*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 08:28:29 -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/t1228232053iw3sxhf94unyo63.htm/, Retrieved Tue, 02 Dec 2008 15:34:15 +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/t1228232053iw3sxhf94unyo63.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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1160,9 1470,1 1653 1390 1550,7 1852,1 1797,9 1256,4 1835,5 1793,2 1874,8 1948,6 1819,9 1823,6 2042,7 1705,9 1876,8 1919,5 1803,4 1380,4 1812,1 1856,4 1934,3 1977,5 1813,7 1937,1 2178,7 1699,9 1943,8 2151 1927,7 1418,2 1899,8 1974,9 1918,7 1818,3 1751 1888 1947,7 1818,1 2110,8 2009 2124,2 1542,5 2044,1 2324,3 2055,6 2137,9 2140,7 2301,5 2412,6 2477 2311,8 2326,6 2745,3 1937,2 2668,7 2636,1 2327,6 2584,6 2282,8 2337 2499,3 2389,6 2327,1 2556,6 2580 1831,4 2382,1 2237,9 2117,7 2240,9 1946,1 2149,6 2690 2171,1 2358,6 2841,4 3064,6 2037,3 2799,9 2852,3 2541,2 2910 2694,6 3081,7 3648,2 2823,3 3670,1 3027,6 3578,5 2655,1 3835,3 3766 3716 3531,7 3194 3442,2 3610 3105,5 3428,4 3489,7 3679 2596,1 3110,7 3401,7 3431,8 3383,1 3797,5 3860,5 4054,1 4044,9 4402,4 4046,4 4329,7 3204,2 4037,2 4678,4 4174,6 4151,4 3874,7 3568 3431 3733,2 3278,3 3583,7 4060,3 2979 4078,4 4002,1 3542,4 3928,2 3626 3998,4 4413,5 3853,1 3920,5 4616,2 4332,7 3362,7 3855,4 4087,1 3860,2 4018,1 3627,7 3996 4420,7 4386,5 4631,8 4875,3 4549,3 3933 4963,3 4419,7 4646,7 5000,2 4302,2 4432,1 5125,5 4299,9 5145,6 4537,8 4880,9 4136,9 4668,8 4818,5 4933,9 4524,4 4676,1 4911 5745 4483,7 4772,7 5021,4 5535,6 4736 5001,7 5486,3 4958,5 4756,4 4763,3 4951,2 4692,8 5301,2 4929,7 5223,3 5708,3 4018

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 3215.95 85.7554911337219 37.5013886281083 Geometric Mean 2993.40557717569 Harmonic Mean 2773.80133785337 Quadratic Mean 3423.08825463828 Winsorized Mean ( 1 / 62 ) 3216.26276595745 85.6612881558779 37.5462806501907 Winsorized Mean ( 2 / 62 ) 3215.74468085106 85.2279770440271 37.7310924462031 Winsorized Mean ( 3 / 62 ) 3215.11117021277 85.0969743895086 37.7817330554722 Winsorized Mean ( 4 / 62 ) 3211.77287234043 84.4869688848907 38.0150088792546 Winsorized Mean ( 5 / 62 ) 3211.08138297872 84.062566523939 38.1987074123449 Winsorized Mean ( 6 / 62 ) 3210.91223404255 83.498789847267 38.4545960476294 Winsorized Mean ( 7 / 62 ) 3210.46914893617 83.3738970651432 38.5068859912798 Winsorized Mean ( 8 / 62 ) 3210.39255319149 82.3896175278792 38.9659844227963 Winsorized Mean ( 9 / 62 ) 3211.69468085106 82.0550922735052 39.1407113423976 Winsorized Mean ( 10 / 62 ) 3211.93404255319 82.014394857032 39.1630523904036 Winsorized Mean ( 11 / 62 ) 3212.41382978723 81.5084304009137 39.4120438092895 Winsorized Mean ( 12 / 62 ) 3214.80106382979 81.2180438500289 39.5823503181879 Winsorized Mean ( 13 / 62 ) 3214.62127659574 81.12979158537 39.6231916017326 Winsorized Mean ( 14 / 62 ) 3213.74255319149 80.9446518976173 39.702963418218 Winsorized Mean ( 15 / 62 ) 3214.10159574468 80.8420698451094 39.7577845532999 Winsorized Mean ( 16 / 62 ) 3212.64627659574 80.6495241313648 39.8346588054614 