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tijdreeks 1 - stap 14

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Sat, 17 Jul 2010 12:50:49 +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/Jul/17/t1279371063jdz4rp7x1yd0pkt.htm/, Retrieved Sat, 17 Jul 2010 14:51:03 +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/Jul/17/t1279371063jdz4rp7x1yd0pkt.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:

Van de Walle Mathias

Dataseries X:

» Textbox « » Textfile « » CSV «

268 267 266 264 284 283 268 258 259 259 260 262 255 259 258 258 288 289 271 268 274 284 284 279 273 280 276 271 298 297 278 270 280 289 288 293 285 283 275 268 295 290 267 252 268 278 280 278 261 263 259 265 294 285 255 231 246 258 265 260 238 241 239 233 265 255 224 194 210 222 230 225 206 204 207 195 230 221 195 162 182 203 211 206 187 181 189 174 213 201 177 140 165 192 197 196 176 164 177 165 208 195 164 123 147 173 176 170 157 145 148 135 175 168 140 109 129 150 150 152

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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	229.375	4.57073357265077	50.1834106832386
Geometric Mean	223.308958213028
Harmonic Mean	216.616641113354
Quadratic Mean	234.731744054073
Winsorized Mean ( 1 / 40 )	229.483333333333	4.54529003819109	50.4881605805428
Winsorized Mean ( 2 / 40 )	229.55	4.52199495525483	50.7629933848664
Winsorized Mean ( 3 / 40 )	229.675	4.49166926510789	51.1335511241127
Winsorized Mean ( 4 / 40 )	229.808333333333	4.45881080717632	51.5402745869961
Winsorized Mean ( 5 / 40 )	229.683333333333	4.44423876158051	51.6811417331797
Winsorized Mean ( 6 / 40 )	229.883333333333	4.39708305612114	52.2808712956456
Winsorized Mean ( 7 / 40 )	230	4.37832706626169	52.5314798367449
Winsorized Mean ( 8 / 40 )	230	4.36024823902061	52.7492902678546
Winsorized Mean ( 9 / 40 )	230.15	4.33674693169660	53.0697325956168
Winsorized Mean ( 10 / 40 )	229.9	4.3093020355521	53.3497067746252
Winsorized Mean ( 11 / 40 )	230.083333333333	4.28096862212328	53.7456247972255
Winsorized Mean ( 12 / 40 )	230.483333333333	4.19508488132615	54.9412800583126
Winsorized Mean ( 13 / 40 )	231.025	4.11704563685058	56.1142674572652
Winsorized Mean ( 14 / 40 )	231.258333333333	4.08446355763987	56.6190223195336
Winsorized Mean ( 15 / 40 )	231.133333333333	4.07099003322373	56.7757060191828
Winsorized Mean ( 16 / 40 )	231.266666666667	4.05259078411478	57.0663753106231
Winsorized Mean ( 17 / 40 )	230.841666666667	4.00788780812783	57.5968384640232
Winsorized Mean ( 18 / 40 )	231.291666666667	3.94649785947092	58.6068141685687
Winsorized Mean ( 19 / 40 )	231.608333333333	3.90416167800847	59.3234482675108
Winsorized Mean ( 20 / 40 )	231.941666666667	3.82129327971546	60.6971644646803
Winsorized Mean ( 21 / 40 )	231.941666666667	3.78066909424598	61.3493698826147
Winsorized Mean ( 22 / 40 )	232.125	3.75715121123567	61.7821820175446
Winsorized Mean ( 23 / 40 )	232.316666666667	3.73275649555241	62.2372948633196
Winsorized Mean ( 24 / 40 )	231.916666666667	3.69211786924670	62.8139931821798
Winsorized Mean ( 25 / 40 )	231.916666666667	3.64487252245516	63.6281969363497
Winsorized Mean ( 26 / 40 )	231.7	3.62348393874483	63.9439842750512
Winsorized Mean ( 27 / 40 )	232.375	3.48863827141702	66.6090840956158
Winsorized Mean ( 28 / 40 )	232.141666666667	3.41465157150896	67.9839983099891
Winsorized Mean ( 29 / 40 )	233.35	3.26803657035996	71.4037297245721
Winsorized Mean ( 30 / 40 )	233.6	3.18470609965448	73.3505675846647
Winsorized Mean ( 31 / 40 )	233.858333333333	3.04516183821403	76.7966846289162
Winsorized Mean ( 32 / 40 )	234.391666666667	2.98402130855908	78.5489252353394
Winsorized Mean ( 33 / 40 )	234.666666666667	2.95285868548314	79.4710115388643
Winsorized Mean ( 34 / 40 )	234.666666666667	2.95285868548314	79.4710115388643
Winsorized Mean ( 35 / 40 )	234.666666666667	2.95285868548314	79.4710115388643
Winsorized Mean ( 36 / 40 )	234.666666666667	2.89073506332718	81.1788910176257
Winsorized Mean ( 37 / 40 )	234.975	2.85618064154613	82.2689561654621
Winsorized Mean ( 38 / 40 )	235.925	2.68636359596907	87.8231823696571
Winsorized Mean ( 39 / 40 )	236.25	2.58524822857716	91.383874627012
