Tijdreeks 1 - 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: Tue, 10 Aug 2010 11:25:43 +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/10/t1281439680r0hc9mle5euqw3t.htm/, Retrieved Tue, 10 Aug 2010 13:28:01 +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/10/t1281439680r0hc9mle5euqw3t.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:

Van Boxel Dieter

Dataseries X:

» Textbox « » Textfile « » CSV «

356 355 354 352 372 371 356 346 347 347 348 350 353 350 343 346 373 363 349 350 353 356 355 346 349 348 342 342 379 375 363 361 363 373 367 360 358 367 357 346 386 383 367 354 363 370 361 354 363 366 353 351 389 385 364 348 347 352 342 338 343 354 329 320 353 345 324 310 314 313 310 301 294 296 274 269 292 287 271 256 260 265 263 256 246 245 220 224 240 238 222 203 209 214 216 214 206 196 169 177 193 183 164 142 141 137 140 146 136 124 105 114 135 123 100 74 64 57 62 64

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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 287.658333333333 8.42939447962805 34.1256224309514 Geometric Mean 265.854094921594 Harmonic Mean 233.574429143917 Quadratic Mean 301.998054629496 Winsorized Mean ( 1 / 40 ) 287.675 8.41742026606574 34.1761479059971 Winsorized Mean ( 2 / 40 ) 287.691666666667 8.4083108466079 34.2151559231101 Winsorized Mean ( 3 / 40 ) 287.641666666667 8.40349563647364 34.2288113315865 Winsorized Mean ( 4 / 40 ) 287.841666666667 8.31771081139056 34.605875726346 Winsorized Mean ( 5 / 40 ) 288.758333333333 8.07903012172561 35.7417077276173 Winsorized Mean ( 6 / 40 ) 288.908333333333 8.02151340381488 36.016686476636 Winsorized Mean ( 7 / 40 ) 289.433333333333 7.92207967677861 36.5350192300801 Winsorized Mean ( 8 / 40 ) 289.966666666667 7.80641958527488 37.1446427519251 Winsorized Mean ( 9 / 40 ) 289.966666666667 7.78637153617437 37.2402813453639 Winsorized Mean ( 10 / 40 ) 290.8 7.61818736389993 38.1718099213476 Winsorized Mean ( 11 / 40 ) 290.616666666667 7.57879415969537 38.3460297961632 Winsorized Mean ( 12 / 40 ) 290.716666666667 7.56168084975663 38.4460376525972 Winsorized Mean ( 13 / 40 ) 291.041666666667 7.50644350503648 38.7722450014299 Winsorized Mean ( 14 / 40 ) 291.041666666667 7.47686811702617 38.9256119154908 Winsorized Mean ( 15 / 40 ) 290.916666666667 7.4349521230399 39.1282501692453 Winsorized Mean ( 16 / 40 ) 291.316666666667 7.33468586330403 39.7176746347305 Winsorized Mean ( 17 / 40 ) 293.866666666667 6.92104262369958 42.4598839574236 Winsorized Mean ( 18 / 40 ) 294.616666666667 6.80372265186023 43.3022746137534 Winsorized Mean ( 19 / 40 ) 295.883333333333 6.6096990033611 44.7650238207328 Winsorized Mean ( 20 / 40 ) 296.883333333333 6.46003858394585 45.9568978536958 Winsorized Mean ( 21 / 40 ) 298.283333333333 6.17496054414564 48.3053019045005 Winsorized Mean ( 22 / 40 ) 298.833333333333 6.09657723384638 49.0165746895323 Winsorized Mean ( 23 / 40 ) 299.983333333333 5.89225127745867 50.9114970165896 Winsorized Mean ( 24 / 40 ) 300.183333333333 5.7756974591059 51.9735210264637 Winsorized Mean ( 25 / 40 ) 300.6 5.6730035420115 52.987804039589 Winsorized Mean ( 26 / 40 ) 301.466666666667 5.50915882862949 54.7209975323333 Winsorized Mean ( 27 / 40 ) 301.466666666667 5.50915882862949 54.7209975323333 Winsorized Mean ( 28 / 40 ) 301.933333333333 5.44709381221457 55.430169507321 Winsorized Mean ( 29 / 40 ) 302.658333333333 5.29954936820755 57.1102016992249 Winsorized Mean ( 30 / 40 ) 303.158333333333 5.23420654769567 57.9186798554591 Winsorized Mean ( 31 / 40 ) 303.416666666667 5.14561877947795 58.9660213222111 Winsorized Mean ( 32 / 40 ) 307.15 4.67081123478956 65.7594547414499 Winsorized Mean ( 33 / 40 ) 307.7 4.60260552198822 66.8534373693362 Winsorized Mean ( 34 / 40 ) 309.116666666667 4.42885217175855 69.796113005941 Winsorized Mean ( 35 / 40 ) 309.116666666667 4.36867483278219 70.7575359802656 Winsorized Mean ( 36 / 40 ) 312.116666666667 4.00991738588756 77.8361837989793 Winsorized Mean ( 37 / 40 ) 312.116666666667 4.00991738588756 77.8361837989793 Winsorized Mean ( 38 / 40 ) 313.383333333333 3.86185075102028 81.148483858559 