opgave 5 oefening 2 rosewijnen - Kathleen Geets

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 01 Jun 2009 04:55:52 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t12438538089jsi69pemlpqd16.htm/, Retrieved Mon, 01 Jun 2009 12:56:50 +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/2009/Jun/01/t12438538089jsi69pemlpqd16.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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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 30 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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 89.427807486631 2.92977324297935 30.5237982840235 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 97.9483384511835 Winsorized Mean ( 1 / 62 ) 89.2085561497326 2.86583471855378 31.1282976551945 Winsorized Mean ( 2 / 62 ) 89.4652406417112 2.81220082233303 31.8132474506177 Winsorized Mean ( 3 / 62 ) 89.3689839572193 2.77672139514477 32.1850741358082 Winsorized Mean ( 4 / 62 ) 89.1764705882353 2.73153929065648 32.6469660873167 Winsorized Mean ( 5 / 62 ) 88.2941176470588 2.52881060716785 34.9152749505208 Winsorized Mean ( 6 / 62 ) 88.2941176470588 2.51958474194615 35.0431228516091 Winsorized Mean ( 7 / 62 ) 88.1818181818182 2.49027675140686 35.4104491125337 Winsorized Mean ( 8 / 62 ) 88.139037433155 2.4369360878006 36.1679725103923 Winsorized Mean ( 9 / 62 ) 88.0909090909091 2.41685054122017 36.448637426473 Winsorized Mean ( 10 / 62 ) 87.9304812834225 2.39264402919136 36.7503398794932 Winsorized Mean ( 11 / 62 ) 87.7540106951872 2.35212223269260 37.308439789173 Winsorized Mean ( 12 / 62 ) 87.6256684491979 2.31834600857978 37.796630927787 Winsorized Mean ( 13 / 62 ) 87.3475935828877 2.26451074730242 38.5723908296482 Winsorized Mean ( 14 / 62 ) 87.3475935828877 2.24702415297411 38.8725655072628 Winsorized Mean ( 15 / 62 ) 87.427807486631 2.23877784851249 39.0515778708105 Winsorized Mean ( 16 / 62 ) 87.2566844919786 2.21703276746222 39.3574176135697 Winsorized Mean ( 17 / 62 ) 87.0748663101604 2.19450487438828 39.678593256455 Winsorized Mean ( 18 / 62 ) 86.9786096256685 2.16136398860929 40.2424626689714 Winsorized Mean ( 19 / 62 ) 86.9786096256685 2.16136398860929 40.2424626689714 Winsorized Mean ( 20 / 62 ) 86.8716577540107 2.14866987635128 40.4304349914981 Winsorized Mean ( 21 / 62 ) 86.6470588235294 2.12252423626386 40.8226475548041 Winsorized Mean ( 22 / 62 ) 86.4117647058823 2.07065200815535 41.7316692353645 Winsorized Mean ( 23 / 62 ) 86.5347593582888 2.05816828705457 42.0445499537495 Winsorized Mean ( 24 / 62 ) 86.5347593582888 2.05816828705457 42.0445499537495 Winsorized Mean ( 25 / 62 ) 86.9358288770053 2.01878098906357 43.0635266271906 Winsorized Mean ( 26 / 62 ) 86.6577540106952 1.98803343337731 43.5896864488235 Winsorized Mean ( 27 / 62 ) 86.8021390374332 1.97422933902752 43.9676066612357 Winsorized Mean ( 28 / 62 ) 86.6524064171123 1.95794439950468 44.2568269247246 Winsorized Mean ( 29 / 62 ) 86.3422459893048 1.92487340032736 44.8560648064547 Winsorized Mean ( 30 / 62 ) 86.5026737967914 1.90961261514479 45.2985454278813 Winsorized Mean ( 31 / 62 ) 86.5026737967914 1.90961261514479 45.2985454278813 Winsorized Mean ( 32 / 62 ) 86.5026737967914 1.87558473172520 46.1203763997507 Winsorized Mean ( 33 / 62 ) 86.1497326203209 1.83913937966367 46.8424163893851 Winsorized Mean ( 34 / 62 ) 85.7860962566845 1.76755302182415 48.5338177681095 Winsorized Mean ( 35 / 62 ) 85.7860962566845 1.76755302182415 48.5338177681095 Winsorized Mean ( 36 / 62 ) 85.7860962566845 1.76755302182415 48.5338177681095 Winsorized Mean ( 37 / 62 ) 85.7860962566845 1.76755302182415 48.5338177681095 Winsorized Mean ( 38 / 62 ) 85.7860962566845 1.72880632639784 49.6215770076626 Winsorized Mean ( 39 / 62 ) 