*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, 02 Dec 2008 09:03:18 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9.htm/, Retrieved Tue, 02 Dec 2008 16:08:32 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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283,9 340,5 383 335,4 356 431 441,9 262,3 481,1 442,8 458,4 472,7 498,8 463,3 538,3 494,2 446,2 497,5 433,2 359,4 455,9 483 469,7 423,8 371 435,7 438,1 364,5 392,7 405,1 391,4 282,8 408 480,9 416,9 350,7 399,9 438,1 454,9 407,7 485 494 521,2 333 520,3 570,5 501,8 408,4 512,7 596,9 581,5 605,7 581,1 538,3 731,4 457,7 706,3 708,5 683,5 722,1 727,7 674,4 675,3 628,7 658,3 786 736,9 567,3 736,3 719,6 676,8 589,5 596,5 661 840,9 683,1 677,4 780,2 928,8 557,5 760,6 847 719 699,8 745,4 871,5 1199,8 795,4 1054,4 753,7 1093,8 768,4 1405,7 1149,2 1086,8 994,4 958,8 1096,3 1107,4 862,4 983 1021,1 1036,3 684,5 874,1 1031,6 1126,3 1041,9 1419,4 1402,2 1503,6 1503,6 1571,2 1351,2 1448,3 1292,9 1293,7 1824,7 1641,4 1529,1 1417 1060 1117 1226,2 1124 1246 1398,1 1095,2 1507,1 1467 1262,1 1262,9 1184,9 1412,9 1581,8 1348,8 1443,7 1628,7 1393,2 1108,1 1074,8 1323,5 1367 1190,7 1149,2 1281,9 1406,8 1392,4 1674,5 1529,8 1180,9 1282,8 1524,2 1524,6 1520,1 1881,9 1375,2 1336,5 1653,9 1404,3 1731,4 1330,8 1616,3 1503,7 1476,7 1565,6 1581,8 1407,7 1393,7 1662,4 1929 1352,8 1339,8 1251,2 1491,7 1540,7 1383,7 1827,5 1591,3 1289,2 1291,9 1368,3 1264,6 1448,8 1239,2 1280,9 1269,4 1068,5

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 4 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 964.154255319149 32.2742300746928 29.8738111827235 Geometric Mean 853.375756789506 Harmonic Mean 744.127249874803 Quadratic Mean 1060.36667072432 Winsorized Mean ( 1 / 62 ) 964.012765957447 32.2226172333253 29.9172708094129 Winsorized Mean ( 2 / 62 ) 963.445744680851 32.1356063421332 29.9806306569567 Winsorized Mean ( 3 / 62 ) 964.184574468085 32.0436157531851 30.0897558469895 Winsorized Mean ( 4 / 62 ) 962.250531914894 31.7671214349866 30.2907688341923 Winsorized Mean ( 5 / 62 ) 960.872872340426 31.5633644051038 30.4426632094091 Winsorized Mean ( 6 / 62 ) 960.812234042553 31.4830334462617 30.5184135347873 Winsorized Mean ( 7 / 62 ) 960.693085106383 31.4251298487568 30.5708549091131 Winsorized Mean ( 8 / 62 ) 960.30585106383 31.3479962685408 30.6337235349088 Winsorized Mean ( 9 / 62 ) 959.942021276596 31.2529396492115 30.7152553344150 Winsorized Mean ( 10 / 62 ) 959.628191489362 31.1429291415493 30.8136780303390 Winsorized Mean ( 11 / 62 ) 958.86755319149 30.9099656972989 31.0213075802695 Winsorized Mean ( 12 / 62 ) 958.797340425532 30.7908590408611 31.1390253566215 Winsorized Mean ( 13 / 62 ) 958.887234042553 30.7820088671741 31.1508985063385 Winsorized Mean ( 14 / 62 ) 958.634042553191 30.6445996501104 31.2823157586834 Winsorized Mean ( 15 / 62 ) 958.602127659575 30.5566657804640 31.3712934044147 Winsorized Mean ( 16 / 62 ) 956.70425531915 30.3135032212655 31.5603329755699 Winsorized Mean ( 17 / 62 ) 955.745744680851 30.2100237118642 