Opgave 5 oef2 - Stien Philipsen

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Sat, 02 Apr 2011 10:16:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Apr/02/t1301739197fpgl5hlc5lbbo8a.htm/, Retrieved Sat, 02 Apr 2011 12:13:20 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

112 118 129 99 116 168 118 129 205 147 150 267 126 129 124 97 102 127 222 214 118 141 154 226 89 77 82 97 127 121 117 117 106 112 134 169 75 108 115 85 101 108 109 124 105 95 135 164 88 85 112 87 91 87 87 142 95 108 139 159 61 82 124 93 108 75 87 103 90 108 123 129 57 65 67 71 76 67 110 118 99 85 107 141 58 65 70 86 93 74 87 73 101 100 96 157 63 115 70 66 67 83 79 77 102 116 100 135 71 60 89 74 73 91 86 74 87 87 109 137 43 69 73 77 69 76 78 70 83 65 110 132 54 55 66 65 60 65 96 55 71 63 74 106 34 47 56 53 53 55 67 52 46 51 58 91 33 40 46 45 41 55 57 54 46 52 48 77 77 35 42 48 44 45 0 0 46 51 63 84 30 39 45 52 28 40 62

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 'George Udny Yule' @ 216.218.223.82 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 89.6791443850267 2.9130971544779 30.7848106772462 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 98.0855241790344 Winsorized Mean ( 1 / 62 ) 89.4598930481283 2.84888837045699 31.4016842414145 Winsorized Mean ( 2 / 62 ) 89.716577540107 2.79480519471538 32.101191778858 Winsorized Mean ( 3 / 62 ) 89.620320855615 2.75914922680425 32.4811430947563 Winsorized Mean ( 4 / 62 ) 89.4919786096257 2.70639185934395 33.0668961705047 Winsorized Mean ( 5 / 62 ) 88.5561497326203 2.50888914989939 35.2969559201815 Winsorized Mean ( 6 / 62 ) 88.5561497326203 2.49965876820092 35.4272954609549 Winsorized Mean ( 7 / 62 ) 88.5561497326203 2.45776327639688 36.0311957555347 Winsorized Mean ( 8 / 62 ) 88.3850267379679 2.41869859045162 36.5423898152868 Winsorized Mean ( 9 / 62 ) 88.288770053476 2.4037471371792 36.7296412704552 Winsorized Mean ( 10 / 62 ) 88.1818181818182 2.37371404697341 37.1493012371284 Winsorized Mean ( 11 / 62 ) 88.0053475935829 2.33310123121145 37.7203296694874 Winsorized Mean ( 12 / 62 ) 87.8770053475936 2.29927106625474 38.2195064502488 Winsorized Mean ( 13 / 62 ) 87.5989304812834 2.24531226532652 39.0141415223349 Winsorized Mean ( 14 / 62 ) 87.5989304812834 2.22785565113952 39.3198412278091 Winsorized Mean ( 15 / 62 ) 87.5989304812834 2.22785565113952 39.3198412278091 Winsorized Mean ( 16 / 62 ) 87.427807486631 2.20607427598114 39.6304913386237 Winsorized Mean ( 17 / 62 ) 87.336898395722 2.17413517585651 40.1708685667695 Winsorized Mean ( 18 / 62 ) 87.144385026738 2.15079755553613 40.5172419888749 Winsorized Mean ( 19 / 62 ) 87.144385026738 2.15079755553613 40.5172419888749 Winsorized Mean ( 20 / 62 ) 87.0374331550802 2.13808529208497 40.7081202407061 Winsorized Mean ( 21 / 62 ) 86.9251336898396 2.1003308229009 41.3864010098094 Winsorized Mean ( 22 / 62 ) 86.6898395721925 2.0483770978117 42.3212306292645 Winsorized Mean ( 23 / 62 ) 