Verschillende gemiddelden mannen

*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: Tue, 07 Dec 2010 10:36:33 +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/Dec/07/t1291718209cihziq1yvdhw63n.htm/, Retrieved Tue, 07 Dec 2010 11:36:50 +0100

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/Dec/07/t1291718209cihziq1yvdhw63n.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:

Dataseries X:

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60747 60194 58401 58280 59246 60363 59486 60288 61064 61128 60793 61681 63587 65952 66995 66366 65038 64644 63404 63654 61635 62560 63514 65319 67256 67334 67931 66922 66512 66495 65767 68454 71116 73792 75737 77046 76729 76567 78036 80147 82512 84873 83387 81732 81449 80647 80680 79230 77543 75689 74454 73085 71606 71043 67799 67936 67596 67730 66928 66809 55559 55167 51834 45337 40839 44921 47745 48088 45971 43752 42504 42367 41333 41358 40150 38294 34220 31910 29516 27291 25732 23261 20671 18158 16021 13587 8757 4977 4025 3711 3613 3241 2300 1659 1085 807 470 296 190 102 89 37 16 4 0 2 2 0 0 0 0

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 46341.2342342342 2723.92530276244 17.0126670460595 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 54439.733526216 Winsorized Mean ( 1 / 37 ) 46327.8468468468 2722.23609813481 17.0183059722811 Winsorized Mean ( 2 / 37 ) 46312.0810810811 2720.30687735477 17.0245796408511 Winsorized Mean ( 3 / 37 ) 46291 2717.78214684452 17.0326381949878 Winsorized Mean ( 4 / 37 ) 46280.8018018018 2716.57754461533 17.0364368554608 Winsorized Mean ( 5 / 37 ) 46246.2522522523 2712.52644601044 17.0491433623701 Winsorized Mean ( 6 / 37 ) 46244.4684684685 2712.32067726472 17.0497791268193 Winsorized Mean ( 7 / 37 ) 46213.0630630631 2708.6876495843 17.0610528202302 Winsorized Mean ( 8 / 37 ) 46147.8378378378 2701.11055225175 17.0847645607719 Winsorized Mean ( 9 / 37 ) 46052.7297297297 2690.2245031723 17.1185451903455 Winsorized Mean ( 10 / 37 ) 46013 2684.72489393263 17.1388137771537 Winsorized Mean ( 11 / 37 ) 45965.036036036 2679.29820189867 17.1556253064564 Winsorized Mean ( 12 / 37 ) 45940.2792792793 2674.2176743655 17.1789603066547 Winsorized Mean ( 13 / 37 ) 45933.7207207207 2670.30562131909 17.2016717315039 Winsorized Mean ( 14 / 37 ) 45850.981981982 2656.08323588226 17.2626299366525 Winsorized Mean ( 15 / 37 ) 45890.036036036 2648.35913357643 17.3277239684879 Winsorized Mean ( 16 / 37 ) 45752.0900900901 2624.20202226366 17.4346676444613 Winsorized Mean ( 17 / 37 ) 45738.6126126126 2600.61531521262 17.587611802891 Winsorized Mean ( 18 / 37 ) 45727.9099099099 2573.47167951507 17.7689578921368 Winsorized Mean ( 19 / 37 ) 45635.8198198198 2524.74863092077 18.0753914512181 Winsorized Mean ( 20 / 37 ) 45614.5585585586 2506.315553165 18.1998465839451 Winsorized Mean ( 21 / 37 ) 45619.2882882883 2502.21518250443 18.2315608214912 Winsorized Mean ( 22 / 37 ) 45168.3873873874 2446.80824933975 18.4601255123182 Winsorized Mean ( 23 / 37 ) 45258.3153153153 2407.48913625644 18.798969695743 Winsorized Mean ( 24 / 37 ) 46074.5315315315 2284.51498870798 20.1681896416837 Winsorized Mean ( 25 / 37 ) 47132.6396396396 2122.90352108498 22.201969694577 Winsorized Mean ( 26 / 37 ) 47686.6036036036 2040.28192882844 23.3725559834694 Winsorized Mean ( 27 / 37 ) 48173.8198198198 1964.58069366371 24.5211713497914 Winsorized Mean ( 28 / 37 ) 48741.6396396396 1871.36201375748 26.0460772855873 Winsorized Mean ( 29 / 37 ) 49397.9279279279 1778.14070861717 27.7806630760644 Winsorized Mean ( 30 / 37 ) 49995.2252252252 1683.28213238687 29.7010371959053 Winsorized Mean ( 31 / 37 ) 50411.9099099099 1624.86430260863 31.0253045924983 Winsorized Mean ( 32 / 37 ) 51051.6216216216 1542.48816236916 33.0969292777001 Winsorized Mean ( 33 / 37 ) 51729.7567567568 1449.90933305262 35.6779252174656 Winsorized Mean ( 34 / 37 ) 52346.3513513514 1354.23963226485 38.6536844028162 Winsorized Mean ( 35 / 37 ) 53625.5855855856 1200.80028374941 44.6582052913444 Winsorized Mean ( 36 / 37 ) 54185.6936936937 