OPGAVE 5 OEF 2 STAP 1

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 03 Jun 2010 21:36:24 +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/Jun/03/t127560104668dyol07o39zjau.htm/, Retrieved Thu, 03 Jun 2010 23:37:28 +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/Jun/03/t127560104668dyol07o39zjau.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:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

1954 2302 3054 2414 2226 2725 2589 3470 2400 3180 4009 3924 2072 2434 2956 2828 2687 2629 3150 4119 3030 3055 3821 4001 2529 2472 3134 2789 2758 2993 3282 3437 2804 3076 3782 3889 2271 2452 3084 2522 2769 3438 2839 3746 2632 2851 3871 3618 2389 2344 2678 2492 2858 2246 2800 3869 3007 3023 3907 4209 2353 2570 2903 2910 3782 2759 2931 3641 2794 3070 3576 4106 2452 2206 2488 2416 2534 2521 3093 3903 2907 3025 3812 4209 2138 2419 2622 2912 2708 2798 3254 2895 3263 3736 4077 4097 2175 3138 2823 2498 2822 2738 4137 3515 3785 3632 4504 4451 2550 2867 3458 2961 3163 2880 3331 3062 3534 3622 4464 5411 2564 2820 3508 3088 3299 2939 3320 3418 3604 3495 4163 4882 2211 3260 2992 2425 2707 3244 3965 3315 3333 3583 4021 4904 2252 2952 3573 3048 3059 2731 3563 3092 3478 3478 4308 5029 2075 3264 3308 3688 3136 2824 3644 4694 2914 3686 4358 5587 2265 3685 3754 etc...

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 3262.60962566845 53.2627657523588 61.2549795261798 Geometric Mean 3185.88686223051 Harmonic Mean 3112.92857306567 Quadratic Mean 3342.49743479397 Winsorized Mean ( 1 / 62 ) 3262.50267379679 53.0042594460639 61.5517075022367 Winsorized Mean ( 2 / 62 ) 3260.65240641711 52.5714314281439 62.0232760995649 Winsorized Mean ( 3 / 62 ) 3256.44919786096 51.37773634507 63.3824965737993 Winsorized Mean ( 4 / 62 ) 3256.02139037433 51.0558310798398 63.7737418333794 Winsorized Mean ( 5 / 62 ) 3253.50802139037 50.3556938031321 64.6105291312262 Winsorized Mean ( 6 / 62 ) 3252.96256684492 50.2140186257856 64.7819604140283 Winsorized Mean ( 7 / 62 ) 3246.48663101604 48.9777316994177 66.284952740158 Winsorized Mean ( 8 / 62 ) 3239.21390374332 47.6552773253518 67.9717774304108 Winsorized Mean ( 9 / 62 ) 3237.57754010695 47.351545589275 68.3732178077046 Winsorized Mean ( 10 / 62 ) 3237.57754010695 47.1776542128948 68.6252335798003 Winsorized Mean ( 11 / 62 ) 3235.16577540107 46.7615882408845 69.1842577873029 Winsorized Mean ( 12 / 62 ) 3234.65240641711 46.2106402962763 69.9980001505793 Winsorized Mean ( 13 / 62 ) 3237.08556149733 45.8352842897719 70.6243151244003 Winsorized Mean ( 14 / 62 ) 3234.01604278075 45.2799446460636 71.4227030986862 Winsorized Mean ( 15 / 62 ) 3233.69518716578 44.9798259219721 71.8921232104225 Winsorized Mean ( 16 / 62 ) 3229.67379679144 44.0557932359319 73.308719684051 Winsorized Mean ( 17 / 62 ) 3229.58288770053 43.822076983847 73.6976225223412 Winsorized Mean ( 18 / 62 ) 3230.93048128342 43.6857554937414 73.9584435422273 Winsorized Mean ( 19 / 62 ) 3226.45989304813 43.1109856378091 74.840782350809 Winsorized Mean ( 20 / 