paper

*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: Tue, 22 Dec 2009 13:33:49 -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/Dec/22/t1261514095f3crxh82g4kqp9w.htm/, Retrieved Tue, 22 Dec 2009 21:34:57 +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/Dec/22/t1261514095f3crxh82g4kqp9w.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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-0.0554261742134966 -0.0103723682328288 -0.09774824332988 0.0682838841648196 -0.0598771056498073 -0.0115369164551084 0.0737440480162446 0.0652896783949673 0.0212699541543462 -0.00207374705171083 0.110078189837195 -0.0146254989919655 0.114698886599707 0.122494579401249 -0.093917668042089 0.317437918041701 0.0233064257137833 0.0907672748631918 0.137726863879236 0.0413595313391135 0.0478723668655492 0.0876104346350919 0.0384005884923821 0.0522380787977494 0.0585774158784047 -0.0318052895741221 -0.181701157975095 0.312417016566173 -0.161245884995846 -0.0498906070653545 0.0164895011305049 -0.106532442482812 -0.0111252289051036 -0.0112051724184589 -0.0621266808603795 -0.0194747117256815 -0.005215993087672 0.00868532947685446 0.0189643082939415 0.132126779126779 0.114222267764316 -0.0512424762283885 0.111763287181735 -0.0727789247238114 0.099056920984902 -0.0133229962434981 -0.0540901157284234 0.0131003534096119 -0.105083618822970 -0.0532746009105622 -0.1526034260688 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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.58441362933677e-18 0.0118782279568637 1.33388047029459e-16 Geometric Mean NaN Harmonic Mean -0.136917376752570 Quadratic Mean 0.157134185949981 Winsorized Mean ( 1 / 58 ) -0.000492768967897822 0.0116815888901826 -0.042183385542009 Winsorized Mean ( 2 / 58 ) -0.00235172656640445 0.0112770174088465 -0.208541539056203 Winsorized Mean ( 3 / 58 ) -0.00260453124803225 0.0111662771264589 -0.233249741031478 Winsorized Mean ( 4 / 58 ) -0.00201151499163493 0.0110269289260503 -0.182418423581464 Winsorized Mean ( 5 / 58 ) -0.0023025477869701 0.0109783102564924 -0.209736082618762 Winsorized Mean ( 6 / 58 ) -0.00235707717565503 0.0109166293372145 -0.215916204795910 Winsorized Mean ( 7 / 58 ) -0.00278277041202036 0.0108127251663837 -0.257360690223762 Winsorized Mean ( 8 / 58 ) -0.00311800823060777 0.0107047211851493 -0.291274118837713 Winsorized Mean ( 9 / 58 ) -0.0036404341272724 0.0103165815752762 -0.352872131210278 Winsorized Mean ( 10 / 58 ) -0.00252887694416614 0.0101395829626164 -0.249406405913325 Winsorized Mean ( 11 / 58 ) -0.00219787933816002 0.00997204803597766 -0.220404006301454 Winsorized Mean ( 12 / 58 ) -0.00260450710597991 0.00974914481531597 -0.267152366214543 Winsorized Mean ( 13 / 58 ) -0.00155761609114517 0.00959958859395594 -0.162258629721473 Winsorized Mean ( 14 / 58 ) -0.00176329804570185 0.00951079180601877 -0.185399710314968 Winsorized Mean ( 15 / 58 ) -0.00159080307504903 0.00947129295773462 -0.167960497278243 Winsorized Mean ( 16 / 58 ) -0.00419522586016167 0.00904031317550016 -0.464057580608049 Winsorized Mean ( 17 / 58 ) -0.00468363306394894 0.0089332237540746 -0.52429371444017 Winsorized Mean ( 18 / 58 ) -0.00497871724974751 0.00877456069228574 -0.567403591398554 Winsorized Mean ( 19 / 58 ) -0.00330668010117033 0.00819960221786449 -0.40327323366566 Winsorized Mean ( 20 / 58 ) -0.00349044931646601 0.00815789809051926 -0.427861353223137 Winsorized Mean ( 21 / 58 ) -0.00315520091935977 0.00801952961169704 -0.39343964947242 Winsorized Mean ( 22 / 58 ) -0.00192528463863552 0.00775349880190027 -0.248311721949794 Winsorized Mean ( 23 / 58 ) -0.00250229530696349 0.00767856330875653 -0.325880663653564 Winsorized Mean ( 24 / 58 ) -0.00200354660981398 0.00760187707230681 -0.263559459164735 Winsorized Mean ( 25 / 58 ) -0.00157566605706142 0.00752831979736594 -0.209298502118989 Winsorized Mean ( 26 / 58 ) -0.000821553917995276 0.00743640899402589 -0.110477236883458 Winsorized Mean ( 27 / 58 ) -0.00109200409527677 0.00726317840580074 -0.150347965348701 Winsorized Mean ( 28 / 58 ) -0.000220137207973718 0.00708741486634932 -0.0310602966137792 Winsorized Mean ( 29 / 58 ) -0.000452386528133094 0.00703724212578674 -0.0642846331058304 Winsorized Mean ( 30 / 58 ) -0.000260197284977978 0.00697725059319249 -0.0372922373222262 Winsorized Mean ( 31 / 58 ) -0.00039178223015898 0.00695956108979055 -0.0562941003181525 Winsorized Mean ( 32 / 58 ) -0.000598823047283892 0.00684840946013547 -0.0874397260808712 Winsorized Mean ( 33 / 58 ) -0.000329687087590579 0.00680142296188912 -0.0484732517648053 Winsorized Mean ( 34 / 58 ) 0.00048284938957598 0.00668630100188276 0.0722147252180267 Winsorized Mean ( 35 / 58 ) 0.000876374705069047 0.00660077421108678 0.132768471855479 Winsorized Mean ( 36 / 58 ) 0.000877424129288666 0.00657581843150326 0.133431927664718 Winsorized Mean ( 37 / 58 ) 0.00309123941593829 0.00627982390735815 0.492249378571944 Winsorized Mean ( 38 / 58 ) 0.0033735892422635 0.00624573384647921 0.540142972016848 Winsorized Mean ( 39 / 58 ) 0.00446304575566331 0.00606367582918717 0.736029741923322 Winsorized Mean ( 40 / 58 ) 0.00429124608930011 0.00603625486931262 0.71091201120684 Winsorized Mean ( 41 / 58 ) 0.00249623851208034 0.0058439748942757 0.427147370965857 Winsorized Mean ( 42 / 58 ) 0.00298775969620484 0.0057150098652315 0.522791695318239 Winsorized Mean ( 43 / 58 ) 0.002659081765209 0.00544437073301773 0.488409385694996 Winsorized Mean ( 44 / 58 ) 0.00489564494599242 0.00512677707703406 0.954916680095763 Winsorized Mean ( 45 / 58 ) 0.00535981943153301 0.00504878164738834 1.06160650348299 Winsorized Mean ( 46 / 58 ) 0.00589475765027746 0.00499558471395473 1.17999353185043 Winsorized Mean ( 47 / 58 ) 0.00460123739906978 0.00481154563865419 0.956290918682166 Winsorized Mean ( 48 / 58 ) 0.00443179450974795 0.00476001436842184 0.931046456319269 Winsorized Mean ( 49 / 58 ) 0.00456452237537082 0.00457416809321462 0.997891262925359 Winsorized Mean ( 50 / 58 ) 0.00469896655597496 0.004416417528862 1.06397697347827 Winsorized Mean ( 51 / 58 ) 0.00349375083057106 0.00424341761657745 0.823334195748793 Winsorized Mean ( 52 / 58 ) 0.00327374634714739 0.00410810147963519 0.796900067677517 Winsorized Mean ( 53 / 58 ) 0.00371681564655513 0.00405384658221663 0.916861447806145 Winsorized Mean ( 54 / 58 ) 0.0035944807057487 