Central tendency Schiphol

*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: Fri, 10 Dec 2010 09:59:23 +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/10/t129197508794kn64okhmr9q9f.htm/, Retrieved Fri, 10 Dec 2010 10:58:09 +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/10/t129197508794kn64okhmr9q9f.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:

» Textbox « » Textfile « » CSV «

1149822 1086979 1276674 1522522 1742117 1737275 1979900 2061036 1867943 1707752 1298756 1281814 1281151 1164976 1454329 1645288 1817743 1895785 2236311 2295951 2087315 1980891 1465446 1445026 1488120 1338333 1715789 1806090 2083316 2092278 2430800 2424894 2299016 2130688 1652221 1608162 1647074 1479691 1884978 2007898 2208954 2217164 2534291 2560312 2429069 2315077 1799608 1772590 1744799 1659093 2099821 2135736 2427894 2468882 2703217 2766841 2655236 2550373 2052097 1998055 1920748 1876694 2380930 2467402 2770771 2781340 3143926 3172235 2952540 2920877 2384552 2248987 2208616 2178756 2632870 2706905 3029745 3015402 3391414 3507805 3177852 3142961 2545815 2414007 2372578 2332664 2825328 2901478 3263955 3226738 3610786 3709274 3467185 3449646 2802951 2462530 2490645 2561520 3067554 3226951 3546493 3492787 3952263 3932072 3720284 3651555 2914972 2713514 2703997 2591373 3163748 3355137 3613702 3686773 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 3029148.61946903 60924.0429657037 49.7200854049401 Geometric Mean 2875809.02747788 Harmonic Mean 2706349.92716820 Quadratic Mean 3163997.88817418 Winsorized Mean ( 1 / 75 ) 3029097.58849558 60840.6792622338 49.7873729423638 Winsorized Mean ( 2 / 75 ) 3029001.36725664 60791.9470611549 49.8257008318807 Winsorized Mean ( 3 / 75 ) 3029464.21681416 60464.2664047706 50.1033816656897 Winsorized Mean ( 4 / 75 ) 3029298.71238938 60423.3243905451 50.1345919468045 Winsorized Mean ( 5 / 75 ) 3028989.22123894 60381.0254074054 50.1645873153299 Winsorized Mean ( 6 / 75 ) 3027490.07079646 60085.8937529546 50.386037082915 Winsorized Mean ( 7 / 75 ) 3027640.69469027 59803.1911157788 50.6267414531235 Winsorized Mean ( 8 / 75 ) 3028743.27876106 59032.4256846919 51.306434449067 Winsorized Mean ( 9 / 75 ) 3028959.51769911 58970.909325412 51.3636223749709 Winsorized Mean ( 10 / 75 ) 3027510.93362832 58695.9067910196 51.5795921580604 Winsorized Mean ( 11 / 75 ) 3025693.3539823 58340.9208177024 51.8622831380511 Winsorized Mean ( 12 / 75 ) 3025711.51327434 58242.3618066689 51.9503574274332 Winsorized Mean ( 13 / 75 ) 3026447.04867257 57879.8802325666 52.2884124243524 Winsorized Mean ( 14 / 75 ) 3031563.73893805 57260.4509422028 52.9434136311297 Winsorized Mean ( 15 / 75 ) 3032613.60619469 56841.0933663819 53.3524854394919 Winsorized Mean ( 16 / 75 ) 3029813.88938053 56522.84346682 53.6033522651614 Winsorized Mean ( 17 / 75 ) 3027970.74336283 56253.2729810298 53.8274589708576 Winsorized Mean ( 18 / 75 ) 3027667.69026549 56108.0888242168 53.9613405787352 Winsorized Mean ( 19 / 75 ) 3031675.93362832 55661.7860058626 54.4660197089078 Winsorized Mean ( 20 / 75 ) 3032262.48230089 55574.1597889051 54.5624530144718 Winsorized Mean ( 21 / 75 ) 3032140.75663717 55152.0908382883 54.9778024830958 Winsorized Mean ( 22 / 75 ) 3030145.96017699 54858.1196673655 55.2360521751458 Winsorized Mean ( 23 / 75 ) 3030350.51769911 54822.913759443 55.2752546315937 Winsorized Mean ( 24 / 75 ) 3033023.86283186 54489.9975406723 55.6620297251428 Winsorized Mean ( 25 / 75 ) 3034232.93362832 54009.292200505 56.1798314698142 Winsorized Mean ( 26 / 75 ) 3031941.71238938 53637.3087593299 56.5267307872152 Winsorized Mean ( 27 / 75 ) 3030325.29646018 53206.3941262286 56.9541564735797 Winsorized Mean ( 28 / 75 ) 3032800.19911504 52226.5704461588 58.0700622921738 Winsorized Mean ( 29 / 75 ) 3032083.41150442 51941.7608206017 58.3746750899867 Winsorized Mean ( 30 / 75 ) 3033054.69469027 51821.1880754776 58.5292388563655 Winsorized Mean ( 31 / 75 ) 3031020.07964602 51346.4203484724 59.0307962867794 Winsorized Mean ( 32 / 75 ) 3033694.20353982 50921.0986681275 59.5763697737863 Winsorized Mean ( 33 / 75 ) 3041804.90265487 50045.1140039968 60.7812563362717 Winsorized Mean ( 34 / 75 ) 3039249.33628319 49779.2225738862 61.054576169246 Winsorized Mean ( 35 / 75 ) 3041706.15044248 49510.442625181 61.4356485048968 Winsorized Mean ( 36 / 75 ) 3041312.38053097 49182.1270067466 61.837756226236 Winsorized Mean ( 37 / 75 ) 3047915.07964602 48455.0769203588 62.9018726903602 Winsorized Mean ( 38 / 75 ) 3044924.51327434 47903.3639556688 63.5638974350987 Winsorized Mean ( 39 / 75 ) 3045671.55309734 47270.7888959508 64.4303093777696 Winsorized Mean ( 40 / 75 ) 3040854.73893805 46707.0526532651 65.1048303456904 Winsorized Mean ( 41 / 75 ) 3036041.41150442 46116.6507064575 65.8339529214618 Winsorized Mean ( 42 / 75 ) 3035382.60619469 45807.7555244258 66.2635086885265 Winsorized Mean ( 43 / 75 ) 3039523.54424779 45126.1021624903 67.3562173241344 Winsorized Mean ( 44 / 75 ) 3039558.19911504 44954.9304550822 67.6134557065346 Winsorized Mean ( 45 / 75 ) 3046435.03539823 44048.7388172059 69.16055072633 Winsorized Mean ( 46 / 75 ) 3051816.01769911 43459.2027859473 70.2225494731333 Winsorized Mean ( 47 / 75 ) 3049849.29646018 43273.689541796 70.4781433881313 Winsorized Mean ( 48 / 75 ) 3048728.30530973 42872.1351574642 71.1121173254404 Winsorized Mean ( 49 / 75 ) 3046883.65044248 41993.6249152542 72.5558619097847 Winsorized Mean ( 50 / 75 ) 3046698.47345133 41495.4459377047 73.4224781684526 Winsorized Mean ( 51 / 75 ) 3055587.36725664 40452.7491388493 75.5347271150521 Winsorized Mean ( 52 / 75 ) 3048380.30530973 39714.6341586201 76.7571040220215 Winsorized Mean ( 53 / 75 ) 3049350.95575221 39162.1758919919 77.8646968994325 Winsorized Mean ( 54 / 75 ) 3045028.80530973 38094.5910484192 79.9333638058858 Winsorized Mean ( 55 / 75 ) 3049926.96902655 36892.7056114052 82.6701896345515 Winsorized Mean ( 56 / 75 ) 3050264.70353982 36579.9901012315 83.3861544275576 Winsorized Mean ( 57 / 75 ) 3047798.82300885 36225.6190245652 84.1337955037314 Winsorized Mean ( 58 / 75 ) 3055146.08849558 35597.9136562358 85.823740065182 Winsorized Mean ( 59 / 75 ) 3055113.97787611 35132.580453535 86.9595668304719 Winsorized Mean ( 60 / 75 ) 3052982.91592920 34827.3966701810 87.6603825672434 Winsorized Mean ( 61 / 75 ) 3052538.64159292 34739.4364844773 87.8695497250478 Winsorized Mean ( 62 / 75 ) 3050639.68584071 34506.6694178886 88.4072481437233 Winsorized Mean ( 63 / 75 ) 3055540.30530973 33482.2517155258 91.2585070822127 Winsorized Mean ( 64 / 75 ) 3055789.50884956 33281.186597203 91.817324479241 Winsorized Mean ( 65 / 75 ) 3051303.93362832 32847.2729998556 92.8936759420405 Winsorized Mean ( 66 / 75 ) 3046605.08407080 31455.2832300287 96.855115301024 Winsorized Mean ( 67 / 75 ) 3058679.90707965 30381.0140124843 100.677347563935 Winsorized Mean ( 68 / 75 ) 3044305.73008850 28692.2545417159 106.102004834174 Winsorized Mean ( 69 / 75 ) 3044635.46460177 28501.5114215532 106.823649439846 Winsorized Mean ( 70 / 75 ) 3047243.73893805 28227.9345319882 