Mini tutorial Univariate

*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: Mon, 15 Nov 2010 15:51:43 +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/15/t1289836235hd07d5mdtijkw78.htm/, Retrieved Mon, 15 Nov 2010 16:50:36 +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/15/t1289836235hd07d5mdtijkw78.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)

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Dataseries X:

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43 30 30 54 30 16 42 0 30 44 70 30 5 30 62 91 41 73 60 20 4 60 62 60 76 65 60 88 16 65 35 70 21 60 100 65 80 65 60 31 55 74 32 10 20 40 55 70 80 50 55 29 70 50 60 60 27 38 70 15 40 37 10 75 60 55 91 29 50 10 57 45 70 38 70 40 61 15 25 54 36 50 68 14 68 100 74 59 50 60 60 70 45 60 21 0 65 33 70 20 60 65 60 53 71 32 70 60 60 50 25 20 80 53 39 53 39 70 60 77 80 50 69 70 36 30 57 80 91 8 60 63 60 18 39 41 50 65 80 68 58 30 60 100

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 50.8263888888889 1.8627990720367 27.2849550184269 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 55.4935556819188 Winsorized Mean ( 1 / 48 ) 50.8263888888889 1.8627990720367 27.2849550184269 Winsorized Mean ( 2 / 48 ) 50.8819444444444 1.85258215939836 27.465418570677 Winsorized Mean ( 3 / 48 ) 50.7152777777778 1.81696077671118 27.9121478172886 Winsorized Mean ( 4 / 48 ) 50.7986111111111 1.80271044368622 28.1790185933783 Winsorized Mean ( 5 / 48 ) 50.8680555555556 1.791405671015 28.3956093131792 Winsorized Mean ( 6 / 48 ) 50.7430555555556 1.77242394702765 28.6291863978997 Winsorized Mean ( 7 / 48 ) 50.3541666666667 1.72033263117576 29.2700177594447 Winsorized Mean ( 8 / 48 ) 50.5763888888889 1.68522822163782 30.0115961977751 Winsorized Mean ( 9 / 48 ) 50.6388888888889 1.67583756301087 30.2170628028586 Winsorized Mean ( 10 / 48 ) 50.6388888888889 1.67583756301087 30.2170628028586 Winsorized Mean ( 11 / 48 ) 50.7152777777778 1.66458679065094 30.4671874501332 Winsorized Mean ( 12 / 48 ) 50.7152777777778 1.66458679065094 30.4671874501332 Winsorized Mean ( 13 / 48 ) 50.625 1.60636466587834 31.5152599377669 Winsorized Mean ( 14 / 48 ) 50.7222222222222 1.56816883683491 32.3448732246182 Winsorized Mean ( 15 / 48 ) 50.6180555555556 1.55659225898752 32.5185065409999 Winsorized Mean ( 16 / 48 ) 50.5069444444444 1.54459721239583 32.6991037139728 Winsorized Mean ( 17 / 48 ) 50.5069444444444 1.54459721239583 32.6991037139728 Winsorized Mean ( 18 / 48 ) 50.5069444444444 1.51430617970199 33.3531917926824 Winsorized Mean ( 19 / 48 ) 50.2430555555556 1.48771995258552 33.7718503191663 Winsorized Mean ( 20 / 48 ) 50.6597222222222 1.40038162167643 36.1756548629768 Winsorized Mean ( 21 / 48 ) 50.6597222222222 1.40038162167643 36.1756548629768 Winsorized Mean ( 22 / 48 ) 50.9652777777778 1.36199572743191 37.4195577499164 Winsorized Mean ( 23 / 48 ) 51.2847222222222 1.32352390967294 38.7486178733977 Winsorized Mean ( 24 / 48 ) 51.2847222222222 1.32352390967294 38.7486178733977 Winsorized Mean ( 25 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 26 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 