Geschatte tijd serieus invullen F&P

*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, 26 Nov 2010 16:23:58 +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/26/t1290788553wrngwgn40cf946s.htm/, Retrieved Fri, 26 Nov 2010 17:22:33 +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/26/t1290788553wrngwgn40cf946s.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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299 157 169 85 105 132 169 74 98 82 141 200 266 122 175 158 137 131 164 169 116 180 150 171 128 140 165 151 180 110 181 182 115 254 125 111 136 132 216 303 331 118 118 104 395 106 115 621 114 98 153 140 179 759 106 83 133 169 204 178 129 110 149 100 221 158 127 158 107 213 578 569 151 170 287 175 76 131 209 152 246 191 129 169 287 124 174 154 247 110 157 196 128 129 128 147 125 140 155 378 157 86 118 202 523 239 108 155 314 221 232 558 471 131 103 175 81 221 258 165 114 223 191 144 149 115 88 130 117 201 136 136 289 142 143 191 106 180 81 252 126 250 192 223 133 139 858 147 84 144 82 129 94 105 122 140

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 183.339743589744 9.60613730961387 19.0856884177843 Geometric Mean 161.5500678816 Harmonic Mean 147.965362269767 Quadratic Mean 218.898451459306 Winsorized Mean ( 1 / 52 ) 182.717948717949 9.33478865029752 19.5738709855124 Winsorized Mean ( 2 / 52 ) 181.012820512821 8.68635062103874 20.8387651396893 Winsorized Mean ( 3 / 52 ) 180.185897435897 8.42514259027308 21.3866881783015 Winsorized Mean ( 4 / 52 ) 179.980769230769 8.3533793855617 21.5458631678881 Winsorized Mean ( 5 / 52 ) 179.628205128205 8.24823824058862 21.7777663409715 Winsorized Mean ( 6 / 52 ) 178.320512820513 7.85531376041496 22.7006225669963 Winsorized Mean ( 7 / 52 ) 176.032051282051 7.2126704745199 24.4059467161181 Winsorized Mean ( 8 / 52 ) 172.185897435897 6.24049851462993 27.5916895152259 Winsorized Mean ( 9 / 52 ) 171.262820512821 6.01345411999 28.4799413274821 Winsorized Mean ( 10 / 52 ) 168.378205128205 5.37077179338641 31.3508396196514 Winsorized Mean ( 11 / 52 ) 167.602564102564 5.10245468333288 32.8474380478158 Winsorized Mean ( 12 / 52 ) 167.064102564103 4.920422822444 33.9532004855471 Winsorized Mean ( 13 / 52 ) 166.730769230769 4.86145816907656 34.2964525111691 Winsorized Mean ( 14 / 52 ) 166.012820512821 4.69039801228054 35.394186181676 Winsorized Mean ( 15 / 52 ) 166.108974358974 4.63216515122793 35.8598989750918 Winsorized Mean ( 16 / 52 ) 166.211538461538 4.62320548080359 35.9515792996179 Winsorized Mean ( 17 / 52 ) 164.032051282051 4.24263503213085 38.6627767978587 Winsorized Mean ( 18 / 52 ) 163.108974358974 4.10214466812694 39.7618776408124 Winsorized Mean ( 19 / 52 ) 162.74358974359 4.0189879326622 40.4936746440509 Winsorized Mean ( 20 / 52 ) 162.487179487179 3.98161082302486 40.8094077270306 Winsorized Mean ( 21 / 52 ) 162.217948717949 3.94274883717377 41.1433635306654 Winsorized Mean ( 22 / 52 ) 161.935897435897 3.86941175789344 41.8502624088932 Winsorized Mean ( 23 / 52 ) 161.935897435897 3.83508982112254 42.2247991543777 Winsorized Mean ( 24 / 52 ) 161.166666666667 3.65710649309632 44.0694486121495 Winsorized Mean ( 25 / 52 ) 160.044871794872 3.50575968186036 45.65198026065 Winsorized Mean ( 26 / 52 ) 158.544871794872 3.31213702786804 47.8678479968941 Winsorized Mean ( 27 / 52 ) 158.717948717949 3.29587047524037 48.1566098881277 Winsorized Mean ( 28 / 52 ) 