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tijdreeks A-Stap 14

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

Title produced by software: Central Tendency

Date of computation: Wed, 04 Aug 2010 14:40:37 +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/Aug/04/t1280932817o9f0zmdksfl0c2w.htm/, Retrieved Wed, 04 Aug 2010 16:40:20 +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/Aug/04/t1280932817o9f0zmdksfl0c2w.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:

Keirsebelik gilian

Dataseries X:

» Textbox « » Textfile « » CSV «

668 667 666 664 684 683 668 658 659 659 660 662 659 655 655 655 674 674 665 644 638 648 641 637 651 649 652 650 661 666 652 624 613 623 615 613 621 612 611 609 631 632 624 596 584 587 581 574 593 582 571 572 594 588 571 546 535 537 527 515 545 538 520 523 541 529 504 473 455 458 450 442 469 455 439 443 461 451 425 393 366 359 351 343 366 355 344 351 367 364 353 313 278 274 261 255 274 262 265 274 291 289 277 238 203 198 190 187 201 181 181 196 207 202 186 154 120 107 99 100

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	476.675	16.0989245882810	29.6091206208263
Geometric Mean	433.045752022036
Harmonic Mean	376.569979032633
Quadratic Mean	507.996973416181
Winsorized Mean ( 1 / 40 )	476.675	16.0963840716502	29.6137938730939
Winsorized Mean ( 2 / 40 )	476.641666666667	16.0578034811029	29.6828683466942
Winsorized Mean ( 3 / 40 )	476.966666666667	15.9958937891545	29.8180691215928
Winsorized Mean ( 4 / 40 )	477.9	15.7712881341939	30.3019002591082
Winsorized Mean ( 5 / 40 )	479.025	15.5837731741879	30.7387045900688
Winsorized Mean ( 6 / 40 )	478.975	15.5786900278295	30.7455247613483
Winsorized Mean ( 7 / 40 )	479.208333333333	15.5261994887639	30.8644967289084
Winsorized Mean ( 8 / 40 )	479.275	15.5156330421120	30.8898127906974
Winsorized Mean ( 9 / 40 )	479.425	15.4725684002094	30.9854826683791
Winsorized Mean ( 10 / 40 )	479.841666666667	15.3861227255497	31.1866527536438
Winsorized Mean ( 11 / 40 )	479.841666666667	15.3393910630096	31.2816633134666
Winsorized Mean ( 12 / 40 )	480.041666666667	15.2832518310495	31.409654959124
Winsorized Mean ( 13 / 40 )	480.041666666667	15.2558864785225	31.4659962462672
Winsorized Mean ( 14 / 40 )	480.041666666667	15.2264898717397	31.5267452124748
Winsorized Mean ( 15 / 40 )	480.541666666667	15.1503339390276	31.7182227534127
Winsorized Mean ( 16 / 40 )	484.675	14.5417835266949	33.3298181141443
Winsorized Mean ( 17 / 40 )	486.941666666667	14.1903491084538	34.31498851403
Winsorized Mean ( 18 / 40 )	487.391666666667	14.0220878971808	34.7588511953814
Winsorized Mean ( 19 / 40 )	487.55	14.0006294876488	34.8234342198765
Winsorized Mean ( 20 / 40 )	488.05	13.9331529849076	35.0279653520388
Winsorized Mean ( 21 / 40 )	489.1	13.6703506683244	35.778160477865
Winsorized Mean ( 22 / 40 )	489.1	13.6703506683244	35.778160477865
Winsorized Mean ( 23 / 40 )	488.908333333333	13.6511919418504	35.8143329473297
Winsorized Mean ( 24 / 40 )	489.308333333333	13.5520668099960	36.1058088182106
Winsorized Mean ( 25 / 40 )	489.308333333333	13.5039251262065	36.2345265365662
Winsorized Mean ( 26 / 40 )	491.475	13.1716738475707	37.3130253366123
Winsorized Mean ( 27 / 40 )	491.025	13.0242150174484	37.7009285659197
Winsorized Mean ( 28 / 40 )	495.458333333333	12.3034513606069	40.269865650844
Winsorized Mean ( 29 / 40 )	501.983333333333	11.3515382497228	44.2216131673247
Winsorized Mean ( 30 / 40 )	501.983333333333	11.2969963493521	44.4351151235102
Winsorized Mean ( 31 / 40 )	502.5	10.9566436108731	45.8625850987187
Winsorized Mean ( 32 / 40 )	502.233333333333	10.9302008390889	45.9491404345684
Winsorized Mean ( 33 / 40 )	500.858333333333	10.6776376692199	46.9072231938662
Winsorized Mean ( 34 / 40 )	501.425	10.6118140291279	47.2515819278081
Winsorized Mean ( 35 / 40 )	502.3	10.4486311810410	48.0732826431293
Winsorized Mean ( 36 / 40 )	503.2	10.2181430886082	49.2457382556129
Winsorized Mean ( 37 / 40 )	501.966666666667	9.96975070090932	50.3489687681842
Winsorized Mean ( 38 / 40 )	501.333333333333	9.9095936690331	50.5907053384006
Winsorized Mean ( 39 / 40 )	501.658333333333	9.8723185972932	50.8146418077389
Winsorized Mean ( 40 / 40 )	509.991666666667	8.8621444616103	57.547207549582
