vraag 8, deel 1, mannen ouder dan 25
| 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 | 5.96164383561644 | 0.0588974722818897 | 101.220707861338 |
| Geometric Mean | 5.94001142157706 | ||
| Harmonic Mean | 5.91764401898883 | ||
| Quadratic Mean | 5.98255454668903 | ||
| Winsorized Mean ( 1 / 24 ) | 5.96849315068493 | 0.0569010968805784 | 104.892409424222 |
| Winsorized Mean ( 2 / 24 ) | 5.96849315068493 | 0.0569010968805784 | 104.892409424222 |
| Winsorized Mean ( 3 / 24 ) | 5.96438356164384 | 0.0561095994458704 | 106.298808413304 |
| Winsorized Mean ( 4 / 24 ) | 5.97534246575343 | 0.0536981253664966 | 111.276556210686 |
| Winsorized Mean ( 5 / 24 ) | 5.97534246575342 | 0.0536981253664966 | 111.276556210686 |
| Winsorized Mean ( 6 / 24 ) | 5.97534246575342 | 0.0536981253664966 | 111.276556210686 |
| Winsorized Mean ( 7 / 24 ) | 5.97534246575342 | 0.0501057034093616 | 119.254736670098 |
| Winsorized Mean ( 8 / 24 ) | 5.97534246575342 | 0.0501057034093616 | 119.254736670098 |
| Winsorized Mean ( 9 / 24 ) | 5.97534246575342 | 0.0501057034093616 | 119.254736670098 |
| Winsorized Mean ( 10 / 24 ) | 5.97534246575342 | 0.0453206410985182 | 131.845938647783 |
| Winsorized Mean ( 11 / 24 ) | 5.97534246575343 | 0.0453206410985182 | 131.845938647783 |
| Winsorized Mean ( 12 / 24 ) | 5.97534246575342 | 0.0453206410985182 | 131.845938647783 |
| Winsorized Mean ( 13 / 24 ) | 5.99315068493151 | 0.0423046484955872 | 141.666481061925 |
| Winsorized Mean ( 14 / 24 ) | 5.99315068493151 | 0.0423046484955872 | 141.666481061925 |
| Winsorized Mean ( 15 / 24 ) | 5.97260273972603 | 0.0326563837354673 | 182.892349260317 |
| Winsorized Mean ( 16 / 24 ) | 5.95068493150685 | 0.0298476144787684 | 199.368861981910 |
| Winsorized Mean ( 17 / 24 ) | 5.95068493150685 | 0.0298476144787684 | 199.368861981910 |
| Winsorized Mean ( 18 / 24 ) | 5.95068493150685 | 0.0298476144787684 | 199.368861981910 |
| Winsorized Mean ( 19 / 24 ) | 5.97671232876712 | 0.0257697736246986 | 231.927234434021 |
| Winsorized Mean ( 20 / 24 ) | 5.97671232876712 | 0.0257697736246986 | 231.927234434021 |
| Winsorized Mean ( 21 / 24 ) | 5.97671232876712 | 0.0257697736246986 | 231.927234434021 |
| Winsorized Mean ( 22 / 24 ) | 5.97671232876712 | 0.0257697736246986 | 231.927234434021 |
| Winsorized Mean ( 23 / 24 ) | 5.97671232876712 | 0.0257697736246986 | 231.927234434021 |
| Winsorized Mean ( 24 / 24 ) | 6.00958904109589 | 0.0210244099236920 | 285.838654350238 |
| Trimmed Mean ( 1 / 24 ) | 5.97042253521127 | 0.0556430983243703 | 107.298527849884 |
| Trimmed Mean ( 2 / 24 ) | 5.97246376811594 | 0.0541493962094765 | 110.296036266250 |
| Trimmed Mean ( 3 / 24 ) | 5.97462686567164 | 0.0523678705423311 | 114.089551547491 |
| Trimmed Mean ( 4 / 24 ) | 5.97846153846154 | 0.0505970273000068 | 118.158355490200 |
| Trimmed Mean ( 5 / 24 ) | 5.97936507936508 | 0.0493770061475157 | 121.096144660968 |
| Trimmed Mean ( 6 / 24 ) | 5.98032786885246 | 0.0478967281661562 | 124.858797204402 |
| Trimmed Mean ( 7 / 24 ) | 5.98135593220339 | 0.0460905631694171 | 129.773982370696 |
| Trimmed Mean ( 8 / 24 ) | 5.98245614035088 | 0.0448558542780894 | 133.370687876367 |
| Trimmed Mean ( 9 / 24 ) | 5.98363636363636 | 0.0433266223524167 | 138.105304285336 |
| Trimmed Mean ( 10 / 24 ) | 5.98490566037736 | 0.0414186234532216 | 144.497937434757 |
| Trimmed Mean ( 11 / 24 ) | 5.98627450980392 | 0.0401975131259983 | 148.921513901621 |
| Trimmed Mean ( 12 / 24 ) | 5.98775510204082 | 0.0386455035029537 | 154.940537948565 |
| Trimmed Mean ( 13 / 24 ) | 5.98936170212766 | 0.0366539991991504 | 163.402680007329 |
| Trimmed Mean ( 14 / 24 ) | 5.98888888888889 | 0.034847752883544 | 171.858682219851 |
| Trimmed Mean ( 15 / 24 ) | 5.98837209302326 | 0.032471011405231 | 184.422099400900 |
| Trimmed Mean ( 16 / 24 ) | 5.99024390243902 | 0.0317775956604765 | 188.505259064939 |
| Trimmed Mean ( 17 / 24 ) | 5.9948717948718 | 0.0313761554402721 | 191.064574698569 |
| Trimmed Mean ( 18 / 24 ) | 6 | 0.0307562342614623 | 195.082400172704 |
| Trimmed Mean ( 19 / 24 ) | 6.00571428571429 | 0.0298294471683158 | 201.335085153486 |
| Trimmed Mean ( 20 / 24 ) | 6.00909090909091 | 0.0296327382054666 | 202.785543051245 |
| Trimmed Mean ( 21 / 24 ) | 6.01290322580645 | 0.0292346588696197 | 205.677215274607 |
| Trimmed Mean ( 22 / 24 ) | 6.01724137931035 | 0.0285395609607704 | 210.838610572232 |
| Trimmed Mean ( 23 / 24 ) | 6.02222222222222 | 0.0273904621520719 | 219.865666697657 |
| Trimmed Mean ( 24 / 24 ) | 6.028 | 0.0255081686262786 | 236.31645565452 |
| Median | 6.1 | ||
| Midrange | 5.65 | ||
| Midmean - Weighted Average at Xnp | 5.98095238095238 | ||
| Midmean - Weighted Average at X(n+1)p | 5.98095238095238 | ||
| Midmean - Empirical Distribution Function | 5.98095238095238 | ||
| Midmean - Empirical Distribution Function - Averaging | 5.98095238095238 | ||
| Midmean - Empirical Distribution Function - Interpolation | 5.98095238095238 | ||
| Midmean - Closest Observation | 5.98095238095238 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | 5.98095238095238 | ||
| Midmean - MS Excel (old versions) | 5.98095238095238 | ||
| Number of observations | 73 | ||
 |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/qjmm6utp6lsu0iz1193043313/1xz4o1193043570.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/qjmm6utp6lsu0iz1193043313/1xz4o1193043570.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/qjmm6utp6lsu0iz1193043313/2dq8b1193043570.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/qjmm6utp6lsu0iz1193043313/2dq8b1193043570.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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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.