27 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 28 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 29 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 30 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 31 / 48 ) 51.4583333333333 1.30330682610952 39.4829001908481 Winsorized Mean ( 32 / 48 ) 51.2361111111111 1.28147953289604 39.9819972117085 Winsorized Mean ( 33 / 48 ) 51.2361111111111 1.23304295198366 41.5525761115497 Winsorized Mean ( 34 / 48 ) 51.4722222222222 1.20616393330181 42.6743171480178 Winsorized Mean ( 35 / 48 ) 51.4722222222222 1.20616393330181 42.6743171480178 Winsorized Mean ( 36 / 48 ) 50.9722222222222 1.10865355647261 45.9766912076661 Winsorized Mean ( 37 / 48 ) 51.4861111111111 1.05132404915132 48.9726370786184 Winsorized Mean ( 38 / 48 ) 51.75 1.02264093566338 50.6042719348311 Winsorized Mean ( 39 / 48 ) 51.75 1.02264093566338 50.6042719348311 Winsorized Mean ( 40 / 48 ) 52.0277777777778 0.992979652447751 52.395613192603 Winsorized Mean ( 41 / 48 ) 52.3125 0.963114845142548 54.315952312268 Winsorized Mean ( 42 / 48 ) 52.3125 0.963114845142548 54.315952312268 Winsorized Mean ( 43 / 48 ) 52.0138888888889 0.878440406458636 59.211630642742 Winsorized Mean ( 44 / 48 ) 51.7083333333333 0.85216912087168 60.6784874819697 Winsorized Mean ( 45 / 48 ) 51.7083333333333 0.85216912087168 60.6784874819697 Winsorized Mean ( 46 / 48 ) 51.7083333333333 0.792402430761423 65.2551422433757 Winsorized Mean ( 47 / 48 ) 51.3819444444444 0.766174751466117 67.0629570422704 Winsorized Mean ( 48 / 48 ) 51.3819444444444 0.766174751466117 67.0629570422704 Trimmed Mean ( 1 / 48 ) 50.8380281690141 1.82181580251524 27.9051417266367 Trimmed Mean ( 2 / 48 ) 50.85 1.77703533264932 28.6150753818663 Trimmed Mean ( 3 / 48 ) 50.8333333333333 1.7339304157594 29.3168242919771 Trimmed Mean ( 4 / 48 ) 50.875 1.70106456159122 29.9077419803574 Trimmed Mean ( 5 / 48 ) 50.8955223880597 1.66957595148719 30.4841012729754 Trimmed Mean ( 6 / 48 ) 50.9015151515151 1.63804453446388 31.0745612103733 Trimmed Mean ( 7 / 48 ) 50.9307692307692 1.60775507519564 31.6781890578524 Trimmed Mean ( 8 / 48 ) 51.0234375 1.58477099908973 32.1960949116984 Trimmed Mean ( 9 / 48 ) 51.0873015873016 1.56582631327228 32.626416579077 Trimmed Mean ( 10 / 48 ) 51.1451612903226 1.54649479504876 33.0716672659153 Trimmed Mean ( 11 / 48 ) 51.2049180327869 1.52515548211544 33.5735724214584 Trimmed Mean ( 12 / 48 ) 51.2583333333333 1.50324093544271 34.0985480935148 Trimmed Mean ( 13 / 48 ) 51.3135593220339 1.47898990790862 34.6950030204022 Trimmed Mean ( 14 / 48 ) 51.3793103448276 1.45983996023787 35.1951664184173 Trimmed Mean ( 15 / 48 ) 51.4385964912281 1.4433910054936 35.6373264731807 Trimmed Mean ( 16 / 48 ) 51.5089285714286 1.42638965272308 36.1114008876146 Trimmed Mean ( 17 / 48 ) 51.5909090909091 1.40873572967192 36.6221343040145 Trimmed Mean ( 18 / 48 ) 51.6759259259259 1.38895090434986 37.2050054210622 Trimmed Mean ( 19 / 48 ) 51.7641509433962 1.37023629672315 37.7775359382811 Trimmed Mean ( 20 / 48 ) 51.875 1.3519608957472 38.3701926314442 Trimmed