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 11:02:22 +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/t1281092568t6jwxdh4osiwndi.htm/, Retrieved Fri, 06 Aug 2010 13:02:50 +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/t1281092568t6jwxdh4osiwndi.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:

Jacobs Jeff

Dataseries X:

» Textbox « » Textfile « » CSV «

130 129 128 126 146 145 130 120 121 121 122 124 123 125 120 124 146 149 138 133 135 149 146 141 139 141 138 139 166 179 167 154 151 162 148 143 145 143 148 139 169 186 174 161 151 158 144 135 139 137 149 136 169 185 177 164 145 147 142 126 130 136 139 120 151 166 156 150 141 141 130 110 110 123 133 108 136 148 146 142 132 128 116 90 94 112 130 106 124 139 140 129 113 110 102 78 79 94 121 99 126 137 141 119 96 96 88 64 66 92 120 101 135 146 149 134 101 100 91 70

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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 131.591666666667 2.22238723418628 59.2118532011135 Geometric Mean 129.089602859236 Harmonic Mean 126.234962997056 Quadratic Mean 133.806234284256 Winsorized Mean ( 1 / 40 ) 131.6 2.21648395447569 59.3733149902861 Winsorized Mean ( 2 / 40 ) 131.566666666667 2.18101476309203 60.3236020650058 Winsorized Mean ( 3 / 40 ) 131.716666666667 2.12699678250621 61.9261240778496 Winsorized Mean ( 4 / 40 ) 131.65 2.10254114625819 62.6147080328452 Winsorized Mean ( 5 / 40 ) 131.816666666667 1.99435419274639 66.0949129026796 Winsorized Mean ( 6 / 40 ) 131.916666666667 1.97620945959631 66.7523708208612 Winsorized Mean ( 7 / 40 ) 131.858333333333 1.94782417694113 67.6951928692067 Winsorized Mean ( 8 / 40 ) 131.858333333333 1.92613224126253 68.4575703103874 Winsorized Mean ( 9 / 40 ) 132.008333333333 1.90048260984593 69.4604268670657 Winsorized Mean ( 10 / 40 ) 131.841666666667 1.87594965057788 70.2799601396836 Winsorized Mean ( 11 / 40 ) 131.841666666667 1.81924799903132 72.4704200509592 Winsorized Mean ( 12 / 40 ) 131.741666666667 1.80547315404873 72.9679454780257 Winsorized Mean ( 13 / 40 ) 131.741666666667 1.70911610132425 77.0817538753462 Winsorized Mean ( 14 / 40 ) 131.625 1.66093113249489 79.2477167926196 Winsorized Mean ( 15 / 40 ) 131.5 1.61088732790139 81.6320283376454 Winsorized Mean ( 16 / 40 ) 131.1 1.56602585380635 83.7150930052341 Winsorized Mean ( 17 / 40 ) 131.241666666667 1.54330558656414 85.0393258530543 Winsorized Mean ( 18 / 40 ) 131.841666666667 1.45053819014981 90.8915515371917 Winsorized Mean ( 19 / 40 ) 132 1.38635810507899 95.2134946349083 Winsorized Mean ( 20 / 40 ) 132.166666666667 1.32056208500578 100.083644811057 Winsorized Mean ( 21 / 40 ) 132.166666666667 1.32056208500578 100.083644811057 Winsorized Mean ( 22 / 40 ) 132.166666666667 1.32056208500578 100.083644811057 Winsorized Mean ( 23 / 40 ) 132.55 1.26739161769559 104.584879802982 Winsorized Mean ( 24 / 40 ) 132.55 1.21871781362369 108.761846686955 Winsorized Mean ( 25 / 40 ) 133.175 1.13683319175786 117.145594415725 Winsorized Mean ( 26 / 40 ) 133.825 1.05716592355030 126.588454109997 Winsorized Mean ( 27 / 40 ) 133.825 1.00584165601490 133.047780632002 Winsorized Mean ( 28 / 40 ) 133.591666666667 0.980588618270669 136.236199541316 Winsorized Mean ( 29 / 40 ) 133.591666666667 0.980588618270669 136.236199541316 Winsorized Mean ( 30 / 40 ) 133.591666666667 0.980588618270669 136.236199541316 Winsorized Mean ( 31 / 40 ) 133.85 0.950869423771963 140.765910285595 Winsorized Mean ( 32 / 40 ) 133.85 0.950869423771963 140.765910285595 Winsorized Mean ( 33 / 40 ) 133.575 0.921776881380532 144.910338605961 Winsorized Mean ( 34 / 40 ) 133.858333333333 0.88966174239034 150.459806188455 Winsorized Mean ( 35 / 40 ) 134.15 0.857382669533742 156.464557503772 Winsorized Mean ( 36 / 40 ) 133.85 0.825932119212117 162.059322898937 Winsorized Mean ( 37 / 40 ) 133.85 0.760606605916347 175.977961483444 Winsorized Mean ( 38 / 40 ) 133.85 0.760606605916347 175.977961483444 Winsorized Mean ( 39 / 40 ) 133.525 0.728276667731368 183.343783916543 Winsorized Mean ( 40 / 40 ) 133.858333333333 0.692020934550545 193.431046157978 Trimmed Mean ( 1 / 40 ) 131.703389830508 2.13614918066103 61.6545843440357 Trimmed Mean ( 2 / 40 ) 131.810344827586 2.04594577687303 64.4251408407519 Trimmed Mean ( 3 / 40 ) 131.938596491228 1.96599270676239 67.1104201136665 Trimmed Mean ( 4 / 40 ) 132.017857142857 1.89937426797742 69.5059732926879 Trimmed Mean ( 5 / 40 ) 132.118181818182 1.83278145894717 72.0861623589734 Trimmed Mean ( 6 / 40 ) 132.185185185185 1.78859063134830 73.9046615074458 Trimmed Mean ( 7 / 40 ) 132.235849056604 1.74350556632349 75.844810369862 Trimmed Mean ( 8 / 40 ) 132.298076923077 1.69906839824041 77.8650683280835 Trimmed Mean ( 9 / 40 ) 132.362745098039 1.65341984040479 80.0539233070012 Trimmed Mean ( 10 / 40 ) 132.41 1.60680968808922 82.4055275378996 Trimmed Mean ( 11 / 40 ) 132.479591836735 1.55840294313979 85.0098444820832 Trimmed Mean ( 12 / 40 ) 132.552083333333 1.51323617662369 87.5951060257371 Trimmed Mean ( 13 / 40 ) 132.638297872340 1.46403939910125 90.5974920850934 Trimmed Mean ( 14 / 40 ) 132.728260869565 1.42339869550368 93.2474234301573 Trimmed Mean ( 15 / 40 ) 132.833333333333 1.38425694539937 95.9600266228102 Trimmed Mean ( 16 / 40 ) 132.954545454545 1.34674314863227 98.7230160328435 Trimmed Mean ( 17 / 40 ) 133.116279069767 1.30958986328838 101.647304092224 Trimmed Mean ( 18 / 40 ) 133.273809523810 1.27008829054147 104.932712565196 Trimmed Mean ( 19 / 40 ) 133.390243902439 1.23846755964157 107.705884473102 Trimmed Mean ( 20 / 40 ) 133.5 1.21084236814968 110.253822885306 Trimmed Mean ( 21 / 40 ) 133.602564102564 1.18772999680817 112.485636012898 Trimmed Mean ( 22 / 40 ) 133.710526315789 1.16058300374737 115.209791875339 Trimmed Mean ( 23 / 40 ) 133.824324324324 1.12859866782679 118.575653276301 Trimmed Mean ( 24 / 40 ) 133.916666666667 1.09893069380947 121.860884786502 Trimmed Mean ( 25 / 40 ) 134.014285714286 1.07068849526339 125.166457197542 Trimmed Mean ( 26 / 40 ) 134.073529411765 1.04933529202027 127.769961070912 Trimmed Mean ( 27 / 40 ) 134.090909090909 1.03498865971902 129.557853442869 Trimmed Mean ( 28 / 40 ) 134.109375 1.02428768984958 130.929402285109 Trimmed Mean ( 29 / 40 ) 134.145161290323 1.01413770782125 132.275094650131 Trimmed Mean ( 30 / 40 ) 134.183333333333 1.00126802844365 134.013400529632 Trimmed Mean ( 31 / 40 ) 134.224137931034 0.985080201146603 136.257065947323 Trimmed Mean ( 32 / 40 ) 134.25 0.969418757438047 138.485044744536 Trimmed Mean ( 33 / 40 ) 134.277777777778 0.949609749800364 141.403116181154 Trimmed Mean ( 34 / 40 ) 134.326923076923 0.929133442856896 144.572261508417 Trimmed Mean ( 35 / 40 ) 134.36 0.908805001423432 147.842496233577 Trimmed Mean ( 36 / 40 ) 134.375 0.888572546398304 151.225694001766 Trimmed Mean ( 37 / 40 ) 134.413043478261 0.867878799479519 154.875362272786 Trimmed Mean ( 38 / 40 ) 134.454545454545 0.853084532327576 157.609873769129 Trimmed Mean ( 39 / 40 ) 134.5 0.8328686276882 161.490054407900 Trimmed Mean ( 40 / 40 ) 134.575 0.81168351480434 165.797379822898 Median 135.5 Midrange 125 Midmean - Weighted Average at Xnp 133.892307692308 Midmean - Weighted Average at X(n+1)p 133.892307692308 Midmean - Empirical Distribution Function 133.892307692308 Midmean - Empirical Distribution Function - Averaging 133.892307692308 Midmean - Empirical Distribution Function - Interpolation 133.892307692308 Midmean - Closest Observation 133.892307692308 Midmean - True Basic - Statistics Graphics Toolkit 133.892307692308 Midmean - MS Excel (old versions) 133.892307692308 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281092568t6jwxdh4osiwndi/1w8hu1281092540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281092568t6jwxdh4osiwndi/1w8hu1281092540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281092568t6jwxdh4osiwndi/26hyx1281092540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/06/t1281092568t6jwxdh4osiwndi/26hyx1281092540.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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