Kristof Nollekens - Datareeks - Central Tendency 2a

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 01 Jun 2009 06:42:40 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi.htm/, Retrieved Mon, 01 Jun 2009 14:59:44 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

4000 3600 1600 2500 3800 3500 4100 2000 3300 4000 4100 4200 1100 1900 4800 4000 3250 1200 3600 3400 3600 3300 1300 2500 3800 3800 3000 1600 2100 3900 4000 3900 2300 3600 3600 4200 4400 1900 2700 3600 4100 5250 1200 3000 4000 5200 2100 3400 5200 6900 2300 4100 4300 4500 4700 2200 2900 4400 5100 2300 3500 4100 4700 5400 2500 3500 3900 4100 4500 1800 4900 4500 3700 3100 1700 4000 4200 4300 3600 2600 4600 4000 2900 1800 3000 4200 4400 4900 2300 2400 3600 3900 2800 3800 4400 4200 5400 2300 3500 3800 4400 4400 2500 3700 3500 3300 3700 2200 3200 3100 4800 4400 2500 3100 4700 4300 4600 3100 3200 4300 4000 4500 2600 3600 4700 3800 4100 1700 3200 4000 3700 3600 2500 3800 4500 3500 4100 2300 3400 4800 4900 2600 3300 3900 3800 5000 1700 2700 4000 3300 4000 2500 3600 3800 5900 3600 3300 4000 4200 4300 2900 3900 5200 4000 4700 2200 2400 3700 3700 3000 2100 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 3873.4126984127 74.8369150683211 51.7580487500926 Geometric Mean 3665.38256710701 Harmonic Mean 3413.62984882109 Quadratic Mean 4050.81071642671 Winsorized Mean ( 1 / 84 ) 3871.03174603175 73.985639456532 52.3213933739946 Winsorized Mean ( 2 / 84 ) 3870.23809523810 73.6180664450826 52.5718520212047 Winsorized Mean ( 3 / 84 ) 3866.66666666667 73.0979432164315 52.8970651775806 Winsorized Mean ( 4 / 84 ) 3868.25396825397 72.8711474578411 53.083478210521 Winsorized Mean ( 5 / 84 ) 3872.22222222222 71.8067502234777 53.9256018434346 Winsorized Mean ( 6 / 84 ) 3872.22222222222 71.8067502234777 53.9256018434346 Winsorized Mean ( 7 / 84 ) 3875 71.4632259575761 54.2236926485852 Winsorized Mean ( 8 / 84 ) 3868.65079365079 70.6343211043248 54.7701277957625 Winsorized Mean ( 9 / 84 ) 3868.65079365079 70.6343211043248 54.7701277957625 Winsorized Mean ( 10 / 84 ) 3864.68253968254 69.1908170772939 55.8554256609697 Winsorized Mean ( 11 / 84 ) 3864.68253968254 69.1908170772939 55.8554256609697 Winsorized Mean ( 12 / 84 ) 3859.92063492063 67.5571572348594 57.1356284501699 Winsorized Mean ( 13 / 84 ) 3849.60317460317 66.486780680613 57.9002793517071 Winsorized Mean ( 14 / 84 ) 3849.60317460317 66.486780680613 57.9002793517071 Winsorized Mean ( 15 / 84 ) 3855.55555555556 65.8046650333219 58.5909153036976 Winsorized Mean ( 16 / 84 ) 3855.55555555556 65.8046650333219 58.5909153036976 Winsorized Mean ( 17 / 84 ) 3855.55555555556 64.4010319850023 59.8679157261554 Winsorized Mean ( 18 / 84 ) 3855.55555555556 64.4010319850023 59.8679157261554 Winsorized Mean ( 19 / 84 ) 3855.55555555556 64.4010319850023 59.8679157261554 Winsorized Mean ( 20 / 84 ) 3863.49206349206 63.556149675029 60.7886425349335 Winsorized Mean ( 21 / 84 ) 3863.49206349206 63.556149675029 60.7886425349335 Winsorized