Parental Expectations (H0)

*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: Tue, 16 Nov 2010 18:08:48 +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/Nov/16/t1289930861vikcwew3zu98ozx.htm/, Retrieved Tue, 16 Nov 2010 19:07:43 +0100

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/Nov/16/t1289930861vikcwew3zu98ozx.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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11 7 17 10 12 12 11 11 12 13 14 16 11 10 11 15 9 11 17 17 11 18 14 10 11 15 15 13 16 13 9 18 18 12 17 9 9 12 18 12 18 14 15 16 10 11 14 9 12 17 5 12 12 6 24 12 12 14 7 13 12 13 14 8 11 9 11 13 10 11 12 9 15 18 15 12 13 14 10 13 13 11 13 16 8 16 11 9 16 12 14 8 9 15 11 21 14 18 12 13 15 12 19 15 11 11 10 13 15 12 12 16 9 18 8 13 17 9 15 8 7 12 14 6 8 17 10 11 14 11 13 12 11 9 12 20 12 13 12 12 9 15 24 7 17 11 17 11 12 14 11 16 21 14 20 13 11 15 19

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 12.8553459119497 0.273244514898604 47.0470410603487 Geometric Mean 12.3969474677806 Harmonic Mean 11.9267389310756 Quadratic Mean 13.3062618256814 Winsorized Mean ( 1 / 53 ) 12.8616352201258 0.272170432781824 47.2558135307662 Winsorized Mean ( 2 / 53 ) 12.8238993710692 0.263559830075703 48.6565018932731 Winsorized Mean ( 3 / 53 ) 12.8427672955975 0.26067444371751 49.2674583378609 Winsorized Mean ( 4 / 53 ) 12.8176100628931 0.255946762037282 50.0792038190585 Winsorized Mean ( 5 / 53 ) 12.8176100628931 0.255946762037282 50.0792038190585 Winsorized Mean ( 6 / 53 ) 12.7798742138365 0.249615227942347 51.1982955494534 Winsorized Mean ( 7 / 53 ) 12.8238993710692 0.243624965983030 52.6378703402773 Winsorized Mean ( 8 / 53 ) 12.7735849056604 0.236055160323219 54.1127119956629 Winsorized Mean ( 9 / 53 ) 12.7735849056604 0.236055160323219 54.1127119956629 Winsorized Mean ( 10 / 53 ) 12.7735849056604 0.236055160323219 54.1127119956629 Winsorized Mean ( 11 / 53 ) 12.7735849056604 0.236055160323219 54.1127119956629 Winsorized Mean ( 12 / 53 ) 12.7735849056604 0.236055160323219 54.1127119956629 Winsorized Mean ( 13 / 53 ) 12.8553459119497 0.22639961379731 56.7816600758814 Winsorized Mean ( 14 / 53 ) 12.8553459119497 0.22639961379731 56.7816600758814 Winsorized Mean ( 15 / 53 ) 12.8553459119497 0.22639961379731 56.7816600758814 Winsorized Mean ( 16 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 17 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 18 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 19 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 20 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 21 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 22 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 23 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 24 / 53 ) 12.7547169811321 0.212782540664551 59.9424978257015 Winsorized Mean ( 25 / 53 ) 12.5974842767296 0.194076662389153 64.9098357404236 Winsorized Mean ( 26 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 27 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 28 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 29 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 30 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 31 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 32 / 53 ) 12.7610062893082 0.176309485255175 72.3784444769891 Winsorized Mean ( 33 / 53 ) 12.5534591194969 0.153676852561816 81.6873778336088 Winsorized Mean ( 34 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 35 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 36 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 37 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 38 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 39 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 40 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 41 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 42 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 43 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 44 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 45 / 53 ) 12.7672955974843 0.133299982460224 95.7786742492178 Winsorized Mean ( 46 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 47 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 48 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 49 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 50 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 51 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 52 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Winsorized Mean ( 53 / 53 ) 12.4779874213836 0.104373160852142 119.551686654966 Trimmed Mean ( 1 / 53 ) 12.8343949044586 0.262658790428349 48.8633747362045 Trimmed Mean ( 2 / 53 ) 12.8064516129032 0.252195516381460 50.7798544425061 Trimmed Mean ( 3 / 53 ) 12.7973856209150 0.245769061952934 52.070775382484 