Verbetering seatbelt case Q2

*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: Sat, 29 Nov 2008 04:05:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/29/t1227956784m3ny5uhbqwq44ex.htm/, Retrieved Sat, 29 Nov 2008 11:06:24 +0000

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/2008/Nov/29/t1227956784m3ny5uhbqwq44ex.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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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 4 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 9553976186712.7 29637752726055.3 0.322358320316019 Geometric Mean NaN Harmonic Mean -40.6195863705367 Quadratic Mean 409713300139596 Winsorized Mean ( 1 / 64 ) 9660989286401.01 29615802527381 0.326210619397163 Winsorized Mean ( 2 / 64 ) 9751183850666.99 29457748962279.5 0.331022708597093 Winsorized Mean ( 3 / 64 ) 10133697991392.2 29330852774278.1 0.345496193696728 Winsorized Mean ( 4 / 64 ) 10637828318105.7 29156631773913.7 0.364851070610403 Winsorized Mean ( 5 / 64 ) 10639465385802.0 29113546452821.8 0.365447246457697 Winsorized Mean ( 6 / 64 ) 9937685436017.05 28962456352845.0 0.343123018122076 Winsorized Mean ( 7 / 64 ) 8999288735873.62 28797779774439.4 0.312499394271404 Winsorized Mean ( 8 / 64 ) 9350244586762.99 28715629572609.5 0.325615169366920 Winsorized Mean ( 9 / 64 ) 9041812183796.97 28613369798426.8 0.315999557112428 Winsorized Mean ( 10 / 64 ) 7853480474747.96 28320064497573.1 0.277311532091354 Winsorized Mean ( 11 / 64 ) 7366819823445.19 27758739486165.1 0.265387404464702 Winsorized Mean ( 12 / 64 ) 5836763260543.75 27139765163413.1 0.215063145366204 Winsorized Mean ( 13 / 64 ) 6690288078686.63 26932334056955.0 0.24841100160641 Winsorized Mean ( 14 / 64 ) 3832663512091.91 25881636180673.1 0.148084282049909 Winsorized Mean ( 15 / 64 ) -701136478941.292 25278514836938.4 -0.0277364585484568 Winsorized Mean ( 16 / 64 ) -448416488506.13 24926511912375.8 -0.0179895402165553 Winsorized Mean ( 17 / 64 ) 1081318314395.11 24631250579453.3 0.0439002603991662 Winsorized Mean ( 18 / 64 ) 245258992959.427 24508182870699.0 0.0100072287796028 Winsorized Mean ( 19 / 64 ) 2632078621725.32 24068017701134.9 0.109360008556135 Winsorized Mean ( 20 / 64 ) 5479509615139.07 23537013855804.0 0.232803942280378 Winsorized Mean ( 21 / 64 ) 7343945366576.07 22603295012584.7 0.324905964483817 Winsorized Mean ( 22 / 64 ) 10048308442038.4 21972045930979.0 0.457322384706608 Winsorized Mean ( 23 / 64 ) 14412884302870.8 20619451960953.9 0.698994538272102 Winsorized Mean ( 24 / 64 ) 13871654974175.2 20536514772569.9 0.675462955997928 Winsorized Mean ( 25 / 64 ) 14878414736402.0 20300972273070.5 0.73289173229099 Winsorized Mean ( 26 / 64 ) 24321555589901.4 19080390917558.7 1.27468853730452 Winsorized Mean ( 27 / 64 ) 25269418449645.7 18922364503253.2 1.33542604811895 Winsorized Mean ( 28 / 64 ) 26500201044696 18783654064015 1.41081181299351 Winsorized Mean ( 29 / 64 ) 