Mean ( 22 / 84 ) 3863.49206349206 63.556149675029 60.7886425349335 Winsorized Mean ( 23 / 84 ) 3863.49206349206 61.7570784998142 62.5595017987078 Winsorized Mean ( 24 / 84 ) 3863.49206349206 61.7570784998142 62.5595017987078 Winsorized Mean ( 25 / 84 ) 3863.49206349206 61.7570784998142 62.5595017987078 Winsorized Mean ( 26 / 84 ) 3858.33333333333 61.2846644190982 62.9575664630866 Winsorized Mean ( 27 / 84 ) 3852.97619047619 60.8059016718855 63.3651682573053 Winsorized Mean ( 28 / 84 ) 3852.97619047619 60.8059016718855 63.3651682573053 Winsorized Mean ( 29 / 84 ) 3852.97619047619 60.8059016718855 63.3651682573053 Winsorized Mean ( 30 / 84 ) 3864.88095238095 59.6173023115831 64.8281757564538 Winsorized Mean ( 31 / 84 ) 3864.88095238095 59.6173023115831 64.8281757564538 Winsorized Mean ( 32 / 84 ) 3864.88095238095 59.6173023115831 64.8281757564538 Winsorized Mean ( 33 / 84 ) 3877.97619047619 58.3601271713032 66.4490702546492 Winsorized Mean ( 34 / 84 ) 3877.97619047619 58.3601271713032 66.4490702546492 Winsorized Mean ( 35 / 84 ) 3877.97619047619 58.3601271713032 66.4490702546492 Winsorized Mean ( 36 / 84 ) 3877.97619047619 58.3601271713032 66.4490702546492 Winsorized Mean ( 37 / 84 ) 3877.97619047619 58.3601271713032 66.4490702546492 Winsorized Mean ( 38 / 84 ) 3862.89682539683 57.0277071551939 67.7371933415528 Winsorized Mean ( 39 / 84 ) 3862.89682539683 57.0277071551939 67.7371933415528 Winsorized Mean ( 40 / 84 ) 3878.76984126984 55.5437011930095 69.8327579538039 Winsorized Mean ( 41 / 84 ) 3878.76984126984 55.5437011930095 69.8327579538039 Winsorized Mean ( 42 / 84 ) 3878.76984126984 55.5437011930095 69.8327579538039 Winsorized Mean ( 43 / 84 ) 3878.76984126984 55.5437011930095 69.8327579538039 Winsorized Mean ( 44 / 84 ) 3861.30952380952 54.0457171397985 71.4452453988461 Winsorized Mean ( 45 / 84 ) 3879.16666666667 52.4148291437822 74.0089537643153 Winsorized Mean ( 46 / 84 ) 3879.16666666667 52.4148291437822 74.0089537643153 Winsorized Mean ( 47 / 84 ) 3879.16666666667 52.4148291437822 74.0089537643153 Winsorized Mean ( 48 / 84 ) 3879.16666666667 52.4148291437822 74.0089537643153 Winsorized Mean ( 49 / 84 ) 3859.72222222222 50.7926920081006 75.989715638751 Winsorized Mean ( 50 / 84 ) 3879.56349206349 49.0192260765055 79.1437116124307 Winsorized Mean ( 51 / 84 ) 3879.56349206349 49.0192260765055 79.1437116124307 Winsorized Mean ( 52 / 84 ) 3900.19841269841 47.2430476752754 82.5560289739643 Winsorized Mean ( 53 / 84 ) 3900.19841269841 47.2430476752754 82.5560289739643 Winsorized Mean ( 54 / 84 ) 3900.19841269841 47.2430476752754 82.5560289739643 Winsorized Mean ( 55 / 84 ) 3900.19841269841 43.6059768751939 89.4418309641657 Winsorized Mean ( 56 / 84 ) 3900.19841269841 43.6059768751939 89.4418309641657 Winsorized Mean ( 57 / 84 ) 3900.19841269841 43.6059768751939 89.4418309641657 Winsorized Mean ( 58 / 84 ) 3877.18253968254 41.7555113510213 92.8543900968826 Winsorized Mean ( 59 / 84 ) 3900.59523809524 39.8374635816711 