2MAR03A_Robbe Leys_opgave5_deel3

*Unverified author*

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Sun, 04 Jan 2009 07:15:21 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jan/04/t1231078626bq6qfs4ogbbilvh.htm/, Retrieved Sun, 04 Jan 2009 15:17:06 +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/2009/Jan/04/t1231078626bq6qfs4ogbbilvh.htm/}, year = {2009}, } @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 = {2009}, 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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284,4 212,8 226,9 308,4 262 227,9 236,1 320,4 271,9 232,8 237 313,4 261,4 226,8 249,9 314,3 286,1 226,5 260,4 311,4 294,7 232,6 257,2 339,2 279,1 249,8 269,8 345,7 293,8 254,7 277,5 363,4 313,4 272,8 300,1 369,5 330,8 287,8 305,9 386,1 335,2 288 308,3 402,3 352,8 316,1 324,9 404,8 393 318,9 327 442,3 383,1 331,6 361,4 445,9 386,6 357,2 373,6 466,2 409,6 369,8 378,6 487 419,2 376,7 392,8 506,1 458,4 387,4 426,9 565 464,8 444,5 449,5 556,1 499,6 451,9 434,9 553,8 510 432,9 453,2 547,6 485,8 452,6 456,6 565,7 514,8 464,3 430,9 588,3 503,1 442,6 448 554,5 504,5 427,3 473,1 526,2 547,5 440,2 468,7 574,5 492,6 432,6 479,8 575,7 474,6 405,3 434,6 535,1 452,6 429,5 417,2 551,8 464 416,6 422,9 553,6 458,6 427,6 429,2 534,2 481,7 416 440,2 538,7 473,8 439,9 446,8 597,5 467,2 439,4 447,4 568,5 485,9 442,1 430,5 600 464,5 423,6 437 574 443 410 420 532 432 420 411 512 449 382

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 408.267532467532 7.86471831318563 51.9112721154996 Geometric Mean 395.722160169163 Harmonic Mean 382.298871380141 Quadratic Mean 419.697520330331 Winsorized Mean ( 1 / 51 ) 408.34025974026 7.84818429358794 52.0299020085293 Winsorized Mean ( 2 / 51 ) 408.224675324675 7.82920198970994 52.1412879449543 Winsorized Mean ( 3 / 51 ) 407.981168831169 7.793191239789 52.3509761634202 Winsorized Mean ( 4 / 51 ) 407.975974025974 7.78488449920634 52.4061691689281 Winsorized Mean ( 5 / 51 ) 408.112337662338 7.75980052195281 52.5931480464955 Winsorized Mean ( 6 / 51 ) 407.905844155844 7.72912607585453 52.7751572626206 Winsorized Mean ( 7 / 51 ) 407.928571428571 7.68989118106435 53.0473789321048 Winsorized Mean ( 8 / 51 ) 407.938961038961 7.67820669730148 53.1294581041081 Winsorized Mean ( 9 / 51 ) 408.166883116883 7.50386342250713 54.3942313625572 Winsorized Mean ( 10 / 51 ) 408.069480519481 7.48963561530522 54.4845572574428 Winsorized Mean ( 11 / 51 ) 408.362337662338 7.43642905603507 54.9137676948493 Winsorized Mean ( 12 / 51 ) 408.541558441558 7.40827546536746 55.1466478739117 Winsorized Mean ( 13 / 51 ) 408.65974025974 7.35305145433361 55.5768911448174 Winsorized Mean ( 14 / 51 ) 408.368831168831 7.29296081247818 55.9949301345644 Winsorized Mean ( 15 / 51 ) 408.417532467533 7.28405754943138 56.0700584387084 Winsorized Mean ( 16 / 51 ) 408.313636363636 7.06668510864438 57.7800807714162 Winsorized Mean ( 17 / 51 ) 408.148051948052 6.98951512175164 58.3943299125113 Winsorized Mean ( 18 / 51 ) 408.148051948052 6.96366527695354 58.6110956968064 Winsorized Mean ( 19 / 51 ) 408.456493506494 6.85861112613285 59.553820153206 Winsorized Mean ( 20 / 51 ) 407.911038961039 6.74515872698158 60.474638992458 Winsorized Mean ( 21 / 51 ) 407.079220779221 6.48173156917958 62.8040850557387 Winsorized Mean ( 22 / 51 ) 406.922077922078 6.4085878056696 63.4963724086107 Winsorized Mean ( 23 / 51 ) 406.877272727273 6.3455044699341 64.1205556871191 Winsorized Mean ( 24 / 51 ) 406.300649350649 6.27781500928886 