residuals
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 6 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 | 1364.90966659252 | 4209.6005202829 | 0.324237337964980 |
| Geometric Mean | NaN | ||
| Harmonic Mean | 14323.3042952571 | ||
| Quadratic Mean | 42951.36294224 | ||
| Winsorized Mean ( 1 / 35 ) | -75.6262973553848 | 3179.52971351086 | -0.0237853721051966 |
| Winsorized Mean ( 2 / 35 ) | -1934.81811046051 | 2376.18444350324 | -0.814254177848241 |
| Winsorized Mean ( 3 / 35 ) | -1861.85262271237 | 2308.79538964672 | -0.806417333931552 |
| Winsorized Mean ( 4 / 35 ) | -1904.07231586760 | 2230.47530575434 | -0.85366213692451 |
| Winsorized Mean ( 5 / 35 ) | -2089.30933034865 | 2168.44788571635 | -0.963504515884844 |
| Winsorized Mean ( 6 / 35 ) | -2108.96917477715 | 2023.17550015103 | -1.04240545351588 |
| Winsorized Mean ( 7 / 35 ) | -1714.98219440031 | 1940.54854139404 | -0.883761553920376 |
| Winsorized Mean ( 8 / 35 ) | -1680.49552208913 | 1899.33426987923 | -0.884781340883184 |
| Winsorized Mean ( 9 / 35 ) | -2063.76431521503 | 1841.8951651005 | -1.12045699142840 |
| Winsorized Mean ( 10 / 35 ) | -1770.60149265331 | 1774.75701028249 | -0.997658542772277 |
| Winsorized Mean ( 11 / 35 ) | -1762.16375398657 | 1772.53467316939 | -0.99414910222079 |
| Winsorized Mean ( 12 / 35 ) | -1925.41892474641 | 1747.37825438272 | -1.10189017169988 |
| Winsorized Mean ( 13 / 35 ) | -1749.92074009162 | 1714.77011646128 | -1.02049873816491 |
| Winsorized Mean ( 14 / 35 ) | -1980.62966940742 | 1674.85816483078 | -1.18256561122448 |
| Winsorized Mean ( 15 / 35 ) | -1935.57321679985 | 1614.98641437431 | -1.19850742989051 |
| Winsorized Mean ( 16 / 35 ) | -2083.89382981678 | 1573.49951289977 | -1.32436890684282 |
| Winsorized Mean ( 17 / 35 ) | -1546.16835213333 | 1494.21345154337 | -1.03477073542358 |
| Winsorized Mean ( 18 / 35 ) | -1424.60962660235 | 1462.85745083995 | -0.97385403190472 |
| Winsorized Mean ( 19 / 35 ) | -1515.81852411195 | 1450.33990102811 | -1.04514708796015 |
| Winsorized Mean ( 20 / 35 ) | -1439.85224075315 | 1405.93793724497 | -1.02412219103685 |
| Winsorized Mean ( 21 / 35 ) | -1296.03409931431 | 1368.33778174103 | -0.947159478170129 |
| Winsorized Mean ( 22 / 35 ) | -1548.88101796196 | 1296.54960588030 | -1.19461763046878 |
| Winsorized Mean ( 23 / 35 ) | -1729.88501274649 | 1239.31348277066 | -1.39584135636053 |
| Winsorized Mean ( 24 / 35 ) | -1657.68029979662 | 1220.63492404308 | -1.35804757601554 |
| Winsorized Mean ( 25 / 35 ) | -1642.78566137571 | 1212.88849242005 | -1.35444080114726 |
| Winsorized Mean ( 26 / 35 ) | -807.128243458261 | 1093.04029366566 | -0.73842496762077 |
| Winsorized Mean ( 27 / 35 ) | -920.846284463298 | 1049.44427832773 | -0.877460865221591 |
| Winsorized Mean ( 28 / 35 ) | -926.250335229138 | 1011.25800351951 | -0.915938694186328 |
| Winsorized Mean ( 29 / 35 ) | -948.146938862023 | 1003.61913557885 | -0.944727840721335 |
| Winsorized Mean ( 30 / 35 ) | -857.687712790858 | 963.642049587666 | -0.890048035116209 |
| Winsorized Mean ( 31 / 35 ) | -595.488810493849 | 925.460998463603 | -0.643451006020183 |
| Winsorized Mean ( 32 / 35 ) | -591.167977673091 | 924.193487907471 | -0.639658237596539 |
| Winsorized Mean ( 33 / 35 ) | -514.630992058529 | 906.215149521574 | -0.56789051951981 |
| Winsorized Mean ( 34 / 35 ) | -471.056596801364 | 901.10207657936 | -0.522756088399578 |
| Winsorized Mean ( 35 / 35 ) | -301.482520780367 | 856.83738727889 | -0.351855002193361 |
| Trimmed Mean ( 1 / 35 ) | -938.615261027066 | 2750.61541588534 | -0.3412382754806 |
| Trimmed Mean ( 2 / 35 ) | -1835.78200543822 | 2189.53714052306 | -0.83843382761695 |
| Trimmed Mean ( 3 / 35 ) | -1783.26285883549 | 2075.68795254827 | -0.859118952174975 |
| Trimmed Mean ( 4 / 35 ) | -1754.90572753971 | 1975.22938114426 | -0.888456674598008 |
| Trimmed Mean ( 5 / 35 ) | -1713.68864392279 | 1887.99685238359 | -0.907675583123598 |
