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centrummaten

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 01 Jul 2010 12:37:45 +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/01/t1277989719x1nvofxcsuj0hxu.htm/, Retrieved Thu, 01 Jul 2010 15:08:39 +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/01/t1277989719x1nvofxcsuj0hxu.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:

thomas talboom

Dataseries X:

» Textbox « » Textfile « » CSV «

68 67 66 64 84 83 68 58 59 59 60 62 58 58 59 62 87 83 68 58 68 63 65 68 62 69 74 72 94 102 92 81 99 95 92 93 85 92 99 107 125 137 125 115 135 128 120 123 119 128 139 155 164 176 162 155 174 171 162 160 156 163 180 195 203 212 203 184 200 198 195 177 176 180 194 204 206 219 213 196 214 209 213 194 197 211 240 251 254 273 271 245 264 264 262 237 237 251 272 282 278 291 293 271 284 290 288 262 263 275 297 301 296 309 310 292 300 314 310 288

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	169.858333333333	7.57702091293357	22.4175616360507
Geometric Mean	147.624474747113
Harmonic Mean	126.056261984515
Quadratic Mean	188.901539609043
Winsorized Mean ( 1 / 40 )	169.825	7.57176369098751	22.4287242617118
Winsorized Mean ( 2 / 40 )	169.825	7.57176369098751	22.4287242617118
Winsorized Mean ( 3 / 40 )	169.8	7.5678869683199	22.4369101587806
Winsorized Mean ( 4 / 40 )	169.566666666667	7.52357390688758	22.5380475775527
Winsorized Mean ( 5 / 40 )	169.525	7.51747695266793	22.5507841350729
Winsorized Mean ( 6 / 40 )	169.375	7.49580706791735	22.595965779981
Winsorized Mean ( 7 / 40 )	169.375	7.48029215059568	22.6428322036208
Winsorized Mean ( 8 / 40 )	169.308333333333	7.43581118183076	22.7693158410254
Winsorized Mean ( 9 / 40 )	169.233333333333	7.42535905189011	22.7912660048749
Winsorized Mean ( 10 / 40 )	169.15	7.41381527034125	22.8155131780366
Winsorized Mean ( 11 / 40 )	169.15	7.39008770980043	22.8887675819707
Winsorized Mean ( 12 / 40 )	169.05	7.35070691545124	22.9977880963606
Winsorized Mean ( 13 / 40 )	169.158333333333	7.33774055082511	23.0531908510055
Winsorized Mean ( 14 / 40 )	168.808333333333	7.26097446054683	23.2487160298619
Winsorized Mean ( 15 / 40 )	168.683333333333	7.21296668660236	23.3861239989715
Winsorized Mean ( 16 / 40 )	168.283333333333	7.12748244958164	23.6104872265537
Winsorized Mean ( 17 / 40 )	167.858333333333	7.07294224643997	23.7324620341445
Winsorized Mean ( 18 / 40 )	167.558333333333	7.03495470146947	23.8179690479505
Winsorized Mean ( 19 / 40 )	167.4	7.01506382352857	23.8629332834492
Winsorized Mean ( 20 / 40 )	167.233333333333	6.99423265146505	23.9101759502243
Winsorized Mean ( 21 / 40 )	167.408333333333	6.97342389884739	24.0066193826255
Winsorized Mean ( 22 / 40 )	166.675	6.75051779837802	24.6906985476059
Winsorized Mean ( 23 / 40 )	167.058333333333	6.70557583773332	24.9133463517436
Winsorized Mean ( 24 / 40 )	168.258333333333	6.5208295410805	25.8032098942818
Winsorized Mean ( 25 / 40 )	168.466666666667	6.44878480000887	26.1237848511296
Winsorized Mean ( 26 / 40 )	168.466666666667	6.44878480000887	26.1237848511296
Winsorized Mean ( 27 / 40 )	166.891666666667	6.20774563420653	26.8844241534389
Winsorized Mean ( 28 / 40 )	166.425	6.09950935368922	27.2849815205775
Winsorized Mean ( 29 / 40 )	166.908333333333	6.04555530222258	27.6084371061781
Winsorized Mean ( 30 / 40 )	166.658333333333	5.73390447085221	29.0654185434246
Winsorized Mean ( 31 / 40 )	165.366666666667	5.58723702322428	29.5972170107143
Winsorized Mean ( 32 / 40 )	164.566666666667	5.49804820643642	29.9318340777756
Winsorized Mean ( 33 / 40 )	164.841666666667	5.4676153251307	30.1487315519451
Winsorized Mean ( 34 / 40 )	160.025	4.89352259373042	32.7013918777087
Winsorized Mean ( 35 / 40 )	158.858333333333	4.71484181176746	33.6932477642939
Winsorized Mean ( 36 / 40 )	159.758333333333	4.54970981057153	35.1139611062941
Winsorized Mean ( 37 / 40 )	159.758333333333	4.54970981057153	35.1139611062941
Winsorized Mean ( 38 / 40 )	160.391666666667	4.41244255219002	36.3498594643594
Winsorized Mean ( 39 / 40 )	161.691666666667	4.20117023568509	38.4872922532974
Winsorized Mean ( 40 / 40 )	163.691666666667	3.84747353957396	42.5452351999262