64.7200735844357 Winsorized Mean ( 25 / 51 ) 406.982467532468 6.13607731470885 66.3261635502678 Winsorized Mean ( 26 / 51 ) 406.898051948052 6.0933032374277 66.7779094676772 Winsorized Mean ( 27 / 51 ) 407.231168831169 5.91733475675029 68.8200322563488 Winsorized Mean ( 28 / 51 ) 407.012987012987 5.66561695075917 71.8391290040265 Winsorized Mean ( 29 / 51 ) 406.410389610390 5.51047756693825 73.7522990836166 Winsorized Mean ( 30 / 51 ) 406.215584415584 5.48779952062148 74.0215787565034 Winsorized Mean ( 31 / 51 ) 406.799350649351 5.41594312589408 75.11145172563 Winsorized Mean ( 32 / 51 ) 406.362987012987 5.28785461795764 76.8483659957239 Winsorized Mean ( 33 / 51 ) 405.955844155844 5.25018340244266 77.3222215374366 Winsorized Mean ( 34 / 51 ) 405.006493506494 5.12348736740343 79.0489884064553 Winsorized Mean ( 35 / 51 ) 405.233766233766 5.06014661809359 80.0834040627933 Winsorized Mean ( 36 / 51 ) 405.724675324675 4.9706064387702 81.6247836803304 Winsorized Mean ( 37 / 51 ) 405.027922077922 4.83710770823139 83.7334925142723 Winsorized Mean ( 38 / 51 ) 405.768181818182 4.67909035134383 86.7194585592145 Winsorized Mean ( 39 / 51 ) 406.046753246753 4.59747582350782 88.319497227273 Winsorized Mean ( 40 / 51 ) 406.67012987013 4.45603866251310 91.2627022945932 Winsorized Mean ( 41 / 51 ) 406.803246753247 4.42555204730185 91.921469322966 Winsorized Mean ( 42 / 51 ) 407.730519480519 4.31239494130628 94.5485107532875 Winsorized Mean ( 43 / 51 ) 408.763636363636 4.18309283873351 97.7180407230444 Winsorized Mean ( 44 / 51 ) 409.077922077922 3.85074127164988 106.233551729288 Winsorized Mean ( 45 / 51 ) 411.094155844156 3.62510044374515 113.402142154011 Winsorized Mean ( 46 / 51 ) 411.870779220779 3.44202692909771 119.659371557778 Winsorized Mean ( 47 / 51 ) 412.114935064935 3.22220997340278 127.898224655337 Winsorized Mean ( 48 / 51 ) 412.551298701299 3.14270202908908 131.272801201862 Winsorized Mean ( 49 / 51 ) 414.492207792208 2.94657455613477 140.669173610162 Winsorized Mean ( 50 / 51 ) 414.362337662338 2.91769130794819 142.01719576487 Winsorized Mean ( 51 / 51 ) 414.825974025974 2.72620226080596 152.162581621269 Trimmed Mean ( 1 / 51 ) 408.292105263158 7.7608863671061 52.6089528888957 Trimmed Mean ( 2 / 51 ) 408.242666666667 7.66637023140556 53.2511024571036 Trimmed Mean ( 3 / 51 ) 408.252027027027 7.5745957818916 53.8975331202525 Trimmed Mean ( 4 / 51 ) 408.347260273973 7.4889650954239 54.5265273733872 Trimmed Mean ( 5 / 51 ) 408.347260273973 7.39843683701549 55.1937212237793 Trimmed Mean ( 6 / 51 ) 408.519014084507 7.30628660310962 55.9133573969898 Trimmed Mean ( 7 / 51 ) 408.631428571429 7.21259184168438 56.655282531002 Trimmed Mean ( 8 / 51 ) 408.743478260870 7.1180323243616 57.4236614326598 Trimmed Mean ( 9 / 51 ) 408.857352941176 7.01688351646291 58.2676557166613 Trimmed Mean ( 10 / 51 ) 408.857352941176 6.93447008359123 58.9601437474854 Trimmed Mean ( 11 / 51 ) 409.047727272727 6.84635808958339 59.746761989427 Trimmed Mean ( 12 / 51 ) 409.121538461538 6.75733299287842 60.5448242513301 Trimmed Mean ( 13 / 51 ) 409.1796875 6.66343943822324 61.4066791322268 Trimmed Mean ( 14 / 51 ) 409.228571428571 6.56741990914534 62.3119241787339 Trimmed Mean ( 15 / 51 ) 409.304838709677 6.46924099198484 63.2693756836067 Trimmed Mean ( 16 / 51 ) 409.379508196721 6.36212116712577 64.3463865969826 Trimmed Mean ( 17 / 51 ) 409.465 6.26955346338574 65.3100738978111 Trimmed Mean ( 18 / 51 ) 409.566101694915 6.17598145650489 66.3159539223579 