Gemiddelde voor Q9

*The author of this computation has been verified*

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Sun, 26 Oct 2008 08:26:52 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/26/t12250312930gzifw7ksbigtbs.htm/, Retrieved Sun, 26 Oct 2008 14:28:15 +0000

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/2008/Oct/26/t12250312930gzifw7ksbigtbs.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0,97 0,88 0,94 1,03 1,52 1,48 1,78 1,30 1,45 1,32 1,38 1,48 1,78 1,23 1,18 1,22 1,16 1,28 1,05 0,86 0,75 0,36 0,07 0,79 0,95 0,87 0,91 1,12 0,95 1,12 0,75 0,89 0,94 0,89 0,94 0,73 0,48 0,35 0,09 0,62 0,91 0,69 0,46 0,57 0,78 0,31 0,81 1,00 1,07 0,90 0,94 0,83 0,76 0,95 0,83 0,94 0,81 0,68 0,71 0,65 0,68 0,67 0,81 0,67 0,50 0,44 0,47 0,56 0,45 0,79 0,91 0,70 0,82 0,94 0,82 0,83 0,87 0,89 0,94 1,13 1,06 1,24 1,67 0,69 1,29 1,11 1,06 2,10 1,17 1,25 1,38 1,31 1,31 1,38 2,20 1,88 2,60 4,00 4,00 12,00 1,67 2,00 1,20 2,00 0,91

