Centrummaten - Koers BEL 20 van Januari 2000 tot Januari 2009 - Claus Wesley

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 23 Apr 2009 04:38:53 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Apr/23/t1240483221v3mo5139ezw8b7j.htm/, Retrieved Thu, 23 Apr 2009 12:40:25 +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/2009/Apr/23/t1240483221v3mo5139ezw8b7j.htm/}, year = {2009}, } @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 = {2009}, 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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1635,25 1765,9 1833,42 1862,83 1905,41 1910,43 1940,49 1946,81 1959,67 1969,6 1995,37 2014,45 2042 2061,41 2065,81 2091,48 2120,88 2174,56 2196,72 2197,82 2214,95 2304,98 2350,44 2407,6 2408,64 2440,25 2448,05 2452,62 2472,81 2497,84 2555,28 2604,42 2638,53 2641,65 2645,64 2659,81 2720,25 2735,7 2745,88 2756,76 2767,63 2794,83 2799,43 2803,47 2811,7 2833,18 2845,26 2848,96 2849,27 2863,36 2882,6 2892,63 2897,06 2915,02 2921,44 2962,34 2981,85 2987,1 2995,55 3012,61 3013,24 3030,29 3032,6 3032,93 3045,78 3047,03 3061,26 3080,58 3097,31 3106,22 3110,52 3119,31 3142,95 3161,69 3257,16 3277,01 3295,32 3363,99 3494,17 3504,37 3570,12 3667,03 3674,4 3701,61 3720,98 3801,06 3801,06 3813,06 3844,49 3857,62 3862,27 3895,51 3917,96 3970,1 4105,18 4116,68 4138,52 4199,75 4202,52 4290,89 4296,49 4356,98 4435,23 4443,91 4502,64 4562,84 4591,27 4621,4 4696,96