Trimmed Mean ( 19 / 51 ) 409.670689655172 6.07523372336121 67.432910124899 Trimmed Mean ( 20 / 51 ) 409.670689655172 5.97518882483787 68.5619654314922 Trimmed Mean ( 21 / 51 ) 409.883928571429 5.87632957566263 69.7516916459215 Trimmed Mean ( 22 / 51 ) 410.070909090909 5.79418366807673 70.7728530164154 Trimmed Mean ( 23 / 51 ) 410.275 5.70999426306635 71.8520862015151 Trimmed Mean ( 24 / 51 ) 410.489622641509 5.6222997998217 73.0109807830823 Trimmed Mean ( 25 / 51 ) 410.748076923077 5.53073883245294 74.2664026210954 Trimmed Mean ( 26 / 51 ) 410.975490196078 5.44290166017917 75.5066903381367 Trimmed Mean ( 27 / 51 ) 411.217 5.34832968450351 76.8869954280265 Trimmed Mean ( 28 / 51 ) 411.448979591837 5.26013818685293 78.2201845229471 Trimmed Mean ( 29 / 51 ) 411.703125 5.18611664140488 79.3856277186379 Trimmed Mean ( 30 / 51 ) 412.002127659574 5.11724266103291 80.5125249965011 Trimmed Mean ( 31 / 51 ) 412.325 5.04063318041809 81.8002392242715 Trimmed Mean ( 32 / 51 ) 412.63 4.96080554299961 83.178023493035 Trimmed Mean ( 33 / 51 ) 412.972727272727 4.88230382243978 84.58562643616 Trimmed Mean ( 34 / 51 ) 413.353488372093 4.79543435898268 86.1972988114851 Trimmed Mean ( 35 / 51 ) 413.803571428571 4.70778960647714 87.8976347751918 Trimmed Mean ( 36 / 51 ) 414.263414634146 4.61331468952165 89.7973458379231 Trimmed Mean ( 37 / 51 ) 414.72 4.51416385573848 91.870834390028 Trimmed Mean ( 38 / 51 ) 415.237179487180 4.41284305704026 94.0974274679243 Trimmed Mean ( 39 / 51 ) 415.237179487180 4.31390296809025 96.2555677674418 Trimmed Mean ( 40 / 51 ) 415.742105263158 4.20756046254584 98.8083496277574 Trimmed Mean ( 41 / 51 ) 416.772222222222 4.10056068068966 101.637862398887 Trimmed Mean ( 42 / 51 ) 417.307142857143 3.97726318578235 104.923190486590 Trimmed Mean ( 43 / 51 ) 417.823529411765 3.84727941550867 108.602335387309 Trimmed Mean ( 44 / 51 ) 418.315151515152 3.71182275571046 112.698040570928 Trimmed Mean ( 45 / 51 ) 418.8203125 3.59790309003387 116.406779732374 Trimmed Mean ( 46 / 51 ) 419.246774193548 3.49727378734321 119.878167877740 Trimmed Mean ( 47 / 51 ) 419.658333333333 3.40331036225696 123.308863625071 Trimmed Mean ( 48 / 51 ) 420.084482758621 3.32147843681027 126.475149771570 Trimmed Mean ( 49 / 51 ) 420.516071428571 3.23293198194877 130.072662764494 Trimmed Mean ( 50 / 51 ) 420.866666666667 3.15857622570438 133.245689384245 Trimmed Mean ( 51 / 51 ) 421.251923076923 3.06874784746431 137.271598715744 Median 427.45 Midrange 406.4 Midmean - Weighted Average at Xnp 414.562337662338 Midmean - Weighted Average at X(n+1)p 415.237179487180 Midmean - Empirical Distribution Function 415.237179487180 Midmean - Empirical Distribution Function - Averaging 415.237179487180 Midmean - Empirical Distribution Function - Interpolation 415.742105263158 Midmean - Closest Observation 415.237179487180 Midmean - True Basic - Statistics Graphics Toolkit 415.237179487180 Midmean - MS Excel (old versions) 415.237179487180 Number of observations 154

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/04/t1231078626bq6qfs4ogbbilvh/1l8ph1231078515.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/04/t1231078626bq6qfs4ogbbilvh/1l8ph1231078515.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/04/t1231078626bq6qfs4ogbbilvh/2jxpi1231078515.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/04/t1231078626bq6qfs4ogbbilvh/2jxpi1231078515.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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