Central Tendency

*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, 04 Oct 2010 21:49:14 +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/Oct/04/t1286228942ed4tr4aj4aqfcuv.htm/, Retrieved Mon, 04 Oct 2010 23:49:03 +0200

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/Oct/04/t1286228942ed4tr4aj4aqfcuv.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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426,113 383,703 232,444 70,939 226,731 947,293 611,281 158,047 33,999 37,028 388,3 506,652 392,25 180,818 198,296 217,465 275,562 1030,944 57,47 136,452 556,277 213,361 274,482 220,553 236,71 260,642 2763,544 213,923 169,861 403,064 449,594 406,167 206,893 156,187 257,102 62,156 662,883 251,422 171,328 350,089 221,588 4,813 183,186 190,379 223,166 232,669 356,725 109,215 475,834 315,955 694,87 8,95 278,741 308,16 207,533 192,797 601,162 289,714 293,671 386,688 699,645 85,094 131,812 645,285 197,549 308,174 86,58 242,205 238,502 187,881 140,321 440,31 421,403 218,761 1305,923 137,55 262,517 348,821 150,034 64,016 261,596 259,7 171,26 203,077 249,148 211,655 252,64 438,555 239,89 401,915 216,886 184,641 380,155 653,641 313,906 366,936 236,302 229,641 235,577 103,898 263,906 241,171 216,548 295,281 193,299 204,386 257,567 136,813 240,755 59,609 213,511 380,531 242,344 250,407 183,613 191,835 266,79 etc...

