WS 3 Review 1 part 2

*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: Sat, 24 Oct 2009 03:23:42 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Oct/24/t1256376260p329l50ip23fj1p.htm/, Retrieved Sat, 24 Oct 2009 11:24:21 +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/2009/Oct/24/t1256376260p329l50ip23fj1p.htm/}, year = {2009}, } @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 = {2009}, 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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4.241 4.336 4.452 4.567 4.737 4.800 4.091 3.738 3.750 3.914 4.092 4.206 4.269 4.355 4.421 4.534 4.684 4.720 3.959 3.662 3.667 3.814 4.032 4.143 4.212 4.297 4.396 4.485 4.632 4.641 3.765 3.559 3.568 3.733 3.907 3.997 4.086 4.208 4.332 4.484 4.622 4.564 3.777 3.643 3.660 3.827 3.980 4.052 4.130 4.217 4.423 4.543 4.701 4.671 3.884 3.776 3.806 4.090 4.268 4.424 4.534 4.655 4.775 4.884 5.060 5.041 4.174 4.000 3.996 4.173 4.361 4.520 4.735 4.808 4.972 5.049 5.172 5.111 4.316 4.174 4.156 4.374 4.611 4.820 4.809 4.933 5.082 5.166 5.405 5.359 4.474 4.331 4.370 4.584 4.831 4.901 4.933 5.011 5.210 5.257 5.586 5.459 4.560 4.434 4.427 4.652 4.802 4.835 4.831 4.883 4.953 5.036 5.278 5.181 4.489 4.341 4.310