31.6367095172291 Winsorized Mean ( 18 / 62 ) 955.717021276596 30.1995036017653 31.6467791616525 Winsorized Mean ( 19 / 62 ) 956.121276595745 30.0706108917692 31.7958713920724 Winsorized Mean ( 20 / 62 ) 956.812765957447 29.9962374140291 31.8977594673241 Winsorized Mean ( 21 / 62 ) 957.159042553192 29.8739128958652 32.0399622871522 Winsorized Mean ( 22 / 62 ) 955.895212765957 29.6975239414640 32.1877074550086 Winsorized Mean ( 23 / 62 ) 955.785106382979 29.6275638658906 32.2599964920959 Winsorized Mean ( 24 / 62 ) 956.078723404255 29.5975840643561 32.3025933915885 Winsorized Mean ( 25 / 62 ) 956.078723404255 29.5975840643561 32.3025933915885 Winsorized Mean ( 26 / 62 ) 954.958510638298 29.3866819361856 32.4963026690808 Winsorized Mean ( 27 / 62 ) 952.933510638298 29.1659510572153 32.6728077122845 Winsorized Mean ( 28 / 62 ) 951.995212765957 28.9806235392539 32.8493695615793 Winsorized Mean ( 29 / 62 ) 950.529787234042 28.5915607564485 33.2451171634504 Winsorized Mean ( 30 / 62 ) 950.609574468085 28.5693420411453 33.2737650415269 Winsorized Mean ( 31 / 62 ) 950.147872340426 28.4713855057023 33.3720279313463 Winsorized Mean ( 32 / 62 ) 946.13085106383 28.0822109183195 33.691465882646 Winsorized Mean ( 33 / 62 ) 946.569680851064 27.9645931928623 33.8488628932627 Winsorized Mean ( 34 / 62 ) 946.985638297872 27.7913706683914 34.0748086734322 Winsorized Mean ( 35 / 62 ) 946.576063829787 27.6535757558517 34.2297890221117 Winsorized Mean ( 36 / 62 ) 947.973936170213 27.4949339877464 34.4781310110689 Winsorized Mean ( 37 / 62 ) 947.796808510638 27.4720503495159 34.5004030078644 Winsorized Mean ( 38 / 62 ) 947.897872340425 27.4119819398873 34.5796912612559 Winsorized Mean ( 39 / 62 ) 947.877127659575 27.335630150429 34.6755177196711 Winsorized Mean ( 40 / 62 ) 948.919680851064 27.0854868444104 35.0342486476994 Winsorized Mean ( 41 / 62 ) 948.003723404255 26.9966601429612 35.1155927579222 Winsorized Mean ( 42 / 62 ) 948.629255319149 26.9206284432663 35.2380055806769 Winsorized Mean ( 43 / 62 ) 948.743617021277 26.8778413032284 35.2983562302424 Winsorized Mean ( 44 / 62 ) 947.409574468085 26.6359611653774 35.5688149785851 Winsorized Mean ( 45 / 62 ) 947.984042553191 26.2257334655335 36.1470936093763 Winsorized Mean ( 46 / 62 ) 948.155319148936 25.9143892712456 36.5879862814672 Winsorized Mean ( 47 / 62 ) 948.055319148936 25.8663655305618 36.6520498609975 Winsorized Mean ( 48 / 62 ) 948.79574468085 25.1706912102263 37.6944652316650 Winsorized Mean ( 49 / 62 ) 948.378723404255 25.1349244361441 37.7315128125265 Winsorized Mean ( 50 / 62 ) 952.846808510638 24.6382686197796 38.6734483341777 Winsorized Mean ( 51 / 62 ) 953.063829787234 24.2016662900089 39.3800913691916 Winsorized Mean ( 52 / 62 ) 953.036170212766 24.0483828491574 39.6299483499847 Winsorized Mean ( 53 / 62 ) 954.41755319149 23.6583297368350 40.3417132066388 Winsorized Mean ( 54 / 62 ) 952.435638297872 23.4707959462676 40.5796054159523 Winsorized Mean ( 55 / 62 ) 946.057978723404 22.5447679419222 41.9635270214603 Winsorized Mean ( 56 / 