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Tijdreeks1-Stap14

*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 14:55:02 +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/t1277996124a1v53syqdn0qx0h.htm/, Retrieved Thu, 01 Jul 2010 16:55:24 +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/t1277996124a1v53syqdn0qx0h.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:

Steffi Poppe

Dataseries X:

» Textbox « » Textfile « » CSV «

300 299 298 296 316 315 300 290 291 291 292 294 302 296 282 282 298 300 289 275 270 269 268 260 271 269 260 262 285 286 272 248 240 234 231 222 233 236 226 232 248 253 233 216 209 202 204 193 201 201 188 198 220 225 215 198 195 183 180 170 175 180 161 174 195 198 188 173 162 149 140 129 132 133 116 128 148 154 152 141 136 119 114 100 108 115 101 116 131 133 135 124 131 113 118 99 107 112 106 121 138 134 132 121 132 113 112 91 96 99 99 110 127 122 124 114 129 113 115 94

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	190.783333333333	6.39305671305372	29.8422713729069
Geometric Mean	177.937353398701
Harmonic Mean	165.808487345071
Quadratic Mean	203.130376851913
Winsorized Mean ( 1 / 40 )	190.8	6.38845904452595	29.8663572342207
Winsorized Mean ( 2 / 40 )	190.616666666667	6.35057931292182	30.0156343656413
Winsorized Mean ( 3 / 40 )	190.641666666667	6.33401289108164	30.0980863071959
Winsorized Mean ( 4 / 40 )	190.641666666667	6.33401289108164	30.0980863071959
Winsorized Mean ( 5 / 40 )	190.641666666667	6.33401289108164	30.0980863071959
Winsorized Mean ( 6 / 40 )	190.641666666667	6.32073192577392	30.161327660376
Winsorized Mean ( 7 / 40 )	190.641666666667	6.30535759734837	30.2348699060047
Winsorized Mean ( 8 / 40 )	190.975	6.26645131751982	30.4757813191766
Winsorized Mean ( 9 / 40 )	190.9	6.23655304134704	30.6098575181471
Winsorized Mean ( 10 / 40 )	190.983333333333	6.22717662814938	30.6693297360493
Winsorized Mean ( 11 / 40 )	190.983333333333	6.18098855479222	30.8985094601511
Winsorized Mean ( 12 / 40 )	190.983333333333	6.13130123290237	31.1489072349700
Winsorized Mean ( 13 / 40 )	190.875	6.11635043527256	31.2073354886989
Winsorized Mean ( 14 / 40 )	190.991666666667	6.10376537863192	31.2907942587851
Winsorized Mean ( 15 / 40 )	190.866666666667	6.08660573508885	31.358473831537
Winsorized Mean ( 16 / 40 )	190.733333333333	6.06840942124279	31.4305314775996
Winsorized Mean ( 17 / 40 )	190.45	5.9958184541874	31.7638036333459
Winsorized Mean ( 18 / 40 )	190.3	5.97578681105714	31.8451788888926
Winsorized Mean ( 19 / 40 )	189.983333333333	5.89605317512353	32.2221200675235
Winsorized Mean ( 20 / 40 )	189.983333333333	5.89605317512353	32.2221200675235
Winsorized Mean ( 21 / 40 )	188.933333333333	5.71950240403539	33.0331766623669
Winsorized Mean ( 22 / 40 )	188.383333333333	5.65052714792934	33.3390723381211
Winsorized Mean ( 23 / 40 )	188.575	5.5857419341733	33.7600630717125
Winsorized Mean ( 24 / 40 )	188.575	5.53981846141497	34.0399241082413
Winsorized Mean ( 25 / 40 )	188.783333333333	5.47043471115763	34.5097498281601
Winsorized Mean ( 26 / 40 )	188.783333333333	5.47043471115763	34.5097498281601
Winsorized Mean ( 27 / 40 )	188.783333333333	5.41938867180581	34.8348023671881
Winsorized Mean ( 28 / 40 )	187.85	5.20126641010702	36.116204244984
Winsorized Mean ( 29 / 40 )	187.366666666667	5.1436362103139	36.4268892677448
Winsorized Mean ( 30 / 40 )	188.116666666667	5.06679727643833	37.1273323962355
Winsorized Mean ( 31 / 40 )	186.566666666667	4.82812043816772	38.6416762083651
Winsorized Mean ( 32 / 40 )	185.5	4.64846892055031	39.9056126157857
Winsorized Mean ( 33 / 40 )	185.5	4.64846892055031	39.9056126157857
Winsorized Mean ( 34 / 40 )	183.8	4.33899005920658	42.3600878296571
Winsorized Mean ( 35 / 40 )	182.633333333333	4.21338957697453	43.3459403638804
Winsorized Mean ( 36 / 40 )	182.333333333333	4.11898204316254	44.2666006849932
Winsorized Mean ( 37 / 40 )	182.025	4.08657127380771	44.5422305898989
Winsorized Mean ( 38 / 40 )	182.025	4.08657127380771	44.5422305898989