62 ) 3224 42.7565112829262 75.4037198841203 Winsorized Mean ( 21 / 62 ) 3222.65240641711 42.4576273249172 75.902790840265 Winsorized Mean ( 22 / 62 ) 3222.18181818182 42.1780147649103 76.3948193422913 Winsorized Mean ( 23 / 62 ) 3223.28877005348 41.8325629867774 77.0521464599792 Winsorized Mean ( 24 / 62 ) 3220.72192513369 41.5462279375222 77.5214041086246 Winsorized Mean ( 25 / 62 ) 3222.05882352941 41.1342737038754 78.330271411255 Winsorized Mean ( 26 / 62 ) 3218.02673796791 40.2350594625706 79.9806631567562 Winsorized Mean ( 27 / 62 ) 3218.45989304813 40.1632974492574 80.134353936311 Winsorized Mean ( 28 / 62 ) 3217.5614973262 39.8836802918078 80.6736357774661 Winsorized Mean ( 29 / 62 ) 3219.88770053476 39.408347386835 81.705727695915 Winsorized Mean ( 30 / 62 ) 3214.27272727273 38.7847069835497 82.8747456732376 Winsorized Mean ( 31 / 62 ) 3210.79144385027 38.194545740388 84.0641348551263 Winsorized Mean ( 32 / 62 ) 3209.42245989305 37.885985902794 84.7126551788207 Winsorized Mean ( 33 / 62 ) 3209.24598930481 37.3136404692042 86.0073139192483 Winsorized Mean ( 34 / 62 ) 3211.42780748663 37.0365431822352 86.7097069962786 Winsorized Mean ( 35 / 62 ) 3212.17647058824 36.8935395923954 87.0660962888565 Winsorized Mean ( 36 / 62 ) 3213.13903743315 36.2826009065539 88.5586743273619 Winsorized Mean ( 37 / 62 ) 3216.10695187166 35.329595513652 91.0315248480243 Winsorized Mean ( 38 / 62 ) 3217.12299465241 35.1608264249405 91.4973657266034 Winsorized Mean ( 39 / 62 ) 3207.73796791444 34.1215142464788 94.0092501388759 Winsorized Mean ( 40 / 62 ) 3217.14973262032 33.2039807220628 96.8904830884523 Winsorized Mean ( 41 / 62 ) 3217.58823529412 32.8828941045122 97.8499102015658 Winsorized Mean ( 42 / 62 ) 3216.01604278075 31.9099916828377 100.783982482529 Winsorized Mean ( 43 / 62 ) 3215.55614973262 31.8242001982354 101.041224279092 Winsorized Mean ( 44 / 62 ) 3219.55614973262 31.4837881475265 102.260761463851 Winsorized Mean ( 45 / 62 ) 3214.26203208556 30.7198833747608 104.631322745397 Winsorized Mean ( 46 / 62 ) 3214.01604278075 30.3890037014767 105.762468370247 Winsorized Mean ( 47 / 62 ) 3216.52941176471 29.7325912911068 108.181940156921 Winsorized Mean ( 48 / 62 ) 3209.59893048128 29.042057720888 110.515548220704 Winsorized Mean ( 49 / 62 ) 3206.97860962567 28.3430300209548 113.148756757999 Winsorized Mean ( 50 / 62 ) 3211.79144385027 27.8537868967883 115.308968785878 Winsorized Mean ( 51 / 62 ) 3212.88235294118 27.7177784175598 115.914136571124 Winsorized Mean ( 52 / 62 ) 3209.82352941176 27.2470268255413 117.804542490591 Winsorized Mean ( 53 / 62 ) 3203.02139037433 26.5370505388882 120.699977025726 Winsorized Mean ( 54 / 62 ) 3203.31016042781 26.3655080599209 121.496242482703 Winsorized Mean ( 55 / 62 ) 3205.36898395722 25.7485281918287 124.487464296093 Winsorized Mean ( 56 / 62 ) 3202.97326203209 25.4343018524463 125.931243586465 Winsorized Mean ( 57 / 62 ) 3202.05882352941 25.3018749479636 126.554211105495 Winsorized Mean ( 58 / 62 ) 3198.02673796791 