vraag 2

*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: Mon, 01 Dec 2008 02:21:47 -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/01/t1228123338623cxsh8l5y2yvm.htm/, Retrieved Mon, 01 Dec 2008 09:22:20 +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/01/t1228123338623cxsh8l5y2yvm.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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-183.9235445 -177.0726091 -228.6351091 -237.4476091 -127.7601091 -193.0101091 -220.6351091 -164.5101091 -268.3226091 -333.6976091 -34.26010911 -154.8851091 -97.74528053 101.1056549 2.543154874 -43.26934513 -163.5818451 -162.8318451 46.54315487 26.66815487 -107.1443451 42.48065487 76.91815487 196.2931549 201.4329835 12.28391886 -0.278581137 42.90891886 87.59641886 84.34641886 57.72141886 173.8464189 -185.9660811 47.65891886 89.09641886 -68.52858114 272.6112475 146.4621829 162.8996829 10.08718285 279.7746829 212.5246829 248.8996829 -41.97531715 -5.787817149 52.83718285 274.2746829 414.6496829 310.7895114 362.6404468 26.07794684 403.2654468 327.9529468 193.7029468 317.0779468 202.2029468 321.3904468 178.0154468 16.45294684 -68.17205316 -157.0322246 -76.18128917 -81.74378917 -134.5562892 77.13121083 199.8812108 105.2562108 198.3812108 262.5687108 196.1937108 11.63121083 -145.9937892 -166.8539606 -202.0030252 43.43447482 -113.3780252 -113.6905252 -155.9405252 -210.5655252 -124.4405252 -64.25302518 -298.6280252 -154.1905252 23.18447482 -249.6756966 118.1752388 -180.3872612 -79.19976119 -81.51226119 -246.7622612 -105.3872612 -319.2622612 -72.07476119 -90.44976119 -80.01226119 119.3627388 -53.49743261 -114.6464972 -155.2089972 -50.02149721 -196.3339972 -14.58399721 -82.20899721 17.91600279 -162.8964972 -132.2714972 -16.83399721 81.54100279 275.6808314 -32.46823322 17.96926678 27.15676678 -123.1557332 108.5942668 67.96926678 34.09426678 -13.71823322 -113.0932332 54.34426678 149.7192668 153.8590954 -28.28996923 238.1475308 50.33503077 8.022530771 -61.22746923 -140.8524692 -28.72746923 9.460030771 -121.9149692 41.52253077 115.8975308 27.03735936 -91.11170524 3.325794759 -29.48670524 -73.79920524 50.95079476 -86.67420524 -9.549205241 -66.36170524 73.26329476 -216.2992052 -128.9242052 -142.7843767 27.06655875 60.50405875 35.69155875 16.37905875 -64.87094125 115.5040587 -30.37094125 87.81655875 205.4415587 -64.12094125 -322.7459413 -139.6061127 35.24482274 -4.317677263 17.86982274 2.557322737 129.3073227 -16.31767726 164.8073227 21.99482274 138.6198227 87.05732274 51.43232274 -80.42784867 -105.1918797 5.245620328 68.43312033 -0.879379672 -105.1293797 -82.75437967 -132.6293797 102.5581203 23.18312033 -180.3793797 -267.0043797 30.13544892 23.98638432 90.42388432 31.61138432 81.29888432 25.04888432 -13.57611568 33.54888432 140.7363843 132.3613843 94.79888432 4.173884316

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 -8.12500874729036e-10 10.6188693305822 -7.65148199337048e-11 Geometric Mean NaN Harmonic Mean -66.3453559004962 Quadratic Mean 146.755693924481 Winsorized Mean ( 1 / 64 ) -0.00225296070833429 10.5976619071988 -0.000212590355123887 Winsorized Mean ( 2 / 64 ) -0.389141709666668 10.5114574691667 -0.0370207186594378 Winsorized Mean ( 3 / 64 ) -0.608723959666668 10.3675898727051 -0.0587141242217983 Winsorized Mean ( 4 / 64 ) -0.114079874250001 10.2542623571223 -0.0111251175635042 Winsorized Mean ( 5 / 64 ) -0.192055671125001 10.2312339214556 -0.0187715062131702 Winsorized Mean ( 6 / 64 ) 0.152952069499998 10.127553490917 0.0151025684176514 Winsorized Mean ( 7 / 64 ) -0.871577887270833 9.9393750375757 -0.0876894054179303 