97.9127406065548 Winsorized Mean ( 60 / 84 ) 3900.59523809524 39.8374635816711 97.9127406065548 Winsorized Mean ( 61 / 84 ) 3900.59523809524 39.8374635816711 97.9127406065548 Winsorized Mean ( 62 / 84 ) 3900.59523809524 39.8374635816711 97.9127406065548 Winsorized Mean ( 63 / 84 ) 3900.59523809524 39.8374635816711 97.9127406065548 Winsorized Mean ( 64 / 84 ) 3925.99206349206 37.8491674430131 103.727303101268 Winsorized Mean ( 65 / 84 ) 3925.99206349206 37.8491674430131 103.727303101268 Winsorized Mean ( 66 / 84 ) 3899.80158730159 35.7593919471388 109.056708600260 Winsorized Mean ( 67 / 84 ) 3899.80158730159 35.7593919471388 109.056708600260 Winsorized Mean ( 68 / 84 ) 3886.30952380952 32.6444824996604 119.049506263423 Winsorized Mean ( 69 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 70 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 71 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 72 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 73 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 74 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 75 / 84 ) 3900 31.5947670509250 123.438162836077 Winsorized Mean ( 76 / 84 ) 3900 27.0904012055099 143.962430471749 Winsorized Mean ( 77 / 84 ) 3900 27.0904012055099 143.962430471749 Winsorized Mean ( 78 / 84 ) 3900 27.0904012055099 143.962430471749 Winsorized Mean ( 79 / 84 ) 3900 27.0904012055099 143.962430471749 Winsorized Mean ( 80 / 84 ) 3931.74603174603 24.8202389867576 158.408870834956 Winsorized Mean ( 81 / 84 ) 3931.74603174603 24.8202389867576 158.408870834956 Winsorized Mean ( 82 / 84 ) 3931.74603174603 24.8202389867576 158.408870834956 Winsorized Mean ( 83 / 84 ) 3931.74603174603 24.8202389867576 158.408870834956 Winsorized Mean ( 84 / 84 ) 3931.74603174603 24.8202389867576 158.408870834956 Trimmed Mean ( 1 / 84 ) 3870 72.7409443155619 53.2024987634381 Trimmed Mean ( 2 / 84 ) 3868.95161290323 71.4280360703747 54.1657285535798 Trimmed Mean ( 3 / 84 ) 3868.29268292683 70.2442682686859 55.069157644728 Trimmed Mean ( 4 / 84 ) 3868.85245901639 69.1914978834232 55.91514242884 Trimmed Mean ( 5 / 84 ) 3869.00826446281 68.1475535659143 56.7739861816256 Trimmed Mean ( 6 / 84 ) 3868.33333333333 67.2992563975932 57.4795850711907 Trimmed Mean ( 7 / 84 ) 3867.64705882353 66.4058849694384 58.2425346880553 Trimmed Mean ( 8 / 84 ) 3866.52542372881 65.5252228501444 59.0081995229764 Trimmed Mean ( 9 / 84 ) 3866.23931623932 64.7289499183099 59.7296776962803 Trimmed Mean ( 10 / 84 ) 3865.94827586207 63.8895745123173 60.5098453913909 Trimmed Mean ( 11 / 84 ) 3866.08695652174 63.1960730907312 61.1760631229596 Trimmed Mean ( 12 / 84 ) 3866.22807017544 62.4649149750117 61.8943941846727 Trimmed Mean ( 13 / 84 ) 3866.81415929204 61.880734432435 62.4881749507037 Trimmed Mean ( 14 / 84 ) 3868.30357142857 61.3776476752868 63.0246305934287 Trimmed Mean ( 15 / 84 ) 3869.81981981982 60.8466204010946 63.5995852244606 Trimmed Mean ( 16 / 84 ) 3870.90909090909 60.3519527620798 