computation13

*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 11:36:35 +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/t1289907392hksyy1gs464k8c5.htm/, Retrieved Tue, 16 Nov 2010 12:36:34 +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/t1289907392hksyy1gs464k8c5.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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13 12 15 12 9 12 15 9 12 11 11 11 15 7 11 11 10 14 10 6 11 15 11 12 14 15 9 13 13 16 13 12 14 11 9 16 12 10 13 16 14 15 5 8 11 16 17 9 9 13 10 6 12 8 14 12 11 16 8 15 7 16 14 16 9 14 11 13 15 5 15 13 11 11 12 12 12 12 14 6 7 14 14 10 13 12 9 12 16 10 14 10 16 15 12 10 8 8 11 13 16 16 14 11 4 14 9 14 8 8 11 12 11 14 15 16 16 11 14 14 12 14 8 13 16 12 16 12 11 4 16 15 10 13 15 12 14 7 19 12 12 13 15 8 12 10 8 10 15 16 13 16 9 14 14 12

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 12.0384615384615 0.235565001133148 51.1046270904101 Geometric Mean 11.6153175798654 Harmonic Mean 11.0980496169828 Quadratic Mean 12.3905464377339 Winsorized Mean ( 1 / 52 ) 12.0256410256410 0.233460095181334 51.5104776955583 Winsorized Mean ( 2 / 52 ) 12.0256410256410 0.229169161056951 52.4749533060102 Winsorized Mean ( 3 / 52 ) 12.0256410256410 0.229169161056951 52.4749533060102 Winsorized Mean ( 4 / 52 ) 12.0512820512821 0.224399736342042 53.7045285691104 Winsorized Mean ( 5 / 52 ) 12.0512820512821 0.224399736342042 53.7045285691104 Winsorized Mean ( 6 / 52 ) 12.0512820512821 0.224399736342042 53.7045285691104 Winsorized Mean ( 7 / 52 ) 12.0961538461538 0.217090095790076 55.7195103817666 Winsorized Mean ( 8 / 52 ) 12.0961538461538 0.217090095790076 55.7195103817666 Winsorized Mean ( 9 / 52 ) 12.0961538461538 0.217090095790076 55.7195103817666 Winsorized Mean ( 10 / 52 ) 12.0961538461538 0.217090095790076 55.7195103817666 Winsorized Mean ( 11 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 12 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 13 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 14 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 15 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 16 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 17 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 18 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 19 / 52 ) 12.1666666666667 0.207157558888342 58.7314637803032 Winsorized Mean ( 20 / 52 ) 12.0384615384615 0.193116578398452 62.337794291399 Winsorized Mean ( 21 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 22 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 23 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 24 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 25 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 26 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 27 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 28 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 29 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 30 / 52 ) 12.1730769230769 0.176155868817872 69.1040100154862 Winsorized Mean ( 31 / 52 ) 12.3717948717949 0.154667644080959 79.9895475573355 Winsorized Mean ( 32 / 52 ) 12.3717948717949 0.154667644080959 79.9895475573355 Winsorized Mean ( 33 / 52 ) 12.3717948717949 0.154667644080959 79.9895475573355 Winsorized Mean ( 34 / 52 ) 12.3717948717949 0.154667644080959 79.9895475573355 Winsorized Mean ( 35 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 36 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 37 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 38 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 39 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 40 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 41 / 52 ) 12.1474358974359 0.132046463731185 91.993647949294 Winsorized Mean ( 42 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 43 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 44 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 45 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 46 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 47 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 48 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 49 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 50 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 51 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Winsorized Mean ( 52 / 52 ) 12.4166666666667 0.106044894800559 117.088773486163 Trimmed Mean ( 1 / 52 ) 12.0454545454545 0.228356637780494 52.7484318499782 Trimmed Mean ( 2 / 52 ) 12.0657894736842 0.222800830861999 54.1550470301328 Trimmed Mean ( 3 / 52 ) 12.0866666666667 0.219198594573024 55.1402562147364 Trimmed Mean ( 4 / 52 ) 