workshop 3

*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: Mon, 19 Oct 2009 03:04:18 -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/Oct/19/t1255943095sb4mk3jivgfomt2.htm/, Retrieved Mon, 19 Oct 2009 11:04:58 +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/Oct/19/t1255943095sb4mk3jivgfomt2.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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5.10 5.21 5.31 5.34 5.44 5.47 5.60 5.68 5.76 6.03 6.14 6.54 6.50 6.43 6.93 6.77 6.58 6.55 6.41 6.28 6.18 6.35 6.17 6.04 5.68 5.43 5.43 5.38 5.28 5.33 5.35 5.31 5.24 5.12 5.04 5.03 4.98 4.98 5.09 5.12 5.07 5.05 5.05 5.05 5.03 5.11 5.19 5.14 4.97 4.94 4.93 4.92 4.90 4.90 4.82 4.81 4.72 4.54 4.51 4.30 4.08 4.05 4.03 3.85 3.80 3.91 3.99 4.08 5.15 4.78 4.79 4.79 4.69 4.82 4.98 5.12 5.55 5.65 5.66 5.74 5.76 5.91 5.92 5.74 5.25 5.06 4.84 4.66 4.38 4.14 4.11 3.96 3.51 3.00 2.75 2.63 2.58 2.63 2.69 2.69 2.69 2.67 2.63 2.57 2.56 2.52 2.29 2.18 2.10 2.02 1.91 1.92 1.85 1.64 1.62 1.64 1.65 1.65 1.67 1.66 1.61 1.59 1.57 1.60 1.67 1.81 1.88 1.92 2.01 2.12 2.24 2.34 2.41 2.48 2.59 2.65 2.70 2.77 2.87 2.97 3.03 3.19 3.36 3.48 3.56 3.68 3.82 3.93 4.04 4.19 4.30 4.33 4.36 4.44 4.49 4.52

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 4.16814102564103 0.118840441315388 35.073422645573 Geometric Mean 3.85412346700355 Harmonic Mean 3.50070533851884 Quadratic Mean 4.42294838720705 Winsorized Mean ( 1 / 52 ) 4.16724358974359 0.118672961993611 35.1153583742853 Winsorized Mean ( 2 / 52 ) 4.1649358974359 0.118322277124014 35.1999302132312 Winsorized Mean ( 3 / 52 ) 4.16455128205128 0.118219886774695 35.2271635142753 Winsorized Mean ( 4 / 52 ) 4.16455128205128 0.118150880776211 35.2477379321389 Winsorized Mean ( 5 / 52 ) 4.16391025641026 0.117896983453117 35.3182086127427 Winsorized Mean ( 6 / 52 ) 4.16121794871795 0.117557276853372 35.3973659487552 Winsorized Mean ( 7 / 52 ) 4.16076923076923 0.117383930263257 35.4458163177692 Winsorized Mean ( 8 / 52 ) 4.15769230769231 0.117007767203948 35.5334727518159 Winsorized Mean ( 9 / 52 ) 4.15423076923077 0.116446100321474 35.6751386071506 Winsorized Mean ( 10 / 52 ) 4.14846153846154 0.115616596136905 35.8811941976672 Winsorized Mean ( 11 / 52 ) 4.14775641025641 0.115536815687922 35.8998678088894 Winsorized Mean ( 12 / 52 ) 4.15621794871795 0.113815413042613 36.5171802096949 Winsorized Mean ( 13 / 52 ) 4.15121794871795 0.112453550486170 36.9149567156485 Winsorized Mean ( 14 / 52 ) 4.15301282051282 0.112002430401367 37.0796669824956 Winsorized Mean ( 15 / 52 ) 4.14532051282051 0.110505471454754 37.5123553453893 Winsorized Mean ( 16 / 52 ) 4.14532051282051 0.110265692354588 37.5939281230843 Winsorized Mean ( 17 / 52 ) 4.12897435897436 0.108629715315478 38.0096214648374 Winsorized Mean ( 18 / 52 ) 4.13935897435897 0.107283537792541 38.5833563986624 Winsorized Mean ( 19 / 52 ) 4.13814102564103 0.106891246901134 38.7135630428979 Winsorized Mean ( 20 / 52 ) 4.14839743589744 0.105593736529839 39.2863968283304 Winsorized Mean ( 21 / 52 ) 4.14301282051282 0.104481049285583 39.6532466781468 Winsorized Mean ( 22 / 52 ) 4.15147435897436 0.103432240258809 40.1371404949415 Winsorized Mean ( 23 / 52 ) 4.15737179487179 0.102071639650810 40.7299403545811 Winsorized Mean ( 24 / 52 ) 4.16352564102564 0.100999095250482 41.2233954244831 Winsorized Mean ( 25 / 52 ) 4.16352564102564 0.0992904439616125 41.9327930755888 Winsorized Mean ( 26 / 52 ) 4.16685897435897 0.097146841029332 42.8923774587879 Winsorized Mean ( 27 / 52 ) 