| Trimmed Mean ( 6 / 35 ) | -1628.87106956856 | 1806.57605883879 | -0.90163437160545 |
| Trimmed Mean ( 7 / 35 ) | -1536.54451087461 | 1751.46612705648 | -0.877290452346304 |
| Trimmed Mean ( 8 / 35 ) | -1506.47074398825 | 1707.62409187748 | -0.882202793433261 |
| Trimmed Mean ( 9 / 35 ) | -1480.21700591269 | 1666.05355781283 | -0.888457036072654 |
| Trimmed Mean ( 10 / 35 ) | -1400.12227718492 | 1629.3336933946 | -0.85932199331609 |
| Trimmed Mean ( 11 / 35 ) | -1353.25442462566 | 1599.34382032219 | -0.846131024130288 |
| Trimmed Mean ( 12 / 35 ) | -1305.06645651916 | 1564.99622665076 | -0.833910289555219 |
| Trimmed Mean ( 13 / 35 ) | -1236.35653124082 | 1529.28808780722 | -0.808452338769984 |
| Trimmed Mean ( 14 / 35 ) | -1182.48615968305 | 1493.17421249101 | -0.79192779368346 |
| Trimmed Mean ( 15 / 35 ) | -1102.67180871061 | 1457.26676416219 | -0.756671212044387 |
| Trimmed Mean ( 16 / 35 ) | -1022.80455040068 | 1424.65454431918 | -0.717931623830576 |
| Trimmed Mean ( 17 / 35 ) | -924.728516651834 | 1392.46707206267 | -0.664093632951786 |
| Trimmed Mean ( 18 / 35 ) | -869.100909895178 | 1366.91140911235 | -0.635813633642547 |
| Trimmed Mean ( 19 / 35 ) | -820.735723117191 | 1341.13500992013 | -0.611970992514816 |
| Trimmed Mean ( 20 / 35 ) | -761.639614530592 | 1311.96074841642 | -0.580535366968803 |
| Trimmed Mean ( 21 / 35 ) | -705.121895678712 | 1283.68663207599 | -0.549294413495902 |
| Trimmed Mean ( 22 / 35 ) | -656.686469151205 | 1255.31631261992 | -0.523124301460451 |
| Trimmed Mean ( 23 / 35 ) | -584.51356651243 | 1231.66182055699 | -0.474573098521557 |
| Trimmed Mean ( 24 / 35 ) | -492.779011322059 | 1211.00300038228 | -0.406918076310714 |
| Trimmed Mean ( 25 / 35 ) | -400.116408829765 | 1188.02531299213 | -0.336791147843512 |
| Trimmed Mean ( 26 / 35 ) | -301.640732212917 | 1160.23394557514 | -0.259982681392235 |
| Trimmed Mean ( 27 / 35 ) | -301.640732212917 | 1146.13716683123 | -0.263180307682434 |
| Trimmed Mean ( 28 / 35 ) | -209.293373625067 | 1134.37520240411 | -0.184501012699773 |
| Trimmed Mean ( 29 / 35 ) | -152.089360731125 | 1124.47953269133 | -0.135253116050156 |
| Trimmed Mean ( 30 / 35 ) | -88.0387509964553 | 1111.52274167392 | -0.0792055328205628 |
| Trimmed Mean ( 31 / 35 ) | -25.3929052690039 | 1100.57450610371 | -0.0230724091173988 |
| Trimmed Mean ( 32 / 35 ) | 21.7039240375332 | 1091.88136191055 | 0.019877547867981 |
| Trimmed Mean ( 33 / 35 ) | 73.2676657679944 | 1078.40640935398 | 0.0679406809274117 |
| Trimmed Mean ( 34 / 35 ) | 123.824061404182 | 1062.17713049499 | 0.116575717786804 |
| Trimmed Mean ( 35 / 35 ) | 176.313531245848 | 1039.15338205597 | 0.169670362711047 |
| Median | 1332.23572230402 | ||
| Midrange | 119996.443439001 | ||
| Midmean - Weighted Average at Xnp | -539.31890018999 | ||
| Midmean - Weighted Average at X(n+1)p | -301.640732212917 | ||
| Midmean - Empirical Distribution Function | -301.640732212917 | ||
| Midmean - Empirical Distribution Function - Averaging | -301.640732212917 | ||
| Midmean - Empirical Distribution Function - Interpolation | -301.640732212917 | ||
| Midmean - Closest Observation | -634.998622140083 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | -301.640732212917 | ||
| Midmean - MS Excel (old versions) | -301.640732212917 | ||
| Number of observations | 105 | ||
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/03/t1196681767fj44mk0fgcvs9dm/12gzx1196682461.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/03/t1196681767fj44mk0fgcvs9dm/12gzx1196682461.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/03/t1196681767fj44mk0fgcvs9dm/2v8br1196682461.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/03/t1196681767fj44mk0fgcvs9dm/2v8br1196682461.ps (open in new window) |
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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