12.1081081081081 0.215277655887442 56.2441469282759 Trimmed Mean ( 5 / 52 ) 12.1232876712329 0.212480327458371 57.0560475703712 Trimmed Mean ( 6 / 52 ) 12.1388888888889 0.209434412237912 57.9603359313245 Trimmed Mean ( 7 / 52 ) 12.1549295774648 0.206113673330571 58.9719710539064 Trimmed Mean ( 8 / 52 ) 12.1642857142857 0.203921148910156 59.6519084915762 Trimmed Mean ( 9 / 52 ) 12.1739130434783 0.201526241283802 60.408574912754 Trimmed Mean ( 10 / 52 ) 12.1838235294118 0.198908296570809 61.2534707674926 Trimmed Mean ( 11 / 52 ) 12.1940298507463 0.196043691393163 62.2005725565090 Trimmed Mean ( 12 / 52 ) 12.1969696969697 0.194309335172809 62.7708889339895 Trimmed Mean ( 13 / 52 ) 12.2 0.192401885101416 63.4089421398825 Trimmed Mean ( 14 / 52 ) 12.203125 0.190303637081421 64.124496973113 Trimmed Mean ( 15 / 52 ) 12.2063492063492 0.187994414073536 64.9293185997248 Trimmed Mean ( 16 / 52 ) 12.2096774193548 0.185451085484328 65.837724203489 Trimmed Mean ( 17 / 52 ) 12.2131147540984 0.182646959163678 66.867331435579 Trimmed Mean ( 18 / 52 ) 12.2166666666667 0.179551001365916 68.0400920837511 Trimmed Mean ( 19 / 52 ) 12.2203389830508 0.176126819942143 69.3837485231676 Trimmed Mean ( 20 / 52 ) 12.2241379310345 0.172331314604996 70.933933040862 Trimmed Mean ( 21 / 52 ) 12.2368421052632 0.169614955954621 72.1448296607581 Trimmed Mean ( 22 / 52 ) 12.2410714285714 0.168385061426377 72.6968967726607 Trimmed Mean ( 23 / 52 ) 12.2454545454545 0.166995909033991 73.3278714208625 Trimmed Mean ( 24 / 52 ) 12.25 0.165428945154025 74.049919067032 Trimmed Mean ( 25 / 52 ) 12.2547169811321 0.163662770298562 74.8778537646428 Trimmed Mean ( 26 / 52 ) 12.2596153846154 0.161672545618185 75.829914953949 Trimmed Mean ( 27 / 52 ) 12.2647058823529 0.159429232502456 76.9288397732456 Trimmed Mean ( 28 / 52 ) 12.27 0.156898603298983 78.2033730193155 Trimmed Mean ( 29 / 52 ) 12.2755102040816 0.154039931489699 79.6904418572955 Trimmed Mean ( 30 / 52 ) 12.28125 0.150804222013818 81.438369801574 Trimmed Mean ( 31 / 52 ) 12.28125 0.147131763258901 83.4710991561302 Trimmed Mean ( 32 / 52 ) 12.2826086956522 0.145259613180465 84.5562536394253 Trimmed Mean ( 33 / 52 ) 12.2777777777778 0.143113041914380 85.7907680078746 Trimmed Mean ( 34 / 52 ) 12.2727272727273 0.140650148026338 87.2571230456797 Trimmed Mean ( 35 / 52 ) 12.2674418604651 0.137820060133787 89.0105681898312 Trimmed Mean ( 36 / 52 ) 12.2738095238095 0.136918792466208 89.6429869321168 Trimmed Mean ( 37 / 52 ) 12.2804878048780 0.135832927653201 90.4087691920456 Trimmed Mean ( 38 / 52 ) 12.2875 0.134533594223464 91.3340647064715 Trimmed Mean ( 39 / 52 ) 12.2948717948718 0.132986112608966 92.4522986172532 Trimmed Mean ( 40 / 52 ) 12.3026315789474 0.131148418457474 93.8069381518058 Trimmed Mean ( 41 / 52 ) 12.3108108108108 0.128968906239563 95.4556502785496 Trimmed Mean ( 42 / 52 ) 12.3194444444444 0.126383407162418 97.4767552247776 Trimmed Mean ( 43 / 52 ) 12.3142857142857 0.126332003919963 97.4755828466661 Trimmed Mean ( 44 / 52 ) 12.3088235294118 0.126159856753958 97.565294112666 Trimmed Mean ( 45 / 52 ) 12.3088235294118 0.125843905549539 97.810247350964 Trimmed Mean ( 46 / 52 ) 12.296875 0.12535601582838 98.0956112775246 Trimmed Mean ( 47 / 52 ) 12.2903225806452 0.124661565696999 98.5895092198493 Trimmed Mean ( 48 / 52 ) 12.2833333333333 0.123717526508367 99.2853129221158 Trimmed Mean ( 49 / 52 ) 12.2758620689655 0.122469803056851 100.235827629011 Trimmed Mean ( 50 / 52 ) 12.2678571428571 0.120849459397776 101.513545894132 Trimmed Mean ( 51 / 52 ) 12.2592592592593 0.118767209382842 103.220908556856 Trimmed Mean ( 52 / 52 ) 12.25 0.116105091003206 105.507862697095 Median 12 Midrange 11.5 Midmean - Weighted Average at Xnp 12.1666666666667 Midmean - Weighted Average at X(n+1)p 12.1666666666667 Midmean - Empirical Distribution Function 12.1666666666667 Midmean - Empirical Distribution Function - Averaging 12.1666666666667 Midmean - Empirical Distribution Function - Interpolation 12.1666666666667 Midmean - Closest Observation 12.1666666666667 Midmean - True Basic - Statistics Graphics Toolkit 12.1666666666667 Midmean - MS Excel (old versions) 12.1666666666667 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/16/t1289907392hksyy1gs464k8c5/1jvip1289907392.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289907392hksyy1gs464k8c5/1jvip1289907392.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289907392hksyy1gs464k8c5/2jvip1289907392.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289907392hksyy1gs464k8c5/2jvip1289907392.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

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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