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Paper central tendency graan

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Fri, 30 Nov 2007 06:58:34 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/30/t1196430501l1tflxj2uub5r6n.htm/, Retrieved Fri, 30 Nov 2007 14:48:21 +0100

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G29 paper

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174,1 180,4 182,6 207,1 213,7 186,5 179,1 168,3 156,5 144,3 138,9 137,8 136,3 140,3 149,1 149,2 140,4 129 124,7 130,8 130,1 133,2 130,1 126,6 124,8 125,3 126,9 120,1 118,7 117,7 113,4 107,5 107,6 114,3 114,9 111,2 109,9 108,6 109,2 106,4 103,7 103 96,9 104,7 102,2 99 95,8 94,5 102,7 103,2 105,6 103,9 107,2 100,7 92,1 90,3 93,4 98,5 100,8 102,3 104,7 101,1 101,4 99,5 98,4 96,3 100,7 101,2 100,3 97,8 97,4 98,6 99,7 99 98,1 97 98,5 103,8 114,4 124,5 134,2 131,8 125,6 119,9 114,9 115,5 112,5 111,4 115,3 110,8 103,7 111,1 113 111,2 117,6 121,7 127,3 129,8 137,1 141,4 137,4 130,7 117,2 110,8 111,4 108,2 108,8 110,2 109,5 109,5 116 111,2 112,1 114 119,1 114,1 115,1 115,4 110,8 116 119,2 126,5 127,8 131,3 140,3 137,3 143 134,5 139,9 159,3 170,4 175 175,8 180,9 180,3 169,6 172,3 184,8 177,7 184,6 211,4