Winsorized Mean ( 17 / 62 ) 3210.32234042553 80.3079813352592 39.9751343147763 Winsorized Mean ( 18 / 62 ) 3209.80531914894 80.2466295601932 39.9992540090578 Winsorized Mean ( 19 / 62 ) 3204.22659574468 79.6017630924239 40.2532113770435 Winsorized Mean ( 20 / 62 ) 3199.74787234043 79.0416728578098 40.4817833005197 Winsorized Mean ( 21 / 62 ) 3199.56914893617 78.8491423181818 40.5783633767996 Winsorized Mean ( 22 / 62 ) 3199.2414893617 78.719135422886 40.6412173123485 Winsorized Mean ( 23 / 62 ) 3198.77659574468 78.2685656172335 40.8692374840246 Winsorized Mean ( 24 / 62 ) 3193.81063829787 77.643718770266 41.1341791568201 Winsorized Mean ( 25 / 62 ) 3194.34255319149 77.2219880881946 41.3657124385771 Winsorized Mean ( 26 / 62 ) 3194.30106382979 77.1640463193034 41.3962358921904 Winsorized Mean ( 27 / 62 ) 3194.86117021277 76.9099798699717 41.5402679290019 Winsorized Mean ( 28 / 62 ) 3193.32712765957 76.4145342809202 41.7895254837261 Winsorized Mean ( 29 / 62 ) 3193.94414893617 75.9183554726911 42.0707762839393 Winsorized Mean ( 30 / 62 ) 3191.58244680851 75.6554835648132 42.1857385139085 Winsorized Mean ( 31 / 62 ) 3181.90319148936 74.4364215584194 42.7465899739973 Winsorized Mean ( 32 / 62 ) 3181.06914893617 74.1433818976458 42.904289870775 Winsorized Mean ( 33 / 62 ) 3179.2085106383 73.8695757222477 43.0381314574249 Winsorized Mean ( 34 / 62 ) 3171.86595744681 73.156610340679 43.3572023454329 Winsorized Mean ( 35 / 62 ) 3163.48829787234 72.1332282468557 43.85618632021 Winsorized Mean ( 36 / 62 ) 3161.74574468085 71.888530893371 43.9812262872714 Winsorized Mean ( 37 / 62 ) 3161.86382978723 71.8416345904923 44.0115797449531 Winsorized Mean ( 38 / 62 ) 3160.79255319149 71.7079879007484 44.078667464026 Winsorized Mean ( 39 / 62 ) 3163.94574468085 71.002129515922 44.5612795876967 Winsorized Mean ( 40 / 62 ) 3161.11595744681 70.6378842476258 44.750999992657 Winsorized Mean ( 41 / 62 ) 3156.25265957447 68.944794593775 45.7794192900452 Winsorized Mean ( 42 / 62 ) 3161.90478723404 68.3240290852395 46.2780785847586 Winsorized Mean ( 43 / 62 ) 3156.85 67.6430472412664 46.6692458241906 Winsorized Mean ( 44 / 62 ) 3156.63936170213 67.5654665206175 46.7197153258593 Winsorized Mean ( 45 / 62 ) 3129.4 64.6677087494073 48.3920036834254 Winsorized Mean ( 46 / 62 ) 3137.22978723404 62.9897253199359 49.8054209841288 Winsorized Mean ( 47 / 62 ) 3135.32978723404 62.5281911146545 50.1426593564007 Winsorized Mean ( 48 / 62 ) 3124.27446808511 61.3049819812374 50.9628152087427 Winsorized Mean ( 49 / 62 ) 3125.57765957447 60.8039131056856 51.4042189051514 Winsorized Mean ( 50 / 62 ) 3121.50851063830 60.337236877489 51.734362927098 Winsorized Mean ( 51 / 62 ) 3122.24095744681 59.9877997501124 52.0479325871751 Winsorized Mean ( 52 / 62 ) 3120.49840425532 59.777553638744 52.2018419006162 Winsorized Mean ( 53 / 62 ) 3125.74202127660 59.2526635131814 52.7527681617424 Winsorized Mean ( 54 / 62 ) 3125.71329787234 58.8814678593772 53.084839959107 Winsorized Mean ( 55 / 62 ) 3137.44468085106 56.942564846853 55.0984081818095 Winsorized Mean ( 56 / 62 ) 3138.3085106383 