ws6tut

*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: Sat, 13 Nov 2010 15:04:19 +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/13/t128966061175t18uo7txs04sc.htm/, Retrieved Sat, 13 Nov 2010 16:03:33 +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/13/t128966061175t18uo7txs04sc.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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2 1 4 2 2 2 2 2 1 4 2 3 2 2 1 3 1 2 4 4 3 2 3 2 2 3 3 2 4 2 2 3 5 2 3 1 2 2 4 2 4 3 2 4 2 2 2 2 2 3 1 2 2 1 5 2 2 2 1 2 2 2 4 2 1 2 2 2 1 2 2 2 3 3 2 2 3 2 2 2 2 2 1 3 2 4 2 1 4 3 2 3 2 3 2 5 3 4 2 2 2 3 4 3 2 2 3 2 2 2 2 3 2 4 1 2 4 2 2 1 1 3 3 1 1 3 1 2 3 2 2 2 2 2 2 4 2 2 3 2 2 4 4 1 2 4 2 1 2 2 3 5 2 4 2 2 3 4

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 2.39873417721519 0.0761590403098942 31.4963813547892 Geometric Mean 2.21477813286791 Harmonic Mean 2.03302594895990 Quadratic Mean 2.58158032259802 Winsorized Mean ( 1 / 52 ) 2.39873417721519 0.0761590403098942 31.4963813547892 Winsorized Mean ( 2 / 52 ) 2.39873417721519 0.0761590403098942 31.4963813547892 Winsorized Mean ( 3 / 52 ) 2.39873417721519 0.0761590403098942 31.4963813547892 Winsorized Mean ( 4 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 5 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 6 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 7 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 8 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 9 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 10 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 11 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 12 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 13 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 14 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 15 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 16 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 17 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 18 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 19 / 52 ) 2.37341772151899 0.0715433670958277 33.1745320057406 Winsorized Mean ( 20 / 52 ) 2.5 0.0600666135634787 41.6204585490405 Winsorized Mean ( 21 / 52 ) 2.5 0.0600666135634787 41.6204585490405 Winsorized Mean ( 22 / 52 ) 2.5 0.0600666135634787 41.6204585490405 Winsorized Mean ( 23 / 52 ) 2.5 0.0600666135634787 41.6204585490405 Winsorized Mean ( 24 / 52 ) 2.5 0.0600666135634787 41.6204585490405 Winsorized Mean ( 25 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 26 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 27 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 28 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 29 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 30 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 31 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 32 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 33 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 34 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 35 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 36 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 37 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 38 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 39 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 40 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 41 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 42 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 43 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 44 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 45 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 46 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 47 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 48 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 49 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 50 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 51 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Winsorized Mean ( 52 / 52 ) 2.34177215189873 0.0378535518687214 61.8640005043689 Trimmed Mean ( 1 / 52 ) 2.39102564102564 0.0747611127694354 31.9822104360539 Trimmed Mean ( 2 / 52 ) 2.38311688311688 0.0732420966047127 32.5375295573331 Trimmed Mean ( 3 / 52 ) 2.375 0.071587888104403 33.1760031324898 Trimmed Mean ( 4 / 52 ) 2.36666666666667 0.0697818054571253 33.9152398130595 Trimmed Mean ( 5 / 52 ) 2.36486486486486 0.0692383966174574 34.155396144292 Trimmed Mean ( 6 / 52 ) 2.36301369863014 0.0686464981530215 34.4229314270728 Trimmed Mean ( 7 / 52 ) 