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Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 124.848936170213 2.31110828315669 54.0212404066522 Geometric Mean 122.241326366027 Harmonic Mean 119.978668092102 Quadratic Mean 127.808559464304 Winsorized Mean ( 1 / 47 ) 124.845390070922 2.30535201170002 54.1545887297523 Winsorized Mean ( 2 / 47 ) 124.802836879433 2.28749562055209 54.5587216684206 Winsorized Mean ( 3 / 47 ) 124.387943262411 2.18409894969622 56.9516061896884 Winsorized Mean ( 4 / 47 ) 124.376595744681 2.17086997121263 57.2934341503674 Winsorized Mean ( 5 / 47 ) 124.387234042553 2.16780739616160 57.3792829855632 Winsorized Mean ( 6 / 47 ) 124.327659574468 2.14877574210317 57.8597650459228 Winsorized Mean ( 7 / 47 ) 124.248226950355 2.13213926249854 58.273973532458 Winsorized Mean ( 8 / 47 ) 124.242553191489 2.12470946438499 58.4750787220934 Winsorized Mean ( 9 / 47 ) 124.261702127660 2.12121518468807 58.5804321148741 Winsorized Mean ( 10 / 47 ) 124.197872340426 2.10336042900668 59.0473561390896 Winsorized Mean ( 11 / 47 ) 124.112056737589 2.0810637222308 59.6387584924822 Winsorized Mean ( 12 / 47 ) 123.958865248227 2.05083966566562 60.4429821226395 Winsorized Mean ( 13 / 47 ) 123.885106382979 2.03757234222931 60.8003474602702 Winsorized Mean ( 14 / 47 ) 123.805673758865 2.02073841997302 61.2675408826632 Winsorized Mean ( 15 / 47 ) 123.656737588652 1.98315482112305 62.3535471217654 Winsorized Mean ( 16 / 47 ) 123.441134751773 1.94568081419368 63.4436716707459 Winsorized Mean ( 17 / 47 ) 123.404964539007 1.92371440939581 64.1493165182277 Winsorized Mean ( 18 / 47 ) 123.264539007092 1.89311238527843 65.1121084863447 Winsorized Mean ( 19 / 47 ) 122.132624113475 1.68688050926463 72.4014673491707 Winsorized Mean ( 20 / 47 ) 121.792198581560 1.61995943227940 75.1822521939269 Winsorized Mean ( 21 / 47 ) 120.704964539007 1.46062932344512 82.6390122404952 Winsorized Mean ( 22 / 47 ) 120.704964539007 1.45693162958800 82.848750131906 Winsorized Mean ( 23 / 47 ) 119.970921985816 1.34693187285721 89.0697773238704 Winsorized Mean ( 24 / 47 ) 119.766666666667 1.31700385560964 90.938736554592 Winsorized Mean ( 25 / 47 ) 119.518439716312 1.27819750351452 93.5054554461927 Winsorized Mean ( 26 / 47 ) 119.481560283688 1.24101373849185 96.2773872502725 Winsorized Mean ( 27 / 47 ) 119.481560283688 1.23680742507647 96.6048213013439 Winsorized Mean ( 28 / 47 ) 119.560992907801 1.22897424014001 97.2851903667085 Winsorized Mean ( 29 / 47 ) 119.540425531915 1.21305628512650 98.5448301102111 Winsorized Mean ( 30 / 47 ) 119.370212765957 1.18361352534807 100.852356119244 Winsorized Mean ( 31 / 47 ) 119.238297872340 1.14465794148892 104.169371084991 Winsorized Mean ( 32 / 47 ) 119.147517730496 1.13418269988464 105.051432844651 Winsorized Mean ( 33 / 47 ) 119.147517730496 1.12921935608828 105.513173404355 Winsorized Mean ( 34 / 47 ) 119.123404255319 1.12134970702663 106.232162463561 Winsorized Mean ( 35 / 47 ) 119.123404255319 1.07958801022968 110.341540593782 Winsorized Mean ( 36 / 47 ) 118.663829787234 1.02817318476414 115.412297797336 Winsorized Mean ( 37 / 47 ) 118.821276595745 0.996859205755726 119.195645593367 Winsorized Mean ( 38 / 47 ) 118.767375886525 0.947065802197447 125.405621880710 Winsorized Mean ( 39 / 47 ) 118.601418439716 0.884838513716046 134.037360039435 Winsorized Mean ( 40 / 47 ) 118.544680851064 0.86197355225914 137.527051195911 Winsorized Mean ( 41 / 47 ) 118.428368794326 0.844014821845307 140.315508364416 Winsorized Mean ( 42 / 47 ) 118.577304964539 0.82466865336097 143.787816453642 Winsorized Mean ( 43 / 47 ) 118.516312056738 0.794614359202969 149.149471921970 Winsorized Mean ( 44 / 47 ) 118.578723404255 0.789070354805877 150.276490153311 Winsorized Mean ( 45 / 47 ) 118.610638297872 0.767855803574961 154.469937904550 Winsorized Mean ( 46 / 47 ) 118.447517730496 0.732281816048841 161.751275444202 Winsorized Mean ( 47 / 47 ) 118.047517730496 0.69153631409972 170.703280975457 Trimmed Mean ( 1 / 47 ) 124.458273381295 2.24091625475108 55.539011383145 Trimmed Mean ( 2 / 47 ) 124.059854014599 2.16953576204026 57.1826729871147 Trimmed Mean ( 3 / 47 ) 123.671851851852 2.10086550693329 58.8670961771276 Trimmed Mean ( 4 / 47 ) 123.418796992481 2.06738529711024 59.6980142816116 Trimmed Mean ( 5 / 47 ) 123.16106870229 2.03458978063822 60.5336121680785 Trimmed Mean ( 6 / 47 ) 122.893023255814 1.99913837606727 61.4729949297311 Trimmed Mean ( 7 / 47 ) 122.627559055118 1.96427271542017 62.4289886493115 Trimmed Mean ( 8 / 47 ) 122.3664 1.9290749727065 63.4326823639827 Trimmed Mean ( 9 / 47 ) 122.097560975610 1.8913671536754 64.555187361874 Trimmed Mean ( 10 / 47 ) 121.817355371901 1.84997047020061 65.848270193573 Trimmed Mean ( 11 / 47 ) 121.535294117647 1.80678727489123 67.2659675029889 Trimmed Mean ( 12 / 47 ) 121.252991452991 1.76209540410240 68.81181982014 Trimmed Mean ( 13 / 47 ) 120.976521739130 1.71676630479681 70.4676701780027 Trimmed Mean ( 14 / 47 ) 120.697345132743 1.66787120092770 72.3661066067989 Trimmed Mean ( 15 / 47 ) 120.415315315315 1.61527880930020 74.5476970427685 Trimmed Mean ( 16 / 47 ) 120.135779816514 1.56135887202268 76.9430923083574 Trimmed Mean ( 17 / 47 ) 119.863551401869 1.50579694080910 79.6014045143855 Trimmed Mean ( 18 / 47 ) 119.583809523810 1.44561109415658 82.7219782742322 Trimmed Mean ( 19 / 47 ) 119.303883495146 1.38124758572488 86.374003276563 Trimmed Mean ( 20 / 47 ) 119.096039603960 1.33789469667469 89.017498836023 Trimmed Mean ( 21 / 47 ) 118.904040404040 1.29766366275573 91.6293210765685 Trimmed Mean ( 22 / 47 ) 118.779381443299 1.27284783840245 93.3178168353404 Trimmed Mean ( 23 / 47 ) 118.649473684211 1.24503751674628 95.2979103748484 Trimmed Mean ( 24 / 47 ) 118.562365591398 1.22674336544455 96.6480593505662 Trimmed Mean ( 25 / 47 ) 118.484615384615 1.20928027624395 97.9794491915733 Trimmed Mean ( 26 / 47 ) 118.419101123596 1.19366611995813 99.2062178389964 Trimmed Mean ( 27 / 47 ) 118.352873563218 1.17970547010915 100.324086445295 Trimmed Mean ( 28 / 47 ) 118.283529411765 1.16393980691200 101.623407593196 Trimmed Mean ( 29 / 47 ) 118.206024096386 1.14641393551437 103.109374750708 Trimmed Mean ( 30 / 47 ) 118.125925925926 1.12782673371819 104.737653749784 Trimmed Mean ( 31 / 47 ) 118.051898734177 1.10967370187511 106.384334903760 Trimmed Mean ( 32 / 47 ) 117.981818181818 1.09302412309748 107.940726731148 Trimmed Mean ( 33 / 47 ) 117.913333333333 1.07468886481444 109.718577342562 Trimmed Mean ( 34 / 47 ) 117.841095890411 1.05360480652976 111.845632404186 Trimmed Mean ( 35 / 47 ) 117.766197183099 1.02963521047397 114.376621919221 Trimmed Mean ( 36 / 47 ) 117.686956521739 1.00661476575219 116.913600441573 Trimmed Mean ( 37 / 47 ) 117.629850746269 0.98642072549954 119.249167931563 Trimmed Mean ( 38 / 47 ) 117.56 0.966147683407587 121.679120096182 Trimmed Mean ( 39 / 47 ) 117.488888888889 0.94844608707486 123.875136910777 Trimmed Mean ( 40 / 47 ) 117.422950819672 0.935444704281569 125.526340875332 Trimmed Mean ( 41 / 47 ) 117.355932203390 0.92238310762517 127.231224458937 Trimmed Mean ( 42 / 47 ) 117.291228070175 0.908613339042345 129.088164382219 Trimmed Mean ( 43 / 47 ) 117.212727272727 0.893760845034922 131.145516078350 Trimmed Mean ( 44 / 47 ) 117.132075471698 0.879506943879172 133.179250359381 Trimmed Mean ( 45 / 47 ) 117.041176470588 0.861791005420816 135.811554929651 Trimmed Mean ( 46 / 47 ) 116.940816326531 0.842402613355184 138.818202214224 Trimmed Mean ( 47 / 47 ) 116.842553191489 0.823968784384272 141.804587025469 Median 115.3 Midrange 152 Midmean - Weighted Average at Xnp 117.501428571429 Midmean - Weighted Average at X(n+1)p 117.766197183099 Midmean - Empirical Distribution Function 117.766197183099 Midmean - Empirical Distribution Function - Averaging 117.766197183099 Midmean - Empirical Distribution Function - Interpolation 117.766197183099 Midmean - Closest Observation 117.573611111111 Midmean - True Basic - Statistics Graphics Toolkit 117.766197183099 Midmean - MS Excel (old versions) 117.766197183099 Number of observations 141

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430501l1tflxj2uub5r6n/1jgor1196431108.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430501l1tflxj2uub5r6n/1jgor1196431108.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430501l1tflxj2uub5r6n/2xjh41196431108.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430501l1tflxj2uub5r6n/2xjh41196431108.ps (open in new window)

Parameters:

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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