107.951353489391 Winsorized Mean ( 71 / 75 ) 3046913.24336283 28143.0551252644 108.265191174201 Winsorized Mean ( 72 / 75 ) 3038773.73893805 26972.7037293414 112.661072817569 Winsorized Mean ( 73 / 75 ) 3040051.5619469 26129.0749510117 116.347462267477 Winsorized Mean ( 74 / 75 ) 3037614.1460177 25678.2679013950 118.295134145425 Winsorized Mean ( 75 / 75 ) 3035509.16814159 25474.0887016185 119.160657862855 Trimmed Mean ( 1 / 75 ) 3029369.46428571 60259.7510187555 50.2718549790032 Trimmed Mean ( 2 / 75 ) 3029646.23873874 59646.7124187226 50.7931806445674 Trimmed Mean ( 3 / 75 ) 3029977.46818182 59025.8891764137 51.3330253971435 Trimmed Mean ( 4 / 75 ) 3030154.83027523 58491.8283636915 51.804754869933 Trimmed Mean ( 5 / 75 ) 3030378.76851852 57939.1545139179 52.3027785604047 Trimmed Mean ( 6 / 75 ) 3030672.26168224 57364.7366061856 52.8316251582936 Trimmed Mean ( 7 / 75 ) 3031237.65094340 56816.2906273125 53.3515584610558 Trimmed Mean ( 8 / 75 ) 3031790.65238095 56285.6435861655 53.8643685887628 Trimmed Mean ( 9 / 75 ) 3032204.53846154 55844.9309812754 54.296862493719 Trimmed Mean ( 10 / 75 ) 3032600.10194175 55386.6057352327 54.7533119548548 Trimmed Mean ( 11 / 75 ) 3033163.90196078 54936.7226600448 55.2119557755634 Trimmed Mean ( 12 / 75 ) 3033923.73267327 54502.6590501474 55.665609450023 Trimmed Mean ( 13 / 75 ) 3034697.05 54053.0538513187 56.1429342798541 Trimmed Mean ( 14 / 75 ) 3035421.40909091 53614.8305705808 56.615331556349 Trimmed Mean ( 15 / 75 ) 3035739.13265306 53212.9457897599 57.0488832669971 Trimmed Mean ( 16 / 75 ) 3035981.87113402 52826.3316102442 57.4709955923813 Trimmed Mean ( 17 / 75 ) 3036435.63541667 52444.2626747275 57.8983377886234 Trimmed Mean ( 18 / 75 ) 3037027.91578947 52061.2185191898 58.3357055822661 Trimmed Mean ( 19 / 75 ) 3037653.03723404 51665.2897718619 58.7948514495397 Trimmed Mean ( 20 / 75 ) 3038035.27419355 51280.8148687793 59.2431162017895 Trimmed Mean ( 21 / 75 ) 3038389.79891304 50877.3965698725 59.7198363862874 Trimmed Mean ( 22 / 75 ) 3038759.31318681 50480.343511436 60.1968826241922 Trimmed Mean ( 23 / 75 ) 3039250.88333333 50078.7628204104 60.6894162747694 Trimmed Mean ( 24 / 75 ) 3039742.20786517 49651.7055326623 61.2213049935524 Trimmed Mean ( 25 / 75 ) 3040101.66477273 49220.0341096684 61.7655334817322 Trimmed Mean ( 26 / 75 ) 3040406.56896552 48793.6958890171 62.3114628553865 Trimmed Mean ( 27 / 75 ) 3040834.35465116 48363.9257381873 62.8740183564167 Trimmed Mean ( 28 / 75 ) 3041351.79411765 47934.0669069957 63.4486491625809 Trimmed Mean ( 29 / 75 ) 3041762.64880952 47542.8509120573 63.9793910221338 Trimmed Mean ( 30 / 75 ) 3042217.05421687 47142.0852323304 64.532933560828 Trimmed Mean ( 31 / 75 ) 3042217.05421687 46719.1758189853 65.1170959437302 Trimmed Mean ( 32 / 75 ) 3043160.75308642 46296.7815115409 65.7315833569946 Trimmed Mean ( 33 / 75 ) 3043578.6125 45871.1597942478 66.3505920964671 Trimmed Mean ( 34 / 75 ) 3043655.49367089 45472.5974058733 66.9338385600504 Trimmed Mean ( 35 / 75 ) 3043843.23717949 45060.0350538206 67.5508404186517 Trimmed Mean ( 36 / 75 ) 3043932.84415584 44632.3294590339 68.2001786832511 Trimmed Mean ( 37 / 75 ) 3044041.07236842 44191.9622543576 68.8822337158894 Trimmed Mean ( 38 / 75 ) 3043883.32 43764.7894169728 69.550964612194 Trimmed Mean ( 39 / 75 ) 3043841.47972973 43339.2211666300 70.2329529187157 Trimmed Mean ( 40 / 75 ) 3043768.84246575 42920.2836017172 70.9167924124335 Trimmed Mean ( 41 / 75 ) 3043883.18055556 42503.2254640431 71.61534559608 