158.897435897436 3.20129961573559 49.6352903415836 Winsorized Mean ( 29 / 52 ) 158.897435897436 3.20129961573559 49.6352903415836 Winsorized Mean ( 30 / 52 ) 159.089743589744 3.18400899807522 49.9652305272742 Winsorized Mean ( 31 / 52 ) 158.096153846154 3.06102502126711 51.6481089660319 Winsorized Mean ( 32 / 52 ) 157.480769230769 2.98656311422059 52.7297643505073 Winsorized Mean ( 33 / 52 ) 156.846153846154 2.86671312760964 54.7128878489971 Winsorized Mean ( 34 / 52 ) 155.974358974359 2.72056696150918 57.3315640383412 Winsorized Mean ( 35 / 52 ) 155.75 2.64905721637893 58.7945020730428 Winsorized Mean ( 36 / 52 ) 155.519230769231 2.62315311615422 59.2871341788984 Winsorized Mean ( 37 / 52 ) 155.282051282051 2.59671158832395 59.7994987122456 Winsorized Mean ( 38 / 52 ) 155.282051282051 2.40047201850919 64.6881322026362 Winsorized Mean ( 39 / 52 ) 154.282051282051 2.29264647594696 67.2943050316233 Winsorized Mean ( 40 / 52 ) 154.538461538462 2.21938568940325 69.6311877094306 Winsorized Mean ( 41 / 52 ) 154.801282051282 2.19621487812204 70.4854901008826 Winsorized Mean ( 42 / 52 ) 154.801282051282 2.19621487812204 70.4854901008826 Winsorized Mean ( 43 / 52 ) 152.596153846154 1.91653332537551 79.6209237928357 Winsorized Mean ( 44 / 52 ) 152.596153846154 1.86358111208046 81.8832906477566 Winsorized Mean ( 45 / 52 ) 152.596153846154 1.80987957882259 84.3128767414597 Winsorized Mean ( 46 / 52 ) 152.596153846154 1.80987957882259 84.3128767414597 Winsorized Mean ( 47 / 52 ) 152.596153846154 1.80987957882259 84.3128767414597 Winsorized Mean ( 48 / 52 ) 152.596153846154 1.75304954301379 87.0461159835876 Winsorized Mean ( 49 / 52 ) 152.282051282051 1.72266059998644 88.3993348911851 Winsorized Mean ( 50 / 52 ) 151.320512820513 1.63129514073508 92.7609658374428 Winsorized Mean ( 51 / 52 ) 151.320512820513 1.63129514073508 92.7609658374428 Winsorized Mean ( 52 / 52 ) 151.653846153846 1.6020472284744 94.6625314524984 Trimmed Mean ( 1 / 52 ) 179.668831168831 8.64782704427098 20.7761822997904 Trimmed Mean ( 2 / 52 ) 176.539473684211 7.84961742311988 22.4902010082988 Trimmed Mean ( 3 / 52 ) 174.213333333333 7.3479735091064 23.709031220299 Trimmed Mean ( 4 / 52 ) 172.114864864865 6.89516834738367 24.9616624560259 Trimmed Mean ( 5 / 52 ) 170.013698630137 6.4034929820692 26.5501499113378 Trimmed Mean ( 6 / 52 ) 167.930555555556 5.86846441498253 28.615757663422 Trimmed Mean ( 7 / 52 ) 166.028169014085 5.36065146448276 30.9716403153818 Trimmed Mean ( 8 / 52 ) 164.435714285714 4.94196754733156 33.2733294403162 Trimmed Mean ( 9 / 52 ) 163.340579710145 4.6887520839348 34.8366850680383 Trimmed Mean ( 10 / 52 ) 162.330882352941 4.44916891638434 36.4856640428166 Trimmed Mean ( 11 / 52 ) 161.626865671642 4.29914235981312 37.5951415757881 Trimmed Mean ( 12 / 52 ) 160.984848484848 4.17598058961704 38.5501907947354 Trimmed Mean ( 13 / 52 ) 160.376923076923 4.06688658972011 39.4348156848801 Trimmed Mean ( 14 / 52 ) 159.78125 3.95452648393032 40.4046478508337 Trimmed Mean ( 15 / 52 ) 159.230158730159 3.85372485568061 41.3185073385415 Trimmed Mean ( 16 / 52 ) 158.653225806452 3.74909805316944 42.3177051003849 Trimmed Mean ( 17 / 52 ) 158.049180327869 3.63332310400287 43.4998968722997 Trimmed Mean ( 18 / 52 ) 157.591666666667 3.5541134063939 44.3406410113861 Trimmed Mean ( 19 / 52 ) 157.186440677966 3.48330487986196 45.1256625817362 Trimmed Mean ( 20 / 52 ) 156.793103448276 