25073182776199.8 18570305073848.2 1.35017613746741 Winsorized Mean ( 30 / 64 ) 18375342260219.6 17768690229724.2 1.03414162904819 Winsorized Mean ( 31 / 64 ) 20165186767304.1 17449812210565.1 1.15561053173368 Winsorized Mean ( 32 / 64 ) 19094491399939.1 17178002624658.4 1.11156645025363 Winsorized Mean ( 33 / 64 ) 18303484992669.3 16975313281963.2 1.07824136666257 Winsorized Mean ( 34 / 64 ) 16732176290265.5 16566496432389.4 1.01000089901642 Winsorized Mean ( 35 / 64 ) 16559121028366.2 16398552677091.2 1.00979161725043 Winsorized Mean ( 36 / 64 ) 17523257447132.2 15974440824774.8 1.09695592098318 Winsorized Mean ( 37 / 64 ) 16958064372451.4 15260619660863.3 1.11123039229798 Winsorized Mean ( 38 / 64 ) 16724440358821.7 15135147725957.7 1.10500674731692 Winsorized Mean ( 39 / 64 ) 18330855142223.1 14938503513011.3 1.22708778200287 Winsorized Mean ( 40 / 64 ) 19820243600588.1 14744118379227.5 1.34428136635908 Winsorized Mean ( 41 / 64 ) 21305729201079.3 14574495356938.8 1.46185021705989 Winsorized Mean ( 42 / 64 ) 22056878098139.9 14474647638689.6 1.52382832720457 Winsorized Mean ( 43 / 64 ) 23275589064149.2 14358659130077.6 1.62101411094808 Winsorized Mean ( 44 / 64 ) 24391735035137.1 14116478362046.3 1.72789093777929 Winsorized Mean ( 45 / 64 ) 24756467081076.1 13919018028066.0 1.77860730054072 Winsorized Mean ( 46 / 64 ) 25124142604229.8 13814671348888.1 1.81865655502922 Winsorized Mean ( 47 / 64 ) 25612950739315.4 13562921017105.3 1.88845387413322 Winsorized Mean ( 48 / 64 ) 25769388338419.6 13489848092437.1 1.91028009817745 Winsorized Mean ( 49 / 64 ) 25695285805472.9 13330172022495.8 1.92760346694026 Winsorized Mean ( 50 / 64 ) 25283494454223.9 13293512795795.2 1.90194231145745 Winsorized Mean ( 51 / 64 ) 23305814987359 12972813871209.1 1.7965119378674 Winsorized Mean ( 52 / 64 ) 23963900567949.5 12635406341744.6 1.89656746445724 Winsorized Mean ( 53 / 64 ) 24370362698492.8 12602447326868.2 1.93378016716924 Winsorized Mean ( 54 / 64 ) 21687483948869.2 12264575507510.1 1.76830286018371 Winsorized Mean ( 55 / 64 ) 19826856714986.0 12064511367024.1 1.64340321060815 Winsorized Mean ( 56 / 64 ) 17879091661436.4 11883349016284.5 1.50454990734814 Winsorized Mean ( 57 / 64 ) 16311570140632.5 11740933061276.1 1.38929078766586 Winsorized Mean ( 58 / 64 ) 15877975456569.7 11702326529802.7 1.35682211704935 Winsorized Mean ( 59 / 64 ) 17018508328495.4 11508152405015.7 1.47882194548258 Winsorized Mean ( 60 / 64 ) 17328588123009.8 11478629424857.4 1.50963912864754 Winsorized Mean ( 61 / 64 ) 16769664181863.4 11395819966405.0 1.47156275119304 Winsorized Mean ( 62 / 64 ) 12350100446148.9 11023802341972.2 1.1203122174213 Winsorized Mean ( 63 / 64 ) 12403238336213.8 10991822568747.1 1.12840598168677 Winsorized Mean ( 64 / 64 ) 14981550148741.8 10764198247414.7 1.39179433566639 Trimmed Mean ( 1 / 64 ) 9809617841800.18 29075467868090.9 0.337384694420200 Trimmed Mean ( 2 / 64 ) 9961408706888.69 28497539539964.7 