86.6898395721925 2.0483770978117 42.3212306292645 Winsorized Mean ( 24 / 62 ) 87.0748663101604 2.01024453369836 43.3155593016158 Winsorized Mean ( 25 / 62 ) 87.0748663101604 2.01024453369836 43.3155593016158 Winsorized Mean ( 26 / 62 ) 86.9358288770053 1.96606854049113 44.2181068902555 Winsorized Mean ( 27 / 62 ) 86.9358288770053 1.96606854049113 44.2181068902555 Winsorized Mean ( 28 / 62 ) 86.7860962566845 1.94977063748332 44.5109258434131 Winsorized Mean ( 29 / 62 ) 86.6310160427807 1.90180376829009 45.552026706031 Winsorized Mean ( 30 / 62 ) 86.6310160427807 1.90180376829009 45.552026706031 Winsorized Mean ( 31 / 62 ) 86.7967914438503 1.88617410478874 46.0173804865018 Winsorized Mean ( 32 / 62 ) 86.6256684491979 1.86814352555099 46.3699214029332 Winsorized Mean ( 33 / 62 ) 86.4491978609626 1.81509926516686 47.6278072059136 Winsorized Mean ( 34 / 62 ) 85.903743315508 1.760437281503 48.7968212319195 Winsorized Mean ( 35 / 62 ) 85.903743315508 1.760437281503 48.7968212319195 Winsorized Mean ( 36 / 62 ) 85.903743315508 1.760437281503 48.7968212319195 Winsorized Mean ( 37 / 62 ) 86.1016042780749 1.74190810547814 49.4294756464439 Winsorized Mean ( 38 / 62 ) 86.1016042780749 1.70321945603088 50.5522667517684 Winsorized Mean ( 39 / 62 ) 86.1016042780749 1.70321945603088 50.5522667517684 Winsorized Mean ( 40 / 62 ) 86.1016042780749 1.66290533122321 51.7778148048512 Winsorized Mean ( 41 / 62 ) 86.1016042780749 1.66290533122321 51.7778148048512 Winsorized Mean ( 42 / 62 ) 86.3262032085562 1.60096268696359 53.9214335921118 Winsorized Mean ( 43 / 62 ) 86.3262032085562 1.60096268696359 53.9214335921118 Winsorized Mean ( 44 / 62 ) 85.855614973262 1.51337264126062 56.7313116627676 Winsorized Mean ( 45 / 62 ) 86.096256684492 1.49230163138642 57.693602200585 Winsorized Mean ( 46 / 62 ) 86.3422459893048 1.47113073563446 58.691076121163 Winsorized Mean ( 47 / 62 ) 85.8395721925134 1.42463667257126 60.2536589470113 Winsorized Mean ( 48 / 62 ) 85.8395721925134 1.42463667257126 60.2536589470113 Winsorized Mean ( 49 / 62 ) 86.1016042780749 1.35627723556139 63.4837789948128 Winsorized Mean ( 50 / 62 ) 86.1016042780749 1.35627723556139 63.4837789948128 Winsorized Mean ( 51 / 62 ) 85.8288770053476 1.33169200696255 64.4509965942608 Winsorized Mean ( 52 / 62 ) 85.8288770053476 1.33169200696255 64.4509965942608 Winsorized Mean ( 53 / 62 ) 85.8288770053476 1.33169200696255 64.4509965942608 Winsorized Mean ( 54 / 62 ) 86.1176470588235 1.30760583140065 65.8590264671567 Winsorized Mean ( 55 / 62 ) 86.1176470588235 1.30760583140065 65.8590264671567 Winsorized Mean ( 56 / 62 ) 86.1176470588235 1.25610935030456 68.5590367096171 Winsorized Mean ( 57 / 62 ) 85.812834224599 1.22902678909304 69.8217768612875 Winsorized Mean ( 58 / 62 ) 85.812834224599 1.22902678909304 69.8217768612875 Winsorized Mean ( 59 / 62 ) 85.4973262032086 1.20132501003905 71.1691885948746 Winsorized Mean ( 60 / 62 ) 85.4973262032086 1.09305875078572 