Trimmed Mean ( 4 / 53 ) 12.7814569536424 0.239921628067539 53.2734670758585 Trimmed Mean ( 5 / 53 ) 12.7718120805369 0.235040511069357 54.3387691867643 Trimmed Mean ( 6 / 53 ) 12.7619047619048 0.229729075388325 55.5519789575286 Trimmed Mean ( 7 / 53 ) 12.7619047619048 0.225372374293587 56.6258610972442 Trimmed Mean ( 8 / 53 ) 12.7482517482517 0.221797615315873 57.4769558730211 Trimmed Mean ( 9 / 53 ) 12.7446808510638 0.219237939451984 58.1317306800134 Trimmed Mean ( 10 / 53 ) 12.7410071942446 0.216448555110265 58.863905040872 Trimmed Mean ( 11 / 53 ) 12.7372262773723 0.213405918334812 59.6854406698735 Trimmed Mean ( 12 / 53 ) 12.7333333333333 0.210083033940884 60.6109550803442 Trimmed Mean ( 13 / 53 ) 12.7293233082707 0.206448728847728 61.6585211220146 Trimmed Mean ( 14 / 53 ) 12.7293233082707 0.203687327063180 62.4944295347462 Trimmed Mean ( 15 / 53 ) 12.7054263565891 0.200653939620849 63.3200941910087 Trimmed Mean ( 16 / 53 ) 12.6929133858268 0.197317281590465 64.3274288167576 Trimmed Mean ( 17 / 53 ) 12.688 0.195238811583115 64.9870786301042 Trimmed Mean ( 18 / 53 ) 12.6829268292683 0.192943995678997 65.7337212523007 Trimmed Mean ( 19 / 53 ) 12.6776859504132 0.190408751286638 66.5814247756314 Trimmed Mean ( 20 / 53 ) 12.6722689075630 0.187605259329867 67.5475141412818 Trimmed Mean ( 21 / 53 ) 12.6666666666667 0.184501143551216 68.6535943510317 Trimmed Mean ( 22 / 53 ) 12.6608695652174 0.181058400514610 69.9269933305068 Trimmed Mean ( 23 / 53 ) 12.6548672566372 0.177231979011664 71.4028434778373 Trimmed Mean ( 24 / 53 ) 12.6486486486486 0.172967854142113 73.1271640697834 Trimmed Mean ( 25 / 53 ) 12.6422018348624 0.168200352081486 75.1615658256041 Trimmed Mean ( 26 / 53 ) 12.6448598130841 0.164924832564742 76.6704420216422 Trimmed Mean ( 27 / 53 ) 12.6380952380952 0.163032059098013 77.5190800387141 Trimmed Mean ( 28 / 53 ) 12.6380952380952 0.160897853964146 78.547320096087 Trimmed Mean ( 29 / 53 ) 12.6310679611650 0.158490081613970 79.696267630994 Trimmed Mean ( 30 / 53 ) 12.6161616161616 0.155770652325680 80.9919033386606 Trimmed Mean ( 31 / 53 ) 12.6082474226804 0.152693950964934 82.5720163962216 Trimmed Mean ( 32 / 53 ) 12.6 0.149204685160715 84.447750326526 Trimmed Mean ( 33 / 53 ) 12.5913978494624 0.145234860929838 86.6968010906502 Trimmed Mean ( 34 / 53 ) 12.5934065934066 0.143204236929242 87.9401815438539 Trimmed Mean ( 35 / 53 ) 12.5842696629213 0.142759350181916 88.1502307686707 Trimmed Mean ( 36 / 53 ) 12.5747126436782 0.142186860939033 88.4379369558626 Trimmed Mean ( 37 / 53 ) 12.5647058823529 0.141467953061057 88.8166232032072 Trimmed Mean ( 38 / 53 ) 12.5542168674699 0.140580439050830 89.3027291153264 Trimmed Mean ( 39 / 53 ) 12.5432098765432 0.139497978564107 89.9167859323418 Trimmed Mean ( 40 / 53 ) 12.5316455696203 0.13818906071205 90.6847872403803 Trimmed Mean ( 41 / 53 ) 12.5194805194805 0.136615657488249 91.6401586001033 Trimmed Mean ( 42 / 53 ) 12.5066666666667 0.134731408931075 92.8266598404292 Trimmed Mean ( 43 / 53 ) 12.4931506849315 0.132479123985425 94.3027875569738 Trimmed Mean ( 44 / 53 ) 12.4788732394366 0.129787250352085 96.1486833690069 Trimmed Mean ( 45 / 53 ) 12.4637681159420 0.126564733537156 98.4774175839667 Trimmed Mean ( 46 / 53 ) 12.4477611940299 0.122693246700326 101.454330444389 Trimmed Mean ( 47 / 53 ) 12.4461538461538 0.122141880832311 101.899150081381 Trimmed Mean ( 48 / 53 ) 12.4444444444444 0.121388489045707 102.517500154061 Trimmed Mean ( 49 / 53 ) 12.4426229508197 0.120392323530163 103.350633877436 Trimmed Mean ( 50 / 53 ) 12.4406779661017 0.119102396016233 104.453633026879 Trimmed Mean ( 51 / 53 ) 12.4385964912281 0.117454048657958 105.901811247485 Trimmed Mean ( 52 / 53 ) 12.4363636363636 0.115363972002176 107.801104803579 Trimmed Mean ( 53 / 53 ) 12.4339622641509 0.11272271604614 110.305736947125 Median 12 Midrange 14.5 Midmean - Weighted Average at Xnp 12.6195652173913 Midmean - Weighted Average at X(n+1)p 12.6195652173913 Midmean - Empirical Distribution Function 12.6195652173913 Midmean - Empirical Distribution Function - Averaging 12.6195652173913 Midmean - Empirical Distribution Function - Interpolation 12.6195652173913 Midmean - Closest Observation 12.6195652173913 Midmean - True Basic - Statistics Graphics Toolkit 12.6195652173913 Midmean - MS Excel (old versions) 12.6195652173913 Number of observations 159

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/16/t1289930861vikcwew3zu98ozx/1r77r1289930926.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289930861vikcwew3zu98ozx/1r77r1289930926.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289930861vikcwew3zu98ozx/21gpc1289930926.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289930861vikcwew3zu98ozx/21gpc1289930926.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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