IKO opgave 5 Lorenz Van Look

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 04 Apr 2011 10:15:50 +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/04/t1301912029v97zyw5kp99prmt.htm/, Retrieved Mon, 04 Apr 2011 12:13:50 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

131.676 135.050 129.070 137.792 139.762 142.917 144.198 142.648 152.170 136.022 138.142 138.135 135.027 132.911 133.976 137.012 119.610 118.106 120.383 133.185 131.416 134.248 134.397 127.728 131.837 125.955 134.187 143.291 145.074 149.812 144.668 147.253 145.568 155.564 155.872 156.323 158.010 155.598 154.785 157.294 162.938 157.283 166.074 169.282 172.552 174.055 175.409 173.696 171.283 173.322 170.717 174.229 175.339 173.511 175.839 173.816 173.990 174.777 174.819 176.726 176.199 180.952 176.663 182.346 180.605 182.497 187.856 190.020 190.108 193.288 193.230 199.068 195.076 191.563 191.067 186.665 185.508 184.371 183.046 175.714 175.768 171.029 170.465 170.102 156.389 124.291 99.360 86.675 85.056 128.236 164.257 162.401 152.779 156.005 153.387 153.190 148.840 144.211 145.953 145.542 150.271 147.489 143.824 134.754 131.736 126.304 125.511 125.495 130.133 126.257 110.323 98.417 105.749 120.665 12 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 145.214837078652 1.81537112599316 79.991818201475 Geometric Mean 143.192289620066 Harmonic Mean 141.1541097096 Quadratic Mean 147.209596186255 Winsorized Mean ( 1 / 59 ) 145.201505617978 1.81006367785371 80.21900411269 Winsorized Mean ( 2 / 59 ) 145.313348314607 1.78511740384461 81.4026842165366 Winsorized Mean ( 3 / 59 ) 145.328264044944 1.78263179180154 81.5245552745777 Winsorized Mean ( 4 / 59 ) 145.434376404494 1.75738374006508 82.756186420109 Winsorized Mean ( 5 / 59 ) 145.548926966292 1.73977182360911 83.6597793981712 Winsorized Mean ( 6 / 59 ) 145.547005617978 1.73159116630122 84.053908596031 Winsorized Mean ( 7 / 59 ) 145.579882022472 1.72706645951836 84.2931557266594 Winsorized Mean ( 8 / 59 ) 145.541949438202 1.70679039382105 85.2723040656283 Winsorized Mean ( 9 / 59 ) 145.517275280899 1.69469832999281 85.866182025161 Winsorized Mean ( 10 / 59 ) 145.455308988764 1.68555942200175 86.2949754782437 Winsorized Mean ( 11 / 59 ) 145.388134831461 1.67590195648902 86.7521720280378 Winsorized Mean ( 12 / 59 ) 145.299280898876 1.66425927313106 87.3056760113568 Winsorized Mean ( 13 / 59 ) 145.303005617978 1.65456122268645 87.8196609624723 Winsorized Mean ( 14 / 59 ) 145.317162921348 1.65036077164306 88.051755360541 Winsorized Mean ( 15 / 59 ) 145.212078651685 1.63438039732163 88.84839715996 Winsorized Mean ( 16 / 59 ) 145.203988764045 1.62816012144677 89.1828677360172 Winsorized Mean ( 17 / 59 ) 144.849949438202 1.58260850605659 91.52607791748 Winsorized Mean ( 18 / 59 ) 144.851061797753 1.58110823810246 91.6136279016507 Winsorized Mean ( 19 / 59 ) 144.951719101124 1.56018433195708 92.9067906478063 Winsorized Mean ( 20 / 59 ) 144.925202247191 1.55422767706304 93.2457994320693 Winsorized Mean ( 21 / 59 ) 144.923078651685 1.55266153201937 93.3384872768766 Winsorized Mean ( 22 / 59 ) 144.984876404494 1.54511137473244 93.834579678504 Winsorized Mean ( 23 / 59 ) 144.958904494382 1.53936997415478 94.1676834862097 Winsorized Mean ( 24 / 59 ) 145.038185393258 1.52963759662547 94.818658820381 Winsorized Mean ( 25 / 59 ) 145.036640449438 1.51458972738793 95.7596884666379 Winsorized Mean ( 26 / 59 ) 145.079730337079 1.50919587168483 96.130484491132 Winsorized Mean ( 27 / 59 ) 145.029674157303 1.49684547116159 96.8902114155826 Winsorized Mean ( 28 / 59 ) 145.089449438202 1.48559049542784 97.664497642345 Winsorized Mean ( 29 / 59 ) 145.07902247191 1.48440927672978 97.7351898470519 Winsorized Mean ( 30 / 59 ) 145.064696629213 1.4797770936185 98.0314516657958 Winsorized Mean ( 31 / 59 ) 145.057382022472 1.47620993198254 98.2633830593869 