0.349553290132956 Trimmed Mean ( 3 / 64 ) 10069911858487.0 27967349822736.7 0.360059566684449 Trimmed Mean ( 4 / 64 ) 10047725377476.5 27448837403806.9 0.366052857892005 Trimmed Mean ( 5 / 64 ) 9892093832695.14 26945806559434.6 0.367110697201662 Trimmed Mean ( 6 / 64 ) 9732654568032.35 26417270125539.4 0.368420147947957 Trimmed Mean ( 7 / 64 ) 9695795086147.46 25882580984611.2 0.374606964116608 Trimmed Mean ( 8 / 64 ) 9804341530345.99 25339652781504.2 0.386916964288569 Trimmed Mean ( 9 / 64 ) 9866975591529.85 24769739963443.6 0.39834796837157 Trimmed Mean ( 10 / 64 ) 9969321440550.98 24171868733497.0 0.412434865937182 Trimmed Mean ( 11 / 64 ) 10208287008453.4 23570895231595.7 0.433088642079648 Trimmed Mean ( 12 / 64 ) 10503504378324.4 23001909694823.6 0.456636188806887 Trimmed Mean ( 13 / 64 ) 10953310751122.6 22470430658296.1 0.487454420330774 Trimmed Mean ( 14 / 64 ) 11337222736632.7 21922372513023.2 0.51715309234426 Trimmed Mean ( 15 / 64 ) 11972529337652.1 21457967728489.8 0.557952621102888 Trimmed Mean ( 16 / 64 ) 12986422602979.5 21024726426788.3 0.617673797002805 Trimmed Mean ( 17 / 64 ) 14006790128915.2 20595324556871.8 0.68009562511321 Trimmed Mean ( 18 / 64 ) 14942570893767.3 20162749944861.5 0.741097862872393 Trimmed Mean ( 19 / 64 ) 15960566523260.5 19706246246850.2 0.809924240432729 Trimmed Mean ( 20 / 64 ) 16846670982088.3 19257284015094.6 0.87482071557356 Trimmed Mean ( 21 / 64 ) 17574169309573.1 18824575661667.7 0.93357585453356 Trimmed Mean ( 22 / 64 ) 18206152255858.6 18447744461331.6 0.9869039705109 Trimmed Mean ( 23 / 64 ) 18693794227294.9 18099578162529.2 1.03283038198072 Trimmed Mean ( 24 / 64 ) 18941962918565.9 17846708975090.7 1.06137007921202 Trimmed Mean ( 25 / 64 ) 19227614070362.5 17579086020185.3 1.09377780211578 Trimmed Mean ( 26 / 64 ) 19466198719539.8 17308751578734.2 1.12464487291251 Trimmed Mean ( 27 / 64 ) 19206380291694.4 17119058585948.8 1.12192970164018 Trimmed Mean ( 28 / 64 ) 18889358688664.2 16924874762362.1 1.11607081020598 Trimmed Mean ( 29 / 64 ) 18499891275988.8 16723807776839 1.10620090369668 Trimmed Mean ( 30 / 64 ) 18170196404504.9 16521549194389.4 1.09978768883703 Trimmed Mean ( 31 / 64 ) 18160096916223.5 16365391474673.5 1.10966468136906 Trimmed Mean ( 32 / 64 ) 18063076439558.4 16218725429973.5 1.1137173828824 Trimmed Mean ( 33 / 64 ) 18013961441445 16078277035563.2 1.12039128331974 Trimmed Mean ( 34 / 64 ) 18000376758689.6 15938805157436.4 1.12934292005516 Trimmed Mean ( 35 / 64 ) 18059078419716.0 15815965869825.5 1.14182583399285 Trimmed Mean ( 36 / 64 ) 18127647900463.4 15692174843542.1 1.15520302833763 Trimmed Mean ( 37 / 64 ) 18154964983099.8 15586691614524.1 1.16477347676415 Trimmed Mean ( 38 / 64 ) 18208507601283.5 15521710825779.9 1.17309926757823 Trimmed Mean ( 39 / 64 ) 18274283434744.5 15456158690562.2 1.18233021545664 Trimmed Mean ( 40 / 64 ) 18271796766283.9 15395075211085.5 1.18685985717867 Trimmed Mean ( 41 / 64 ) 