WS2Q9TOT WERKLOOSHEID
| Summary of compuational 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 | 543438.583333333 | 6511.85500900879 | 83.4537290190761 |
| Geometric Mean | 540583.977041319 | ||
| Harmonic Mean | 537651.489721945 | ||
| Quadratic Mean | 546201.60747391 | ||
| Winsorized Mean ( 1 / 24 ) | 543495.625 | 6494.47376260947 | 83.6858604509358 |
| Winsorized Mean ( 2 / 24 ) | 543616.652777778 | 6439.54888464242 | 84.4184371477078 |
| Winsorized Mean ( 3 / 24 ) | 543618.611111111 | 6356.35416552633 | 85.5236503433722 |
| Winsorized Mean ( 4 / 24 ) | 543947.333333333 | 6271.42872259691 | 86.7341968463117 |
| Winsorized Mean ( 5 / 24 ) | 543985.736111111 | 6254.79141037037 | 86.9710435441842 |
| Winsorized Mean ( 6 / 24 ) | 543776.569444444 | 6086.55822662512 | 89.3405680513728 |
| Winsorized Mean ( 7 / 24 ) | 543843.166666667 | 6056.74280772819 | 89.7913588096794 |
| Winsorized Mean ( 8 / 24 ) | 544397.944444444 | 5921.82497548297 | 91.9307724727283 |
| Winsorized Mean ( 9 / 24 ) | 544753.069444444 | 5833.3751351395 | 93.3855712729534 |
| Winsorized Mean ( 10 / 24 ) | 542936.819444444 | 5537.95716342644 | 98.039187271091 |
| Winsorized Mean ( 11 / 24 ) | 542691.763888889 | 5500.45719719072 | 98.6630282599165 |
| Winsorized Mean ( 12 / 24 ) | 543273.097222222 | 5392.3615153756 | 100.748641513213 |
| Winsorized Mean ( 13 / 24 ) | 543310.472222222 | 5315.49043308318 | 102.212670507448 |
| Winsorized Mean ( 14 / 24 ) | 544735.555555556 | 5028.76963304713 | 108.323823779034 |
| Winsorized Mean ( 15 / 24 ) | 548218.888888889 | 4462.46028890225 | 122.851264413996 |
| Winsorized Mean ( 16 / 24 ) | 548092.444444444 | 4317.54249325921 | 126.945466153525 |
| Winsorized Mean ( 17 / 24 ) | 548882.708333333 | 4149.64931364417 | 132.272070926233 |
| Winsorized Mean ( 18 / 24 ) | 549248.458333333 | 4049.49364127887 | 135.633861165880 |
| Winsorized Mean ( 19 / 24 ) | 548815.152777778 | 3981.28079048093 | 137.848893775584 |
| Winsorized Mean ( 20 / 24 ) | 548047.652777778 | 3844.18693293346 | 142.565297249884 |
| Winsorized Mean ( 21 / 24 ) | 546773.361111111 | 3657.54349416383 | 149.491964205914 |
| Winsorized Mean ( 22 / 24 ) | 547451.694444444 | 3555.13433236242 | 153.989031992684 |
| Winsorized Mean ( 23 / 24 ) | 547121.708333333 | 3454.66168504998 | 158.372008090111 |
| Winsorized Mean ( 24 / 24 ) | 547166.041666667 | 3117.33919399316 | 175.523421615783 |
| Trimmed Mean ( 1 / 24 ) | 543819.514285714 | 6384.67846424697 | 85.1757088992037 |
| Trimmed Mean ( 2 / 24 ) | 544162.455882353 | 6253.19822344539 | 87.0214626880212 |
| Trimmed Mean ( 3 / 24 ) | 544460.166666667 | 6130.25205460205 | 88.8152985908523 |
| Trimmed Mean ( 4 / 24 ) | 544775.75 | 6019.29468521358 | 90.5049143611865 |
| Trimmed Mean ( 5 / 24 ) | 545016.258064516 | 5915.1432440418 | 92.1391478749902 |
| Trimmed Mean ( 6 / 24 ) | 545263.583333333 | 5792.45545668492 | 94.1334098139778 |
| Trimmed Mean ( 7 / 24 ) | 545571.24137931 | 5688.43755433995 | 95.9088038090654 |
| Trimmed Mean ( 8 / 24 ) | 545888.642857143 | 5566.38593023375 | 98.0687738326148 |
| Trimmed Mean ( 9 / 24 ) | 546137.092592593 | 5447.48219944365 | 100.254956803415 |
| Trimmed Mean ( 10 / 24 ) | 546350.019230769 | 5318.6884733493 | 102.722696012071 |
| Trimmed Mean ( 11 / 24 ) | 546841.52 | 5219.63023221111 | 104.766333183021 |
| Trimmed Mean ( 12 / 24 ) | 547407.395833333 | 5097.8371586956 | 107.380322044928 |
| Trimmed Mean ( 13 / 24 ) | 547946.652173913 | 4964.19846503039 | 110.379682849880 |
| Trimmed Mean ( 14 / 24 ) | 548530.227272727 | 4805.46420131611 | 114.147188344988 |
| Trimmed Mean ( 15 / 24 ) | 548994.880952381 | 4666.25528187601 | 117.652131696418 |
| Trimmed Mean ( 16 / 24 ) | 549088 | 4613.56655211228 | 119.015948680442 |
| Trimmed Mean ( 17 / 24 ) | 549205.894736842 | 4566.48673357254 | 120.268803301040 |
| Trimmed Mean ( 18 / 24 ) | 549243.916666667 | 4530.7732085442 | 121.225206247555 |
| Trimmed Mean ( 19 / 24 ) | 549243.382352941 | 4492.51402151822 | 122.257466470261 |
| Trimmed Mean ( 20 / 24 ) | 549294.09375 | 4441.75885960208 | 123.665897026929 |
| Trimmed Mean ( 21 / 24 ) | 549443.666666667 | 4389.96954832822 | 125.158878807236 |
| Trimmed Mean ( 22 / 24 ) | 549770.642857143 | 4346.28129293152 | 126.492190864694 |
| Trimmed Mean ( 23 / 24 ) | 550062.538461538 | 4291.93206958601 | 128.161986150586 |
| Trimmed Mean ( 24 / 24 ) | 550446.125 | 4215.82771039578 | 130.566560782989 |
| Median | 556303 | ||
| Midrange | 530106 | ||
| Midmean - Weighted Average at Xnp | 548101.405405405 | ||
| Midmean - Weighted Average at X(n+1)p | 549243.916666667 | ||
| Midmean - Empirical Distribution Function | 548101.405405405 | ||
| Midmean - Empirical Distribution Function - Averaging | 549243.916666667 | ||
| Midmean - Empirical Distribution Function - Interpolation | 549243.916666667 | ||
| Midmean - Closest Observation | 548101.405405405 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | 549243.916666667 | ||
| Midmean - MS Excel (old versions) | 549205.894736842 | ||
| Number of observations | 72 | ||
 |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/18oh2vy8u4qw2rx1193069726/1au0l1193070014.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/18oh2vy8u4qw2rx1193069726/1au0l1193070014.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/18oh2vy8u4qw2rx1193069726/2f4ko1193070014.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/18oh2vy8u4qw2rx1193069726/2f4ko1193070014.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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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.