Winsorized Mean ( 39 / 40 ) 314.033333333333 3.72108710760332 84.392900314445 Winsorized Mean ( 40 / 40 ) 314.7 3.64446935269664 86.3500195898601 Trimmed Mean ( 1 / 40 ) 288.754237288136 8.29978415285713 34.7905718956239 Trimmed Mean ( 2 / 40 ) 289.870689655172 8.16814974096133 35.487925521436 Trimmed Mean ( 3 / 40 ) 291.017543859649 8.02597265074777 36.2594736517742 Trimmed Mean ( 4 / 40 ) 292.223214285714 7.86821053996774 37.1397299044456 Trimmed Mean ( 5 / 40 ) 293.418181818182 7.71868158047826 38.0140285305047 Trimmed Mean ( 6 / 40 ) 294.453703703704 7.61526613729676 38.6662394189454 Trimmed Mean ( 7 / 40 ) 295.5 7.5114796840586 39.3397855587803 Trimmed Mean ( 8 / 40 ) 296.5 7.41473816437932 39.9879258615492 Trimmed Mean ( 9 / 40 ) 297.460784313725 7.32679600738043 40.599026370338 Trimmed Mean ( 10 / 40 ) 298.46 7.22953286558305 41.2834418971729 Trimmed Mean ( 11 / 40 ) 299.397959183673 7.14662256856903 41.893629656676 Trimmed Mean ( 12 / 40 ) 300.395833333333 7.05668587983943 42.568967706434 Trimmed Mean ( 13 / 40 ) 301.425531914894 6.95536788274231 43.3371084026759 Trimmed Mean ( 14 / 40 ) 302.467391304348 6.84668376778794 44.1772106851768 Trimmed Mean ( 15 / 40 ) 303.555555555556 6.7248418869719 45.1394338569709 Trimmed Mean ( 16 / 40 ) 304.704545454545 6.5888580925823 46.2454254095379 Trimmed Mean ( 17 / 40 ) 305.872093023256 6.4452613906983 47.45689499338 Trimmed Mean ( 18 / 40 ) 306.880952380952 6.33940143432988 48.4085060017014 Trimmed Mean ( 19 / 40 ) 307.878048780488 6.2318544841261 49.403921347124 Trimmed Mean ( 20 / 40 ) 308.825 6.13288435971365 50.3555883147974 Trimmed Mean ( 21 / 40 ) 309.74358974359 6.03680673840746 51.3091777102842 Trimmed Mean ( 22 / 40 ) 310.605263157895 5.96133974543308 52.1032647729642 Trimmed Mean ( 23 / 40 ) 311.472972972973 5.88100006647871 52.9625862016814 Trimmed Mean ( 24 / 40 ) 312.305555555556 5.81171846812743 53.737213402249 Trimmed Mean ( 25 / 40 ) 313.171428571429 5.74153338059486 54.5449112304877 Trimmed Mean ( 26 / 40 ) 314.058823529412 5.66855544094508 55.4036785564279 Trimmed Mean ( 27 / 40 ) 314.939393939394 5.6008190704649 56.2309530047454 Trimmed Mean ( 28 / 40 ) 315.875 5.51519815730004 57.2735540937729 Trimmed Mean ( 29 / 40 ) 316.838709677419 5.41753198927141 58.4839573268547 Trimmed Mean ( 30 / 40 ) 317.816666666667 5.31783544527052 59.7642913056516 Trimmed Mean ( 31 / 40 ) 318.827586206897 5.20267614201945 61.2814592920523 Trimmed Mean ( 32 / 40 ) 319.892857142857 5.07071286334437 63.0863678863237 Trimmed Mean ( 33 / 40 ) 320.777777777778 4.9906037963395 64.2763462836023 Trimmed Mean ( 34 / 40 ) 321.692307692308 4.8974813040309 65.6852548732625 Trimmed Mean ( 35 / 40 ) 322.58 4.80833375187161 67.0876891343987 Trimmed Mean ( 36 / 40 ) 323.541666666667 4.6988151454048 68.8560108569228 Trimmed Mean ( 37 / 40 ) 324.369565217391 4.62866083821439 70.0784906380232 Trimmed Mean ( 38 / 40 ) 325.272727272727 4.5296023791121 71.8104372191027 Trimmed Mean ( 39 / 40 ) 326.166666666667 4.42749259398182 73.668483852467 Trimmed Mean ( 40 / 40 ) 327.1 4.31630777638861 75.7823623675139 Median 342.5 Midrange 223 Midmean - Weighted Average at Xnp 316.838709677419 Midmean - Weighted Average at X(n+1)p 318.426229508197 Midmean - Empirical Distribution Function 316.838709677419 Midmean - Empirical Distribution Function - Averaging 318.426229508197 Midmean - Empirical Distribution Function - Interpolation 318.426229508197 Midmean - Closest Observation 316.838709677419 Midmean - True Basic - Statistics Graphics Toolkit 318.426229508197 Midmean - MS Excel (old versions) 316.838709677419 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/10/t1281439680r0hc9mle5euqw3t/1ruci1281439539.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/10/t1281439680r0hc9mle5euqw3t/1ruci1281439539.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/10/t1281439680r0hc9mle5euqw3t/223b31281439539.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/10/t1281439680r0hc9mle5euqw3t/223b31281439539.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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