Winsorized Mean ( 8 / 64 ) -0.6540445289375 9.86473213624517 -0.066301296366112 Winsorized Mean ( 9 / 64 ) -0.306871802374999 9.8039215405298 -0.0313009239319571 Winsorized Mean ( 10 / 64 ) 0.0231576038749992 9.7411096875374 0.00237730655108284 Winsorized Mean ( 11 / 64 ) -0.303784900291665 9.62851670103618 -0.0315505398935407 Winsorized Mean ( 12 / 64 ) -0.799744144041668 9.4666182913089 -0.0844804469169203 Winsorized Mean ( 13 / 64 ) -0.948001838312502 9.30090307559519 -0.101925784045636 Winsorized Mean ( 14 / 64 ) -2.40296787268750 9.01170211561372 -0.266649722977872 Winsorized Mean ( 15 / 64 ) -2.696658193 8.91418257299239 -0.302513233369278 Winsorized Mean ( 16 / 64 ) -2.37954018466667 8.81648245626585 -0.269896775326257 Winsorized Mean ( 17 / 64 ) -2.26686442372917 8.78856082233683 -0.257933519441289 Winsorized Mean ( 18 / 64 ) -2.08081655497917 8.73527438797359 -0.238208493810333 Winsorized Mean ( 19 / 64 ) -2.228474114875 8.71726451071787 -0.255639152871304 Winsorized Mean ( 20 / 64 ) -2.10152466695834 8.65444936006816 -0.242825924507089 Winsorized Mean ( 21 / 64 ) -0.994736685708337 8.537127619339 -0.116518896057613 Winsorized Mean ( 22 / 64 ) -1.01157040966667 8.47548455483976 -0.119352516439786 Winsorized Mean ( 23 / 64 ) -2.77960388883333 8.2432050198789 -0.337199412380279 Winsorized Mean ( 24 / 64 ) -3.21506388883333 8.17498013935355 -0.393280941852853 Winsorized Mean ( 25 / 64 ) -4.3836112976875 8.04255112410791 -0.545052338498343 Winsorized Mean ( 26 / 64 ) -3.8565722445625 7.93390877424924 -0.486087293703151 Winsorized Mean ( 27 / 64 ) -4.974384633625 7.78044088983915 -0.639344826862098 Winsorized Mean ( 28 / 64 ) -5.471428471125 7.70549918371716 -0.710068009959293 Winsorized Mean ( 29 / 64 ) -5.91446325341667 7.64892241045276 -0.77324137127266 Winsorized Mean ( 30 / 64 ) -6.70059055029166 7.54541021544003 -0.888035290192755 Winsorized Mean ( 31 / 64 ) -5.71889572529167 7.37680556624333 -0.775253688596817 Winsorized Mean ( 32 / 64 ) -6.227066708625 7.21781714059571 -0.86273544858897 Winsorized Mean ( 33 / 64 ) -6.4199369445625 7.13245319004065 -0.900102219181324 Winsorized Mean ( 34 / 64 ) -7.96024804664583 6.93823462900093 -1.14730165125478 Winsorized Mean ( 35 / 64 ) -7.256178658625 6.826758119697 -1.06290255658671 Winsorized Mean ( 36 / 64 ) -7.321953377375 6.75061613251325 -1.08463482942098 Winsorized Mean ( 37 / 64 ) -7.328811789875 6.7366739813319 -1.08789764952022 Winsorized Mean ( 38 / 64 ) -8.03388989508333 6.54335984850557 -1.22779276718492 Winsorized Mean ( 39 / 64 ) -8.47547549977083 6.45758888474339 -1.31248297949020 Winsorized Mean ( 40 / 64 ) -8.34599770810417 6.33932914709154 -1.31654273101331 Winsorized Mean ( 41 / 64 ) -8.38180296539583 6.28470994379208 -1.33368175148246 Winsorized Mean ( 42 / 64 ) -9.48999190477083 6.13437862059099 -1.54701763482877 Winsorized Mean ( 43 / 64 ) -8.84197473810417 5.89291733807751 -1.50044099226899 Winsorized Mean ( 44 / 64 ) -8.92710865602083 5.84557313781894 -1.52715712309976 Winsorized Mean ( 45 / 64 ) -9.15383368180208 5.8124302807431 -1.57487199668084 Winsorized Mean ( 46 / 64 ) -9.13834411378125 5.80142125581835 -1.57519058017313 Winsorized Mean ( 47 / 64 ) -7.81407211867708 5.65457334772997 -1.38190304345668 Winsorized Mean ( 48 / 64 ) -8.05252711367708 5.55494447381643 -1.44961433037417 Winsorized Mean ( 49 / 64 ) -8.71862968622917 5.48836172361314 -1.58856688485347 Winsorized Mean ( 50 / 64 ) -8.76540532945834 5.48143726057306 -1.59910711603072 Winsorized Mean ( 51 / 64 ) -7.91104225820834 5.20760440241908 -1.51913272339455 Winsorized