Winsorized Mean ( 39 / 40 )	182.025	4.01918477909581	45.2890349671731
Winsorized Mean ( 40 / 40 )	181.691666666667	3.98443744000982	45.600331138897
Trimmed Mean ( 1 / 40 )	190.567796610169	6.35744716118784	29.9755219002974
Trimmed Mean ( 2 / 40 )	190.327586206897	6.32208683808726	30.1051837915722
Trimmed Mean ( 3 / 40 )	190.175438596491	6.30323926864254	30.1710645100495
Trimmed Mean ( 4 / 40 )	190.008928571429	6.28727616891048	30.2211837792319
Trimmed Mean ( 5 / 40 )	189.836363636364	6.26793376695039	30.2869128319980
Trimmed Mean ( 6 / 40 )	189.657407407407	6.24483396101088	30.3702882400906
Trimmed Mean ( 7 / 40 )	189.471698113208	6.22043710957457	30.4595472593992
Trimmed Mean ( 8 / 40 )	189.278846153846	6.19460449555737	30.5554367981996
Trimmed Mean ( 9 / 40 )	189.029411764706	6.17082671020597	30.6327532179876
Trimmed Mean ( 10 / 40 )	188.78	6.14729274403034	30.7094533904091
Trimmed Mean ( 11 / 40 )	188.510204081633	6.12036923790051	30.8004626443580
Trimmed Mean ( 12 / 40 )	188.229166666667	6.0948727223349	30.8831989184768
Trimmed Mean ( 13 / 40 )	187.936170212766	6.07093748728502	30.9566966562215
Trimmed Mean ( 14 / 40 )	187.641304347826	6.04372864065928	31.0472748702624
Trimmed Mean ( 15 / 40 )	187.322222222222	6.01223944678583	31.1568133438806
Trimmed Mean ( 16 / 40 )	187	5.9765083483266	31.2891723898218
Trimmed Mean ( 17 / 40 )	186.674418604651	5.93596568449603	31.4480285983156
Trimmed Mean ( 18 / 40 )	186.357142857143	5.89661948081548	31.6040645769074
Trimmed Mean ( 19 / 40 )	186.036585365854	5.85176324958241	31.7915434085836
Trimmed Mean ( 20 / 40 )	185.725	5.80783993812118	31.9783261899056
Trimmed Mean ( 21 / 40 )	185.397435897436	5.75473685337667	32.2164923646598
Trimmed Mean ( 22 / 40 )	185.131578947368	5.71318395591525	32.4042741098314
Trimmed Mean ( 23 / 40 )	184.891891891892	5.67086148323834	32.6038455424077
Trimmed Mean ( 24 / 40 )	184.625	5.62639089046159	32.814108296847
Trimmed Mean ( 25 / 40 )	184.342857142857	5.57697446543464	33.0542767024289
Trimmed Mean ( 26 / 40 )	184.029411764706	5.52430858414438	33.3126596680169
Trimmed Mean ( 27 / 40 )	183.696969696970	5.45889363078701	33.6509523946313
Trimmed Mean ( 28 / 40 )	183.34375	5.38494460029203	34.0474719071496
Trimmed Mean ( 29 / 40 )	183.032258064516	5.3240803617656	34.3781922186874
Trimmed Mean ( 30 / 40 )	182.733333333333	5.25587466134267	34.7674450224908
Trimmed Mean ( 31 / 40 )	182.362068965517	5.1799264860035	35.2055322519097
Trimmed Mean ( 32 / 40 )	182.071428571429	5.12048706229222	35.5574433362445
Trimmed Mean ( 33 / 40 )	181.833333333333	5.07076064875411	35.8591828581004
Trimmed Mean ( 34 / 40 )	181.576923076923	5.00439822799089	36.283468022452
Trimmed Mean ( 35 / 40 )	181.42	4.96700049099565	36.5250618212912
Trimmed Mean ( 36 / 40 )	181.333333333333	4.93408083276942	36.7511882109872
Trimmed Mean ( 37 / 40 )	181.260869565217	4.90069844007853	36.986742151454
Trimmed Mean ( 38 / 40 )	181.204545454545	4.85580443683745	37.3171011748081
Trimmed Mean ( 39 / 40 )	181.142857142857	4.78984255177711	37.8181234946126
Trimmed Mean ( 40 / 40 )	181.075	4.70969914385633	38.4472541597922
Median	185.5
Midrange	203.5
Midmean - Weighted Average at Xnp	182.095238095238
Midmean - Weighted Average at X(n+1)p	184
Midmean - Empirical Distribution Function	182.095238095238
Midmean - Empirical Distribution Function - Averaging	184
Midmean - Empirical Distribution Function - Interpolation	184
Midmean - Closest Observation	182.095238095238
Midmean - True Basic - Statistics Graphics Toolkit	184
Midmean - MS Excel (old versions)	182.095238095238
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277996124a1v53syqdn0qx0h/1pl6l1277996098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277996124a1v53syqdn0qx0h/1pl6l1277996098.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277996124a1v53syqdn0qx0h/20dnn1277996098.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/01/t1277996124a1v53syqdn0qx0h/20dnn1277996098.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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