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 4.45921367521368 0.0433938481575692 102.761425053193 Geometric Mean 4.43465726997809 Harmonic Mean 4.41006316007498 Quadratic Mean 4.48363888197206 Winsorized Mean ( 1 / 39 ) 4.45820512820513 0.043150086152103 103.318568414674 Winsorized Mean ( 2 / 39 ) 4.4585641025641 0.0427502314004788 104.293332609053 Winsorized Mean ( 3 / 39 ) 4.45782051282051 0.0424586041968934 104.992158765942 Winsorized Mean ( 4 / 39 ) 4.45511965811966 0.0419599495413928 106.17552468038 Winsorized Mean ( 5 / 39 ) 4.4544358974359 0.0417750004292383 106.629224456410 Winsorized Mean ( 6 / 39 ) 4.45541025641026 0.0408502403761904 109.066928747061 Winsorized Mean ( 7 / 39 ) 4.45397435897436 0.0405323557529676 109.886886074917 Winsorized Mean ( 8 / 39 ) 4.45417948717949 0.0403132930889246 110.48910039064 Winsorized Mean ( 9 / 39 ) 4.4548717948718 0.0400700582664365 111.177073046666 Winsorized Mean ( 10 / 39 ) 4.45111111111111 0.0392299191598855 113.462153540775 Winsorized Mean ( 11 / 39 ) 4.44847863247863 0.0388264663393568 114.573358121169 Winsorized Mean ( 12 / 39 ) 4.44919658119658 0.0380733567608149 116.858532047684 Winsorized Mean ( 13 / 39 ) 4.44886324786325 0.0377760239433233 117.769494601603 Winsorized Mean ( 14 / 39 ) 4.44946153846154 0.0374210270479045 118.902710306843 Winsorized Mean ( 15 / 39 ) 4.4561282051282 0.0363125869322387 122.715801367817 Winsorized Mean ( 16 / 39 ) 4.4558547008547 0.0354225519424022 125.791464943012 Winsorized Mean ( 17 / 39 ) 4.45120512820513 0.034535116352614 128.889246607916 Winsorized Mean ( 18 / 39 ) 4.45520512820513 0.0332465216563925 134.005150200382 Winsorized Mean ( 19 / 39 ) 4.45536752136752 0.032395329186824 137.531169869378 Winsorized Mean ( 20 / 39 ) 4.45810256410256 0.032052359133096 139.088125950121 Winsorized Mean ( 21 / 39 ) 4.45253846153846 0.0313083843955623 142.215529402072 Winsorized Mean ( 22 / 39 ) 4.44990598290598 0.0308462550477759 144.260817918214 Winsorized Mean ( 23 / 39 ) 4.456 0.0300439823971259 148.315890386964 Winsorized Mean ( 24 / 39 ) 4.45025641025641 0.0283665098159839 156.884172184933 Winsorized Mean ( 25 / 39 ) 4.45666666666667 0.0274027202575315 162.635921718092 Winsorized Mean ( 26 / 39 ) 4.45755555555556 0.0272993077246742 163.284563861912 Winsorized Mean ( 27 / 39 ) 4.45524786324786 0.0269748436637581 165.163065216711 Winsorized Mean ( 28 / 39 ) 4.4528547008547 0.0266418160760676 167.137806527187 Winsorized Mean ( 29 / 39 ) 4.46202564102564 0.0255348113995829 174.742846978636 Winsorized Mean ( 30 / 39 ) 4.46382051282051 0.0249838545429966 178.668207707437 Winsorized Mean ( 31 / 39 ) 4.46673504273504 0.0245432799279183 181.994218207733 Winsorized Mean ( 32 / 39 ) 4.46454700854701 0.0232440900896900 192.072350060597 Winsorized Mean ( 33 / 39 ) 4.45411111111111 0.0220017106004370 202.443855025647 Winsorized Mean ( 34 / 39 ) 4.45352991452992 0.0219373574732770 203.011229586562 Winsorized Mean ( 35 / 39 ) 4.45861538461538 0.0204042762567054 218.513772726938 Winsorized Mean ( 36 / 39 ) 4.45338461538462 0.0196987324427672 226.074679085240 Winsorized Mean ( 37 / 39 ) 4.44927350427350 0.0189847947593842 234.359842213950 Winsorized Mean ( 38 / 39 ) 4.44667521367521 0.0183623708193483 242.162368760671 Winsorized Mean ( 39 / 39 ) 4.44934188034188 0.0169469542292145 262.545223180679 Trimmed Mean ( 1 / 39 ) 4.45724347826087 0.0423165499886862 105.330975220158 Trimmed Mean ( 2 / 39 ) 4.45624778761062 0.0413901615060444 107.66442133742 Trimmed Mean ( 3 / 39 ) 4.45502702702703 0.0405933680923497 109.747656732791 Trimmed Mean ( 4 / 39 ) 4.45402752293578 0.039824791390488 111.840573859217 Trimmed Mean ( 5 / 39 ) 4.45372897196262 0.0391276651382828 113.825574723728 Trimmed Mean ( 6 / 39 ) 4.45357142857143 0.0383975251442805 115.985897836825 Trimmed Mean ( 7 / 39 ) 4.45322330097087 0.0377966090216928 117.820709747137 Trimmed Mean ( 8 / 39 ) 4.45309900990099 0.0371876363595483 119.746761177458 Trimmed Mean ( 9 / 39 ) 4.45293939393939 0.0365435128005635 121.853074668583 Trimmed Mean ( 10 / 39 ) 4.45268041237113 0.0358599004838693 124.168788878097 Trimmed Mean ( 11 / 39 ) 4.45287368421053 0.0352297188313695 126.395379580650 Trimmed Mean ( 12 / 39 ) 4.45337634408602 0.0345793695570994 128.787088981838 Trimmed Mean ( 13 / 39 ) 4.45382417582418 0.0339567763513051 131.161572280786 Trimmed Mean ( 14 / 39 ) 4.45432584269663 0.033290652781758 133.801096418795 Trimmed Mean ( 15 / 39 ) 4.45432584269663 0.0325808030040207 136.716269459256 Trimmed Mean ( 16 / 39 ) 4.45467058823529 0.0319282300395005 139.521375996230 Trimmed Mean ( 17 / 39 ) 4.45456626506024 0.0313032488100296 142.303640497308 Trimmed Mean ( 18 / 39 ) 4.45485185185185 0.0307046821092379 145.087053368696 Trimmed Mean ( 19 / 39 ) 4.45482278481013 0.0301870655870166 147.573892930028 Trimmed Mean ( 20 / 39 ) 4.45477922077922 0.0296973433510633 150.005984310368 Trimmed Mean ( 21 / 39 ) 4.45452 0.0291683177499211 152.717754866479 Trimmed Mean ( 22 / 39 ) 4.45467123287671 0.0286465965555081 155.504379874410 Trimmed Mean ( 23 / 39 ) 4.45502816901408 0.028092116165865 158.586421283115 Trimmed Mean ( 24 / 39 ) 4.45495652173913 0.0275453855876218 161.731499730433 Trimmed Mean ( 25 / 39 ) 4.45529850746269 0.0271236511850292 164.258804136287 Trimmed Mean ( 26 / 39 ) 4.4552 0.0267453195042537 166.578679282235 Trimmed Mean ( 27 / 39 ) 4.45503174603175 0.0262968789288745 169.4129466117 Trimmed Mean ( 28 / 39 ) 4.45501639344262 0.0257954277708584 172.705660592903 Trimmed Mean ( 29 / 39 ) 4.45516949152542 0.0252300479589928 176.581887547997 Trimmed Mean ( 30 / 39 ) 4.45516949152542 0.0247041030083634 180.341277318070 Trimmed Mean ( 31 / 39 ) 4.45403636363636 0.0241366475155619 184.534176122208 Trimmed Mean ( 32 / 39 ) 4.4531320754717 0.0234976458342161 189.513966926307 Trimmed Mean ( 33 / 39 ) 4.4523137254902 0.0229200569789539 194.254042630805 Trimmed Mean ( 34 / 39 ) 4.45218367346939 0.0224089555080946 198.678768042587 Trimmed Mean ( 35 / 39 ) 4.45208510638298 0.0217601046692067 204.598515221447 Trimmed Mean ( 36 / 39 ) 4.4516 0.0212175456267741 209.807490381102 Trimmed Mean ( 37 / 39 ) 4.45146511627907 0.0206460266177808 215.608804477921 Trimmed Mean ( 38 / 39 ) 4.45163414634146 0.0200366358386016 222.174729440616 Trimmed Mean ( 39 / 39 ) 4.45202564102564 0.0193557674011189 230.010288342702 Median 4.434 Midrange 4.5725 Midmean - Weighted Average at Xnp 4.44908620689655 Midmean - Weighted Average at X(n+1)p 4.45516949152542 Midmean - Empirical Distribution Function 4.45516949152542 Midmean - Empirical Distribution Function - Averaging 4.45516949152542 Midmean - Empirical Distribution Function - Interpolation 4.45516949152542 Midmean - Closest Observation 4.44911666666667 Midmean - True Basic - Statistics Graphics Toolkit 4.45516949152542 Midmean - MS Excel (old versions) 4.45516949152542 Number of observations 117

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Oct/24/t1256376260p329l50ip23fj1p/14fim1256376216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/24/t1256376260p329l50ip23fj1p/14fim1256376216.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/24/t1256376260p329l50ip23fj1p/2b1vs1256376216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/24/t1256376260p329l50ip23fj1p/2b1vs1256376216.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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