Tijdreeks A - 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: Fri, 06 Aug 2010 10:56:59 +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/06/t128109228465fd7eu9esyhlto.htm/, Retrieved Fri, 06 Aug 2010 12:58:07 +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/06/t128109228465fd7eu9esyhlto.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:

Quaglia Laura

Dataseries X:

» Textbox « » Textfile « » CSV «

239 238 237 235 255 254 239 229 230 230 231 233 239 236 231 235 253 257 236 226 226 221 217 219 225 226 225 229 242 252 233 232 225 218 209 211 220 225 215 222 238 246 226 230 222 214 208 203 208 212 199 200 223 225 203 207 195 198 193 189 184 188 180 186 215 212 191 190 180 190 189 181 174 179 165 185 211 209 183 178 170 182 195 188 175 176 162 193 211 207 179 176 167 175 190 173 159 159 147 181 196 199 171 170 156 164 178 155 138 142 113 148 156 158 141 139 119 120 125 102

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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 198.491666666667 3.09141750035987 64.2073309876651 Geometric Mean 195.321230359402 Harmonic Mean 191.785645157560 Quadratic Mean 201.336058204519 Winsorized Mean ( 1 / 40 ) 198.566666666667 3.06602721380894 64.7635043069258 Winsorized Mean ( 2 / 40 ) 198.65 3.04072843439935 65.3297406478986 Winsorized Mean ( 3 / 40 ) 198.65 3.03145623269680 65.5295622801321 Winsorized Mean ( 4 / 40 ) 198.783333333333 2.99100386297143 66.4604067531532 Winsorized Mean ( 5 / 40 ) 199.075 2.84988973446332 69.853579804374 Winsorized Mean ( 6 / 40 ) 198.925 2.81420870908028 70.6859442791687 Winsorized Mean ( 7 / 40 ) 198.866666666667 2.77162830048350 71.7508428644544 Winsorized Mean ( 8 / 40 ) 198.933333333333 2.76000175915721 72.0772487456962 Winsorized Mean ( 9 / 40 ) 199.308333333333 2.69691689915931 73.9022894607774 Winsorized Mean ( 10 / 40 ) 199.308333333333 2.67318345434407 74.5584194790097 Winsorized Mean ( 11 / 40 ) 199.95 2.57427072156352 77.6724834436052 Winsorized Mean ( 12 / 40 ) 199.95 2.54736228712147 78.4929576020159 Winsorized Mean ( 13 / 40 ) 199.841666666667 2.53424710318838 78.8564250168197 Winsorized Mean ( 14 / 40 ) 200.075 2.50078874112371 80.0047587826623 Winsorized Mean ( 15 / 40 ) 200.075 2.46823411001646 81.0599769236095 Winsorized Mean ( 16 / 40 ) 200.075 2.46823411001646 81.0599769236095 Winsorized Mean ( 17 / 40 ) 200.216666666667 2.37650684421040 84.2483021475113 Winsorized Mean ( 18 / 40 ) 200.516666666667 2.33653192129583 85.8180728622194 Winsorized Mean ( 19 / 40 ) 200.516666666667 2.29748319651787 87.2766629895597 Winsorized Mean ( 20 / 40 ) 200.683333333333 2.2356289677447 89.76593890523 Winsorized Mean ( 21 / 40 ) 201.208333333333 2.17065660958000 92.6946862278068 Winsorized Mean ( 22 / 40 ) 201.025 2.14970073156570 93.5130165088544 Winsorized Mean ( 23 / 40 ) 201.216666666667 2.12663459403474 94.6174144025892 Winsorized Mean ( 24 / 40 ) 201.616666666667 2.07961909803306 96.9488435922518 Winsorized Mean ( 25 / 40 ) 201.616666666667 2.03192941844230 99.2242470809975 Winsorized Mean ( 26 / 40 ) 201.833333333333 2.00738598831144 100.545353264675 Winsorized Mean ( 27 / 40 ) 201.158333333333 1.93250957409485 104.091765458679 Winsorized Mean ( 28 / 40 ) 201.391666666667 1.90617806686461 105.652074256592 Winsorized Mean ( 29 / 40 ) 201.391666666667 1.90617806686461 105.652074256592 Winsorized Mean ( 30 / 40 ) 201.891666666667 1.85106509703577 109.06783720895 Winsorized Mean ( 31 / 40 ) 201.633333333333 1.82301399300009 110.604380497107 Winsorized Mean ( 32 / 40 ) 201.9 1.79418606656517 112.530134840765 Winsorized Mean ( 33 / 40 ) 201.9 1.79418606656517 112.530134840765 Winsorized Mean ( 34 / 40 ) 202.183333333333 1.76401881271581 114.615179767873 Winsorized Mean ( 35 / 40 ) 202.183333333333 1.76401881271581 114.615179767873 Winsorized Mean ( 36 / 40 ) 201.883333333333 1.66783642423406 121.045043986281 Winsorized Mean ( 37 / 40 ) 201.575 1.63524994020693 123.268617869200 Winsorized Mean ( 38 / 40 ) 201.891666666667 1.60198563035085 126.025891145135 Winsorized Mean ( 39 / 40 ) 201.891666666667 1.53405760966353 131.606313475378 Winsorized Mean ( 40 / 40 ) 201.891666666667 