0.00390111977505637 0.921397166201277 Winsorized Mean ( 55 / 58 ) 0.00364223720412324 0.00387787335246237 0.939235728730152 Winsorized Mean ( 56 / 58 ) 0.00378027573216301 0.00384453217619363 0.983286277475187 Winsorized Mean ( 57 / 58 ) 0.00321833097117743 0.00375173181936286 0.857825432662184 Winsorized Mean ( 58 / 58 ) 0.0038768403122443 0.00369342063260897 1.04966119429126 Trimmed Mean ( 1 / 58 ) -0.00134044147626347 0.0112530545129900 -0.11911801144446 Trimmed Mean ( 2 / 58 ) -0.00220782729877716 0.0107840621836909 -0.204730579365179 Trimmed Mean ( 3 / 58 ) -0.00213333826612303 0.0105079963335156 -0.203020461600151 Trimmed Mean ( 4 / 58 ) -0.00196879468513886 0.0102534541208445 -0.192012824355106 Trimmed Mean ( 5 / 58 ) -0.00195747123040496 0.0100214413003936 -0.195328313735479 Trimmed Mean ( 6 / 58 ) -0.00188340601826414 0.0097830750714122 -0.192516770495585 Trimmed Mean ( 7 / 58 ) -0.00179763840128802 0.00953873131098615 -0.188456760409804 Trimmed Mean ( 8 / 58 ) -0.00164283194245865 0.00929448745159831 -0.176753366015481 Trimmed Mean ( 9 / 58 ) -0.00143742764917206 0.00904890111513453 -0.15885107273058 Trimmed Mean ( 10 / 58 ) -0.00116126729294296 0.0088469349194102 -0.131262104166171 Trimmed Mean ( 11 / 58 ) -0.00100496904708888 0.00865519042034004 -0.116111720052648 Trimmed Mean ( 12 / 58 ) -0.000879399542765603 0.00847125277793704 -0.103809857386850 Trimmed Mean ( 13 / 58 ) -0.000710722358806872 0.00830175426238896 -0.0856111053571887 Trimmed Mean ( 14 / 58 ) -0.000633251830276965 0.00813702432827624 -0.077823514435911 Trimmed Mean ( 15 / 58 ) -0.000633251830276965 0.0079688916438928 -0.0794654838558731 Trimmed Mean ( 16 / 58 ) -0.000449997319372373 0.00779053460747131 -0.0577620589658654 Trimmed Mean ( 17 / 58 ) -0.000159873981705597 0.00764562755537025 -0.0209105113409954 Trimmed Mean ( 18 / 58 ) 0.000174656101687188 0.0074993474948215 0.0232895064280983 Trimmed Mean ( 19 / 58 ) 0.000539790445428133 0.00735670960892971 0.0733738959565462 Trimmed Mean ( 20 / 58 ) 0.000801779151388399 0.00726083064151684 0.110425265506661 Trimmed Mean ( 21 / 58 ) 0.00108365684181466 0.00716004834385712 0.151347699033955 Trimmed Mean ( 22 / 58 ) 0.00135279066792097 0.00706325384991227 0.191525137941598 Trimmed Mean ( 23 / 58 ) 0.00155451837909368 0.00698259475431513 0.222627609619334 Trimmed Mean ( 24 / 58 ) 0.0017970452842384 0.00690071949408953 0.260414190980748 Trimmed Mean ( 25 / 58 ) 0.00201824375426261 0.00681776435692203 0.296027209009285 Trimmed Mean ( 26 / 58 ) 0.00222228508548617 0.00673314579957193 0.330051531874960 Trimmed Mean ( 27 / 58 ) 0.00239117400875626 0.00664826519147151 0.359668866973552 Trimmed Mean ( 28 / 58 ) 0.00258038368354324 0.0065698992610576 0.392758485482145 Trimmed Mean ( 29 / 58 ) 0.002729563973164 0.00649877006520883 0.420012394003094 Trimmed Mean ( 30 / 58 ) 0.002729563973164 0.00642409148055392 0.42489494139779 Trimmed Mean ( 31 / 58 ) 0.00305846560047428 0.00634634757214904 0.481925322510914 Trimmed Mean ( 32 / 58 ) 0.00323336295594417 0.00626128157911974 0.516405933048413 Trimmed Mean ( 33 / 58 ) 0.00342497225610558 0.00617633307053067 0.554531664175828 Trimmed