Home » date » 2009 » Jun » 03 »

Opgave 5, centrummaten aantal overvallen

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 03 Jun 2009 04:09:36 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/03/t1244023828evnwbc95z503pnu.htm/, Retrieved Wed, 03 Jun 2009 12:10:30 +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/2009/Jun/03/t1244023828evnwbc95z503pnu.htm/},
    year = {2009},
}
@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 = {2009},
    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:

Dataseries X:

» Textbox « » Textfile « » CSV «

41 39 50 40 43 38 44 35 39 35 29 49 50 59 63 32 47 53 60 57 52 70 90 74 62 55 84 94 70 108 139 120 97 126 149 158 124 140 109 114 77 120 133 110 92 97 78 99 107 112 90 98 125 155 190 236 189 174 178 136 161 171 149 184 155 276 224 213 279 268 287 238 213 257 293 212 246 353 339 308 247 257 322 298 273 312 249 286 279 309 401 309 328 353 354 327 324 285 243 241 287 355 460 364 487 452 391 500 451 375 372 302 316 398 394 431 431

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	197.632478632479	11.8109781132695	16.7329476642109
Geometric Mean	152.340681037301
Harmonic Mean	111.268229489883
Quadratic Mean	235.032985500981
Winsorized Mean ( 1 / 39 )	197.547008547009	11.7838218741402	16.76425616892
Winsorized Mean ( 2 / 39 )	197.136752136752	11.6840082431934	16.8723564750645
Winsorized Mean ( 3 / 39 )	196.931623931624	11.6447484302112	16.9116254517532
Winsorized Mean ( 4 / 39 )	197	11.6261073426248	16.9446224943872
Winsorized Mean ( 5 / 39 )	196.188034188034	11.4650890003532	17.1117759471375
Winsorized Mean ( 6 / 39 )	196.188034188034	11.4650890003532	17.1117759471375
Winsorized Mean ( 7 / 39 )	194.452991452991	11.156091140903	17.4302082151376
Winsorized Mean ( 8 / 39 )	194.316239316239	11.1153733691660	17.4817554806816
Winsorized Mean ( 9 / 39 )	194.162393162393	11.0488623335636	17.5730665565971
Winsorized Mean ( 10 / 39 )	193.991452991453	10.9990119303208	17.6371708859303
Winsorized Mean ( 11 / 39 )	192.769230769231	10.7397020539945	17.9492158907269
Winsorized Mean ( 12 / 39 )	192.666666666667	10.6710037601732	18.0551587270304
Winsorized Mean ( 13 / 39 )	191.888888888889	10.5310844515453	18.2211898282453
Winsorized Mean ( 14 / 39 )	190.811965811966	10.3817917104589	18.3794831502675
Winsorized Mean ( 15 / 39 )	190.940170940171	10.3344833406327	18.4760248429102
Winsorized Mean ( 16 / 39 )	190.940170940171	10.3000897803845	18.5377190889926
Winsorized Mean ( 17 / 39 )	191.230769230769	10.2666941148670	18.6263238284125
Winsorized Mean ( 18 / 39 )	189.384615384615	9.94539205803429	19.0424484303384
Winsorized Mean ( 19 / 39 )	187.923076923077	9.68081188492346	19.4119128805448
Winsorized Mean ( 20 / 39 )	187.923076923077	9.63993035616744	19.4942359519068
Winsorized Mean ( 21 / 39 )	187.743589743590	9.53232323429972	19.6954703621506
Winsorized Mean ( 22 / 39 )	187.555555555556	9.4647689630746	19.8161789566418
Winsorized Mean ( 23 / 39 )	187.752136752137	9.16713611837774	20.4810023902386
Winsorized Mean ( 24 / 39 )	186.931623931624	9.06887980816308	20.6124271007940
Winsorized Mean ( 25 / 39 )	187.145299145299	8.89858591372611	21.030903219873
Winsorized Mean ( 26 / 39 )	187.811965811966	8.82596853301775	21.2794737607964
Winsorized Mean ( 27 / 39 )	187.811965811966	8.7737462011722	21.4061316005327
Winsorized Mean ( 28 / 39 )	187.811965811966	8.45180971613407	22.2215090163995
Winsorized Mean ( 29 / 39 )	188.307692307692	8.18097198467144	23.0177652069365
Winsorized Mean ( 30 / 39 )	187.025641025641	8.03397278840786	23.2793470865994
Winsorized Mean ( 31 / 39 )	185.965811965812	7.79969479954803	23.842703688430
Winsorized Mean ( 32 / 39 )	186.512820512821	7.74311836533205	24.0875590056697
Winsorized Mean ( 33 / 39 )	187.076923076923	7.62490213729485	24.5349933295398
Winsorized Mean ( 34 / 39 )	186.786324786325	7.59244848222078	24.6015926513985
Winsorized Mean ( 35 / 39 )	185.290598290598	7.36335447129947	25.1638840711547
Winsorized Mean ( 36 / 39 )	185.598290598291	7.33196735759919	25.3135729533667
Winsorized Mean ( 37 / 39 )	187.179487179487	6.97352594884696	26.8414412669442