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 333.340820143885 36.8856068928258 9.03715156734264 Geometric Mean 240.575054099351 Harmonic Mean 137.582408146386 Quadratic Mean 546.6916185582 Winsorized Mean ( 1 / 46 ) 323.019381294964 29.7848722512537 10.8450819788682 Winsorized Mean ( 2 / 46 ) 302.406834532374 19.0315242954992 15.8897852761006 Winsorized Mean ( 3 / 46 ) 296.537410071942 16.9472149317598 17.497707515128 Winsorized Mean ( 4 / 46 ) 294.188287769784 16.2107421530595 18.1477371604643 Winsorized Mean ( 5 / 46 ) 287.986489208633 14.3061383908975 20.1302742459048 Winsorized Mean ( 6 / 46 ) 285.626374100719 13.7359772114785 20.7940337773737 Winsorized Mean ( 7 / 46 ) 285.514172661871 13.6683812283685 20.8886603242592 Winsorized Mean ( 8 / 46 ) 283.780244604317 13.2650869583858 21.3930180401055 Winsorized Mean ( 9 / 46 ) 283.63009352518 13.0885248899722 21.6701344047169 Winsorized Mean ( 10 / 46 ) 283.165992805755 12.9499073860639 21.8662562104874 Winsorized Mean ( 11 / 46 ) 281.44445323741 12.3024529952555 22.8771004730479 Winsorized Mean ( 12 / 46 ) 280.699158273381 12.119024888263 23.1618600391878 Winsorized Mean ( 13 / 46 ) 278.120949640288 11.1506951272935 24.9420279601703 Winsorized Mean ( 14 / 46 ) 273.658273381295 10.2230382089752 26.7687812358012 Winsorized Mean ( 15 / 46 ) 272.771115107914 9.41303934450433 28.9780064785523 Winsorized Mean ( 16 / 46 ) 270.284784172662 8.89790619841852 30.3762231412039 Winsorized Mean ( 17 / 46 ) 269.331316546763 8.7491198834848 30.7838182735572 Winsorized Mean ( 18 / 46 ) 269.28081294964 8.71788186049926 30.8883301309407 Winsorized Mean ( 19 / 46 ) 269.419690647482 8.64258287384592 31.1735154386308 Winsorized Mean ( 20 / 46 ) 270.443287769784 8.44159824467725 32.036976877016 Winsorized Mean ( 21 / 46 ) 269.885805755396 8.13735582435763 33.1662780368463 Winsorized Mean ( 22 / 46 ) 269.434726618705 8.00447004931554 33.6605327971393 Winsorized Mean ( 23 / 46 ) 268.868496402878 7.47038816320641 35.9912350642132 Winsorized Mean ( 24 / 46 ) 268.659230215827 7.38740142304778 36.3672169455478 Winsorized Mean ( 25 / 46 ) 268.582971223022 7.37453161335087 36.4203430543003 Winsorized Mean ( 26 / 46 ) 270.143158273381 7.1811597898933 37.618318792123 Winsorized Mean ( 27 / 46 ) 270.507366906475 7.12727048119107 37.9538517052701 Winsorized Mean ( 28 / 46 ) 268.745784172662 6.87635351166044 39.0826015150242 Winsorized Mean ( 29 / 46 ) 268.136158273381 6.75033618166184 39.7218969629702 Winsorized Mean ( 30 / 46 ) 268.487525179856 6.643383980571 40.4142716972352 Winsorized Mean ( 31 / 46 ) 268.329402877698 6.51335165770147 41.1968241512681 Winsorized Mean ( 32 / 46 ) 267.65026618705 6.41547697207526 41.7194648740936 Winsorized Mean ( 33 / 46 ) 267.906669064748 6.3740581897744 42.0307849549504 Winsorized Mean ( 34 / 46 ) 264.908553956835 5.94603498413426 44.5521351057784 Winsorized Mean ( 35 / 46 ) 262.463841726619 5.6186139153174 46.7132722914264 Winsorized Mean ( 36 / 46 ) 261.845884892086 5.31331919837047 49.2810379192711 Winsorized Mean ( 37 / 46 ) 261.707201438849 5.25532630745903 49.7984684732898 Winsorized Mean ( 38 / 46 ) 256.989460431655 4.64395513684505 55.3384890376536 Winsorized Mean ( 39 / 46 ) 255.285244604317 4.42440439611161 57.6993470191545 Winsorized Mean ( 40 / 46 ) 253.076611510791 4.15491855438443 60.9101257216547 Winsorized Mean ( 41 / 46 ) 253.333820143885 4.0098015784832 63.1786424304103 Winsorized Mean ( 42 / 46 ) 252.10554676259 3.79750095955473 66.3872239790428 Winsorized Mean ( 43 / 46 ) 252.732604316547 3.71892166534865 67.958571612681 Winsorized Mean ( 44 / 46 ) 252.766791366906 3.7149857783133 68.0397736224092 Winsorized Mean ( 45 / 46 ) 248.804525179856 3.25460303417396 76.4469652880431 Winsorized Mean ( 46 / 46 ) 248.462007194245 3.18212166055755 78.0806121506714 Trimmed Mean ( 1 / 46 ) 307.497810218978 24.2195560101489 12.6962612398892 Trimmed Mean ( 2 / 46 ) 291.516340740741 16.2442545367473 17.9458121689291 Trimmed Mean ( 3 / 46 ) 285.825443609023 14.4660212854976 19.7584005973755 Trimmed Mean ( 4 / 46 ) 282.036732824427 13.3933832668437 21.057915479999 Trimmed Mean ( 5 / 46 ) 278.763348837209 12.4317309434155 22.4235345911229 Trimmed Mean ( 6 / 46 ) 276.74442519685 11.9214322592045 23.2140248906064 