Winsorized Mean ( 32 / 59 ) 145.044438202247 1.4706646802237 98.6250911935854 Winsorized Mean ( 33 / 59 ) 145.009769662921 1.46680348657009 98.8610751137533 Winsorized Mean ( 34 / 59 ) 144.999646067416 1.43804741245258 100.830921714966 Winsorized Mean ( 35 / 59 ) 144.777848314607 1.40869863139426 102.774181140016 Winsorized Mean ( 36 / 59 ) 144.819511235955 1.39459892785351 103.843125319803 Winsorized Mean ( 37 / 59 ) 144.998275280899 1.36542421944194 106.192839716993 Winsorized Mean ( 38 / 59 ) 145.029657303371 1.35203126763416 107.267975804398 Winsorized Mean ( 39 / 59 ) 144.97291011236 1.34155097929369 108.0636609044 Winsorized Mean ( 40 / 59 ) 144.882348314607 1.31373344923893 110.282910432508 Winsorized Mean ( 41 / 59 ) 144.168073033708 1.23567941206825 116.671097394431 Winsorized Mean ( 42 / 59 ) 143.765061797753 1.19099684934678 120.709859036658 Winsorized Mean ( 43 / 59 ) 143.567938202247 1.14911123907188 124.938242113274 Winsorized Mean ( 44 / 59 ) 143.443353932584 1.13577704566141 126.295345094822 Winsorized Mean ( 45 / 59 ) 143.151612359551 1.08121798049405 132.398475554521 Winsorized Mean ( 46 / 59 ) 143.097859550562 1.07236695073238 133.441131743972 Winsorized Mean ( 47 / 59 ) 142.545213483146 1.02242479798097 139.41877560544 Winsorized Mean ( 48 / 59 ) 142.647146067416 1.00965077649555 141.283649147022 Winsorized Mean ( 49 / 59 ) 142.679078651685 0.997450990927534 143.043698336504 Winsorized Mean ( 50 / 59 ) 142.491157303371 0.978895014863005 145.563267909084 Winsorized Mean ( 51 / 59 ) 142.757617977528 0.953638853469113 149.697778627842 Winsorized Mean ( 52 / 59 ) 142.637550561798 0.918396321497682 155.311543854172 Winsorized Mean ( 53 / 59 ) 142.769157303371 0.902917169663497 158.119883085819 Winsorized Mean ( 54 / 59 ) 142.925696629213 0.871873322476858 163.929429820359 Winsorized Mean ( 55 / 59 ) 142.994292134831 0.858568891238204 166.549584540163 Winsorized Mean ( 56 / 59 ) 143.161033707865 0.834044491381416 171.646758880632 Winsorized Mean ( 57 / 59 ) 143.541140449438 0.79559691789874 180.419427501739 Winsorized Mean ( 58 / 59 ) 143.614780898876 0.787371547774863 182.397727355956 Winsorized Mean ( 59 / 59 ) 143.620084269663 0.784418339006631 183.091186332461 Trimmed Mean ( 1 / 59 ) 145.250664772727 1.77748204843458 81.717092389568 Trimmed Mean ( 2 / 59 ) 145.300954022988 1.74247925360018 83.3874800648436 Trimmed Mean ( 3 / 59 ) 145.294540697674 1.71884703008599 84.5302334381705 Trimmed Mean ( 4 / 59 ) 145.282770588235 1.69444570518693 85.7405876998627 Trimmed Mean ( 5 / 59 ) 145.242613095238 1.67572724794893 86.674375721355 Trimmed Mean ( 6 / 59 ) 145.176921686747 1.65985844159903 87.463435464346 Trimmed Mean ( 7 / 59 ) 145.109975609756 1.64446871413976 88.2412504184763 Trimmed Mean ( 8 / 59 ) 145.036216049383 1.62869706030112 89.0504560882353 Trimmed Mean ( 9 / 59 ) 144.9658875 1.61503001271808 89.7604913583149 Trimmed Mean ( 10 / 59 ) 144.896867088608 1.60207121782376 90.4434618614733 Trimmed Mean ( 11 / 59 ) 144.833147435897 1.58932125016062 91.1289315619862 Trimmed Mean ( 12 / 59 ) 144.774831168831 1.57676789608238 91.8174650362537 Trimmed Mean ( 13 / 59 ) 144.723651315789 1.56458693175375 92.4995910285201 Trimmed Mean ( 14 / 59 ) 144.670766666667 1.55247124255603 93.1874051518513 Trimmed Mean ( 15 / 59 ) 144.615236486486 1.53974808964646 93.9213612011636 Trimmed Mean ( 16 / 59 ) 144.566726027397 1.52762526441099 94.6349405154268 Trimmed Mean ( 17 / 59 ) 144.517493055556 1.51504372761986 95.3883313207025 Trimmed Mean ( 18 / 59 ) 144.492978873239 1.50587799557452 95.9526464281146 Trimmed Mean ( 19 / 59 ) 144.467685714286 1.49594589524112 96.5728013117745 Trimmed Mean ( 20 / 59 ) 144.434826086957 1.48695722705205 97.1344860896265 Trimmed Mean ( 21 / 59 ) 144.402735294118 1.47758196504435 97.72908624381 Trimmed Mean ( 22 / 59 ) 