Mean ( 21 / 48 ) 51.9607843137255 1.34107065603938 38.7457469744231 Trimmed Mean ( 22 / 48 ) 52.05 1.32858054427508 39.1771505493484 Trimmed Mean ( 23 / 48 ) 52.1224489795918 1.31845606093831 39.5329435115932 Trimmed Mean ( 24 / 48 ) 52.1770833333333 1.31080151518359 39.8054798754374 Trimmed Mean ( 25 / 48 ) 52.2340425531915 1.30179693241329 40.1245703170918 Trimmed Mean ( 26 / 48 ) 52.2826086956522 1.29336633835056 40.4236658596887 Trimmed Mean ( 27 / 48 ) 52.3333333333333 1.2834293947218 40.7761685594535 Trimmed Mean ( 28 / 48 ) 52.3863636363636 1.2717729479282 41.1916008448713 Trimmed Mean ( 29 / 48 ) 52.4418604651163 1.25814497142737 41.6818901287827 Trimmed Mean ( 30 / 48 ) 52.5 1.24224502042192 42.2621939608732 Trimmed Mean ( 31 / 48 ) 52.5609756097561 1.22371152930272 42.9520964305255 Trimmed Mean ( 32 / 48 ) 52.625 1.20210456273439 43.7773897807156 Trimmed Mean ( 33 / 48 ) 52.7051282051282 1.1792564093984 44.6935270269302 Trimmed Mean ( 34 / 48 ) 52.7894736842105 1.1582375887943 45.5774136454709 Trimmed Mean ( 35 / 48 ) 52.8648648648649 1.13692758816675 46.4980051632904 Trimmed Mean ( 36 / 48 ) 52.9444444444444 1.11169781067384 47.6248526677885 Trimmed Mean ( 37 / 48 ) 53.0571428571429 1.09334249496598 48.5274679265017 Trimmed Mean ( 38 / 48 ) 53.1470588235294 1.07902516443294 49.2546981992399 Trimmed Mean ( 39 / 48 ) 53.2272727272727 1.0654120217136 49.9593318288846 Trimmed Mean ( 40 / 48 ) 53.3125 1.04863739060942 50.8397854944094 Trimmed Mean ( 41 / 48 ) 53.3870967741936 1.03235721364129 51.7137828541816 Trimmed Mean ( 42 / 48 ) 53.45 1.01664814283393 52.5747284119428 Trimmed Mean ( 43 / 48 ) 53.5172413793103 0.997043839665827 53.6759159930695 Trimmed Mean ( 44 / 48 ) 53.6071428571429 0.984516023043273 54.4502492620038 Trimmed Mean ( 45 / 48 ) 53.7222222222222 0.971435740587052 55.3018794529396 Trimmed Mean ( 46 / 48 ) 53.8461538461538 0.954225585020415 56.4291659031568 Trimmed Mean ( 47 / 48 ) 53.98 0.941336435985714 57.3440036276459 Trimmed Mean ( 48 / 48 ) 54.1458333333333 0.926840503300282 58.4197962222535 Median 55 Midrange 50 Midmean - Weighted Average at Xnp 52.3783783783784 Midmean - Weighted Average at X(n+1)p 52.9444444444444 Midmean - Empirical Distribution Function 52.3783783783784 Midmean - Empirical Distribution Function - Averaging 52.9444444444444 Midmean - Empirical Distribution Function - Interpolation 52.9444444444444 Midmean - Closest Observation 52.3783783783784 Midmean - True Basic - Statistics Graphics Toolkit 52.9444444444444 Midmean - MS Excel (old versions) 52.987012987013 Number of observations 144

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/15/t1289836235hd07d5mdtijkw78/15jcz1289836299.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/15/t1289836235hd07d5mdtijkw78/15jcz1289836299.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/15/t1289836235hd07d5mdtijkw78/2gsuk1289836299.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/15/t1289836235hd07d5mdtijkw78/2gsuk1289836299.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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