78.218417941168 Winsorized Mean ( 61 / 62 ) 85.1711229946524 1.06516074926501 79.9608162931489 Winsorized Mean ( 62 / 62 ) 85.5026737967914 1.03831974408284 82.3471520059716 Trimmed Mean ( 1 / 62 ) 89.6791443850267 2.74042610032766 32.7245257131014 Trimmed Mean ( 2 / 62 ) 89.2054054054054 2.62188873390814 34.0233375473708 Trimmed Mean ( 3 / 62 ) 88.5469613259668 2.52372931655742 35.0857600872793 Trimmed Mean ( 4 / 62 ) 88.5469613259668 2.43113443494843 36.4220752472888 Trimmed Mean ( 5 / 62 ) 87.8248587570621 2.34703838818295 37.4194385567997 Trimmed Mean ( 6 / 62 ) 87.6685714285714 2.30735795384839 37.9952192863491 Trimmed Mean ( 7 / 62 ) 87.6685714285714 2.26667794542171 38.6771184700705 Trimmed Mean ( 8 / 62 ) 87.5086705202312 2.23085165689296 39.2265753080641 Trimmed Mean ( 9 / 62 ) 87.2011834319527 2.19900621592495 39.6548144341074 Trimmed Mean ( 10 / 62 ) 87.065868263473 2.16709022574074 40.1763928558687 Trimmed Mean ( 11 / 62 ) 86.939393939394 2.13707457257077 40.6814975271599 Trimmed Mean ( 12 / 62 ) 86.8282208588957 2.11022467473021 41.1464342629756 Trimmed Mean ( 13 / 62 ) 86.7267080745342 2.08555345101048 41.584505078263 Trimmed Mean ( 14 / 62 ) 86.7267080745342 2.06535095740299 41.9912692144032 Trimmed Mean ( 15 / 62 ) 86.5668789808917 2.04546306442175 42.3214090181403 Trimmed Mean ( 16 / 62 ) 86.5668789808917 2.02395281283448 42.771194284741 Trimmed Mean ( 17 / 62 ) 86.4117647058823 2.00297614416755 43.1416844167137 Trimmed Mean ( 18 / 62 ) 86.3443708609272 1.98346467795819 43.5320940274123 Trimmed Mean ( 19 / 62 ) 86.2885906040269 1.96459445009596 43.9218336383939 Trimmed Mean ( 20 / 62 ) 86.2312925170068 1.94409969540752 44.3553860538675 Trimmed Mean ( 21 / 62 ) 86.1793103448276 1.92303213257188 44.8142851516322 Trimmed Mean ( 22 / 62 ) 86.1328671328671 1.9035711063635 45.2480429257049 Trimmed Mean ( 23 / 62 ) 86.0992907801418 1.88698697784675 45.6279199543762 Trimmed Mean ( 24 / 62 ) 86.0647482014388 1.86886893916647 46.0517837274477 Trimmed Mean ( 25 / 62 ) 86.007299270073 1.8522466239292 46.4340429395003 Trimmed Mean ( 26 / 62 ) 85.9481481481481 1.83402876960864 46.8630315796455 Trimmed Mean ( 27 / 62 ) 85.8947368421053 1.81777705319347 47.2526246776002 Trimmed Mean ( 28 / 62 ) 85.8947368421053 1.79990920348716 47.7217054480815 Trimmed Mean ( 29 / 62 ) 85.7906976744186 1.78164475833123 48.1525271933415 Trimmed Mean ( 30 / 62 ) 85.748031496063 1.76549750504273 48.5687639043069 Trimmed Mean ( 31 / 62 ) 85.704 1.74766291870582 49.0392049191416 Trimmed Mean ( 32 / 62 ) 85.704 1.72920071456935 49.5627831274314 Trimmed Mean ( 33 / 62 ) 85.603305785124 1.71030257415389 50.0515564197598 Trimmed Mean ( 34 / 62 ) 85.563025210084 1.69374456127945 50.5170774661853 Trimmed Mean ( 35 / 62 ) 85.5470085470085 1.67981093269408 50.9265697002034 Trimmed Mean ( 36 / 62 ) 85.5304347826087 1.66423873829807 51.3931281698661 Trimmed Mean ( 37 / 62 ) 85.5132743362832 1.64684289022847 