1127.69194899369 48.0500847257506 Winsorized Mean ( 37 / 37 ) 54277.3603603604 1088.30120975022 49.8734724119419 Trimmed Mean ( 1 / 37 ) 46412.880733945 2717.94813646218 17.0764409045561 Trimmed Mean ( 2 / 37 ) 46501.0934579439 2712.33752047796 17.1442872086766 Trimmed Mean ( 3 / 37 ) 46601 2706.32746137734 17.2192761833349 Trimmed Mean ( 4 / 37 ) 46712.359223301 2699.72672594733 17.3026250302833 Trimmed Mean ( 5 / 37 ) 46830.9306930693 2691.81264329181 17.3975446655901 Trimmed Mean ( 6 / 37 ) 46962.0404040404 2683.0573992389 17.5031814143678 Trimmed Mean ( 7 / 37 ) 46962.0404040404 2672.34528580506 17.5733430307427 Trimmed Mean ( 8 / 37 ) 47246.7578947368 2660.07784921384 17.7614192414332 Trimmed Mean ( 9 / 37 ) 47410.7096774194 2646.64867782605 17.9134881310965 Trimmed Mean ( 10 / 37 ) 47594.7582417582 2632.27305266202 18.081239024054 Trimmed Mean ( 11 / 37 ) 47792.0337078652 2615.81134481977 18.2704436245031 Trimmed Mean ( 12 / 37 ) 48003.9425287356 2596.85868359238 18.48538884 Trimmed Mean ( 13 / 37 ) 48228.5176470588 2574.94357184979 18.7299318611523 Trimmed Mean ( 14 / 37 ) 48228.5176470588 2549.40504476848 18.9175579400482 Trimmed Mean ( 15 / 37 ) 48720.4197530864 2520.98598502381 19.3259383600366 Trimmed Mean ( 16 / 37 ) 48985.5443037975 2488.34500611494 19.6859937763529 Trimmed Mean ( 17 / 37 ) 49276.8701298701 2452.71785761157 20.0907209840497 Trimmed Mean ( 18 / 37 ) 49584.9066666667 2413.51441313384 20.5446905130692 Trimmed Mean ( 19 / 37 ) 49910.7260273973 2370.43124911822 21.0555467685315 Trimmed Mean ( 20 / 37 ) 50262.4788732394 2325.41890394324 21.6143761401478 Trimmed Mean ( 21 / 37 ) 50636.3333333333 2273.63737357291 22.2710683426886 Trimmed Mean ( 22 / 37 ) 51032.1343283582 2211.46001541331 23.0762184134813 Trimmed Mean ( 23 / 37 ) 51487.2923076923 2142.70777678474 24.0290780037921 Trimmed Mean ( 24 / 37 ) 51964.4603174603 2064.70263841107 25.1680117759962 Trimmed Mean ( 25 / 37 ) 52411.0327868852 1991.60026836307 26.3160402312874 Trimmed Mean ( 26 / 37 ) 52808.2542372881 1931.30730966559 27.343268454999 Trimmed Mean ( 27 / 37 ) 53191.8596491228 1871.45935534167 28.42266357391 Trimmed Mean ( 28 / 37 ) 53191.8596491228 1810.64426679778 29.3773109519715 Trimmed Mean ( 29 / 37 ) 53927.8679245283 1751.87707107574 30.7829064121571 Trimmed Mean ( 30 / 37 ) 54267.8431372549 1695.94386985654 31.9986080328505 Trimmed Mean ( 31 / 37 ) 54267.8431372549 1643.50403182888 33.0195984228076 Trimmed Mean ( 32 / 37 ) 54908.8085106383 1587.8180854748 34.581296820422 Trimmed Mean ( 33 / 37 ) 55206.1333333333 1533.74037667081 35.9944448050366 Trimmed Mean ( 34 / 37 ) 55478.0697674419 1483.97787210989 37.3847014905716 Trimmed Mean ( 35 / 37 ) 55727.4390243902 1440.05271628133 38.6981937496677 Trimmed Mean ( 36 / 37 ) 55898.358974359 1418.01321183675 39.4201961644307 Trimmed Mean ( 37 / 37 ) 56041.0810810811 1402.91751525961 39.9460983782149 Median 60194 Midrange 42436.5 Midmean - Weighted Average at Xnp 52934.6428571429 Midmean - Weighted Average at X(n+1)p 53191.8596491228 Midmean - Empirical Distribution Function 53191.8596491228 Midmean - Empirical Distribution Function - Averaging 53191.8596491228 Midmean - Empirical Distribution Function - Interpolation 53566.9454545455 Midmean - Closest Observation 52934.6428571429 Midmean - True Basic - Statistics Graphics Toolkit 53191.8596491228 Midmean - MS Excel (old versions) 53191.8596491228 Number of observations 111

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/07/t1291718209cihziq1yvdhw63n/1dc661291718189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291718209cihziq1yvdhw63n/1dc661291718189.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291718209cihziq1yvdhw63n/25ln91291718189.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/07/t1291718209cihziq1yvdhw63n/25ln91291718189.ps (open in new window)

Parameters (Session):

par1 = 500 ;

Parameters (R input):

par1 = 500 ;

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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