144.369820895522 1.46737151481111 98.386686287901 Trimmed Mean ( 23 / 59 ) 144.332121212121 1.45675681053713 99.0777047810087 Trimmed Mean ( 24 / 59 ) 144.294807692308 1.44553863157379 99.8207896631656 Trimmed Mean ( 25 / 59 ) 144.251734375 1.43396491806999 100.596418055438 Trimmed Mean ( 26 / 59 ) 144.207380952381 1.42244914634939 101.379638999734 Trimmed Mean ( 27 / 59 ) 144.159217741935 1.41013073342888 102.231101219528 Trimmed Mean ( 28 / 59 ) 144.112180327869 1.39753335501972 103.118955844768 Trimmed Mean ( 29 / 59 ) 144.060408333333 1.384478760814 104.053895524286 Trimmed Mean ( 30 / 59 ) 144.007423728814 1.37006413433984 105.109987276766 Trimmed Mean ( 31 / 59 ) 143.953344827586 1.35443358056179 106.283059497002 Trimmed Mean ( 32 / 59 ) 143.897736842105 1.33734665139287 107.599429581129 Trimmed Mean ( 33 / 59 ) 143.840785714286 1.31880288437995 109.069207701888 Trimmed Mean ( 34 / 59 ) 143.783463636364 1.29847617842396 110.732461654308 Trimmed Mean ( 35 / 59 ) 143.724509259259 1.27839147086029 112.426054565696 Trimmed Mean ( 36 / 59 ) 143.673971698113 1.258685777942 114.146019773915 Trimmed Mean ( 37 / 59 ) 143.619509615385 1.23786273815513 116.02216076835 Trimmed Mean ( 38 / 59 ) 143.554480392157 1.21709418519117 117.9485385263 Trimmed Mean ( 39 / 59 ) 143.48538 1.19493323855436 120.078156143342 Trimmed Mean ( 40 / 59 ) 143.416102040816 1.17094659878041 122.478772465107 Trimmed Mean ( 41 / 59 ) 143.348135416667 1.14661313458336 125.018745288274 Trimmed Mean ( 42 / 59 ) 143.310265957447 1.12714480731166 127.144502665327 Trimmed Mean ( 43 / 59 ) 143.289315217391 1.10949635863982 129.148071646721 Trimmed Mean ( 44 / 59 ) 143.2765 1.09348430300181 131.027486729055 Trimmed Mean ( 45 / 59 ) 143.268829545455 1.07645825362272 133.092787447443 Trimmed Mean ( 46 / 59 ) 143.274220930233 1.06235764179022 134.86439527916 Trimmed Mean ( 47 / 59 ) 143.274220930233 1.04692590067644 136.85230333652 Trimmed Mean ( 48 / 59 ) 143.316390243902 1.03400345228747 138.603396271891 Trimmed Mean ( 49 / 59 ) 143.3474125 1.02024475248167 140.502964755583 Trimmed Mean ( 50 / 59 ) 143.378538461538 1.00542440574206 142.604991128813 Trimmed Mean ( 51 / 59 ) 143.378538461538 0.989951570422557 144.833891621928 Trimmed Mean ( 52 / 59 ) 143.451351351351 0.974701969740384 147.174578286284 Trimmed Mean ( 53 / 59 ) 143.490041666667 0.96063186272358 149.370479196728 Trimmed Mean ( 54 / 59 ) 143.524628571429 0.945729056392005 151.76083213408 Trimmed Mean ( 55 / 59 ) 143.524628571429 0.931736719090078 154.039897355975 Trimmed Mean ( 56 / 59 ) 143.581090909091 0.916541230848281 156.655353929035 Trimmed Mean ( 57 / 59 ) 143.601953125 0.901415830083455 159.307112580556 Trimmed Mean ( 58 / 59 ) 143.605016129032 0.888227314239996 161.675973961583 Trimmed Mean ( 59 / 59 ) 143.605016129032 0.873088177571144 164.479396031371 Median 144.4395 Midrange 142.062 Midmean - Weighted Average at Xnp 143.061617977528 Midmean - Weighted Average at X(n+1)p 143.2765 Midmean - Empirical Distribution Function 143.2765 Midmean - Empirical Distribution Function - Averaging 143.2765 Midmean - Empirical Distribution Function - Interpolation 143.268829545455 Midmean - Closest Observation 143.2765 Midmean - True Basic - Statistics Graphics Toolkit 143.2765 Midmean - MS Excel (old versions) 143.2765 Number of observations 178

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/04/t1301912029v97zyw5kp99prmt/1aq151301912146.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/04/t1301912029v97zyw5kp99prmt/1aq151301912146.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/04/t1301912029v97zyw5kp99prmt/2y0f41301912146.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/04/t1301912029v97zyw5kp99prmt/2y0f41301912146.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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