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 1.16980952380952 0.119286414714298 9.80672884344225 Geometric Mean 0.952076782307922 Harmonic Mean 0.746202314744964 Quadratic Mean 1.68768960134149 Winsorized Mean ( 1 / 35 ) 1.09380952380952 0.0645144418270519 16.9544910074828 Winsorized Mean ( 2 / 35 ) 1.098 0.0639524491393078 17.1690062660184 Winsorized Mean ( 3 / 35 ) 1.05914285714286 0.0486278975775766 21.7805603347995 Winsorized Mean ( 4 / 35 ) 1.04428571428571 0.0443222996700045 23.5611807613955 Winsorized Mean ( 5 / 35 ) 1.04333333333333 0.0426145658314293 24.4830215438649 Winsorized Mean ( 6 / 35 ) 1.03819047619048 0.0412134577740325 25.1905695921640 Winsorized Mean ( 7 / 35 ) 1.03885714285714 0.0411225975250942 25.2624397625467 Winsorized Mean ( 8 / 35 ) 1.03047619047619 0.0390325732649621 26.4004164798739 Winsorized Mean ( 9 / 35 ) 1.02276190476190 0.0371755796499454 27.5116599227904 Winsorized Mean ( 10 / 35 ) 1.02466666666667 0.0369117020533292 27.7599408769135 Winsorized Mean ( 11 / 35 ) 1.01942857142857 0.0338705325836293 30.0978016484245 Winsorized Mean ( 12 / 35 ) 1.02057142857143 0.0337225885349671 30.2637333878802 Winsorized Mean ( 13 / 35 ) 1.00819047619048 0.0296634915016015 33.9875862602366 Winsorized Mean ( 14 / 35 ) 1.00685714285714 0.0282976442818043 35.5809527051185 Winsorized Mean ( 15 / 35 ) 1.00971428571429 0.0279574693810352 36.1160830385893 Winsorized Mean ( 16 / 35 ) 1.00514285714286 0.0272285518259215 36.9150318228076 Winsorized Mean ( 17 / 35 ) 0.995428571428571 0.0253156008059331 39.3207563612426 Winsorized Mean ( 18 / 35 ) 0.99542857142857 0.0253156008059331 39.3207563612426 Winsorized Mean ( 19 / 35 ) 0.997238095238095 0.0251007111519059 39.7294757587923 Winsorized Mean ( 20 / 35 ) 0.985809523809524 0.0234789208525984 41.9870031505483 Winsorized Mean ( 21 / 35 ) 0.985809523809524 0.0229654865081589 42.9256973702385 Winsorized Mean ( 22 / 35 ) 0.987904761904762 0.0227168811535035 43.4876933690522 Winsorized Mean ( 23 / 35 ) 0.990095238095238 0.0219107811446339 45.1875828415053 Winsorized Mean ( 24 / 35 ) 0.992380952380953 0.0210865666530785 47.0622348677172 Winsorized Mean ( 25 / 35 ) 0.99 0.0207651264429205 47.6760882107473 Winsorized Mean ( 26 / 35 ) 0.985047619047619 0.0195058570191080 50.5000943092457 Winsorized Mean ( 27 / 35 ) 0.987619047619048 0.0186089130489417 53.0723661839676 Winsorized Mean ( 28 / 35 ) 0.987619047619048 0.0179781734830363 54.9343373814329 Winsorized Mean ( 29 / 35 ) 0.984857142857143 0.0176219530323993 55.8880812499279 Winsorized Mean ( 30 / 35 ) 0.984857142857143 0.0162935043382675 60.4447712665516 Winsorized Mean ( 31 / 35 ) 0.978952380952381 0.0155514765028871 62.949159892994 Winsorized Mean ( 32 / 35 ) 0.975904761904762 0.0151746216939619 64.3116369940929 Winsorized Mean ( 33 / 35 ) 0.975904761904762 0.0144608193889908 67.4861317089495 Winsorized Mean ( 34 / 35 ) 0.966190476190476 0.0132897967915825 72.7016741747657 Winsorized Mean ( 35 / 35 ) 0.966190476190476 0.0125454346090834 77.0153052721599 Trimmed Mean ( 1 / 35 ) 1.07533980582524 0.058506023603605 18.3799844800764 Trimmed Mean ( 2 / 35 ) 1.05613861386139 0.0511257371191926 20.6576701554277 Trimmed Mean ( 3 / 35 ) 1.03393939393939 0.0420167281677777 24.6078035826767 Trimmed Mean ( 4 / 35 ) 1.02484536082474 0.0390496295709948 26.2446884153281 Trimmed Mean ( 5 / 35 ) 1.01947368421053 0.0372243488631816 27.3872805124841 Trimmed Mean ( 6 / 35 ) 1.01408602150538 0.035639891738091 28.4536785060306 Trimmed Mean ( 7 / 35 ) 1.00945054945055 0.0341934318955436 29.5217675878305 Trimmed Mean ( 8 / 35 ) 1.00449438202247 0.0325350487668666 30.874223955227 Trimmed Mean ( 9 / 35 ) 1.00057471264368 0.0311004427408228 32.1723623352253 Trimmed Mean ( 10 / 35 ) 0.997529411764706 0.0298398648073105 33.4294212861286 Trimmed Mean ( 11 / 35 ) 0.994096385542169 0.0284190319945223 34.9799523690244 Trimmed Mean ( 12 / 35 ) 0.991111111111111 0.0273601945700973 36.2245637022744 Trimmed Mean ( 13 / 35 ) 0.987848101265823 0.0261438552006809 37.7850968681962 Trimmed Mean ( 14 / 35 ) 0.985714285714286 0.0254584946090496 38.7184828031387 Trimmed Mean ( 15 / 35 ) 0.9836 0.0248799930477091 39.5337731049152 Trimmed Mean ( 16 / 35 ) 0.98109589041096 0.0242478651980007 40.4611244082574 Trimmed Mean ( 17 / 35 ) 0.97887323943662 0.0236222359657828 41.4386360738472 Trimmed Mean ( 18 / 35 ) 0.977391304347826 0.0231885523226402 42.1497336594638 Trimmed Mean ( 19 / 35 ) 0.975820895522388 0.0226681856786071 43.0480369870673 Trimmed Mean ( 20 / 35 ) 0.974 0.0220737400546441 44.1248287598222 Trimmed Mean ( 21 / 35 ) 0.973015873015873 0.0216245424383960 44.9959057301582 Trimmed Mean ( 22 / 35 ) 0.971967213114754 0.0211575144863980 45.9395745062411 Trimmed Mean ( 23 / 35 ) 0.970677966101695 0.0206227765665214 47.0682482046319 Trimmed Mean ( 24 / 35 ) 0.969122807017544 0.0200942028039299 48.2289751165451 Trimmed Mean ( 25 / 35 ) 0.967272727272727 0.0195719419616915 49.4213976909387 Trimmed Mean ( 26 / 35 ) 0.965471698113208 0.0189770429595172 50.8757713292213 Trimmed Mean ( 27 / 35 ) 0.965471698113208 0.0184626351471896 52.2932772280979 Trimmed Mean ( 28 / 35 ) 0.96204081632653 0.0179639178979592 53.5540644190888 Trimmed Mean ( 29 / 35 ) 0.96 0.0174376117995813 55.0534104689183 Trimmed Mean ( 30 / 35 ) 0.958 0.0168283070803904 56.9278891467538 Trimmed Mean ( 31 / 35 ) 0.955813953488372 0.0163035727631646 58.6260427314365 Trimmed Mean ( 32 / 35 ) 0.95390243902439 0.0157800354291098 60.449955471247 Trimmed Mean ( 33 / 35 ) 0.952051282051282 0.0151652146856373 62.7786221155809 Trimmed Mean ( 34 / 35 ) 0.95 0.0144986278733613 65.5234418248265 Trimmed Mean ( 35 / 35 ) 0.948571428571429 0.0139214051808316 68.1376208974595 Median 0.94 Midrange 6.035 Midmean - Weighted Average at Xnp 0.96 Midmean - Weighted Average at X(n+1)p 0.965471698113208 Midmean - Empirical Distribution Function 0.965471698113208 Midmean - Empirical Distribution Function - Averaging 0.965471698113208 Midmean - Empirical Distribution Function - Interpolation 0.965471698113208 Midmean - Closest Observation 0.957636363636364 Midmean - True Basic - Statistics Graphics Toolkit 0.965471698113208 Midmean - MS Excel (old versions) 0.965471698113208 Number of observations 105

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250312930gzifw7ksbigtbs/10qf51225031210.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250312930gzifw7ksbigtbs/10qf51225031210.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250312930gzifw7ksbigtbs/2sou11225031210.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/26/t12250312930gzifw7ksbigtbs/2sou11225031210.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

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.

FreeStatistics.org is powered by