Trimmed Mean ( 42 / 75 ) 3044187.58450704 42089.3413206294 72.3268050530167 Trimmed Mean ( 43 / 75 ) 3044526.00714286 41659.3114903869 73.0815248313789 Trimmed Mean ( 44 / 75 ) 3044716.52898551 41237.3114900248 73.8340211563504 Trimmed Mean ( 45 / 75 ) 3044911.34558824 40788.0711251191 74.6520063733302 Trimmed Mean ( 46 / 75 ) 3044854.23880597 40360.2591350328 75.4418902172764 Trimmed Mean ( 47 / 75 ) 3044595.12121212 39932.7042106548 76.243149102829 Trimmed Mean ( 48 / 75 ) 3044400.77692308 39476.8442668177 77.1186459674045 Trimmed Mean ( 49 / 75 ) 3044241.59375 39005.1872249487 78.047096048877 Trimmed Mean ( 50 / 75 ) 3044144.88095238 38550.4079240618 78.9653091855438 Trimmed Mean ( 51 / 75 ) 3044051.79838710 38085.8964016216 79.9259591079886 Trimmed Mean ( 52 / 75 ) 3043632.79508197 37649.7056583142 80.8408124808233 Trimmed Mean ( 53 / 75 ) 3043460.85 37222.4315359283 81.7641600619872 Trimmed Mean ( 54 / 75 ) 3043248 36790.2814465566 82.7188018232688 Trimmed Mean ( 55 / 75 ) 3043183.75 36391.0300398773 83.6245565642215 Trimmed Mean ( 56 / 75 ) 3042940.69298246 36036.4478936101 84.4406391541722 Trimmed Mean ( 57 / 75 ) 3042676.78571429 35663.8211664019 85.3155014297998 Trimmed Mean ( 58 / 75 ) 3042492.16363636 35274.4019840431 86.2521259754504 Trimmed Mean ( 59 / 75 ) 3042035.62037037 34886.6290556722 87.1977517666118 Trimmed Mean ( 60 / 75 ) 3041563.00943396 34488.2312485943 88.1913307617925 Trimmed Mean ( 61 / 75 ) 3041563.00943396 34065.1923615265 89.2865355684628 Trimmed Mean ( 62 / 75 ) 3041149.40384615 33596.1300716667 90.5208247902013 Trimmed Mean ( 63 / 75 ) 3040374.7 33087.6829616766 91.888413689211 Trimmed Mean ( 64 / 75 ) 3039819.56122449 32603.5477592776 93.2358522351142 Trimmed Mean ( 65 / 75 ) 3039232.125 32073.2726283589 94.7590275621807 Trimmed Mean ( 66 / 75 ) 3038785.60638298 31513.0521740655 96.4294283396587 Trimmed Mean ( 67 / 75 ) 3038494.56521739 31009.5400241851 97.9857993007182 Trimmed Mean ( 68 / 75 ) 3037738.03333333 30535.4921568866 99.482203126968 Trimmed Mean ( 69 / 75 ) 3037489.98863636 30157.0269388516 100.722461627115 Trimmed Mean ( 70 / 75 ) 3037217.84883721 29738.4854294548 102.130885449498 Trimmed Mean ( 71 / 75 ) 3036832.5 29281.8685408365 103.710338558649 Trimmed Mean ( 72 / 75 ) 3036441.18292683 28761.9605822747 105.571425641899 Trimmed Mean ( 73 / 75 ) 3036349.6625 28287.2845010834 107.339736424106 Trimmed Mean ( 74 / 75 ) 3036202.73076923 27827.1159358263 109.109500882923 Trimmed Mean ( 75 / 75 ) 3036146.01315789 27339.5843030134 111.053115493905 Median 3028973.5 Midrange 3004414 Midmean - Weighted Average at Xnp 3036820.61946903 Midmean - Weighted Average at X(n+1)p 3042940.69298246 Midmean - Empirical Distribution Function 3042940.69298246 Midmean - Empirical Distribution Function - Averaging 3042940.69298246 Midmean - Empirical Distribution Function - Interpolation 3042676.78571429 Midmean - Closest Observation 3042940.69298246 Midmean - True Basic - Statistics Graphics Toolkit 3042940.69298246 Midmean - MS Excel (old versions) 3042940.69298246 Number of observations 226

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t129197508794kn64okhmr9q9f/1jrjh1291975159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129197508794kn64okhmr9q9f/1jrjh1291975159.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129197508794kn64okhmr9q9f/2jrjh1291975159.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129197508794kn64okhmr9q9f/2jrjh1291975159.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