51.9255812704878 Trimmed Mean ( 38 / 62 ) 85.4864864864865 1.62891888019066 52.4805056446277 Trimmed Mean ( 39 / 62 ) 85.4587155963303 1.61207915537966 53.0114884936924 Trimmed Mean ( 40 / 62 ) 85.429906542056 1.59317182512934 53.6225316030307 Trimmed Mean ( 41 / 62 ) 85.4 1.57537134409474 54.2094410439296 Trimmed Mean ( 42 / 62 ) 85.368932038835 1.55531245066735 54.8886058246401 Trimmed Mean ( 43 / 62 ) 85.3267326732673 1.53803970071274 55.4775878891333 Trimmed Mean ( 44 / 62 ) 85.2828282828283 1.51845924932666 56.1640546630711 Trimmed Mean ( 45 / 62 ) 85.2577319587629 1.50409509381141 56.6837378231971 Trimmed Mean ( 46 / 62 ) 85.221052631579 1.48943114623947 57.2171817722127 Trimmed Mean ( 47 / 62 ) 85.1720430107527 1.47438972850079 57.7676589604014 Trimmed Mean ( 48 / 62 ) 85.1428571428571 1.46139340306703 58.2614215748941 Trimmed Mean ( 49 / 62 ) 85.1123595505618 1.44630117168781 58.8482960649454 Trimmed Mean ( 50 / 62 ) 85.0689655172414 1.43502164327449 59.2806149760416 Trimmed Mean ( 51 / 62 ) 85.0689655172414 1.42171794830568 59.8353320492447 Trimmed Mean ( 52 / 62 ) 84.9879518072289 1.40853929626907 60.3376505237335 Trimmed Mean ( 53 / 62 ) 84.9506172839506 1.39298299185861 60.9846766115957 Trimmed Mean ( 54 / 62 ) 84.9113924050633 1.37466644898494 61.7687239459161 Trimmed Mean ( 55 / 62 ) 84.8571428571429 1.35556803257828 62.5989554325394 Trimmed Mean ( 56 / 62 ) 84.8571428571429 1.33301798072529 63.6579131595601 Trimmed Mean ( 57 / 62 ) 84.7397260273973 1.31233861764383 64.5715403693133 Trimmed Mean ( 58 / 62 ) 84.6901408450704 1.29113858777256 65.5933775406525 Trimmed Mean ( 59 / 62 ) 84.6376811594203 1.26581571920744 66.8641413399528 Trimmed Mean ( 60 / 62 ) 84.5970149253731 1.23934804746875 68.2592876941667 Trimmed Mean ( 61 / 62 ) 84.5538461538462 1.22180327976228 69.2041407601207 Trimmed Mean ( 62 / 62 ) 84.5238095238095 1.20416097652138 70.1931146847031 Median 85 Midrange 133.5 Midmean - Weighted Average at Xnp 84.936170212766 Midmean - Weighted Average at X(n+1)p 85.7731958762887 Midmean - Empirical Distribution Function 85.7731958762887 Midmean - Empirical Distribution Function - Averaging 85.7731958762887 Midmean - Empirical Distribution Function - Interpolation 84.936170212766 Midmean - Closest Observation 84.936170212766 Midmean - True Basic - Statistics Graphics Toolkit 85.7731958762887 Midmean - MS Excel (old versions) 85.7731958762887 Number of observations 187

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/02/t1301739197fpgl5hlc5lbbo8a/1zo5i1301739364.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/02/t1301739197fpgl5hlc5lbbo8a/1zo5i1301739364.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/02/t1301739197fpgl5hlc5lbbo8a/2lcif1301739364.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/02/t1301739197fpgl5hlc5lbbo8a/2lcif1301739364.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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