Opgave 5 Oefening 2 stap 1 centrummaten

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Fri, 04 Jun 2010 08:03:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jun/04/t1275640326pf0oje693l4a5pp.htm/, Retrieved Fri, 04 Jun 2010 10:32:13 +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/2010/Jun/04/t1275640326pf0oje693l4a5pp.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

0,9383 0,9217 0,9095 0,8920 0,8742 0,8607 0,8607 0,9005 0,9111 0,9059 0,8883 0,8924 0,8833 0,8700 0,8758 0,8858 0,9170 0,9554 0,9922 0,9778 0,9808 0,9811 1,0014 1,0183 1,0622 1,0773 1,0807 1,0848 1,1582 1,1663 1,1372 1,1139 1,1222 1,1692 1,1702 1,2286 1,2613 1,2646 1,2262 1,1985 1,2007 1,2138 1,2266 1,2176 1,2218 1,2490 1,2991 1,3408 1,3119 1,3014 1,3201 1,2938 1,2694 1,2165 1,2037 1,2292 1,2256 1,2015 1,1786 1,1856 1,2103 1,1938 1,2020 1,2271 1,2770 1,2650 1,2684 1,2811 1,2727 1,2611 1,2881 1,3213 1,2999 1,3074 1,3242 1,3516 1,3511 1,3419 1,3716 1,3622 1,3896 1,4227 1,4684 1,4570 1,4718 1,4748 1,5527 1,5751 1,5557 1,5553 1,5770 1,4975 1,4370 1,3322 1,2732 1,3449 1,3239 1,2785 1,3050 1,3190 1,3650 1,4016 1,4088 1,4268 1,4562 1,4816 1,4914 1,4614 1,4272