85.9946524064171 1.70963804541955 50.2999173636859 Winsorized Mean ( 40 / 62 ) 85.7807486631016 1.68892057995908 50.7902797094105 Winsorized Mean ( 41 / 62 ) 86 1.66898809061713 51.5282286814883 Winsorized Mean ( 42 / 62 ) 85.7754010695187 1.64742413843599 52.0663738428352 Winsorized Mean ( 43 / 62 ) 86.235294117647 1.60638180736558 53.6829374699345 Winsorized Mean ( 44 / 62 ) 85.5294117647059 1.53975351971687 55.5474695589156 Winsorized Mean ( 45 / 62 ) 85.7700534759358 1.51847450735735 56.484355226486 Winsorized Mean ( 46 / 62 ) 86.0160427807487 1.49707851767424 57.4559328487175 Winsorized Mean ( 47 / 62 ) 85.764705882353 1.42909432626981 60.0133275360585 Winsorized Mean ( 48 / 62 ) 85.764705882353 1.42909432626981 60.0133275360585 Winsorized Mean ( 49 / 62 ) 85.5026737967914 1.40537044501466 60.839954404975 Winsorized Mean ( 50 / 62 ) 86.0374331550802 1.36010517592725 63.2579264294204 Winsorized Mean ( 51 / 62 ) 85.764705882353 1.33551996098096 64.218213421054 Winsorized Mean ( 52 / 62 ) 85.764705882353 1.33551996098096 64.218213421054 Winsorized Mean ( 53 / 62 ) 85.764705882353 1.33551996098096 64.218213421054 Winsorized Mean ( 54 / 62 ) 85.764705882353 1.33551996098096 64.218213421054 Winsorized Mean ( 55 / 62 ) 86.0588235294118 1.31112937218753 65.6371715522082 Winsorized Mean ( 56 / 62 ) 85.75935828877 1.28434274501440 66.7729534204741 Winsorized Mean ( 57 / 62 ) 85.75935828877 1.23224753697500 69.5958853359102 Winsorized Mean ( 58 / 62 ) 85.75935828877 1.23224753697500 69.5958853359102 Winsorized Mean ( 59 / 62 ) 85.4438502673797 1.2045445211908 70.9345721675034 Winsorized Mean ( 60 / 62 ) 84.8021390374332 1.14920576364466 73.7919541653593 Winsorized Mean ( 61 / 62 ) 85.1283422459893 1.06778106210365 79.724528994995 Winsorized Mean ( 62 / 62 ) 85.1283422459893 1.06778106210365 79.724528994995 Trimmed Mean ( 1 / 62 ) 89.427807486631 2.75829356256125 32.4214248622586 Trimmed Mean ( 2 / 62 ) 88.9513513513513 2.64082866490801 33.6831209587199 Trimmed Mean ( 3 / 62 ) 88.2872928176796 2.54361018150939 34.709443081915 Trimmed Mean ( 4 / 62 ) 88.2872928176796 2.45200345459015 36.0061861464370 Trimmed Mean ( 5 / 62 ) 87.5762711864407 2.36663551754928 37.0045452867742 Trimmed Mean ( 6 / 62 ) 87.4228571428571 2.32688794454113 37.5707207336523 Trimmed Mean ( 7 / 62 ) 87.4228571428571 2.28613953111150 38.2403855727709 Trimmed Mean ( 8 / 62 ) 87.2658959537572 2.24791629274518 38.8207942775249 Trimmed Mean ( 9 / 62 ) 86.98224852071 2.21588557263334 39.2539441544092 Trimmed Mean ( 10 / 62 ) 86.8443113772455 2.18454501606145 39.7539582561766 Trimmed Mean ( 11 / 62 ) 86.7212121212121 2.15432989941313 40.2543789346452 Trimmed Mean ( 12 / 62 ) 86.6134969325153 2.12725369846011 40.716110633731 Trimmed Mean ( 13 / 62 ) 86.5155279503106 2.10233354504754 41.1521417018317 Trimmed Mean ( 14 / 62 ) 86.5155279503106 2.08184939481996 41.5570541104355 Trimmed Mean ( 15 / 62 ) 86.3630573248408 2.06166476644441 41.8899613217839 Trimmed Mean ( 16 / 62 ) 86.3630573248408 2.04069481180925 42.320417940531 Trimmed Mean ( 17 / 62 ) 86.202614379085 2.02028667831241 42.6685060612744 Trimmed Mean ( 18 / 62 ) 86.1390728476821 2.00046439939675 43.0595380121025 Trimmed Mean ( 19 / 62 ) 86.0805369127517 1.98217882697227 43.4272305512603 Trimmed Mean ( 20 / 62 ) 86.0204081632653 1.96231074023004 43.8362826028161 Trimmed Mean ( 21 / 62 ) 85.9655172413793 1.94191056613043 44.2685254103538 Trimmed Mean ( 22 / 62 ) 85.9230769230769 1.92212918634262 44.7020301931782 Trimmed Mean ( 23 / 62 ) 85.8936170212766 1.90519213059316 45.0839658856529 Trimmed Mean ( 24 / 62 ) 85.8561151079137 1.88775201463545 45.4806110348628 Trimmed Mean ( 25 / 62 ) 85.8175182481752 1.86868211316721 45.9240860944100 Trimmed Mean ( 26 / 62 ) 85.7555555555556 