Trimmed Mean ( 1 / 40 )	478.118644067797	15.95652761421	29.963827696573
Trimmed Mean ( 2 / 40 )	479.612068965517	15.7994067959910	30.3563339534505
Trimmed Mean ( 3 / 40 )	481.175438596491	15.6449170311234	30.7560236746069
Trimmed Mean ( 4 / 40 )	482.678571428571	15.4957762662055	31.1490410765183
Trimmed Mean ( 5 / 40 )	483.981818181818	15.3974315298947	31.4326332441972
Trimmed Mean ( 6 / 40 )	485.083333333333	15.3329502694938	31.6366599256796
Trimmed Mean ( 7 / 40 )	486.235849056604	15.2579118084991	31.8677847374734
Trimmed Mean ( 8 / 40 )	487.394230769231	15.1803658066884	32.1068831262608
Trimmed Mean ( 9 / 40 )	488.588235294118	15.0912217007477	32.3756581794774
Trimmed Mean ( 10 / 40 )	489.81	14.9942864202511	32.6664428217451
Trimmed Mean ( 11 / 40 )	491.030612244898	14.8949293902463	32.9662933861533
Trimmed Mean ( 12 / 40 )	492.302083333333	14.7855216031468	33.2962269811677
Trimmed Mean ( 13 / 40 )	493.606382978723	14.6657350171420	33.6571186102691
Trimmed Mean ( 14 / 40 )	494.967391304348	14.5294521971181	34.0664867876108
Trimmed Mean ( 15 / 40 )	496.388888888889	14.3743311797464	34.5330076705277
Trimmed Mean ( 16 / 40 )	497.829545454545	14.2041874040558	35.0480834484342
Trimmed Mean ( 17 / 40 )	498.976744186047	14.0891530761552	35.4156663277742
Trimmed Mean ( 18 / 40 )	499.988095238095	13.9982325542766	35.7179446261839
Trimmed Mean ( 19 / 40 )	501.012195121951	13.9088582067764	36.021087257749
Trimmed Mean ( 20 / 40 )	502.075	13.8024462952832	36.3757981200461
Trimmed Mean ( 21 / 40 )	503.153846153846	13.6821230741386	36.7745446687939
Trimmed Mean ( 22 / 40 )	504.210526315789	13.5696683923008	37.1571737598149
Trimmed Mean ( 23 / 40 )	505.324324324324	13.4321571757193	37.6204892269855
Trimmed Mean ( 24 / 40 )	506.513888888889	13.2672215577381	38.1778420360716
Trimmed Mean ( 25 / 40 )	507.742857142857	13.0807285700415	38.8160991510618
Trimmed Mean ( 26 / 40 )	509.044117647059	12.8610189877993	39.5803876916726
Trimmed Mean ( 27 / 40 )	510.272727272727	12.6432223005838	40.3593890181909
Trimmed Mean ( 28 / 40 )	511.609375	12.3972286567126	41.2680437835584
Trimmed Mean ( 29 / 40 )	512.725806451613	12.2054761694028	42.0078495369917
Trimmed Mean ( 30 / 40 )	513.466666666667	12.1069263144461	42.410984698403
Trimmed Mean ( 31 / 40 )	514.258620689655	11.9849310595184	42.9087675294747
Trimmed Mean ( 32 / 40 )	515.071428571429	11.8748303376221	43.3750558051822
Trimmed Mean ( 33 / 40 )	515.962962962963	11.7318324679429	43.9797418155114
Trimmed Mean ( 34 / 40 )	517.019230769231	11.5800541833948	44.6473930588867
Trimmed Mean ( 35 / 40 )	518.12	11.3904951425229	45.4870480621831
Trimmed Mean ( 36 / 40 )	519.25	11.1702452718203	46.4851028213262
Trimmed Mean ( 37 / 40 )	520.413043478261	10.9229438126034	47.6440282406088
Trimmed Mean ( 38 / 40 )	521.772727272727	10.6377436267103	49.0491917818559
Trimmed Mean ( 39 / 40 )	523.309523809524	10.2640792002763	50.9845562956525
Trimmed Mean ( 40 / 40 )	524.975	9.76738998152946	53.7477259526598
Median	536
Midrange	391.5
Midmean - Weighted Average at Xnp	510.672131147541
Midmean - Weighted Average at X(n+1)p	513.466666666667
Midmean - Empirical Distribution Function	510.672131147541
Midmean - Empirical Distribution Function - Averaging	513.466666666667
Midmean - Empirical Distribution Function - Interpolation	513.466666666667
Midmean - Closest Observation	510.672131147541
Midmean - True Basic - Statistics Graphics Toolkit	513.466666666667
Midmean - MS Excel (old versions)	512.725806451613
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/04/t1280932817o9f0zmdksfl0c2w/1xekh1280932835.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/04/t1280932817o9f0zmdksfl0c2w/1xekh1280932835.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/04/t1280932817o9f0zmdksfl0c2w/2xekh1280932835.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/04/t1280932817o9f0zmdksfl0c2w/2xekh1280932835.ps (open in new window)

Parameters (Session):

par1 = grey ;

Parameters (R input):

par1 = grey ;

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')

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

Software written by Ed van Stee & Patrick Wessa

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