18204228177150.6 15338373095511.0 1.18684218096626 Trimmed Mean ( 42 / 64 ) 18069745747928.7 15284303521493.2 1.18224201204317 Trimmed Mean ( 43 / 64 ) 17897793948458.7 15227710944834.2 1.17534368844388 Trimmed Mean ( 44 / 64 ) 17666904712722.4 15169304485966.7 1.16464830204090 Trimmed Mean ( 45 / 64 ) 17379211436897.2 15118258753374.6 1.14955113021980 Trimmed Mean ( 46 / 64 ) 17064448529412.3 15071687254907.5 1.13221885783597 Trimmed Mean ( 47 / 64 ) 16721178506704.9 15022607666757.8 1.11306764295693 Trimmed Mean ( 48 / 64 ) 16342805220210.8 14982006533097 1.09082886755573 Trimmed Mean ( 49 / 64 ) 15941674023691.3 14936482641223.8 1.06729773043710 Trimmed Mean ( 50 / 64 ) 15526258437599.4 14892326979927.2 1.04256765638618 Trimmed Mean ( 51 / 64 ) 15109949700890.1 14839533411362.4 1.01822269488073 Trimmed Mean ( 52 / 64 ) 14759324447993.0 14801037726890.2 0.997181732817194 Trimmed Mean ( 53 / 64 ) 14364136922270.4 14778531731215.7 0.97195967661185 Trimmed Mean ( 54 / 64 ) 13932601578821.5 14747798460860.2 0.944724164477688 Trimmed Mean ( 55 / 64 ) 13596346516705.6 14734933275435.5 0.922728746886961 Trimmed Mean ( 56 / 64 ) 13324469708053.3 14729249327122.4 0.90462652998328 Trimmed Mean ( 57 / 64 ) 13124266545267.3 14729575761462.2 0.89101456537567 Trimmed Mean ( 58 / 64 ) 12983001011954.7 14732713289287.4 0.881236250039223 Trimmed Mean ( 59 / 64 ) 12853496097377.3 14728925390274.2 0.872670324330972 Trimmed Mean ( 60 / 64 ) 12665246956987.8 14731575702231.4 0.859734709510359 Trimmed Mean ( 61 / 64 ) 12452065646541.1 14725149208543.9 0.84563256169357 Trimmed Mean ( 62 / 64 ) 12252215376825.1 14713567250182.1 0.832715490981525 Trimmed Mean ( 63 / 64 ) 12247622528997.6 14726353739188.4 0.831680587463093 Trimmed Mean ( 64 / 64 ) 12240212252463.5 14729636531787.6 0.830992144717773 Median 31923419683984 Midrange -14731981046597.5 Midmean - Weighted Average at Xnp 14257146597969.4 Midmean - Weighted Average at X(n+1)p 16342805220210.8 Midmean - Empirical Distribution Function 14257146597969.4 Midmean - Empirical Distribution Function - Averaging 16342805220210.8 Midmean - Empirical Distribution Function - Interpolation 16342805220210.8 Midmean - Closest Observation 14257146597969.4 Midmean - True Basic - Statistics Graphics Toolkit 16342805220210.8 Midmean - MS Excel (old versions) 16721178506704.9 Number of observations 192

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227956784m3ny5uhbqwq44ex/1ykug1227956715.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227956784m3ny5uhbqwq44ex/1ykug1227956715.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227956784m3ny5uhbqwq44ex/2gj731227956715.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/29/t1227956784m3ny5uhbqwq44ex/2gj731227956715.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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Software written by Ed van Stee & Patrick Wessa

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