24.8946009282462 128.462663337549 Winsorized Mean ( 59 / 62 ) 3192.66310160428 24.2149333551682 131.846867169794 Winsorized Mean ( 60 / 62 ) 3193.94652406417 23.7356564855487 134.563226680028 Winsorized Mean ( 61 / 62 ) 3196.88235294118 23.3375917640199 136.984243501503 Winsorized Mean ( 62 / 62 ) 3195.88770053476 22.8661976190093 139.764719687279 Trimmed Mean ( 1 / 62 ) 3262.60962566845 51.673181847401 63.1393211918601 Trimmed Mean ( 2 / 62 ) 3256.37297297297 50.234861184982 64.8229714616288 Trimmed Mean ( 3 / 62 ) 3244.66298342541 48.9275301411045 66.3156912696793 Trimmed Mean ( 4 / 62 ) 3244.66298342541 47.9885816110372 67.6132295329011 Trimmed Mean ( 5 / 62 ) 3236.47457627119 47.0762517237816 68.7496233825305 Trimmed Mean ( 6 / 62 ) 3232.83428571429 46.2709228205243 69.8675126548439 Trimmed Mean ( 7 / 62 ) 3232.83428571429 45.435278308943 71.152514214444 Trimmed Mean ( 8 / 62 ) 3229.20809248555 44.7728443734215 72.1242560680934 Trimmed Mean ( 9 / 62 ) 3224.75147928994 44.2845029405877 72.8189607009089 Trimmed Mean ( 10 / 62 ) 3223.15568862275 43.8060274174222 73.5779041981987 Trimmed Mean ( 11 / 62 ) 3221.52121212121 43.3157877844550 74.3729105921362 Trimmed Mean ( 12 / 62 ) 3220.0981595092 42.8432825735516 75.1599309408907 Trimmed Mean ( 13 / 62 ) 3218.68944099379 42.4023478380130 75.9082835056669 Trimmed Mean ( 14 / 62 ) 3218.68944099379 41.9699775835682 76.6902825855677 Trimmed Mean ( 15 / 62 ) 3215.57961783439 41.565588121742 77.3615811333222 Trimmed Mean ( 16 / 62 ) 3215.57961783439 41.1603721138423 78.1231911349263 Trimmed Mean ( 17 / 62 ) 3212.93464052288 40.8170031931967 78.7155937273318 Trimmed Mean ( 18 / 62 ) 3211.72185430464 40.4674181850569 79.3656229714849 Trimmed Mean ( 19 / 62 ) 3210.38255033557 40.1004990862326 80.0584188099985 Trimmed Mean ( 20 / 62 ) 3209.30612244898 39.7562846205386 80.7244981034021 Trimmed Mean ( 21 / 62 ) 3208.35862068966 39.4142306552812 81.4010210867775 Trimmed Mean ( 22 / 62 ) 3207.46853146853 39.0682777509487 82.0990511001126 Trimmed Mean ( 23 / 62 ) 3206.58156028369 38.7154037554322 82.8244380593284 Trimmed Mean ( 24 / 62 ) 3205.60431654676 38.3596301600328 83.567133029523 Trimmed Mean ( 25 / 62 ) 3204.74452554745 37.9954871633837 84.3453990145404 Trimmed Mean ( 26 / 62 ) 3203.78518518519 37.6314315673948 85.1358838009516 Trimmed Mean ( 27 / 62 ) 3203.01503759398 37.3078673127669 85.8536085898938 Trimmed Mean ( 28 / 62 ) 3203.01503759398 36.9579142339904 86.66655313162 Trimmed Mean ( 29 / 62 ) 3201.40310077519 36.5961462648256 87.4792410547397 Trimmed Mean ( 30 / 62 ) 3200.46456692913 36.2363038360011 88.3220480050574 Trimmed Mean ( 31 / 62 ) 3199.776 35.8912818311616 89.1519008725368 Trimmed Mean ( 32 / 62 ) 3199.776 35.5582075343732 89.9869881491316 Trimmed Mean ( 33 / 62 ) 3198.74380165289 35.2143447029015 90.8363858149355 Trimmed Mean ( 34 / 62 ) 3198.24369747899 34.8792879741082 91.6946383725819 Trimmed Mean ( 35 / 62 ) 3197.62393162393 34.5289418161586 92.6070642028054 Trimmed Mean ( 36 / 62 ) 3196.94782608696 34.1505480242293 93.613368190132 Trimmed Mean ( 37 / 62 ) 3196.20353982301 33.779360875053 94.6200122508386 Trimmed Mean ( 38 / 62 ) 3195.2972972973 33.4427979722889 95.5451544438644 Trimmed Mean ( 39 / 62 ) 3194.31192660550 33.0791062096028 96.565847528196 Trimmed Mean ( 40 / 62 ) 3193.71028037383 32.7597820011057 97.4887525279025 Trimmed Mean ( 41 / 62 ) 3192.66666666667 32.4731691632756 98.3170644852643 Trimmed Mean ( 42 / 62 ) 3191.56310679612 32.174530207014 99.1953289220168 Trimmed Mean ( 43 / 62 ) 3190.48514851485 31.9168617215222 99.9623702465534 Trimmed Mean ( 44 / 62 ) 3189.38383838384 31.6291764766481 100.836765090566 Trimmed Mean ( 45 / 62 ) 3188.0618556701 31.3275011974872 101.765596801759 Trimmed Mean ( 46 / 62 ) 3186.91578947368 31.0502885216966 102.637235955048 Trimmed Mean ( 47 / 62 ) 3185.7311827957 30.7597506169060 103.568173307776 Trimmed Mean ( 48 / 62 ) 3184.38461538462 30.4813831339864 104.469820197695 Trimmed Mean ( 49 / 62 ) 3183.28089887640 30.2234949970648 105.324711757709 Trimmed Mean ( 50 / 62 ) 3182.24137931034 29.9865244862828 106.122381097084 Trimmed Mean ( 51 / 62 ) 3182.24137931034 29.7496479886016 106.967362455166 Trimmed Mean ( 52 / 62 ) 3179.53012048193 29.4808461072998 107.850707842969 Trimmed Mean ( 53 / 62 ) 3178.18518518519 29.2101797699841 108.804026891031 Trimmed Mean ( 54 / 62 ) 3177.07594936709 28.9625202530210 109.696114896482 Trimmed Mean ( 55 / 62 ) 3175.89610389610 28.6824399184803 110.726148574614 Trimmed Mean ( 56 / 62 ) 3175.89610389610 28.4098399398062 111.788595452318 Trimmed Mean ( 57 / 62 ) 3173.2602739726 28.1158929270025 112.863577984571 Trimmed Mean ( 58 / 62 ) 3171.92957746479 27.7756340097816 114.198278114831 Trimmed Mean ( 59 / 62 ) 3170.71014492754 27.4150715346565 115.655731225042 Trimmed Mean ( 60 / 62 ) 3169.67164179104 27.0639926118105 117.117665795099 Trimmed Mean ( 61 / 62 ) 3168.50769230769 26.6945068092104 118.695120121660 Trimmed Mean ( 62 / 62 ) 3167.12698412698 26.2908277774687 120.465091891904 Median 3134 Midrange 3839.5 Midmean - Weighted Average at Xnp 3180.96808510638 Midmean - Weighted Average at X(n+1)p 3186.91578947368 Midmean - Empirical Distribution Function 3186.91578947368 Midmean - Empirical Distribution Function - Averaging 3186.91578947368 Midmean - Empirical Distribution Function - Interpolation 3185.7311827957 Midmean - Closest Observation 3180.96808510638 Midmean - True Basic - Statistics Graphics Toolkit 3186.91578947368 Midmean - MS Excel (old versions) 3186.91578947368 Number of observations 187

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/03/t127560104668dyol07o39zjau/1qp0a1275600982.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t127560104668dyol07o39zjau/1qp0a1275600982.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t127560104668dyol07o39zjau/2qp0a1275600982.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t127560104668dyol07o39zjau/2qp0a1275600982.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