1.85107402127688 46.3274588535367 Trimmed Mean ( 27 / 62 ) 85.7067669172932 1.83438779370159 46.7222727994426 Trimmed Mean ( 28 / 62 ) 85.7067669172932 1.81715333195648 47.1654017358101 Trimmed Mean ( 29 / 62 ) 85.5968992248062 1.79956522593756 47.5653218850186 Trimmed Mean ( 30 / 62 ) 85.5590551181102 1.78295662485732 47.9871769875259 Trimmed Mean ( 31 / 62 ) 85.512 1.76582671856556 48.4260426580613 Trimmed Mean ( 32 / 62 ) 85.512 1.74688819820830 48.9510433968847 Trimmed Mean ( 33 / 62 ) 85.4132231404959 1.72873420712456 49.4079557103028 Trimmed Mean ( 34 / 62 ) 85.3781512605042 1.71166027161786 49.8803136791887 Trimmed Mean ( 35 / 62 ) 85.3589743589744 1.69847895487766 50.2561271741648 Trimmed Mean ( 36 / 62 ) 85.3391304347826 1.68372144923531 50.6848270380713 Trimmed Mean ( 37 / 62 ) 85.3185840707965 1.66721119097071 51.1744310096196 Trimmed Mean ( 38 / 62 ) 85.2972972972973 1.64874531476384 51.7346715308271 Trimmed Mean ( 39 / 62 ) 85.2752293577982 1.63132100337232 52.2737273544045 Trimmed Mean ( 40 / 62 ) 85.2429906542056 1.61333935990856 52.8363670858664 Trimmed Mean ( 41 / 62 ) 85.2190476190476 1.59491987422503 53.4315541465402 Trimmed Mean ( 42 / 62 ) 85.1844660194175 1.57584732693072 54.0562937561543 Trimmed Mean ( 43 / 62 ) 85.1584158415842 1.55625424080808 54.7201180941784 Trimmed Mean ( 44 / 62 ) 85.1111111111111 1.53760589490331 55.3530078111876 Trimmed Mean ( 45 / 62 ) 85.0927835051546 1.52246087941341 55.8916059228663 Trimmed Mean ( 46 / 62 ) 85.0631578947368 1.50696854818603 56.446538315036 Trimmed Mean ( 47 / 62 ) 85.0215053763441 1.49105000910283 57.0212299099893 Trimmed Mean ( 48 / 62 ) 84.989010989011 1.47891113506522 57.4672872317395 Trimmed Mean ( 49 / 62 ) 84.9550561797753 1.46476405491041 57.999140472471 Trimmed Mean ( 50 / 62 ) 84.9310344827586 1.45058432892921 58.5495326186605 Trimmed Mean ( 51 / 62 ) 84.9310344827586 1.43811771672019 59.0570809992205 Trimmed Mean ( 52 / 62 ) 84.8433734939759 1.42585194001241 59.5036350641269 Trimmed Mean ( 53 / 62 ) 84.8024691358025 1.41131010769724 60.0877643214573 Trimmed Mean ( 54 / 62 ) 84.759493670886 1.39412864319119 60.797469505303 Trimmed Mean ( 55 / 62 ) 84.7142857142857 1.37386716434135 61.6611910620186 Trimmed Mean ( 56 / 62 ) 84.7142857142857 1.35254092986755 62.6334359601094 Trimmed Mean ( 57 / 62 ) 84.6027397260274 1.33058216822680 63.5832508102626 Trimmed Mean ( 58 / 62 ) 84.5492957746479 1.31065725020803 64.5090818070307 Trimmed Mean ( 59 / 62 ) 84.4927536231884 1.28679820151653 65.6612307381308 Trimmed Mean ( 60 / 62 ) 84.4477611940299 1.26199062781026 66.916314062141 Trimmed Mean ( 61 / 62 ) 84.4307692307692 1.23975983072119 68.1025204548325 Trimmed Mean ( 62 / 62 ) 84.3968253968254 1.22349831309248 68.9799278786961 Median 85 Midrange 133.5 Midmean - Weighted Average at Xnp 84.7765957446808 Midmean - Weighted Average at X(n+1)p 85.6185567010309 Midmean - Empirical Distribution Function 85.6185567010309 Midmean - Empirical Distribution Function - Averaging 85.6185567010309 Midmean - Empirical Distribution Function - Interpolation 85.0215053763441 Midmean - Closest Observation 84.7765957446808 Midmean - True Basic - Statistics Graphics Toolkit 85.6185567010309 Midmean - MS Excel (old versions) 85.6185567010309 Number of observations 187

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438538089jsi69pemlpqd16/1psv11243853750.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438538089jsi69pemlpqd16/1psv11243853750.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438538089jsi69pemlpqd16/2i57g1243853750.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438538089jsi69pemlpqd16/2i57g1243853750.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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