4.16512820512821 0.0944899453375238 44.080120802802 Winsorized Mean ( 28 / 52 ) 4.16692307692308 0.0931904235888721 44.7140694982381 Winsorized Mean ( 29 / 52 ) 4.1725 0.0921831808615156 45.2631376028154 Winsorized Mean ( 30 / 52 ) 4.17442307692308 0.0919664429928805 45.3907201482853 Winsorized Mean ( 31 / 52 ) 4.16647435897436 0.0908770702096282 45.847366660957 Winsorized Mean ( 32 / 52 ) 4.16237179487179 0.090119400265504 46.1873002107080 Winsorized Mean ( 33 / 52 ) 4.16871794871795 0.0889914410224838 46.8440324240251 Winsorized Mean ( 34 / 52 ) 4.16653846153846 0.0888067973801987 46.9168868200564 Winsorized Mean ( 35 / 52 ) 4.16205128205128 0.0884292541273227 47.0664524214877 Winsorized Mean ( 36 / 52 ) 4.16666666666667 0.0879144601426757 47.39455443285 Winsorized Mean ( 37 / 52 ) 4.16429487179487 0.0867934482829363 47.9793688830033 Winsorized Mean ( 38 / 52 ) 4.16185897435897 0.0856502788112208 48.5913067899289 Winsorized Mean ( 39 / 52 ) 4.15935897435897 0.08544583023632 48.6783142355257 Winsorized Mean ( 40 / 52 ) 4.15166666666667 0.0848223869690523 48.9454118778975 Winsorized Mean ( 41 / 52 ) 4.14903846153846 0.0841088043830176 49.3294191015298 Winsorized Mean ( 42 / 52 ) 4.15173076923077 0.0817619840163244 50.7782537224372 Winsorized Mean ( 43 / 52 ) 4.15448717948718 0.080936071070092 51.3304775554193 Winsorized Mean ( 44 / 52 ) 4.17705128205128 0.0773995369527636 53.9673936886794 Winsorized Mean ( 45 / 52 ) 4.20589743589744 0.0742794375114203 56.6226344302996 Winsorized Mean ( 46 / 52 ) 4.21474358974359 0.0733319299466033 57.4748761257553 Winsorized Mean ( 47 / 52 ) 4.22076923076923 0.0721278009713701 58.5179247658554 Winsorized Mean ( 48 / 52 ) 4.26692307692308 0.0666931057199753 63.978473199893 Winsorized Mean ( 49 / 52 ) 4.31717948717949 0.0609481735438192 70.8336154499464 Winsorized Mean ( 50 / 52 ) 4.34923076923077 0.0565659498906081 76.8877881064787 Winsorized Mean ( 51 / 52 ) 4.35576923076923 0.0553275111136786 78.727004759344 Winsorized Mean ( 52 / 52 ) 4.36910256410256 0.0534172498257677 81.7919787775178 Trimmed Mean ( 1 / 52 ) 4.16707792207792 0.117827176716203 35.3660168919663 Trimmed Mean ( 2 / 52 ) 4.16690789473684 0.116908826432139 35.6423721108481 Trimmed Mean ( 3 / 52 ) 4.16793333333333 0.116105378990701 35.8978487436587 Trimmed Mean ( 4 / 52 ) 4.16912162162162 0.115269013518841 36.1686241111119 Trimmed Mean ( 5 / 52 ) 4.17034246575342 0.114377715604739 36.4611449328565 Trimmed Mean ( 6 / 52 ) 4.17173611111111 0.113467900102806 36.7657822814326 Trimmed Mean ( 7 / 52 ) 4.17366197183099 0.112546130515842 37.0840112645499 Trimmed Mean ( 8 / 52 ) 4.17571428571429 0.111571192359928 37.4264556772277 Trimmed Mean ( 9 / 52 ) 4.17826086956522 0.110568427984376 37.7889144824924 Trimmed Mean ( 10 / 52 ) 4.18132352941176 0.109557627459073 38.1655173298979 Trimmed Mean ( 11 / 52 ) 4.18514925373134 0.108568321379106 38.5485305526402 Trimmed Mean ( 12 / 52 ) 4.18916666666667 0.107492181429851 38.9718267035123 Trimmed Mean ( 13 / 52 ) 4.19246153846154 0.106534842973106 39.3529611670795 Trimmed Mean ( 14 / 52 ) 4.196328125 0.105645156086771 39.7209704679063 Trimmed Mean ( 15 / 52 ) 4.20015873015873 0.104714880258940 40.1104286207702 Trimmed Mean ( 16 / 52 ) 4.20475806451613 0.103852077095214 40.4879534634739 Trimmed Mean ( 17 / 52 ) 4.20950819672131 0.102919702421081 40.9008974734375 Trimmed Mean ( 18 / 52 ) 4.21566666666667 0.102050252171437 41.3097133712579 Trimmed Mean ( 19 / 52 ) 4.22127118644068 0.101224542648191 41.7020524470222 Trimmed