Trimmed Mean ( 1 / 40 )	169.584745762712	7.54788015913036	22.4678641138164
Trimmed Mean ( 2 / 40 )	169.336206896552	7.51988811327732	22.5184476611517
Trimmed Mean ( 3 / 40 )	169.078947368421	7.48735045392422	22.5819464988185
Trimmed Mean ( 4 / 40 )	168.821428571429	7.45129201011254	22.6566652256162
Trimmed Mean ( 5 / 40 )	168.618181818182	7.42327990447262	22.714781604367
Trimmed Mean ( 6 / 40 )	168.416666666667	7.3919366718385	22.7838351630221
Trimmed Mean ( 7 / 40 )	168.235849056604	7.3600699228923	22.857914506129
Trimmed Mean ( 8 / 40 )	168.048076923077	7.32575000312134	22.9393682355357
Trimmed Mean ( 9 / 40 )	167.862745098039	7.29348484760379	23.0154375590688
Trimmed Mean ( 10 / 40 )	167.68	7.25713692606364	23.1055306945892
Trimmed Mean ( 11 / 40 )	167.5	7.21622482287646	23.2115828028253
Trimmed Mean ( 12 / 40 )	167.3125	7.17180016826381	23.3292194532107
Trimmed Mean ( 13 / 40 )	167.127659574468	7.12539633599356	23.4552089025886
Trimmed Mean ( 14 / 40 )	166.923913043478	7.07274388697171	23.601011956754
Trimmed Mean ( 15 / 40 )	166.744444444444	7.02148586113759	23.7477433896633
Trimmed Mean ( 16 / 40 )	166.568181818182	6.96749047918025	23.9064814391794
Trimmed Mean ( 17 / 40 )	166.418604651163	6.91489134622051	24.0666984221122
Trimmed Mean ( 18 / 40 )	166.297619047619	6.85954029832833	24.2432600167311
Trimmed Mean ( 19 / 40 )	166.19512195122	6.79868026197319	24.4452034140791
Trimmed Mean ( 20 / 40 )	166.1	6.72903683366801	24.6840675873457
Trimmed Mean ( 21 / 40 )	166.012820512821	6.64925699443202	24.9671234924198
Trimmed Mean ( 22 / 40 )	165.907894736842	6.55752353853562	25.3003887461289
Trimmed Mean ( 23 / 40 )	165.851351351351	6.47761738620083	25.603758521561
Trimmed Mean ( 24 / 40 )	165.763888888889	6.3878764847308	25.9497642581414
Trimmed Mean ( 25 / 40 )	165.585714285714	6.30411127665629	26.2663057517508
Trimmed Mean ( 26 / 40 )	165.382352941176	6.21240331640309	26.6213161828216
Trimmed Mean ( 27 / 40 )	165.166666666667	6.10149522479892	27.0698673982999
Trimmed Mean ( 28 / 40 )	165.046875	6.00158831851953	27.5005325658048
Trimmed Mean ( 29 / 40 )	164.951612903226	5.89510043221927	27.9811370136621
Trimmed Mean ( 30 / 40 )	164.816666666667	5.77230342387905	28.553015072778
Trimmed Mean ( 31 / 40 )	164.689655172414	5.66824327182572	29.0547965700435
Trimmed Mean ( 32 / 40 )	164.642857142857	5.56142958890574	29.6044127702878
Trimmed Mean ( 33 / 40 )	164.648148148148	5.44171566027928	30.2566613963248
Trimmed Mean ( 34 / 40 )	164.634615384615	5.29599217016803	31.0866425203555
Trimmed Mean ( 35 / 40 )	164.96	5.20721320744168	31.6791330464929
Trimmed Mean ( 36 / 40 )	165.395833333333	5.11929838382873	32.3083010468387
Trimmed Mean ( 37 / 40 )	165.804347826087	5.03328242274065	32.9415943514264
Trimmed Mean ( 38 / 40 )	166.25	4.91697721215612	33.8114237318376
Trimmed Mean ( 39 / 40 )	166.690476190476	4.79019615513707	34.7982568546207
Trimmed Mean ( 40 / 40 )	167.075	4.667177742161	35.7978652689239
Median	172.5
Midrange	186
Midmean - Weighted Average at Xnp	163.540983606557
Midmean - Weighted Average at X(n+1)p	164.816666666667
Midmean - Empirical Distribution Function	163.540983606557
Midmean - Empirical Distribution Function - Averaging	164.816666666667
Midmean - Empirical Distribution Function - Interpolation	164.816666666667
Midmean - Closest Observation	163.540983606557
Midmean - True Basic - Statistics Graphics Toolkit	164.816666666667
Midmean - MS Excel (old versions)	166.31746031746
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277989719x1nvofxcsuj0hxu/15stc1277987862.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277989719x1nvofxcsuj0hxu/15stc1277987862.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277989719x1nvofxcsuj0hxu/25stc1277987862.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277989719x1nvofxcsuj0hxu/25stc1277987862.ps (open in new window)

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.1 ;

Parameters (R input):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.1 ;

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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