56.8646479306371 55.189095947035 Winsorized Mean ( 57 / 62 ) 3146.1914893617 55.4006018336963 56.7898431646296 Winsorized Mean ( 58 / 62 ) 3150.81914893617 54.8269027254009 57.4684870439785 Winsorized Mean ( 59 / 62 ) 3153.29840425532 54.4972848228615 57.8615689663224 Winsorized Mean ( 60 / 62 ) 3137.18138297872 52.521285404582 59.7316185011921 Winsorized Mean ( 61 / 62 ) 3136.37021276596 52.3334204112844 59.9305412892462 Winsorized Mean ( 62 / 62 ) 3133.99574468085 52.1145698922691 60.1366518261482 Trimmed Mean ( 1 / 62 ) 3213.40161290323 84.8810373926335 37.8577089961686 Trimmed Mean ( 2 / 62 ) 3210.47826086957 84.0479390622724 38.1981794757736 Trimmed Mean ( 3 / 62 ) 3210.47826086957 83.3952888186959 38.4971178389855 Trimmed Mean ( 4 / 62 ) 3205.19833333333 82.7459737149211 38.7353993123095 Trimmed Mean ( 5 / 62 ) 3203.46235955056 82.2268068387447 38.9588563962222 Trimmed Mean ( 6 / 62 ) 3203.46235955056 81.7692184325542 39.1768739014288 Trimmed Mean ( 7 / 62 ) 3200.2 81.3894676569724 39.319583874018 Trimmed Mean ( 8 / 62 ) 3198.59651162791 81.0005943252183 39.488556081274 Trimmed Mean ( 9 / 62 ) 3196.96588235294 80.7343235626454 39.5985962509771 Trimmed Mean ( 10 / 62 ) 3195.13452380952 80.489882446246 39.6961012577369 Trimmed Mean ( 11 / 62 ) 3193.23192771084 80.225930055436 39.802990448404 Trimmed Mean ( 12 / 62 ) 3193.23192771084 79.9974011815547 39.9166957994521 Trimmed Mean ( 13 / 62 ) 3188.95370370370 79.7769093859537 39.9733924045092 Trimmed Mean ( 14 / 62 ) 3186.63375 79.541164191959 40.0626993880754 Trimmed Mean ( 15 / 62 ) 3184.32974683544 79.2985547211746 40.1562141710150 Trimmed Mean ( 16 / 62 ) 3181.93782051282 79.0389891869706 40.2578253244837 Trimmed Mean ( 17 / 62 ) 3179.59480519481 78.7695557819252 40.3657831205442 Trimmed Mean ( 18 / 62 ) 3177.35921052632 78.502488479134 40.4746304490826 Trimmed Mean ( 19 / 62 ) 3175.1 78.2110146382097 40.5965836741468 Trimmed Mean ( 20 / 62 ) 3173.1527027027 77.9448005851973 40.7102549352769 Trimmed Mean ( 21 / 62 ) 3171.44041095890 77.6959041005974 40.8186306301637 Trimmed Mean ( 22 / 62 ) 3169.69166666667 77.4326377391198 40.9348274734712 Trimmed Mean ( 23 / 62 ) 3167.91338028169 77.1481332005313 41.0627354008337 Trimmed Mean ( 24 / 62 ) 3167.91338028169 76.865933660753 41.2134898960994 Trimmed Mean ( 25 / 62 ) 3164.53913043478 76.5994344227992 41.3128263188962 Trimmed Mean ( 26 / 62 ) 3162.89117647059 76.3316351558585 41.4361774120574 Trimmed Mean ( 27 / 62 ) 3161.19626865672 76.0335081558183 41.5763568633235 Trimmed Mean ( 28 / 62 ) 3159.42045454545 75.7170784724414 41.7266555747444 Trimmed Mean ( 29 / 62 ) 3157.66923076923 75.3988823970336 41.8795230165568 Trimmed Mean ( 30 / 62 ) 3155.83203125 75.0773750228512 42.0343949197673 Trimmed Mean ( 31 / 62 ) 3155.83203125 74.7343140554213 42.2273499280358 Trimmed Mean ( 32 / 62 ) 3152.69193548387 74.4400784442015 42.3520770178534 Trimmed Mean ( 33 / 62 ) 3151.32540983607 74.1263810523596 42.5128728139326 Trimmed Mean ( 34 / 62 ) 3150.00166666667 73.7895669010874 42.6889843504455 Trimmed Mean ( 35 / 62 ) 3148.97711864407 