Mean ( 34 / 58 ) 0.00361038753233748 0.00608573100417841 0.593254537517124 Trimmed Mean ( 35 / 58 ) 0.0037631196946921 0.00599509610171318 0.627699644984296 Trimmed Mean ( 36 / 58 ) 0.00390269857331123 0.0059014567073986 0.661311057051127 Trimmed Mean ( 37 / 58 ) 0.00404770083424478 0.00579857993303747 0.698050364225034 Trimmed Mean ( 38 / 58 ) 0.00409319737738584 0.00571122374104653 0.716693577939883 Trimmed Mean ( 39 / 58 ) 0.00412720678441633 0.00561583393523065 0.734923224585489 Trimmed Mean ( 40 / 58 ) 0.00411141948234916 0.00552551367417391 0.744079143549281 Trimmed Mean ( 41 / 58 ) 0.00410300206670465 0.00542571608195936 0.756213927291041 Trimmed Mean ( 42 / 58 ) 0.00417797300668712 0.0053313522582747 0.78366103087684 Trimmed Mean ( 43 / 58 ) 0.00423339034601116 0.00523681226068318 0.808390703213579 Trimmed Mean ( 44 / 58 ) 0.00430661400093219 0.00515606046973027 0.835252810981382 Trimmed Mean ( 45 / 58 ) 0.00427921721278986 0.00509499487232445 0.839886461129564 Trimmed Mean ( 46 / 58 ) 0.00422890345868965 0.00503182041743 0.840432111615294 Trimmed Mean ( 47 / 58 ) 0.00415117537339817 0.0049636195002819 0.8363202242159 Trimmed Mean ( 48 / 58 ) 0.00413010864028163 0.0049033713926643 0.842299778976662 Trimmed Mean ( 49 / 58 ) 0.00413010864028163 0.00483801351624417 0.853678607224705 Trimmed Mean ( 50 / 58 ) 0.00409472574730512 0.0047810898362891 0.856441917536376 Trimmed Mean ( 51 / 58 ) 0.00406598348181163 0.00473051863720036 0.859521712870363 Trimmed Mean ( 52 / 58 ) 0.00409341075485584 0.00468865229728792 0.873046345796129 Trimmed Mean ( 53 / 58 ) 0.00413304288006372 0.00465234706219243 0.888378021848614 Trimmed Mean ( 54 / 58 ) 0.00415336918225504 0.00461331538775861 0.900300290172219 Trimmed Mean ( 55 / 58 ) 0.00418096861319363 0.00458227374796528 0.912422269632003 Trimmed Mean ( 56 / 58 ) 0.00420790518364715 0.00454512516548028 0.925806227649289 Trimmed Mean ( 57 / 58 ) 0.00422958225261639 0.00450193157145802 0.939503896378988 Trimmed Mean ( 58 / 58 ) 0.0042816232542343 0.00445907300666867 0.96020478871528 Median -0.0030155289658953 Midrange 0.116618408434922 Midmean - Weighted Average at Xnp 0.00326109950873947 Midmean - Weighted Average at X(n+1)p 0.00430661400093219 Midmean - Empirical Distribution Function 0.00326109950873947 Midmean - Empirical Distribution Function - Averaging 0.00430661400093219 Midmean - Empirical Distribution Function - Interpolation 0.00430661400093219 Midmean - Closest Observation 0.00326109950873947 Midmean - True Basic - Statistics Graphics Toolkit 0.00430661400093219 Midmean - MS Excel (old versions) 0.00423339034601116 Number of observations 176

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/22/t1261514095f3crxh82g4kqp9w/10erz1261514026.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/22/t1261514095f3crxh82g4kqp9w/10erz1261514026.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/22/t1261514095f3crxh82g4kqp9w/2zu4o1261514026.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/22/t1261514095f3crxh82g4kqp9w/2zu4o1261514026.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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