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 3027.43082568807 74.1754435003238 40.8144620756444 Geometric Mean 2930.01419114913 Harmonic Mean 2834.18691102339 Quadratic Mean 3124.02833174110 Winsorized Mean ( 1 / 36 ) 3027.93623853211 73.8349173823063 41.0095432605945 Winsorized Mean ( 2 / 36 ) 3028.62229357798 73.534015479658 41.1866844727907 Winsorized Mean ( 3 / 36 ) 3028.64926605505 73.2606218974908 41.3407528848561 Winsorized Mean ( 4 / 36 ) 3028.00266055046 72.6117271331237 41.7012895864469 Winsorized Mean ( 5 / 36 ) 3025.53889908257 72.0799990828021 41.974735538037 Winsorized Mean ( 6 / 36 ) 3026.71577981651 71.7585750623304 42.1791510936145 Winsorized Mean ( 7 / 36 ) 3022.09642201835 70.8062140747964 42.6812316052648 Winsorized Mean ( 8 / 36 ) 3018.60064220183 69.9103933171067 43.1781384566063 Winsorized Mean ( 9 / 36 ) 3018.95816513762 69.7175791393344 43.302682084013 Winsorized Mean ( 10 / 36 ) 3013.21504587156 68.0479817892988 44.2807408337483 Winsorized Mean ( 11 / 36 ) 3014.86100917431 67.73772668984 44.5078563527659 Winsorized Mean ( 12 / 36 ) 3011.15311926606 66.2477596076726 45.4529049298946 Winsorized Mean ( 13 / 36 ) 3010.86330275229 65.5269434015793 45.9484777780697 Winsorized Mean ( 14 / 36 ) 3009.95137614679 65.2209241857162 46.1500877782101 Winsorized Mean ( 15 / 36 ) 2994.89495412844 61.9433099863603 48.3489654457908 Winsorized Mean ( 16 / 36 ) 2991.55697247706 60.260452000652 49.6437858190094 Winsorized Mean ( 17 / 36 ) 2996.42770642202 58.6594773837005 51.081732058776 Winsorized Mean ( 18 / 36 ) 2994.59798165138 57.4130181866598 52.1588670345707 Winsorized Mean ( 19 / 36 ) 2993.97917431193 57.2750463498012 52.2737101953008 Winsorized Mean ( 20 / 36 ) 2994.71311926605 56.5362311941954 52.969804601576 Winsorized Mean ( 21 / 36 ) 3006.00302752294 53.5315821395747 56.1538237312935 Winsorized Mean ( 22 / 36 ) 3012.75642201835 52.0963207012445 57.8305028352296 Winsorized Mean ( 23 / 36 ) 3024.81770642202 50.7063122557356 59.6536717394562 Winsorized Mean ( 24 / 36 ) 3007.41440366973 48.2216016938361 62.3665390205018 Winsorized Mean ( 25 / 36 ) 3010.22174311927 46.7875148873582 64.338141283341 Winsorized Mean ( 26 / 36 ) 3005.59183486239 45.6935882426931 65.777102443755 Winsorized Mean ( 27 / 36 ) 3004.89825688073 45.3185777641818 66.3061023785194 Winsorized Mean ( 28 / 36 ) 2985.19036697248 41.4149097730614 72.0800886282315 Winsorized Mean ( 29 / 36 ) 2974.35660550459 38.3878577256516 77.4817033751029 Winsorized Mean ( 30 / 36 ) 2987.35844036697 36.2446580977773 82.4220339534716 Winsorized Mean ( 31 / 36 ) 2964.31036697248 30.0072613286956 98.786434873273 Winsorized Mean ( 32 / 36 ) 2954.16431192661 26.4482639774417 111.695962897462 Winsorized Mean ( 33 / 36 ) 2949.56550458716 25.6853900605671 114.834366837801 Winsorized Mean ( 34 / 36 ) 2944.61834862385 24.8204437542981 118.636813175991 Winsorized Mean ( 35 / 36 ) 2918.5128440367 20.8523442964745 139.960898522577 Winsorized Mean ( 36 / 36 ) 2932.28532110092 17.9388883774222 163.459700479071 Trimmed Mean ( 1 / 36 ) 3024.83878504673 72.759115532211 41.5733308867342 Trimmed Mean ( 2 / 36 ) 3021.62333333333 71.5528107680154 42.2292751451774 Trimmed Mean ( 3 / 36 ) 3017.92 70.3741849664328 42.8839069530893 Trimmed Mean ( 4 / 36 ) 3014.06029702970 69.1595385234842 43.5812667547837 Trimmed Mean ( 5 / 36 ) 3010.22262626263 68.0001825026935 44.2678609890994 Trimmed Mean ( 6 / 36 ) 3006.78041237113 66.8351630584256 44.9880014468235 Trimmed Mean ( 7 / 36 ) 3002.96821052632 65.590019240629 45.7839202563636 Trimmed Mean ( 8 / 36 ) 2999.76548387097 64.3843248414205 46.5915499037915 Trimmed Mean ( 9 / 36 ) 2996.94538461538 63.1910134405298 47.4267656339436 Trimmed Mean ( 10 / 36 ) 2993.94988764045 61.8626784835551 48.3967063992602 Trimmed Mean ( 11 / 36 ) 2991.53620689655 60.6419845480747 49.3311066448522 Trimmed Mean ( 12 / 36 ) 2988.81705882353 59.2903605912688 50.4098310251071 Trimmed Mean ( 13 / 36 ) 2986.37265060241 57.9815260862979 51.5055889725563 Trimmed Mean ( 14 / 36 ) 2983.8375308642 56.5837220563015 52.7331434276318 Trimmed Mean ( 15 / 36 ) 2981.26392405063 55.0094018786574 54.195534258432 Trimmed Mean ( 16 / 36 ) 2979.97753246753 53.7022748900984 55.4907131693406 Trimmed Mean ( 17 / 36 ) 2978.92573333333 52.4341561171528 56.8126952720964 Trimmed Mean ( 18 / 36 ) 2977.38849315069 51.1874550252909 58.1663708750436 Trimmed Mean ( 19 / 36 ) 2975.92070422535 49.9072097569812 59.6290740098743 Trimmed Mean ( 20 / 36 ) 2974.41927536232 48.4102251821607 61.4419632251246 Trimmed Mean ( 21 / 36 ) 2972.76850746269 46.7436616504215 63.5972536703461 Trimmed Mean ( 22 / 36 ) 2970.11461538462 45.2276502131359 65.6703278058424 Trimmed Mean ( 23 / 36 ) 2966.76111111111 43.6267757650203 68.003217269378 Trimmed Mean ( 24 / 36 ) 2962.25065573771 41.8960321646379 70.704801927233 Trimmed Mean ( 25 / 36 ) 2958.77406779661 40.2412639278191 73.5258731709763 Trimmed Mean ( 26 / 36 ) 2954.83877192982 38.4533879414718 76.8420919484976 Trimmed Mean ( 27 / 36 ) 2950.97018181818 36.4431483230573 80.9746226000766 Trimmed Mean ( 28 / 36 ) 2946.86245283019 33.9815391494923 86.719510845765 Trimmed Mean ( 29 / 36 ) 2943.9368627451 31.7395344313807 92.7529945062599 Trimmed Mean ( 30 / 36 ) 2941.60346938776 29.5625023429132 99.5045492180038 Trimmed Mean ( 31 / 36 ) 2938.06638297872 27.2069101343536 107.989711748593 Trimmed Mean ( 32 / 36 ) 2936.01577777778 25.6966259910150 114.256859200285 Trimmed Mean ( 33 / 36 ) 2934.57813953488 24.5642045056667 119.465628893373 Trimmed Mean ( 34 / 36 ) 2933.37073170732 23.2512052281121 126.159943234285 Trimmed Mean ( 35 / 36 ) 2932.44615384615 21.7169966136758 135.030004655409 Trimmed Mean ( 36 / 36 ) 2933.61891891892 20.7230143558209 141.563329955176 Median 2921.44 Midrange 3166.105 Midmean - Weighted Average at Xnp 2937.70981481482 Midmean - Weighted Average at X(n+1)p 2950.97018181818 Midmean - Empirical Distribution Function 2950.97018181818 Midmean - Empirical Distribution Function - Averaging 2950.97018181818 Midmean - Empirical Distribution Function - Interpolation 2950.97018181818 Midmean - Closest Observation 2941.98946428571 Midmean - True Basic - Statistics Graphics Toolkit 2950.97018181818 Midmean - MS Excel (old versions) 2950.97018181818 Number of observations 109

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/23/t1240483221v3mo5139ezw8b7j/1pgqt1240483131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/23/t1240483221v3mo5139ezw8b7j/1pgqt1240483131.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/23/t1240483221v3mo5139ezw8b7j/21v5g1240483131.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/23/t1240483221v3mo5139ezw8b7j/21v5g1240483131.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.

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