73.460544925444 42.8662368600723 Trimmed Mean ( 36 / 62 ) 3148.30517241379 73.1618848654729 43.0320402242612 Trimmed Mean ( 37 / 62 ) 3147.68947368421 72.8360512009972 43.2160917812238 Trimmed Mean ( 38 / 62 ) 3147.04642857143 72.4647332003654 43.4286623241933 Trimmed Mean ( 39 / 62 ) 3146.42818181818 72.0496548165616 43.6702741994946 Trimmed Mean ( 40 / 62 ) 3145.6462962963 71.6298367465183 43.9153073519911 Trimmed Mean ( 41 / 62 ) 3144.96037735849 71.1777311258841 44.1846112205565 Trimmed Mean ( 42 / 62 ) 3144.4625 70.7940805657027 44.4170257579895 Trimmed Mean ( 43 / 62 ) 3143.69705882353 70.3982106410085 44.6559227883593 Trimmed Mean ( 44 / 62 ) 3143.122 69.9938007295336 44.9057197528891 Trimmed Mean ( 45 / 62 ) 3142.53265306122 69.5295342916098 45.197090489364 Trimmed Mean ( 46 / 62 ) 3143.10416666667 69.2288131012929 45.4016763521252 Trimmed Mean ( 47 / 62 ) 3143.35957446809 69.0048008986202 45.5527663805046 Trimmed Mean ( 48 / 62 ) 3143.35957446809 68.7643853108331 45.712028985052 Trimmed Mean ( 49 / 62 ) 3144.55444444444 68.5664636719525 45.8614062333616 Trimmed Mean ( 50 / 62 ) 3145.38181818182 68.3554506388585 46.015084222029 Trimmed Mean ( 51 / 62 ) 3145.38181818182 68.1250335797012 46.1707195270877 Trimmed Mean ( 52 / 62 ) 3147.48690476190 67.8624424492667 46.3803952696653 Trimmed Mean ( 53 / 62 ) 3148.67682926829 67.5495757876835 46.612829060022 Trimmed Mean ( 54 / 62 ) 3149.69375 67.2094388371852 46.8638602626951 Trimmed Mean ( 55 / 62 ) 3150.7641025641 66.8215914070422 47.1518866315424 Trimmed Mean ( 56 / 62 ) 3151.36315789474 66.533096258115 47.3653464986663 Trimmed Mean ( 57 / 62 ) 3151.95540540541 66.1691662915792 47.6348060895325 Trimmed Mean ( 58 / 62 ) 3152.21944444444 65.8603097590696 47.8622019236761 Trimmed Mean ( 59 / 62 ) 3152.28428571429 65.5190545083455 48.1124813135507 Trimmed Mean ( 60 / 62 ) 3152.23676470588 65.1123419971436 48.4122774272835 Trimmed Mean ( 61 / 62 ) 3152.95151515152 64.8120668224073 48.6476002036932 Trimmed Mean ( 62 / 62 ) 3152.95151515152 64.427968538208 48.9376211401374 Median 3199.1 Midrange 3452.95 Midmean - Weighted Average at Xnp 3132.49052631579 Midmean - Weighted Average at X(n+1)p 3143.35957446809 Midmean - Empirical Distribution Function 3132.49052631579 Midmean - Empirical Distribution Function - Averaging 3143.35957446809 Midmean - Empirical Distribution Function - Interpolation 3143.35957446809 Midmean - Closest Observation 3132.49052631579 Midmean - True Basic - Statistics Graphics Toolkit 3143.35957446809 Midmean - MS Excel (old versions) 3143.10416666667 Number of observations 188

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228232053iw3sxhf94unyo63/1je4e1228231705.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228232053iw3sxhf94unyo63/1je4e1228231705.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228232053iw3sxhf94unyo63/2h2yk1228231705.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/02/t1228232053iw3sxhf94unyo63/2h2yk1228231705.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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