Winsorized Mean ( 40 / 40 )	236.583333333333	2.54933350254327	92.802033589294
Trimmed Mean ( 1 / 40 )	229.813559322034	4.49628348068868	51.111892813047
Trimmed Mean ( 2 / 40 )	230.155172413793	4.4416355606932	51.8176624959012
Trimmed Mean ( 3 / 40 )	230.473684210526	4.39407602732124	52.4510005692892
Trimmed Mean ( 4 / 40 )	230.758928571429	4.35295735090475	53.0119893142224
Trimmed Mean ( 5 / 40 )	231.018181818182	4.31694648938233	53.514256520524
Trimmed Mean ( 6 / 40 )	231.314814814815	4.27985711250416	54.0473218460959
Trimmed Mean ( 7 / 40 )	231.584905660377	4.24843648476172	54.5106197282284
Trimmed Mean ( 8 / 40 )	231.846153846154	4.2163036017943	54.9880121885646
Trimmed Mean ( 9 / 40 )	232.117647058824	4.18276163296542	55.4938740064566
Trimmed Mean ( 10 / 40 )	232.38	4.14833007657810	56.0177217603878
Trimmed Mean ( 11 / 40 )	232.683673469388	4.11287707381028	56.5744293577496
Trimmed Mean ( 12 / 40 )	232.979166666667	4.07638091892782	57.1534337198167
Trimmed Mean ( 13 / 40 )	233.244680851064	4.04678898244696	57.6369763441504
Trimmed Mean ( 14 / 40 )	233.467391304348	4.02314882771644	58.0310103608235
Trimmed Mean ( 15 / 40 )	233.677777777778	3.99943278680543	58.4277296892466
Trimmed Mean ( 16 / 40 )	233.909090909091	3.97272691405492	58.8787238512557
Trimmed Mean ( 17 / 40 )	234.139534883721	3.94331111401504	59.3763789145519
Trimmed Mean ( 18 / 40 )	234.416666666667	3.91347937352909	59.8998089148671
Trimmed Mean ( 19 / 40 )	234.670731707317	3.88567636873325	60.3937923383519
Trimmed Mean ( 20 / 40 )	234.9125	3.85747645546028	60.897973769219
Trimmed Mean ( 21 / 40 )	235.141025641026	3.83347699968115	61.3388382558663
Trimmed Mean ( 22 / 40 )	235.381578947368	3.80862212526719	61.802292589175
Trimmed Mean ( 23 / 40 )	235.621621621622	3.78072150901532	62.3218666224872
Trimmed Mean ( 24 / 40 )	235.861111111111	3.74929860710100	62.9080625011837
Trimmed Mean ( 25 / 40 )	236.142857142857	3.71501824278397	63.564386958675
Trimmed Mean ( 26 / 40 )	236.441176470588	3.67843669991558	64.2776254586668
Trimmed Mean ( 27 / 40 )	236.772727272727	3.635348051654	65.1306900765667
Trimmed Mean ( 28 / 40 )	237.078125	3.60040952583456	65.8475440915423
Trimmed Mean ( 29 / 40 )	237.419354838710	3.56532661617542	66.5911935701961
Trimmed Mean ( 30 / 40 )	237.7	3.54154519565702	67.1175960966106
Trimmed Mean ( 31 / 40 )	237.982758620690	3.52078706375181	67.5936244684707
Trimmed Mean ( 32 / 40 )	238.267857142857	3.5106971105788	67.8691011038473
Trimmed Mean ( 33 / 40 )	238.537037037037	3.5026906861157	68.1010852549936
Trimmed Mean ( 34 / 40 )	238.807692307692	3.49240054759912	68.3792391659836
Trimmed Mean ( 35 / 40 )	239.1	3.47413181050096	68.8229500323772
Trimmed Mean ( 36 / 40 )	239.416666666667	3.44573347724653	69.4820618737997
Trimmed Mean ( 37 / 40 )	239.760869565217	3.41454473275561	70.2175219042233
Trimmed Mean ( 38 / 40 )	240.113636363636	3.3755769860082	71.1326203961308
Trimmed Mean ( 39 / 40 )	240.428571428571	3.35252480949920	71.7156725424761
Trimmed Mean ( 40 / 40 )	240.75	3.33450299991022	72.1996651394472
Median	249
Midrange	203.5
Midmean - Weighted Average at Xnp	236.868852459016
Midmean - Weighted Average at X(n+1)p	237.7
Midmean - Empirical Distribution Function	236.868852459016
Midmean - Empirical Distribution Function - Averaging	237.7
Midmean - Empirical Distribution Function - Interpolation	237.7
Midmean - Closest Observation	236.868852459016
Midmean - True Basic - Statistics Graphics Toolkit	237.7
Midmean - MS Excel (old versions)	237.952380952381
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/17/t1279371063jdz4rp7x1yd0pkt/18bg51279371045.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/17/t1279371063jdz4rp7x1yd0pkt/18bg51279371045.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/17/t1279371063jdz4rp7x1yd0pkt/28bg51279371045.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/17/t1279371063jdz4rp7x1yd0pkt/28bg51279371045.ps (open in new window)

Parameters (Session):

Parameters (R input):

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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