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 1.21863669724771 0.0185770105835992 65.5991819439229 Geometric Mean 1.20244802806289 Harmonic Mean 1.18540977236868 Quadratic Mean 1.23383417632958 Winsorized Mean ( 1 / 36 ) 1.21861926605505 0.0185739049864107 65.609211791846 Winsorized Mean ( 2 / 36 ) 1.21843394495413 0.0184820814500635 65.925147459511 Winsorized Mean ( 3 / 36 ) 1.21853853211009 0.0184601496994294 66.0091360010887 Winsorized Mean ( 4 / 36 ) 1.21850183486239 0.0184339590903096 66.1009297510556 Winsorized Mean ( 5 / 36 ) 1.21631376146789 0.0179786085687533 67.6533869023565 Winsorized Mean ( 6 / 36 ) 1.21611559633028 0.0179068433196075 67.9134549079714 Winsorized Mean ( 7 / 36 ) 1.21564678899083 0.0177911036750840 68.3289137757828 Winsorized Mean ( 8 / 36 ) 1.21541926605505 0.0176765216621315 68.7589611398969 Winsorized Mean ( 9 / 36 ) 1.21520458715596 0.0176374137721213 68.8992503581669 Winsorized Mean ( 10 / 36 ) 1.21563577981651 0.0174704816589802 69.5822704574175 Winsorized Mean ( 11 / 36 ) 1.21547431192661 0.0172859370426420 70.3157895882761 Winsorized Mean ( 12 / 36 ) 1.21538623853211 0.0171568740548058 70.839608348799 Winsorized Mean ( 13 / 36 ) 1.21548165137615 0.0171129753359060 71.0269037100668 Winsorized Mean ( 14 / 36 ) 1.21377339449541 0.0166745969737657 72.791768005251 Winsorized Mean ( 15 / 36 ) 1.21307155963303 0.0164030041199344 73.9542312349233 Winsorized Mean ( 16 / 36 ) 1.21544954128440 0.0160000077314718 75.9655596224262 Winsorized Mean ( 17 / 36 ) 1.21747706422018 0.0154989684657262 78.5521350606308 Winsorized Mean ( 18 / 36 ) 1.21888073394495 0.0146487611068578 83.207086596172 Winsorized Mean ( 19 / 36 ) 1.21814862385321 0.0144198052781136 84.4774669531853 Winsorized Mean ( 20 / 36 ) 1.21600183486239 0.0141566517326688 85.896146760202 Winsorized Mean ( 21 / 36 ) 1.21467247706422 0.0134439952118837 90.3505585891998 Winsorized Mean ( 22 / 36 ) 1.21519724770642 0.0130175556112613 93.3506476941936 Winsorized Mean ( 23 / 36 ) 1.21817247706422 0.0124173407565285 98.1025246024401 Winsorized Mean ( 24 / 36 ) 1.22550458715596 0.0107632848150107 113.859719241736 Winsorized Mean ( 25 / 36 ) 1.22885321100917 0.0102707283234243 119.646160653139 Winsorized Mean ( 26 / 36 ) 1.22818532110092 0.00999850267635574 122.836924773277 Winsorized Mean ( 27 / 36 ) 1.22845779816514 0.009780009906485 125.609054582916 Winsorized Mean ( 28 / 36 ) 1.23565045871560 0.00875822012056952 141.084654382407 Winsorized Mean ( 29 / 36 ) 1.23557064220183 0.00822338614472615 150.250835903436 Winsorized Mean ( 30 / 36 ) 1.23749724770642 0.00746449524030374 165.784451308199 Winsorized Mean ( 31 / 36 ) 1.24338440366972 0.0067327050521757 184.678282211088 Winsorized Mean ( 32 / 36 ) 1.24499908256881 0.0063727980007607 195.361453857504 Winsorized Mean ( 33 / 36 ) 1.24551376146789 0.00623256064646318 199.839814182103 Winsorized Mean ( 34 / 36 ) 1.24548256880734 0.00615925648449458 202.213135943070 Winsorized Mean ( 35 / 36 ) 1.2459 0.00560628030561295 222.232912391594 Winsorized Mean ( 36 / 36 ) 1.24672568807339 0.00519018117585916 240.208510229321 Trimmed Mean ( 1 / 36 ) 1.21863271028037 0.0183185745672764 66.5244288416028 Trimmed Mean ( 2 / 36 ) 1.21864666666667 0.0180324598990878 67.5807224020675 Trimmed Mean ( 3 / 36 ) 1.21875922330097 0.0177651284691071 68.6040196906175 Trimmed Mean ( 4 / 36 ) 1.21883861386139 0.0174733460368460 69.7541622131918 Trimmed Mean ( 5 / 36 ) 1.21893131313131 0.0171529859234602 71.0623397331759 Trimmed Mean ( 6 / 36 ) 1.21951958762887 0.0169148862739736 72.097415724592 Trimmed Mean ( 7 / 36 ) 1.22017052631579 0.0166613049148024 73.2337912639576 Trimmed Mean ( 8 / 36 ) 1.22092795698925 0.0163971536091903 74.4597499107978 Trimmed Mean ( 9 / 36 ) 1.22175274725275 0.0161181402548909 75.799858292089 Trimmed Mean ( 10 / 36 ) 1.22264382022472 0.0158059324269475 77.3534763529823 Trimmed Mean ( 11 / 36 ) 1.22352183908046 0.0154778176740127 79.0500227389781 Trimmed Mean ( 12 / 36 ) 1.22446 0.0151318421753284 80.9194271135349 Trimmed Mean ( 13 / 36 ) 1.22545301204819 0.0147538735991897 83.0597472460041 Trimmed Mean ( 14 / 36 ) 1.22648518518519 0.0143229932676590 85.6305076924502 Trimmed Mean ( 15 / 36 ) 1.22773797468354 0.0138916984898232 88.3792558255537 Trimmed Mean ( 16 / 36 ) 1.22912207792208 0.0134303252036753 91.5184151747661 Trimmed Mean ( 17 / 36 ) 1.230364 0.0129571490079575 94.9563827076761 Trimmed Mean ( 18 / 36 ) 1.23149589041096 0.0124835121760446 98.6497928663184 Trimmed Mean ( 19 / 36 ) 1.23257183098592 0.0120629167490855 102.178590520353 Trimmed Mean ( 20 / 36 ) 1.23377101449275 0.0115980712270149 106.377257937422 Trimmed Mean ( 21 / 36 ) 1.23521641791045 0.0110800742014814 111.480879590616 Trimmed Mean ( 22 / 36 ) 1.23685692307692 0.0105730960663844 116.981527010742 Trimmed Mean ( 23 / 36 ) 1.23856031746032 0.0100307322285501 123.476560757454 Trimmed Mean ( 24 / 36 ) 1.24014426229508 0.00948179259659032 130.792173490595 Trimmed Mean ( 25 / 36 ) 1.24127118644068 0.00912425750991457 136.040788534507 Trimmed Mean ( 26 / 36 ) 1.24222105263158 0.00877682523800088 141.534212992319 Trimmed Mean ( 27 / 36 ) 1.24329090909091 0.00839258301406636 148.141627792909 Trimmed Mean ( 28 / 36 ) 1.24442075471698 0.00795219997606278 156.487608267254 Trimmed Mean ( 29 / 36 ) 1.24509019607843 0.00761667482124348 163.468997338022 Trimmed Mean ( 30 / 36 ) 1.24582040816327 0.00729652499737687 170.741607629816 Trimmed Mean ( 31 / 36 ) 1.24646382978723 0.00704346123806585 176.967514643343 Trimmed Mean ( 32 / 36 ) 1.24670444444444 0.00687067032116789 181.453102269143 Trimmed Mean ( 33 / 36 ) 1.24683953488372 0.00671513153843314 185.676114867982 Trimmed Mean ( 34 / 36 ) 1.24694634146341 0.0065351993434065 190.804637462434 Trimmed Mean ( 35 / 36 ) 1.24706666666667 0.00630747188098882 197.712600261511 Trimmed Mean ( 36 / 36 ) 1.24716486486486 0.00613100133915039 203.419440948563 Median 1.249 Midrange 1.21885 Midmean - Weighted Average at Xnp 1.24146481481482 Midmean - Weighted Average at X(n+1)p 1.24329090909091 Midmean - Empirical Distribution Function 1.24329090909091 Midmean - Empirical Distribution Function - Averaging 1.24329090909091 Midmean - Empirical Distribution Function - Interpolation 1.24329090909091 Midmean - Closest Observation 1.2403875 Midmean - True Basic - Statistics Graphics Toolkit 1.24329090909091 Midmean - MS Excel (old versions) 1.24329090909091 Number of observations 109

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/04/t1275640326pf0oje693l4a5pp/1jhb61275638635.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/04/t1275640326pf0oje693l4a5pp/1jhb61275638635.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/04/t1275640326pf0oje693l4a5pp/2cqs91275638635.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/04/t1275640326pf0oje693l4a5pp/2cqs91275638635.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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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