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: Tue, 17 Aug 2010 13:41:55 +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/17/t1282052544fk7nrxwy1kl6bxs.htm/, Retrieved Tue, 17 Aug 2010 15:42:27 +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/17/t1282052544fk7nrxwy1kl6bxs.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:

Gregory Goris

Dataseries X:

» Textbox « » Textfile « » CSV «

159 158 157 155 175 174 159 149 150 150 151 153 146 156 154 151 171 167 144 138 138 132 132 132 125 131 129 131 145 156 126 123 127 116 114 114 109 110 113 114 138 155 126 123 124 124 134 131 126 128 128 124 148 174 146 137 152 159 170 166 165 168 175 180 199 228 206 193 201 214 225 224 215 222 238 248 262 288 261 240 248 260 266 268 256 261 287 295 304 331 299 275 293 309 311 311 289 298 319 325 331 348 320 305 322 337 346 343 321 331 343 345 347 363 322 304 323 340 352 351

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 210.816666666667 7.41071016277882 28.4475660275473 Geometric Mean 195.998638521253 Harmonic Mean 182.826573262127 Quadratic Mean 225.785259335797 Winsorized Mean ( 1 / 40 ) 210.733333333333 7.39448402691333 28.498720474118 Winsorized Mean ( 2 / 40 ) 210.766666666667 7.38617390078407 28.5352970966867 Winsorized Mean ( 3 / 40 ) 210.716666666667 7.37155353960762 28.5851097105215 Winsorized Mean ( 4 / 40 ) 210.683333333333 7.3663534351699 28.6007636190041 Winsorized Mean ( 5 / 40 ) 210.641666666667 7.35989392804812 28.6202041396173 Winsorized Mean ( 6 / 40 ) 210.691666666667 7.34124982521813 28.6996998716641 Winsorized Mean ( 7 / 40 ) 210.983333333333 7.28052333796799 28.9791438800908 Winsorized Mean ( 8 / 40 ) 210.983333333333 7.28052333796799 28.9791438800908 Winsorized Mean ( 9 / 40 ) 210.833333333333 7.23892308770127 29.1249583369014 Winsorized Mean ( 10 / 40 ) 210.583333333333 7.20174060167014 29.2406162594217 Winsorized Mean ( 11 / 40 ) 210.033333333333 7.12191683091043 29.4911241341306 Winsorized Mean ( 12 / 40 ) 210.133333333333 7.11181142968451 29.5470901346234 Winsorized Mean ( 13 / 40 ) 210.241666666667 7.10096253223606 29.6074885217656 Winsorized Mean ( 14 / 40 ) 209.541666666667 7.00243950922097 29.9240952229202 Winsorized Mean ( 15 / 40 ) 209.291666666667 6.96797793504457 30.0362126025200 Winsorized Mean ( 16 / 40 ) 209.291666666667 6.93638960965959 30.1729975454677 Winsorized Mean ( 17 / 40 ) 209.433333333333 6.92232548373072 30.2547653711974 Winsorized Mean ( 18 / 40 ) 209.283333333333 6.90187510888701 30.3226775378557 Winsorized Mean ( 19 / 40 ) 209.283333333333 6.86476187853055 30.4866122141635 Winsorized Mean ( 20 / 40 ) 209.45 6.80968631664429 30.7576575866734 Winsorized Mean ( 21 / 40 ) 208.05 6.62358118626868 31.4105004753784 Winsorized Mean ( 22 / 40 ) 208.05 6.62358118626868 31.4105004753784 Winsorized Mean ( 23 / 40 ) 207.858333333333 6.55500638139512 31.7098597986558 Winsorized Mean ( 24 / 40 ) 207.058333333333 6.45211076392762 32.091565211644 Winsorized Mean ( 25 / 40 ) 206.85 6.42558839908921 32.1916044341278 Winsorized Mean ( 26 / 40 ) 207.283333333333 6.38347594753327 32.4718593814758 Winsorized Mean ( 27 / 40 ) 206.833333333333 6.17667444419264 33.486196367011 Winsorized Mean ( 28 / 40 ) 206.833333333333 6.12535179728666 33.766768045054 Winsorized Mean ( 29 / 40 ) 206.108333333333 6.03514213526938 34.1513635824475 Winsorized Mean ( 30 / 40 ) 205.608333333333 5.97346250665341 34.4202936076557 Winsorized Mean ( 31 / 40 ) 206.125 5.70209999278609 36.1489627086118 Winsorized Mean ( 32 / 40 ) 206.125 5.64522507757402 36.5131588497407 Winsorized Mean ( 33 / 40 ) 206.125 5.58679368291529 36.8950442237273 Winsorized Mean ( 34 / 40 ) 202.725 5.18040050283257 39.1330747283252 Winsorized Mean ( 35 / 40 ) 201.266666666667 4.89003350067479 41.1585455680198 Winsorized Mean ( 36 / 40 ) 200.966666666667 4.79403418554515 41.9201571971715 Winsorized Mean ( 37 / 40 ) 200.041666666667 4.62626159390071 43.2404572474680 Winsorized Mean ( 38 / 40 ) 199.725 4.59068235622966 43.5066041389182 Winsorized Mean ( 39 / 40 ) 200.05 4.56120607299606 43.859013778037 Winsorized Mean ( 