3.41422013593713 45.9235483377053 Trimmed Mean ( 21 / 52 ) 156.40350877193 3.34156844794805 46.8054182364489 Trimmed Mean ( 22 / 52 ) 156.017857142857 3.26495130700891 47.7856612464424 Trimmed Mean ( 23 / 52 ) 155.636363636364 3.18759394759854 48.8256553986798 Trimmed Mean ( 24 / 52 ) 155.240740740741 3.10449908477816 50.0050850399377 Trimmed Mean ( 25 / 52 ) 154.877358490566 3.03171919268365 51.0856542599086 Trimmed Mean ( 26 / 52 ) 154.567307692308 2.96745366585163 52.0875218612549 Trimmed Mean ( 27 / 52 ) 154.333333333333 2.91690170170955 52.9100220425257 Trimmed Mean ( 28 / 52 ) 154.08 2.86132977491377 53.849088403186 Trimmed Mean ( 29 / 52 ) 153.80612244898 2.80814608669071 54.7714106391929 Trimmed Mean ( 30 / 52 ) 153.520833333333 2.7476855957407 55.8727801941067 Trimmed Mean ( 31 / 52 ) 153.520833333333 2.68040980778798 57.275134901863 Trimmed Mean ( 32 / 52 ) 152.945652173913 2.6184032132405 58.4118028119243 Trimmed Mean ( 33 / 52 ) 152.7 2.5564024542538 59.732378892811 Trimmed Mean ( 34 / 52 ) 152.477272727273 2.49937177439605 61.0062393635366 Trimmed Mean ( 35 / 52 ) 152.290697674419 2.4508780792864 62.1371984846996 Trimmed Mean ( 36 / 52 ) 152.107142857143 2.40302555139809 63.2981795672737 Trimmed Mean ( 37 / 52 ) 151.926829268293 2.35074036706585 64.6293531164929 Trimmed Mean ( 38 / 52 ) 151.75 2.29332410335695 66.1703244551737 Trimmed Mean ( 39 / 52 ) 151.564102564103 2.24954872031811 67.375336748728 Trimmed Mean ( 40 / 52 ) 151.421052631579 2.21171229672212 68.4632684169606 Trimmed Mean ( 41 / 52 ) 151.256756756757 2.17531001087094 69.5334255811185 Trimmed Mean ( 42 / 52 ) 151.069444444444 2.13449630757369 70.7752193847394 Trimmed Mean ( 43 / 52 ) 150.871428571429 2.08583127383979 72.3315593469315 Trimmed Mean ( 44 / 52 ) 150.779411764706 2.06488743889579 73.0206445758303 Trimmed Mean ( 45 / 52 ) 150.779411764706 2.04530950016685 73.7196066181699 Trimmed Mean ( 46 / 52 ) 150.578125 2.02731896020795 74.2745112907909 Trimmed Mean ( 47 / 52 ) 150.467741935484 2.00445270358185 75.0667459833827 Trimmed Mean ( 48 / 52 ) 150.35 1.97567481141305 76.1005804859487 Trimmed Mean ( 49 / 52 ) 150.224137931034 1.94759583183142 77.1331173931354 Trimmed Mean ( 50 / 52 ) 150.107142857143 1.91688982594821 78.3076527535384 Trimmed Mean ( 51 / 52 ) 150.037037037037 1.89263956694677 79.2739619615366 Trimmed Mean ( 52 / 52 ) 149.961538461538 1.86112114041302 80.5759148102842 Median 149.5 Midrange 466 Midmean - Weighted Average at Xnp 151.189873417722 Midmean - Weighted Average at X(n+1)p 151.189873417722 Midmean - Empirical Distribution Function 151.189873417722 Midmean - Empirical Distribution Function - Averaging 151.189873417722 Midmean - Empirical Distribution Function - Interpolation 151.189873417722 Midmean - Closest Observation 151.189873417722 Midmean - True Basic - Statistics Graphics Toolkit 151.189873417722 Midmean - MS Excel (old versions) 151.75 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290788553wrngwgn40cf946s/14c1y1290788633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290788553wrngwgn40cf946s/14c1y1290788633.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290788553wrngwgn40cf946s/24c1y1290788633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290788553wrngwgn40cf946s/24c1y1290788633.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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