Winsorized Mean ( 38 / 39 )	186.529914529915	6.83471415012628	27.2915458397727
Winsorized Mean ( 39 / 39 )	185.196581196581	6.62092218412692	27.9714178850454
Trimmed Mean ( 1 / 39 )	196.469565217391	11.6301530936752	16.8931194314404
Trimmed Mean ( 2 / 39 )	195.353982300885	11.4583903444054	17.0489899915364
Trimmed Mean ( 3 / 39 )	194.414414414414	11.3243337718239	17.167845661538
Trimmed Mean ( 4 / 39 )	193.51376146789	11.1897012765455	17.2939166726023
Trimmed Mean ( 5 / 39 )	192.560747663551	11.0440053529325	17.4357709463099
Trimmed Mean ( 6 / 39 )	191.752380952381	10.9229802866675	17.554950747868
Trimmed Mean ( 7 / 39 )	190.912621359223	10.7859617394373	17.7001018519450
Trimmed Mean ( 8 / 39 )	190.326732673267	10.6951002838386	17.7956940675788
Trimmed Mean ( 9 / 39 )	189.737373737374	10.5986018869504	17.9021134826274
Trimmed Mean ( 10 / 39 )	189.144329896907	10.4994475972626	18.0146934536080
Trimmed Mean ( 11 / 39 )	188.547368421053	10.3936707463563	18.1405946967439
Trimmed Mean ( 12 / 39 )	188.064516129032	10.3122793679388	18.2369493124606
Trimmed Mean ( 13 / 39 )	187.571428571429	10.2274397229335	18.3400179959827
Trimmed Mean ( 14 / 39 )	187.134831460674	10.1483403974282	18.4399442797660
Trimmed Mean ( 15 / 39 )	187.134831460674	10.0759800668869	18.5723701534170
Trimmed Mean ( 16 / 39 )	186.4	9.99630463934433	18.6468906986238
Trimmed Mean ( 17 / 39 )	186	9.90631994464768	18.7758926664280
Trimmed Mean ( 18 / 39 )	185.555555555556	9.80395800646823	18.9265963229477
Trimmed Mean ( 19 / 39 )	185.240506329114	9.72489511185945	19.0480724160422
Trimmed Mean ( 20 / 39 )	185.025974025974	9.66317526815844	19.1475336927460
Trimmed Mean ( 21 / 39 )	184.8	9.59213266218775	19.2657885903187
Trimmed Mean ( 22 / 39 )	184.575342465753	9.51836560513841	19.3914953598874
Trimmed Mean ( 23 / 39 )	184.352112676056	9.43600916971392	19.5370849434693
Trimmed Mean ( 24 / 39 )	184.101449275362	9.37187901849988	19.6440275116602
Trimmed Mean ( 25 / 39 )	183.895522388060	9.30327591273304	19.7667492733789
Trimmed Mean ( 26 / 39 )	183.661538461538	9.23773295019788	19.8816678780051
Trimmed Mean ( 27 / 39 )	183.365079365079	9.16236602333544	20.0128524551487
Trimmed Mean ( 28 / 39 )	183.049180327869	9.07261014098055	20.1760218375354
Trimmed Mean ( 29 / 39 )	182.711864406780	9.00185222993164	20.2971410482893
Trimmed Mean ( 30 / 39 )	182.711864406780	8.94497793512428	20.426195093151
Trimmed Mean ( 31 / 39 )	181.981818181818	8.88802360343093	20.4749476713327
Trimmed Mean ( 32 / 39 )	181.698113207547	8.84250130870946	20.5482709998112
Trimmed Mean ( 33 / 39 )	181.352941176471	8.7829078006565	20.6483940504209
Trimmed Mean ( 34 / 39 )	180.938775510204	8.71484230088573	20.7621399519547
Trimmed Mean ( 35 / 39 )	180.510638297872	8.6227262667044	20.9342883810300
Trimmed Mean ( 36 / 39 )	180.155555555556	8.53495223490303	21.1079746666681
Trimmed Mean ( 37 / 39 )	179.744186046512	8.41445080246112	21.3613687056009
Trimmed Mean ( 38 / 39 )	179.170731707317	8.31175353802427	21.5563094944591
Trimmed Mean ( 39 / 39 )	178.589743589744	8.19043894301407	21.8046608774331
Median	171
Midrange	264.5
Midmean - Weighted Average at Xnp	180.724137931034
Midmean - Weighted Average at X(n+1)p	182.711864406780
Midmean - Empirical Distribution Function	182.711864406780
Midmean - Empirical Distribution Function - Averaging	182.711864406780
Midmean - Empirical Distribution Function - Interpolation	182.711864406780
Midmean - Closest Observation	181.066666666667
Midmean - True Basic - Statistics Graphics Toolkit	182.711864406780
Midmean - MS Excel (old versions)	182.711864406780
Number of observations	117

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/03/t1244023828evnwbc95z503pnu/17taw1244023774.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/03/t1244023828evnwbc95z503pnu/17taw1244023774.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/03/t1244023828evnwbc95z503pnu/244yq1244023774.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/03/t1244023828evnwbc95z503pnu/244yq1244023774.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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