Mean ( 52 / 64 ) -6.17215160633334 5.04225979307842 -1.22408441048713 Winsorized Mean ( 53 / 64 ) -6.99832114372917 4.93967985958688 -1.41675601307378 Winsorized Mean ( 54 / 64 ) -7.29493259122917 4.7313523406563 -1.54182822711049 Winsorized Mean ( 55 / 64 ) -6.30494060633334 4.62225515591231 -1.36403993151886 Winsorized Mean ( 56 / 64 ) -8.32322306425 4.42817324509418 -1.87960646604579 Winsorized Mean ( 57 / 64 ) -9.01121064471875 4.34929759167664 -2.07187722954707 Winsorized Mean ( 58 / 64 ) -9.96145125826042 4.26188282477517 -2.33733578979519 Winsorized Mean ( 59 / 64 ) -10.0913346602917 4.1961407053366 -2.40490855024419 Winsorized Mean ( 60 / 64 ) -10.4004823571667 4.15038204969002 -2.50590963257068 Winsorized Mean ( 61 / 64 ) -10.2953297883125 4.11580888159069 -2.50141104324881 Winsorized Mean ( 62 / 64 ) -9.51945532029166 4.01521937281708 -2.37084314364941 Winsorized Mean ( 63 / 64 ) -9.61593325122917 3.87918120438278 -2.47885642474367 Winsorized Mean ( 64 / 64 ) -9.41303989789584 3.80076565429347 -2.47661675411704 Trimmed Mean ( 1 / 64 ) -0.426063547136843 10.3568008047626 -0.0411385286990282 Trimmed Mean ( 2 / 64 ) -0.858891380085107 10.0981272767793 -0.0850545211546438 Trimmed Mean ( 3 / 64 ) -1.10134282288172 9.86896178982743 -0.111596624481508 Trimmed Mean ( 4 / 64 ) -1.27268851443478 9.67916990269298 -0.131487361749967 Trimmed Mean ( 5 / 64 ) -1.57825562832967 9.50981514217303 -0.165960705306521 Trimmed Mean ( 6 / 64 ) -1.87397828586667 9.33405392164414 -0.200767887307917 Trimmed Mean ( 7 / 64 ) -2.23837025986517 9.16768192563278 -0.244158804594507 Trimmed Mean ( 8 / 64 ) -2.45137686338636 9.02491922121052 -0.271623136263103 Trimmed Mean ( 9 / 64 ) -2.69928477158621 8.88442780374273 -0.303822016590543 Trimmed Mean ( 10 / 64 ) -2.99601816311628 8.7430222681023 -0.342675343976514 Trimmed Mean ( 11 / 64 ) -3.33700742621177 8.60010010613045 -0.388019602682651 Trimmed Mean ( 12 / 64 ) -3.65214742890476 8.4617525302223 -0.431606504191729 Trimmed Mean ( 13 / 64 ) -3.927077866 8.33366932088543 -0.471230344616405 Trimmed Mean ( 14 / 64 ) -4.19536238631707 8.21579445484382 -0.510645977011218 Trimmed Mean ( 15 / 64 ) -4.347099488 8.12189114744505 -0.535232424207937 Trimmed Mean ( 16 / 64 ) -4.4791347916 8.0312243170661 -0.557715064947443 Trimmed Mean ( 17 / 64 ) -4.63859767313924 7.94349181354005 -0.583949449690694 Trimmed Mean ( 18 / 64 ) -4.81030686766667 7.85155426677626 -0.612656641503634 Trimmed Mean ( 19 / 64 ) -4.99936247374026 7.7573843616848 -0.644464969201869 Trimmed Mean ( 20 / 64 ) -5.18357665826316 7.65727337640406 -0.676948099337342 Trimmed Mean ( 21 / 64 ) -5.38082798570667 7.55477905474283 -0.712241608486038 Trimmed Mean ( 22 / 64 ) -5.65178343281081 7.45428708999418 -0.758192348185401 Trimmed Mean ( 23 / 64 ) -5.92915606682192 7.35082475752669 -0.806597390415397 Trimmed Mean ( 24 / 64 ) -6.11173880177778 7.2595428427418 -0.841890313780349 Trimmed Mean ( 25 / 64 ) -6.27493175461972 7.16630380543271 -0.875616206762369 Trimmed Mean ( 26 / 64 ) -6.37868419111429 7.07639752907355 -0.90140274976177 Trimmed Mean ( 27 / 64 ) -6.51364670330435 6.98759367512289 -0.932173077907222 Trimmed Mean ( 28 / 64 ) -6.59413099452941 6.90372821218928 -0.955155068660837 Trimmed Mean ( 29 / 64 ) -6.65158272280597 6.81835340032511 -0.97554091615269 Trimmed Mean ( 30 / 64 ) -6.68855423224243 6.7296638158485 -0.993891287182985 Trimmed Mean ( 31 / 64 ) -6.68796167504615 6.64113613735978 -1.00705083237535 Trimmed Mean ( 32 / 64 ) -6.7348519629375 6.55793987881812 -1.02697677737041 Trimmed Mean ( 33 / 64 ) -6.75903221314286 