ( 26 / 42 ) 249.8495390625 5.59279364881419 44.6734771120108 Winsorized Mean ( 27 / 42 ) 246.4977421875 5.02077735727394 49.095533350106 Winsorized Mean ( 28 / 42 ) 245.1642421875 4.82552068321565 50.8057592707543 Winsorized Mean ( 29 / 42 ) 243.565390625 4.56978822954339 53.2990542210173 Winsorized Mean ( 30 / 42 ) 243.84453125 4.43366111903272 54.9984594454524 Winsorized Mean ( 31 / 42 ) 243.094234375 4.21853899777792 57.6252191820551 Winsorized Mean ( 32 / 42 ) 243.060234375 4.20213187828811 57.8421242871653 Winsorized Mean ( 33 / 42 ) 243.432 4.16485367128587 58.4491123129524 Winsorized Mean ( 34 / 42 ) 240.266546875 3.73111107914898 64.3954419416004 Winsorized Mean ( 35 / 42 ) 239.963578125 3.66641130854697 65.4491703005628 Winsorized Mean ( 36 / 42 ) 240.045984375 3.42016199889265 70.185559763754 Winsorized Mean ( 37 / 42 ) 239.49734375 3.31206813661634 72.3105123056659 Winsorized Mean ( 38 / 42 ) 237.322140625 3.00865182798937 78.8798951135527 Winsorized Mean ( 39 / 42 ) 236.49034375 2.89094451537879 81.8038334848545 Winsorized Mean ( 40 / 42 ) 236.28096875 2.84230447642488 83.1300695297782 Winsorized Mean ( 41 / 42 ) 234.75371875 2.49339772228939 94.150129620899 Winsorized Mean ( 42 / 42 ) 234.849859375 2.41693713484059 97.1683772778349 Trimmed Mean ( 1 / 42 ) 248.852626984127 10.0041600758233 24.8749145453521 Trimmed Mean ( 2 / 42 ) 247.946120967742 9.56205193920426 25.9302211015155 Trimmed Mean ( 3 / 42 ) 247.172483606557 9.21531609901693 26.8219213481917 Trimmed Mean ( 4 / 42 ) 246.761358333333 8.94313151968609 27.5922765745029 Trimmed Mean ( 5 / 42 ) 246.580355932203 8.70628007590128 28.322125383346 Trimmed Mean ( 6 / 42 ) 246.4605 8.52505953750858 28.9101206760636 Trimmed Mean ( 7 / 42 ) 246.389228070175 8.34261157830342 29.5338247211411 Trimmed Mean ( 8 / 42 ) 246.302732142857 8.1463670409213 30.234671591105 Trimmed Mean ( 9 / 42 ) 246.212136363636 7.93445945366987 31.0307384896595 Trimmed Mean ( 10 / 42 ) 246.078148148148 7.71660145872719 31.8894463403761 Trimmed Mean ( 11 / 42 ) 246.013990566038 7.49776729921812 32.81163321669 Trimmed Mean ( 12 / 42 ) 245.874865384615 7.2884096575885 33.7350501598956 Trimmed Mean ( 13 / 42 ) 245.865088235294 7.09069030696833 34.6743515216948 Trimmed Mean ( 14 / 42 ) 245.70785 6.91170331298606 35.5495366154319 Trimmed Mean ( 15 / 42 ) 245.494959183673 6.72209589426085 36.5205976001132 Trimmed Mean ( 16 / 42 ) 245.04978125 6.55721692079166 37.3710042248251 Trimmed Mean ( 17 / 42 ) 244.541542553191 6.38022949777006 38.3280166706637 Trimmed Mean ( 18 / 42 ) 244.10697826087 6.20580758598824 39.3352476496412 Trimmed Mean ( 19 / 42 ) 243.6888 6.01974599760699 40.4815751523192 Trimmed Mean ( 20 / 42 ) 243.238443181818 5.81755876002084 41.8110848924106 Trimmed Mean ( 21 / 42 ) 242.688906976744 5.61737406193899 43.2032662060204 Trimmed Mean ( 22 / 42 ) 242.077714285714 5.41125469317774 44.7359675364966 Trimmed Mean ( 23 / 42 ) 241.418609756098 5.1766409303832 46.6361513195057 Trimmed Mean ( 24 / 42 ) 240.7441125 4.98539372754739 48.2898895567143 Trimmed Mean ( 25 / 42 ) 240.148 4.80680549103436 49.9599995148386 Trimmed Mean ( 26 / 42 ) 239.606934210526 4.62497873676802 51.807142875205 Trimmed Mean ( 27 / 42 ) 238.925513513514 4.44029939766755 53.8084241884723 Trimmed Mean ( 28 / 42 ) 238.426930555556 4.31054429480763 55.3124882263148 Trimmed Mean ( 29 / 42 ) 237.986942857143 4.18607801590369 56.8520084797718 Trimmed Mean ( 30 / 42 ) 237.624852941176 4.07630173455159 58.2942256033253 Trimmed Mean ( 31 / 42 ) 237.222772727273 3.96369409364168 59.848910416122 Trimmed Mean ( 32 / 42 ) 236.84396875 3.86048419331432 61.3508453577331 Trimmed Mean ( 33 / 42 ) 236.442919354839 3.73760319520281 63.2605728875424 Trimmed Mean ( 34 / 42 ) 235.9911 3.59251679414511 65.6896302849872 Trimmed Mean ( 35 / 42 ) 235.713586206897 3.49151494862037 67.5104044162939 Trimmed Mean ( 36 / 42 ) 235.436035714286 3.37773477963194 69.7023452326651 Trimmed Mean ( 37 / 42 ) 235.1325 3.27802671066422 71.729891411518 Trimmed Mean ( 38 / 42 ) 234.842115384615 3.17268171781293 74.0200676500581 Trimmed Mean ( 39 / 42 ) 234.67504 3.09725542823359 75.7687073080177 Trimmed Mean ( 40 / 42 ) 234.550916666667 3.02254439628466 77.6004868464392 Trimmed Mean ( 41 / 42 ) 234.430565217391 2.93505709935523 79.8725739505684 Trimmed Mean ( 42 / 42 ) 234.407636363636 2.8899606788423 81.1110123676625 Median 235.9395 Midrange 308.047 Midmean - Weighted Average at Xnp 236.125707692308 Midmean - Weighted Average at X(n+1)p 236.84396875 Midmean - Empirical Distribution Function 236.125707692308 Midmean - Empirical Distribution Function - Averaging 236.84396875 Midmean - Empirical Distribution Function - Interpolation 236.84396875 Midmean - Closest Observation 236.125707692308 Midmean - True Basic - Statistics Graphics Toolkit 236.84396875 Midmean - MS Excel (old versions) 237.222772727273 Number of observations 128

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Oct/01/t12859290425qikmjpxhdqnasr/1uw331285929079.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/01/t12859290425qikmjpxhdqnasr/1uw331285929079.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/01/t12859290425qikmjpxhdqnasr/256ko1285929079.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/01/t12859290425qikmjpxhdqnasr/256ko1285929079.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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Software written by Ed van Stee & Patrick Wessa

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