1.46494021605023 137.815635378627 Trimmed Mean ( 1 / 40 ) 198.813559322034 2.99361794362136 66.4124691481273 Trimmed Mean ( 2 / 40 ) 199.068965517241 2.91299945722116 68.3381402711081 Trimmed Mean ( 3 / 40 ) 199.289473684211 2.83811865684949 70.2188660094415 Trimmed Mean ( 4 / 40 ) 199.517857142857 2.75830855969709 72.33340753029 Trimmed Mean ( 5 / 40 ) 199.718181818182 2.68244631537944 74.4537479363986 Trimmed Mean ( 6 / 40 ) 199.861111111111 2.63639549909438 75.8084707623591 Trimmed Mean ( 7 / 40 ) 200.037735849057 2.59306298737195 77.1434156529277 Trimmed Mean ( 8 / 40 ) 200.230769230769 2.55321374787752 78.4230342630814 Trimmed Mean ( 9 / 40 ) 200.421568627451 2.51056537693091 79.8312485582271 Trimmed Mean ( 10 / 40 ) 200.57 2.47387378723158 81.075275964038 Trimmed Mean ( 11 / 40 ) 200.724489795918 2.43626005539001 82.3904202475566 Trimmed Mean ( 12 / 40 ) 200.8125 2.40933457454652 83.3477019428887 Trimmed Mean ( 13 / 40 ) 200.904255319149 2.38249685661424 84.3250872551636 Trimmed Mean ( 14 / 40 ) 201.010869565217 2.35352022914528 85.408600731772 Trimmed Mean ( 15 / 40 ) 201.1 2.32497432630503 86.4955787789703 Trimmed Mean ( 16 / 40 ) 201.193181818182 2.29646142964423 87.6100853343531 Trimmed Mean ( 17 / 40 ) 201.290697674419 2.26348316254070 88.9296200677166 Trimmed Mean ( 18 / 40 ) 201.380952380952 2.23740849560837 90.0063411648908 Trimmed Mean ( 19 / 40 ) 201.451219512195 2.21218277192188 91.06445546413 Trimmed Mean ( 20 / 40 ) 201.525 2.18749141047138 92.1260760318019 Trimmed Mean ( 21 / 40 ) 201.589743589744 2.16613015443809 93.0644648368496 Trimmed Mean ( 22 / 40 ) 201.618421052632 2.14876276969870 93.830004826872 Trimmed Mean ( 23 / 40 ) 201.662162162162 2.13018338158495 94.6689209508887 Trimmed Mean ( 24 / 40 ) 201.694444444444 2.11047010695135 95.568491484487 Trimmed Mean ( 25 / 40 ) 201.7 2.09231562606817 96.400369756369 Trimmed Mean ( 26 / 40 ) 201.705882352941 2.07582967572921 97.168801810334 Trimmed Mean ( 27 / 40 ) 201.696969696970 2.05823941976554 97.9949017398305 Trimmed Mean ( 28 / 40 ) 201.734375 2.04551898315960 98.6225875490984 Trimmed Mean ( 29 / 40 ) 201.758064516129 2.03222134052253 99.2795718129073 Trimmed Mean ( 30 / 40 ) 201.783333333333 2.01464856767231 100.158080456916 Trimmed Mean ( 31 / 40 ) 201.775862068966 1.99940146873534 100.918132363178 Trimmed Mean ( 32 / 40 ) 201.785714285714 1.98299823593229 101.757888952860 Trimmed Mean ( 33 / 40 ) 201.777777777778 1.96522291224316 102.674244494464 Trimmed Mean ( 34 / 40 ) 201.769230769231 1.94137598429043 103.931042931376 Trimmed Mean ( 35 / 40 ) 201.74 1.91467160289664 105.365327241912 Trimmed Mean ( 36 / 40 ) 201.708333333333 1.87932912367226 107.329967270017 Trimmed Mean ( 37 / 40 ) 201.695652173913 1.85015198885188 109.015720540385 Trimmed Mean ( 38 / 40 ) 201.704545454545 1.81681700889794 111.020837248159 Trimmed Mean ( 39 / 40 ) 201.690476190476 1.77820332899656 113.423742325513 Trimmed Mean ( 40 / 40 ) 201.675 1.73994823825161 115.908620478649 Median 201.5 Midrange 179.5 Midmean - Weighted Average at Xnp 202.107692307692 Midmean - Weighted Average at X(n+1)p 202.936507936508 Midmean - Empirical Distribution Function 202.107692307692 Midmean - Empirical Distribution Function - Averaging 202.936507936508 Midmean - Empirical Distribution Function - Interpolation 202.936507936508 Midmean - Closest Observation 202.107692307692 Midmean - True Basic - Statistics Graphics Toolkit 202.936507936508 Midmean - MS Excel (old versions) 202.107692307692 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/06/t128109228465fd7eu9esyhlto/1e9l91281092217.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t128109228465fd7eu9esyhlto/1e9l91281092217.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t128109228465fd7eu9esyhlto/2p12c1281092217.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t128109228465fd7eu9esyhlto/2p12c1281092217.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')

Copyright

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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We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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