62 ) 947.904787234043 22.3492711660731 42.41323039979 Winsorized Mean ( 57 / 62 ) 947.722872340426 22.3140564294683 42.4720119954904 Winsorized Mean ( 58 / 62 ) 949.604787234043 22.0178368238770 43.1288865854548 Winsorized Mean ( 59 / 62 ) 954.814361702128 21.2544707345037 44.9229893150018 Winsorized Mean ( 60 / 62 ) 963.973936170213 20.4656764308653 47.1019826501505 Winsorized Mean ( 61 / 62 ) 964.525531914894 20.3688321727724 47.3530109008508 Winsorized Mean ( 62 / 62 ) 965.152127659574 19.704278815224 48.9818549925245 Trimmed Mean ( 1 / 62 ) 962.740322580645 31.9816781897211 30.1028706770638 Trimmed Mean ( 2 / 62 ) 961.440217391304 31.7240502578993 30.306351477044 Trimmed Mean ( 3 / 62 ) 961.440217391304 31.4961464774099 30.5256459891333 Trimmed Mean ( 4 / 62 ) 959.088333333333 31.2861821153732 30.6553330731288 Trimmed Mean ( 5 / 62 ) 958.253370786517 31.1404156164315 30.7720161024725 Trimmed Mean ( 6 / 62 ) 958.253370786517 31.0307176796131 30.8807994929515 Trimmed Mean ( 7 / 62 ) 957.132183908046 30.9272360973532 30.9478732886176 Trimmed Mean ( 8 / 62 ) 956.576162790698 30.8243212938554 31.0331622121193 Trimmed Mean ( 9 / 62 ) 956.060588235294 30.7238543497242 31.1178596719222 Trimmed Mean ( 10 / 62 ) 955.577976190476 30.6272970301256 31.2002059878334 Trimmed Mean ( 11 / 62 ) 955.119277108434 30.5356146713264 31.2788619907924 Trimmed Mean ( 12 / 62 ) 955.119277108434 30.4629201823159 31.3535035837730 Trimmed Mean ( 13 / 62 ) 954.335185185185 30.3952648357001 31.3974953119768 Trimmed Mean ( 14 / 62 ) 953.92375 30.3201338145826 31.4617262520525 Trimmed Mean ( 15 / 62 ) 953.523417721519 30.2501475967985 31.5212814969020 Trimmed Mean ( 16 / 62 ) 953.115384615385 30.1799331903321 31.5810965718343 Trimmed Mean ( 17 / 62 ) 952.841558441558 30.1237331454373 31.6309254846085 Trimmed Mean ( 18 / 62 ) 952.630263157895 30.0685754812881 31.6819220036121 Trimmed Mean ( 19 / 62 ) 952.415333333333 30.0058411425614 31.7409976546997 Trimmed Mean ( 20 / 62 ) 952.167567567568 29.9449859339629 31.7972287469884 Trimmed Mean ( 21 / 62 ) 951.868493150685 29.8810792361785 31.8552246934311 Trimmed Mean ( 22 / 62 ) 951.539583333333 29.8175294641218 31.9120866293863 Trimmed Mean ( 23 / 62 ) 951.277464788732 29.7583805181364 31.9667081415594 Trimmed Mean ( 24 / 62 ) 951.277464788732 29.6950119620785 32.0349244514043 Trimmed Mean ( 25 / 62 ) 950.726811594203 29.6237403499245 32.0934088796328 Trimmed Mean ( 26 / 62 ) 950.430882352941 29.5415181143366 32.1727163334809 Trimmed Mean ( 27 / 62 ) 950.18656716418 29.4634735758613 32.249645131545 Trimmed Mean ( 28 / 62 ) 950.041666666667 29.3902010646960 32.3251162717588 Trimmed Mean ( 29 / 62 ) 949.94076923077 29.3188909673701 32.4002968014032 Trimmed Mean ( 30 / 62 ) 949.9109375 29.2641902333341 32.4598401639004 Trimmed Mean ( 31 / 62 ) 949.9109375 29.2000035100796 32.5311925792097 Trimmed Mean ( 32 / 62 ) 949.862903225806 29.1308935571572 32.6067204688417 Trimmed Mean ( 33 / 62 ) 950.04262295082 29.0771426051643 32.673176860992 Trimmed Mean ( 34 / 62 ) 950.2075 