Trimmed Mean ( 7 / 46 ) 275.098304 11.4944070311032 23.9332314625366 Trimmed Mean ( 8 / 46 ) 273.416764227642 11.0310198912132 24.7861727133167 Trimmed Mean ( 9 / 46 ) 271.928619834711 10.5958192081226 25.663765537473 Trimmed Mean ( 10 / 46 ) 270.409941176471 10.1400423294762 26.6675357350742 Trimmed Mean ( 11 / 46 ) 268.894478632479 9.65043517179917 27.8634562945152 Trimmed Mean ( 12 / 46 ) 267.515469565217 9.21289317985675 29.0370749277891 Trimmed Mean ( 13 / 46 ) 266.164044247788 8.74775885482701 30.4265410906839 Trimmed Mean ( 14 / 46 ) 265.01227027027 8.38193353647111 31.6170808462224 Trimmed Mean ( 15 / 46 ) 264.224724770642 8.11437886011007 32.5625324286446 Trimmed Mean ( 16 / 46 ) 263.484570093458 7.92719061620299 33.2380767475052 Trimmed Mean ( 17 / 46 ) 262.921933333333 7.78563548407523 33.7701314004637 Trimmed Mean ( 18 / 46 ) 262.41313592233 7.64559967963727 34.3221129692707 Trimmed Mean ( 19 / 46 ) 261.888049504951 7.4916741163256 34.9572132261136 Trimmed Mean ( 20 / 46 ) 261.331484848485 7.32694880885141 35.6671640086771 Trimmed Mean ( 21 / 46 ) 260.678628865979 7.16402174123407 36.3871912009406 Trimmed Mean ( 22 / 46 ) 260.037126315789 7.01618335439862 37.0624758762563 Trimmed Mean ( 23 / 46 ) 259.398677419355 6.86404354380073 37.7909428697653 Trimmed Mean ( 24 / 46 ) 258.769769230769 6.75330890085722 38.3174785915572 Trimmed Mean ( 25 / 46 ) 258.126213483146 6.63572200156017 38.899491784384 Trimmed Mean ( 26 / 46 ) 257.457942528736 6.5017546986396 39.5982245504595 Trimmed Mean ( 27 / 46 ) 256.660094117647 6.36747650780791 40.3079766062625 Trimmed Mean ( 28 / 46 ) 255.801204819277 6.2171722093852 41.1443010108566 Trimmed Mean ( 29 / 46 ) 255.007864197531 6.07557209285507 41.9726505257706 Trimmed Mean ( 30 / 46 ) 254.211341772152 5.92642714776032 42.8945358533973 Trimmed Mean ( 31 / 46 ) 253.352298701299 5.76390235822058 43.9549948898706 Trimmed Mean ( 32 / 46 ) 252.456893333333 5.58913681770749 45.1692097666137 Trimmed Mean ( 33 / 46 ) 251.552835616438 5.39633686693445 46.6154804304017 Trimmed Mean ( 34 / 46 ) 250.582633802817 5.17083457757718 48.4607716691313 Trimmed Mean ( 35 / 46 ) 249.733826086957 4.97132714723551 50.2348404541894 Trimmed Mean ( 36 / 46 ) 248.979253731343 4.78597514530912 52.0226800541109 Trimmed Mean ( 37 / 46 ) 248.214953846154 4.60930695349673 53.850818865047 Trimmed Mean ( 38 / 46 ) 247.410396825397 4.40313325021162 56.1896228812757 Trimmed Mean ( 39 / 46 ) 246.835983606557 4.2618691728126 57.9173066083723 Trimmed Mean ( 40 / 46 ) 246.325576271186 4.12704506981705 59.6857005688348 Trimmed Mean ( 41 / 46 ) 245.914 4.00874969226542 61.3443140324956 Trimmed Mean ( 42 / 46 ) 245.456636363636 3.88497281456921 63.1810434922835 Trimmed Mean ( 43 / 46 ) 245.041452830189 3.76908863121974 65.0134493523145 Trimmed Mean ( 44 / 46 ) 244.553960784314 3.63476406882863 67.2819352655056 Trimmed Mean ( 45 / 46 ) 244.024469387755 3.46333665167593 70.4593557977304 Trimmed Mean ( 46 / 46 ) 243.710319148936 3.34896386410717 72.7718569199638 Median 241.171 Midrange 2103.587 Midmean - Weighted Average at Xnp 248.920442857143 Midmean - Weighted Average at X(n+1)p 250.582633802817 Midmean - Empirical Distribution Function 250.582633802817 Midmean - Empirical Distribution Function - Averaging 250.582633802817 Midmean - Empirical Distribution Function - Interpolation 249.733826086957 Midmean - Closest Observation 248.920442857143 Midmean - True Basic - Statistics Graphics Toolkit 250.582633802817 Midmean - MS Excel (old versions) 250.582633802817 Number of observations 139

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Oct/04/t1286228942ed4tr4aj4aqfcuv/128lf1286228950.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/04/t1286228942ed4tr4aj4aqfcuv/128lf1286228950.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/04/t1286228942ed4tr4aj4aqfcuv/2dh2i1286228950.png (open in new window) http://www.freestatistics.org/blog/date/2010/Oct/04/t1286228942ed4tr4aj4aqfcuv/2dh2i1286228950.ps (open in new window)

Parameters (Session):

par2 = grey ; par3 = FALSE ; par4 = Unknown ;

Parameters (R input):

par2 = grey ; par3 = FALSE ; par4 = Unknown ;

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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