6.47955309447265 -1.04313246833468 Trimmed Mean ( 34 / 64 ) -6.77494284158065 6.40020758527326 -1.05855048470141 Trimmed Mean ( 35 / 64 ) -6.72007818406557 6.32844883550938 -1.06188394008319 Trimmed Mean ( 36 / 64 ) -6.6955707338 6.25813887906533 -1.06989807404210 Trimmed Mean ( 37 / 64 ) -6.6672596538644 6.18640467605326 -1.07772769532399 Trimmed Mean ( 38 / 64 ) -6.63766552289655 6.10794571454933 -1.08672634517452 Trimmed Mean ( 39 / 64 ) -6.57578300224561 6.03669984155528 -1.08930097153074 Trimmed Mean ( 40 / 64 ) -6.49228003532143 5.96435237096563 -1.08851382874790 Trimmed Mean ( 41 / 64 ) -6.41139053687273 5.8934467219023 -1.08788470302880 Trimmed Mean ( 42 / 64 ) -6.32595259959259 5.8187051346851 -1.08717531704499 Trimmed Mean ( 43 / 64 ) -6.18949807430189 5.74750441532225 -1.07690183896185 Trimmed Mean ( 44 / 64 ) -6.07561714419231 5.68799044760658 -1.06814826785598 Trimmed Mean ( 45 / 64 ) -5.9536282025098 5.62470607192737 -1.05847810114453 Trimmed Mean ( 46 / 64 ) -5.81708610206 5.55589541015838 -1.04701144867199 Trimmed Mean ( 47 / 64 ) -5.67563057006123 5.47892242158912 -1.03590270738220 Trimmed Mean ( 48 / 64 ) -5.58463305735417 5.4052913232947 -1.03317892104845 Trimmed Mean ( 49 / 64 ) -5.479616289 5.33065271827516 -1.02794471495285 Trimmed Mean ( 50 / 64 ) -5.34166363321739 5.25160726304157 -1.01714834443345 Trimmed Mean ( 51 / 64 ) -5.19558398751111 5.16190388584832 -1.00652474404941 Trimmed Mean ( 52 / 64 ) -5.07941464972727 5.08655658556664 -0.998595919318064 Trimmed Mean ( 53 / 64 ) -5.03249928844186 5.01650879994221 -1.00318757309861 Trimmed Mean ( 54 / 64 ) -4.94771990923810 4.9456576715787 -1.00041697945882 Trimmed Mean ( 55 / 64 ) -4.84594374958537 4.88449892656841 -0.992106625968669 Trimmed Mean ( 56 / 64 ) -4.7822784322 4.82436660032715 -0.991275918350755 Trimmed Mean ( 57 / 64 ) -4.62663251430769 4.77317384662594 -0.969298974429387 Trimmed Mean ( 58 / 64 ) -4.43230218165789 4.72021827301821 -0.939003648834185 Trimmed Mean ( 59 / 64 ) -4.18495907291892 4.66576487368938 -0.896950272080412 Trimmed Mean ( 60 / 64 ) -3.91800424411111 4.60761724721281 -0.850331968542124 Trimmed Mean ( 61 / 64 ) -3.62166238751429 4.54260523161193 -0.797265490364689 Trimmed Mean ( 62 / 64 ) -3.31275589258824 4.46849340063754 -0.74135857336515 Trimmed Mean ( 63 / 64 ) -3.02153245903030 4.39267562391663 -0.68785695046069 Trimmed Mean ( 64 / 64 ) -2.7075133736875 4.31847906990869 -0.62695993887143 Median 4.709752322 Midrange 40.4760369 Midmean - Weighted Average at Xnp -6.63164039799999 Midmean - Weighted Average at X(n+1)p -5.58463305735416 Midmean - Empirical Distribution Function -6.63164039799999 Midmean - Empirical Distribution Function - Averaging -5.58463305735416 Midmean - Empirical Distribution Function - Interpolation -5.58463305735416 Midmean - Closest Observation -6.63164039799999 Midmean - True Basic - Statistics Graphics Toolkit -5.58463305735416 Midmean - MS Excel (old versions) -5.67563057006121 Number of observations 192

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228123338623cxsh8l5y2yvm/12ont1228123305.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228123338623cxsh8l5y2yvm/12ont1228123305.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228123338623cxsh8l5y2yvm/218cn1228123305.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228123338623cxsh8l5y2yvm/218cn1228123305.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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Software written by Ed van Stee & Patrick Wessa

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