29.0199489524427 32.7432519456592 Trimmed Mean ( 35 / 62 ) 950.358474576271 28.9627455144736 32.8131348632452 Trimmed Mean ( 36 / 62 ) 950.533620689655 28.9026065275073 32.8874705395519 Trimmed Mean ( 37 / 62 ) 950.650877192982 28.8405761803282 32.9622706304117 Trimmed Mean ( 38 / 62 ) 950.780357142857 28.766223301353 33.0519702632684 Trimmed Mean ( 39 / 62 ) 950.91 28.6809328621217 33.1547793292263 Trimmed Mean ( 40 / 62 ) 951.04537037037 28.5846289660387 33.2712162015573 Trimmed Mean ( 41 / 62 ) 951.13962264151 28.4891160529023 33.3860699951277 Trimmed Mean ( 42 / 62 ) 951.277884615385 28.3817717260929 33.5172128715567 Trimmed Mean ( 43 / 62 ) 951.394117647059 28.2601906350648 33.6655237019733 Trimmed Mean ( 44 / 62 ) 951.51 28.1199515244492 33.8375405509749 Trimmed Mean ( 45 / 62 ) 951.688775510204 27.9746458005084 34.0196899112448 Trimmed Mean ( 46 / 62 ) 951.85 27.8371237045128 34.1935470813635 Trimmed Mean ( 47 / 62 ) 952.010638297872 27.6996286406801 34.369075869116 Trimmed Mean ( 48 / 62 ) 952.010638297872 27.53977411249 34.5685710568741 Trimmed Mean ( 49 / 62 ) 952.33 27.4097260069356 34.7442363983875 Trimmed Mean ( 50 / 62 ) 952.502272727273 27.2554597972563 34.9472098365831 Trimmed Mean ( 51 / 62 ) 952.502272727273 27.1144358498237 35.128972551847 Trimmed Mean ( 52 / 62 ) 952.461904761905 26.9821706981576 35.2996767909018 Trimmed Mean ( 53 / 62 ) 952.436585365854 26.8335381001908 35.4942602727101 Trimmed Mean ( 54 / 62 ) 952.34875 26.6880927434693 35.6844064937178 Trimmed Mean ( 55 / 62 ) 952.344871794872 26.5265374980067 35.9015899405053 Trimmed Mean ( 56 / 62 ) 952.627631578947 26.4176286569221 36.0603006405472 Trimmed Mean ( 57 / 62 ) 952.841891891892 26.2959677386889 36.2352852483154 Trimmed Mean ( 58 / 62 ) 953.076388888889 26.1425336885450 36.4569249577558 Trimmed Mean ( 59 / 62 ) 953.237142857143 25.9799790865822 36.6912205618155 Trimmed Mean ( 60 / 62 ) 953.163235294118 25.8571966320099 36.8625898955399 Trimmed Mean ( 61 / 62 ) 952.65 25.7796268786936 36.9535992310016 Trimmed Mean ( 62 / 62 ) 952.65 25.6766503724485 37.1018020723689 Median 988.7 Midrange 1095.65 Midmean - Weighted Average at Xnp 947.466315789474 Midmean - Weighted Average at X(n+1)p 952.010638297872 Midmean - Empirical Distribution Function 947.466315789474 Midmean - Empirical Distribution Function - Averaging 952.010638297872 Midmean - Empirical Distribution Function - Interpolation 952.010638297872 Midmean - Closest Observation 947.466315789474 Midmean - True Basic - Statistics Graphics Toolkit 952.010638297872 Midmean - MS Excel (old versions) 951.85 Number of observations 188

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9/1cc0u1228233790.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9/1cc0u1228233790.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9/23ih31228233790.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228234112wsecmnq33957yj9/23ih31228233790.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')

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