WS2Q9UITVOER
| Summary of compuational 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 | 339.252777777778 | 5.49771825347659 | 61.7079235668807 |
| Geometric Mean | 336.119360125529 | ||
| Harmonic Mean | 333.003770712265 | ||
| Quadratic Mean | 342.400957293574 | ||
| Winsorized Mean ( 1 / 24 ) | 339.225 | 5.47987417110488 | 61.9037936653213 |
| Winsorized Mean ( 2 / 24 ) | 338.622222222222 | 5.12188193717253 | 66.1128519508894 |
| Winsorized Mean ( 3 / 24 ) | 338.418055555556 | 5.02132710013916 | 67.3961382731225 |
| Winsorized Mean ( 4 / 24 ) | 338.5125 | 4.9562538297234 | 68.3000733275381 |
| Winsorized Mean ( 5 / 24 ) | 338.852777777778 | 4.79614924936169 | 70.651007748117 |
| Winsorized Mean ( 6 / 24 ) | 339.711111111111 | 4.55993710813933 | 74.4990781791132 |
| Winsorized Mean ( 7 / 24 ) | 340.333333333333 | 4.41131263755228 | 77.1501277048853 |
| Winsorized Mean ( 8 / 24 ) | 339.755555555556 | 4.21636969805871 | 80.5801150956912 |
| Winsorized Mean ( 9 / 24 ) | 339.393055555556 | 4.09141067842739 | 82.9525760748143 |
| Winsorized Mean ( 10 / 24 ) | 339.309722222222 | 4.06179919185693 | 83.5368038140508 |
| Winsorized Mean ( 11 / 24 ) | 339.004166666667 | 3.97477690907516 | 85.2888537952047 |
| Winsorized Mean ( 12 / 24 ) | 338.004166666667 | 3.78031742499549 | 89.4115833849771 |
| Winsorized Mean ( 13 / 24 ) | 337.480555555556 | 3.54863406841583 | 95.1015373941367 |
| Winsorized Mean ( 14 / 24 ) | 337.208333333333 | 3.46799797816057 | 97.2342935194526 |
| Winsorized Mean ( 15 / 24 ) | 337.416666666667 | 3.38015820392598 | 99.8227438806754 |
| Winsorized Mean ( 16 / 24 ) | 338.083333333333 | 3.17336208414008 | 106.537900299186 |
| Winsorized Mean ( 17 / 24 ) | 337.5875 | 3.01120945544897 | 112.110268313987 |
| Winsorized Mean ( 18 / 24 ) | 337.5375 | 2.96705003424480 | 113.761984497816 |
| Winsorized Mean ( 19 / 24 ) | 337.748611111111 | 2.88566962327789 | 117.043409400226 |
| Winsorized Mean ( 20 / 24 ) | 336.470833333333 | 2.69013545852458 | 125.075795818056 |
| Winsorized Mean ( 21 / 24 ) | 336.529166666667 | 2.60794608376431 | 129.039924851867 |
| Winsorized Mean ( 22 / 24 ) | 335.0625 | 2.17306876242887 | 154.188632128463 |
| Winsorized Mean ( 23 / 24 ) | 334.455555555556 | 2.02770027935170 | 164.943290170324 |
| Winsorized Mean ( 24 / 24 ) | 335.288888888889 | 1.79379860662803 | 186.915569925189 |
| Trimmed Mean ( 1 / 24 ) | 338.884285714286 | 5.19388900185788 | 65.246732379738 |
| Trimmed Mean ( 2 / 24 ) | 338.523529411765 | 4.84770843541909 | 69.8316604477263 |
| Trimmed Mean ( 3 / 24 ) | 338.469696969697 | 4.66967300917027 | 72.4825263578441 |
| Trimmed Mean ( 4 / 24 ) | 338.4890625 | 4.50322752267413 | 75.1658806479751 |
| Trimmed Mean ( 5 / 24 ) | 338.482258064516 | 4.3270902238525 | 78.2239889981214 |
| Trimmed Mean ( 6 / 24 ) | 338.393333333333 | 4.16655086844739 | 81.2166571386254 |
| Trimmed Mean ( 7 / 24 ) | 338.120689655172 | 4.03990212592558 | 83.69526763664 |
| Trimmed Mean ( 8 / 24 ) | 337.714285714286 | 3.92270896703722 | 86.092108426121 |
| Trimmed Mean ( 9 / 24 ) | 337.374074074074 | 3.82689933585935 | 88.158596415841 |
| Trimmed Mean ( 10 / 24 ) | 337.063461538462 | 3.73650717766395 | 90.2081664805452 |
| Trimmed Mean ( 11 / 24 ) | 336.74 | 3.62829020282534 | 92.8095552383823 |
| Trimmed Mean ( 12 / 24 ) | 336.43125 | 3.51067288140472 | 95.8309877807198 |
| Trimmed Mean ( 13 / 24 ) | 336.226086956522 | 3.40624665549822 | 98.7086729065258 |
| Trimmed Mean ( 14 / 24 ) | 336.068181818182 | 3.32386745360363 | 101.107576192254 |
| Trimmed Mean ( 15 / 24 ) | 335.928571428571 | 3.23283331091997 | 103.91150397203 |
| Trimmed Mean ( 16 / 24 ) | 335.75 | 3.13081049553855 | 107.240601268728 |
| Trimmed Mean ( 17 / 24 ) | 335.473684210526 | 3.04096423890243 | 110.318194445985 |
| Trimmed Mean ( 18 / 24 ) | 335.225 | 2.95723319584425 | 113.357648112123 |
| Trimmed Mean ( 19 / 24 ) | 334.952941176471 | 2.8490053156611 | 117.568380562585 |
| Trimmed Mean ( 20 / 24 ) | 334.621875 | 2.71511129091914 | 123.244257470831 |
| Trimmed Mean ( 21 / 24 ) | 334.4 | 2.58532107434865 | 129.345636531528 |
| Trimmed Mean ( 22 / 24 ) | 334.139285714286 | 2.41763439456003 | 138.209187653079 |
| Trimmed Mean ( 23 / 24 ) | 334.023076923077 | 2.32684208377798 | 143.552104052004 |
| Trimmed Mean ( 24 / 24 ) | 333.966666666667 | 2.23514959673995 | 149.415800693506 |
| Median | 332.5 | ||
| Midrange | 352.15 | ||
| Midmean - Weighted Average at Xnp | 334.510810810811 | ||
| Midmean - Weighted Average at X(n+1)p | 335.225 | ||
| Midmean - Empirical Distribution Function | 334.510810810811 | ||
| Midmean - Empirical Distribution Function - Averaging | 335.225 | ||
| Midmean - Empirical Distribution Function - Interpolation | 335.225 | ||
| Midmean - Closest Observation | 334.510810810811 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | 335.225 | ||
| Midmean - MS Excel (old versions) | 335.473684210526 | ||
| Number of observations | 72 | ||
 |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/1ihgungwu42esh61192970232/1ocjt1192970463.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/1ihgungwu42esh61192970232/1ocjt1192970463.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/1ihgungwu42esh61192970232/2if0n1192970463.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/1ihgungwu42esh61192970232/2if0n1192970463.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.