40 / 40 ) 199.716666666667 4.52382710449186 44.1477231674839 Trimmed Mean ( 1 / 40 ) 210.389830508475 7.37388568175286 28.5317456207787 Trimmed Mean ( 2 / 40 ) 210.034482758621 7.34940696680967 28.5784259474469 Trimmed Mean ( 3 / 40 ) 209.649122807018 7.32525541802584 28.6200426938175 Trimmed Mean ( 4 / 40 ) 209.267857142857 7.3023834517941 28.6574730735953 Trimmed Mean ( 5 / 40 ) 208.881818181818 7.27668058732863 28.7056461631088 Trimmed Mean ( 6 / 40 ) 208.490740740741 7.24784370567254 28.7658991014893 Trimmed Mean ( 7 / 40 ) 208.075471698113 7.21784872258125 28.8279069977101 Trimmed Mean ( 8 / 40 ) 207.596153846154 7.1939486751866 28.8570523949101 Trimmed Mean ( 9 / 40 ) 207.098039215686 7.16489790226612 28.9045345852291 Trimmed Mean ( 10 / 40 ) 206.6 7.13695744811202 28.9479097363335 Trimmed Mean ( 11 / 40 ) 206.112244897959 7.10903454155303 28.9930009051457 Trimmed Mean ( 12 / 40 ) 205.666666666667 7.0870165862348 29.0202039411246 Trimmed Mean ( 13 / 40 ) 205.191489361702 7.06072747503544 29.0609558416178 Trimmed Mean ( 14 / 40 ) 204.684782608696 7.0295589364381 29.1177276496966 Trimmed Mean ( 15 / 40 ) 204.222222222222 7.00466721129692 29.1551641301187 Trimmed Mean ( 16 / 40 ) 203.761363636364 6.97779719454036 29.2013880534953 Trimmed Mean ( 17 / 40 ) 203.279069767442 6.94787414785977 29.257736314936 Trimmed Mean ( 18 / 40 ) 202.761904761905 6.91198704023754 29.3348213157148 Trimmed Mean ( 19 / 40 ) 202.231707317073 6.87018395066358 29.4361415603057 Trimmed Mean ( 20 / 40 ) 201.675 6.82328420033059 29.5568811262806 Trimmed Mean ( 21 / 40 ) 201.076923076923 6.77222710907617 29.6914028189396 Trimmed Mean ( 22 / 40 ) 200.552631578947 6.73350863251508 29.7842688744037 Trimmed Mean ( 23 / 40 ) 200 6.68456809179632 29.9196593188199 Trimmed Mean ( 24 / 40 ) 199.430555555556 6.63212372452251 30.0703913013797 Trimmed Mean ( 25 / 40 ) 198.885714285714 6.58027489898628 30.2245297254 Trimmed Mean ( 26 / 40 ) 198.323529411765 6.51861120179802 30.4241997677452 Trimmed Mean ( 27 / 40 ) 197.696969696970 6.44619002404468 30.6688088560139 Trimmed Mean ( 28 / 40 ) 197.0625 6.38333469321134 30.8714033449594 Trimmed Mean ( 29 / 40 ) 196.387096774194 6.31004650163803 31.1229238521797 Trimmed Mean ( 30 / 40 ) 195.716666666667 6.23064649485745 31.4119356359254 Trimmed Mean ( 31 / 40 ) 195.034482758621 6.13923767844008 31.7685180105581 Trimmed Mean ( 32 / 40 ) 194.267857142857 6.06075790722733 32.0533933406574 Trimmed Mean ( 33 / 40 ) 193.444444444444 5.96657746010793 32.421341336436 Trimmed Mean ( 34 / 40 ) 192.557692307692 5.85273095435681 32.900485911513 Trimmed Mean ( 35 / 40 ) 191.84 5.78007555914174 33.1898775434841 Trimmed Mean ( 36 / 40 ) 191.166666666667 5.7314558611592 33.3539455415090 Trimmed Mean ( 37 / 40 ) 190.456521739130 5.67677755444464 33.550111821805 Trimmed Mean ( 38 / 40 ) 189.75 5.62810872517670 33.7147004909793 Trimmed Mean ( 39 / 40 ) 189 5.5608762751688 33.9874492162232 Trimmed Mean ( 40 / 40 ) 188.15 5.46510492075603 34.4275183602462 Median 174 Midrange 236 Midmean - Weighted Average at Xnp 193.854838709677 Midmean - Weighted Average at X(n+1)p 193.854838709677 Midmean - Empirical Distribution Function 193.854838709677 Midmean - Empirical Distribution Function - Averaging 193.854838709677 Midmean - Empirical Distribution Function - Interpolation 193.854838709677 Midmean - Closest Observation 193.854838709677 Midmean - True Basic - Statistics Graphics Toolkit 193.854838709677 Midmean - MS Excel (old versions) 195.460317460317 Number of observations 120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/17/t1282052544fk7nrxwy1kl6bxs/1gwyg1282052513.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/17/t1282052544fk7nrxwy1kl6bxs/1gwyg1282052513.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/17/t1282052544fk7nrxwy1kl6bxs/295g11282052513.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/17/t1282052544fk7nrxwy1kl6bxs/295g11282052513.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 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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