Mean ( 20 / 52 ) 4.22715517241379 0.100338916385650 42.1287704181179 Trimmed Mean ( 21 / 52 ) 4.23254385964912 0.0994820315759688 42.5458124708377 Trimmed Mean ( 22 / 52 ) 4.23848214285714 0.0986259533037876 42.9753224265602 Trimmed Mean ( 23 / 52 ) 4.24409090909091 0.0977683681075382 43.4096527459956 Trimmed Mean ( 24 / 52 ) 4.24953703703704 0.096934618448252 43.8392094079952 Trimmed Mean ( 25 / 52 ) 4.25481132075472 0.0960962409856581 44.2765635483049 Trimmed Mean ( 26 / 52 ) 4.26028846153846 0.0953065351234827 44.7009059349254 Trimmed Mean ( 27 / 52 ) 4.26578431372549 0.094608434599612 45.088837287907 Trimmed Mean ( 28 / 52 ) 4.2716 0.0940492800116358 45.4187421686962 Trimmed Mean ( 29 / 52 ) 4.27755102040816 0.0935155159287998 45.7416181467145 Trimmed Mean ( 30 / 52 ) 4.2834375 0.092982953083588 46.0669118150007 Trimmed Mean ( 31 / 52 ) 4.2834375 0.0923746025341858 46.3702942420217 Trimmed Mean ( 32 / 52 ) 4.29619565217391 0.0917528310022398 46.8235759621307 Trimmed Mean ( 33 / 52 ) 4.30344444444444 0.0910844857719598 47.2467337107068 Trimmed Mean ( 34 / 52 ) 4.31068181818182 0.0904061420406063 47.6812937803015 Trimmed Mean ( 35 / 52 ) 4.31837209302326 0.0896152072766814 48.1879384565897 Trimmed Mean ( 36 / 52 ) 4.32666666666667 0.0887101317421203 48.7730835441013 Trimmed Mean ( 37 / 52 ) 4.33512195121951 0.0876969961896856 49.4329582491376 Trimmed Mean ( 38 / 52 ) 4.344125 0.0866118019244198 50.1562708947081 Trimmed Mean ( 39 / 52 ) 4.35371794871795 0.0854423971412868 50.955007050173 Trimmed Mean ( 40 / 52 ) 4.36394736842105 0.0840684931589292 51.9094277111775 Trimmed Mean ( 41 / 52 ) 4.37513513513514 0.0824891011263726 53.0389478778835 Trimmed Mean ( 42 / 52 ) 4.38708333333333 0.0806823495551162 54.3747592568112 Trimmed Mean ( 43 / 52 ) 4.39957142857143 0.0788166716592397 55.8203148642558 Trimmed Mean ( 44 / 52 ) 4.41264705882353 0.0766806478714939 57.5457717339388 Trimmed Mean ( 45 / 52 ) 4.41264705882353 0.074637029904458 59.1214182085234 Trimmed Mean ( 46 / 52 ) 4.4371875 0.0726631541138548 61.0651650635402 Trimmed Mean ( 47 / 52 ) 4.44935483870968 0.0704067171179154 63.1950333837894 Trimmed Mean ( 48 / 52 ) 4.462 0.0678298802547021 65.7822184448082 Trimmed Mean ( 49 / 52 ) 4.47293103448276 0.0656651981407631 68.1172243612875 Trimmed Mean ( 50 / 52 ) 4.48178571428571 0.0640399160948254 69.9842533779936 Trimmed Mean ( 51 / 52 ) 4.48944444444444 0.0627761080277376 71.515176481804 Trimmed Mean ( 52 / 52 ) 4.49730769230769 0.0613775469682626 73.2728483696682 Median 4.53 Midrange 4.25 Midmean - Weighted Average at Xnp 4.3326582278481 Midmean - Weighted Average at X(n+1)p 4.3326582278481 Midmean - Empirical Distribution Function 4.3326582278481 Midmean - Empirical Distribution Function - Averaging 4.3326582278481 Midmean - Empirical Distribution Function - Interpolation 4.3326582278481 Midmean - Closest Observation 4.3326582278481 Midmean - True Basic - Statistics Graphics Toolkit 4.3326582278481 Midmean - MS Excel (old versions) 4.344125 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Oct/19/t1255943095sb4mk3jivgfomt2/15wcs1255943056.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/19/t1255943095sb4mk3jivgfomt2/15wcs1255943056.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/19/t1255943095sb4mk3jivgfomt2/24lcl1255943056.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/19/t1255943095sb4mk3jivgfomt2/24lcl1255943056.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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