2.36111111111111 0.0680017190290103 34.7213444722454 Trimmed Mean ( 8 / 52 ) 2.35915492957746 0.0672991295594086 35.0547614066079 Trimmed Mean ( 9 / 52 ) 2.35714285714286 0.0665331703094893 35.4280856628092 Trimmed Mean ( 10 / 52 ) 2.35507246376812 0.0656975401900102 35.8471939277602 Trimmed Mean ( 11 / 52 ) 2.35294117647059 0.0647850575215654 36.3191956059829 Trimmed Mean ( 12 / 52 ) 2.35074626865672 0.0637874854851547 36.8527815570314 Trimmed Mean ( 13 / 52 ) 2.34848484848485 0.0626953099004418 37.4587006941057 Trimmed Mean ( 14 / 52 ) 2.34615384615385 0.0614974520481333 38.1504236032013 Trimmed Mean ( 15 / 52 ) 2.34375 0.0601808911977409 38.9450862782833 Trimmed Mean ( 16 / 52 ) 2.34126984126984 0.0587301587301587 39.8648648648649 Trimmed Mean ( 17 / 52 ) 2.33870967741935 0.057126644836261 40.9390343879406 Trimmed Mean ( 18 / 52 ) 2.33606557377049 0.0553476232629208 42.2071524674755 Trimmed Mean ( 19 / 52 ) 2.33333333333333 0.053364836634082 43.7241727044495 Trimmed Mean ( 20 / 52 ) 2.33050847457627 0.0511423675301914 45.5690377102796 Trimmed Mean ( 21 / 52 ) 2.33050847457627 0.0498861341102609 46.7165579402336 Trimmed Mean ( 22 / 52 ) 2.30701754385965 0.0484621328835521 47.6045400107151 Trimmed Mean ( 23 / 52 ) 2.29464285714286 0.0468409932107711 48.9879206193947 Trimmed Mean ( 24 / 52 ) 2.28181818181818 0.0449850650442332 50.7239053578004 Trimmed Mean ( 25 / 52 ) 2.26851851851852 0.0428446796805219 52.9474962920503 Trimmed Mean ( 26 / 52 ) 2.26415094339623 0.0430254877395901 52.6234811584218 Trimmed Mean ( 27 / 52 ) 2.25961538461538 0.0431991590944756 52.3069298565201 Trimmed Mean ( 28 / 52 ) 2.25490196078431 0.0433643270799318 51.999007309117 Trimmed Mean ( 29 / 52 ) 2.25 0.0435194139889245 51.7010638188423 Trimmed Mean ( 30 / 52 ) 2.24489795918367 0.043662594429121 51.4146717238228 Trimmed Mean ( 31 / 52 ) 2.23958333333333 0.0437917511725694 51.141671053707 Trimmed Mean ( 32 / 52 ) 2.23404255319149 0.0439044216656677 50.8842268827438 Trimmed Mean ( 33 / 52 ) 2.22826086956522 0.0439977328325180 50.6449020463697 Trimmed Mean ( 34 / 52 ) 2.22222222222222 0.0440683210854502 50.4267502706365 Trimmed Mean ( 35 / 52 ) 2.21590909090909 0.044112233475599 50.233436766126 Trimmed Mean ( 36 / 52 ) 2.20930232558140 0.044124804560489 50.0693962859993 Trimmed Mean ( 37 / 52 ) 2.20238095238095 0.0441005016629023 49.9400430683448 Trimmed Mean ( 38 / 52 ) 2.19512195121951 0.0440327284804117 49.8520538466298 Trimmed Mean ( 39 / 52 ) 2.1875 0.043913573058158 49.81375569469 Trimmed Mean ( 40 / 52 ) 2.17948717948718 0.0437334802780739 49.8356674481239 Trimmed Mean ( 41 / 52 ) 2.17105263157895 0.0434808201101534 49.9312714451762 Trimmed Mean ( 42 / 52 ) 2.17105263157895 0.0431413089590404 50.3242178775847 Trimmed Mean ( 43 / 52 ) 2.15277777777778 0.0426972190266123 50.4196251384896 Trimmed Mean ( 44 / 52 ) 2.14285714285714 0.0421262731871135 50.8674748734395 Trimmed Mean ( 45 / 52 ) 2.13235294117647 0.0414000577075105 51.5060378959239 Trimmed Mean ( 46 / 52 ) 2.12121212121212 0.040481665913459 52.39932876643 Trimmed Mean ( 47 / 52 ) 2.12121212121212 0.0393220543503685 53.9445905422854 Trimmed Mean ( 48 / 52 ) 2.09677419354839 0.0378541092928044 55.3909267110125 Trimmed Mean ( 49 / 52 ) 2.08333333333333 0.0359823141323644 57.8988145584409 Trimmed Mean ( 50 / 52 ) 2.06896551724138 0.0335630526477440 61.6441400296898 Trimmed Mean ( 51 / 52 ) 2.05357142857143 0.0303619171188469 67.6364216572047 Trimmed Mean ( 52 / 52 ) 2.03703703703704 0.0259408961471287 78.5260858176833 Median 2 Midrange 3 Midmean - Weighted Average at Xnp 2.25663716814159 Midmean - Weighted Average at X(n+1)p 2.25663716814159 Midmean - Empirical Distribution Function 2.25663716814159 Midmean - Empirical Distribution Function - Averaging 2.25663716814159 Midmean - Empirical Distribution Function - Interpolation 2.25663716814159 Midmean - Closest Observation 2.25663716814159 Midmean - True Basic - Statistics Graphics Toolkit 2.25663716814159 Midmean - MS Excel (old versions) 2.25663716814159 Number of observations 158

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/13/t128966061175t18uo7txs04sc/1g0ts1289660657.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/13/t128966061175t18uo7txs04sc/1g0ts1289660657.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/13/t128966061175t18uo7txs04sc/2r9sv1289660657.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/13/t128966061175t18uo7txs04sc/2r9sv1289660657.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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