64.1389203456106 Trimmed Mean ( 17 / 84 ) 3872.01834862385 59.829361761454 64.7176943665553 Trimmed Mean ( 18 / 84 ) 3873.14814814815 59.4004025417348 65.2040724038337 Trimmed Mean ( 19 / 84 ) 3874.29906542056 58.9465616991028 65.7256157737785 Trimmed Mean ( 20 / 84 ) 3875.47169811321 58.4662405102425 66.2856319183765 Trimmed Mean ( 21 / 84 ) 3876.19047619048 58.0255269406165 66.8014696386537 Trimmed Mean ( 22 / 84 ) 3876.92307692308 57.5586057566281 67.3560977712986 Trimmed Mean ( 23 / 84 ) 3877.66990291262 57.0637361820671 67.9533126001522 Trimmed Mean ( 24 / 84 ) 3878.43137254902 56.6712612069711 68.4373576650159 Trimmed Mean ( 25 / 84 ) 3879.20792079208 56.2544676139496 68.958219414917 Trimmed Mean ( 26 / 84 ) 3880 55.8117348659197 69.519430086185 Trimmed Mean ( 27 / 84 ) 3881.06060606061 55.3735750817012 70.0886767801117 Trimmed Mean ( 28 / 84 ) 3882.39795918367 54.9396160031361 70.6666380588109 Trimmed Mean ( 29 / 84 ) 3883.76288659794 54.4777598705804 71.290796387817 Trimmed Mean ( 30 / 84 ) 3885.15625 53.9860212622113 71.9659674701662 Trimmed Mean ( 31 / 84 ) 3886.05263157895 53.5421817989605 72.5792730331807 Trimmed Mean ( 32 / 84 ) 3886.96808510638 53.069018611517 73.2436398260969 Trimmed Mean ( 33 / 84 ) 3887.90322580645 52.5643714241506 73.9646098767989 Trimmed Mean ( 34 / 84 ) 3888.31521739130 52.1079125326364 74.6204372504072 Trimmed Mean ( 35 / 84 ) 3888.73626373626 51.6203976411815 75.3333263871242 Trimmed Mean ( 36 / 84 ) 3889.16666666667 51.0994499624803 76.1097559664983 Trimmed Mean ( 37 / 84 ) 3889.60674157303 50.5424370629447 76.9572455860998 Trimmed Mean ( 38 / 84 ) 3890.05681818182 49.9464321593509 77.8845785374787 Trimmed Mean ( 39 / 84 ) 3891.09195402299 49.3929303472909 78.7783175985713 Trimmed Mean ( 40 / 84 ) 3892.1511627907 48.7996484324077 79.7577705540585 Trimmed Mean ( 41 / 84 ) 3892.64705882353 48.2601247286829 80.6596974356756 Trimmed Mean ( 42 / 84 ) 3893.15476190476 47.6809760467165 81.6500643378264 Trimmed Mean ( 43 / 84 ) 3893.67469879518 47.0587471373505 82.740721665001 Trimmed Mean ( 44 / 84 ) 3894.20731707317 46.3895463455494 83.9457943405127 Trimmed Mean ( 45 / 84 ) 3895.37037037037 45.7658244600958 85.1152670431368 Trimmed Mean ( 46 / 84 ) 3895.9375 45.2021400599399 86.1892267674456 Trimmed Mean ( 47 / 84 ) 3896.51898734177 44.5943425054957 87.376980316765 Trimmed Mean ( 48 / 84 ) 3897.11538461538 43.9382612211122 88.6952573066963 Trimmed Mean ( 49 / 84 ) 3897.72727272727 43.2291477093136 90.1643330776915 Trimmed Mean ( 50 / 84 ) 3899.01315789474 42.5692964131506 91.5921447245308 Trimmed Mean ( 51 / 84 ) 3899.66666666667 41.9771341470639 92.8997833202349 Trimmed Mean ( 52 / 84 ) 3900.33783783784 41.3353159283788 94.3584862057401 Trimmed Mean ( 53 / 84 ) 3900.34246575342 40.760648446991 95.688920916599 Trimmed Mean ( 54 / 84 ) 3900.34722222222 40.136520764652 97.1770135506423 Trimmed Mean ( 55 / 84 ) 3900.35211267606 39.4577180922764 98.8489020970406 Trimmed Mean ( 56 / 84 ) 3900.35714285714 38.9695655242112 100.087262723879 Trimmed Mean ( 57 / 84 ) 3900.36231884058 38.4374825632778 101.472886847353 Trimmed Mean ( 58 / 84 ) 3900.36764705882 37.8568580353359 103.029354507397 Trimmed Mean ( 59 / 84 ) 3901.11940298507 37.3501810961857 104.447134886408 Trimmed Mean ( 60 / 84 ) 3901.13636363636 36.9292713405013 105.638054097154 Trimmed Mean ( 61 / 84 ) 3901.15384615385 36.4677154604591 106.975548012701 Trimmed Mean ( 62 / 84 ) 3901.171875 35.9611456947223 108.482969594947 Trimmed Mean ( 63 / 84 ) 3901.19047619048 35.4045297331229 110.189021167557 Trimmed Mean ( 64 / 84 ) 3901.20967741935 34.7920261999201 112.129418821497 Trimmed Mean ( 65 / 84 ) 3900.40983606557 34.2655531634842 113.828888664261 Trimmed Mean ( 66 / 84 ) 3899.58333333333 33.6842306224102 115.768811140336 Trimmed Mean ( 67 / 84 ) 3899.57627118644 33.2054029356654 117.438004855468 Trimmed Mean ( 68 / 84 ) 3899.56896551724 32.6749454106949 119.344314627098 Trimmed Mean ( 69 / 84 ) 3900 32.3244305315828 120.651777490387 Trimmed Mean ( 70 / 84 ) 3900 32.0121035668507 121.828919860129 Trimmed Mean ( 71 / 84 ) 3900 31.6623133246908 123.174828067876 Trimmed Mean ( 72 / 84 ) 3900 31.2705453403740 124.718004037001 Trimmed Mean ( 73 / 84 ) 3900 30.8315424068888 126.493833767091 Trimmed Mean ( 74 / 84 ) 3900 30.3391336744067 128.546847838636 Trimmed Mean ( 75 / 84 ) 3900 29.7860095967149 130.93395365152 Trimmed Mean ( 76 / 84 ) 3900 29.1634197326900 133.729172907264 Trimmed Mean ( 77 / 84 ) 3900 28.8644765320667 135.114177306051 Trimmed Mean ( 78 / 84 ) 3900 28.5236128712348 136.72882245338 Trimmed Mean ( 79 / 84 ) 3900 28.1350470755229 138.617148552524 Trimmed Mean ( 80 / 84 ) 3900 27.6919095368733 140.835358240895 Trimmed Mean ( 81 / 84 ) 3898.88888888889 27.3915723957793 142.338995095063 Trimmed Mean ( 82 / 84 ) 3897.72727272727 27.0442800689815 144.123905786561 Trimmed Mean ( 83 / 84 ) 3896.51162790698 26.6428819474177 146.249630036162 Trimmed Mean ( 84 / 84 ) 3895.2380952381 26.1787383074718 148.793958268277 Median 3900 Midrange 4300 Midmean - Weighted Average at Xnp 3907.03703703704 Midmean - Weighted Average at X(n+1)p 3907.03703703704 Midmean - Empirical Distribution Function 3907.03703703704 Midmean - Empirical Distribution Function - Averaging 3907.03703703704 Midmean - Empirical Distribution Function - Interpolation 3907.03703703704 Midmean - Closest Observation 3907.03703703704 Midmean - True Basic - Statistics Graphics Toolkit 3907.03703703704 Midmean - MS Excel (old versions) 3907.03703703704 Number of observations 252

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi/12dg91243860158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi/12dg91243860158.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi/2kz3w1243860158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243861182yw9w3fhjro4adoi/2kz3w1243860158.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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