Task 6 - Q2 Part 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: Tue, 25 Nov 2008 00:23:52 -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/Nov/25/t1227597875m9jt3vbfler2vz8.htm/, Retrieved Tue, 25 Nov 2008 07:24:35 +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/Nov/25/t1227597875m9jt3vbfler2vz8.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}, }

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Original text written by user:

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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 -9.54920524 -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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -8.0729257479187e-10 10.6188693305822 -7.60243439917735e-11 Geometric Mean NaN Harmonic Mean -66.3453559002448 Quadratic Mean 146.755693924481 Winsorized Mean ( 1 / 64 ) -0.00225296070312601 10.5976619071988 -0.000212590354632432 Winsorized Mean ( 2 / 64 ) -0.389141709661460 10.5114574691667 -0.0370207186589424 Winsorized Mean ( 3 / 64 ) -0.60872395966146 10.3675898727051 -0.0587141242212961 Winsorized Mean ( 4 / 64 ) -0.114079874244793 10.2542623571223 -0.0111251175629963 Winsorized Mean ( 5 / 64 ) -0.192055671119792 10.2312339214555 -0.0187715062126612 Winsorized Mean ( 6 / 64 ) 0.152952069505207 10.1275534909170 0.0151025684181657 Winsorized Mean ( 7 / 64 ) -0.871577887265624 9.93937503757568 -0.0876894054174065 Winsorized Mean ( 8 / 64 ) -0.654044528932291 9.86473213624514 -0.0663012963655841 Winsorized Mean ( 9 / 64 ) -0.306871802369791 9.80392154052977 -0.031300923931426 Winsorized Mean ( 10 / 64 ) 0.0231576038802075 9.74110968753737 0.00237730655161752 Winsorized Mean ( 11 / 64 ) -0.303784900286457 9.62851670103616 -0.0315505398929999 Winsorized Mean ( 12 / 64 ) -0.79974414403646 9.46661829130887 -0.0844804469163704 Winsorized Mean ( 13 / 64 ) -0.948001838307294 9.30090307559516 -0.101925784045076 Winsorized Mean ( 14 / 64 ) -2.40296787268229 9.0117021156137 -0.266649722977295 Winsorized Mean ( 15 / 64 ) -2.69665819299479 8.91418257299237 -0.302513233368694 Winsorized Mean ( 16 / 64 ) -2.37954018466146 8.81648245626583 -0.269896775325666 Winsorized Mean ( 17 / 64 ) -2.26686442372396 8.7885608223368 -0.257933519440697 Winsorized Mean ( 18 / 64 ) -2.08081655497396 8.73527438797356 -0.238208493809738 Winsorized Mean ( 19 / 64 ) -2.22847411486979 8.71726451071785 -0.255639152870707 Winsorized Mean ( 20 / 64 ) -2.10152466695313 8.65444936006814 -0.242825924506488 Winsorized Mean ( 21 / 64 ) -0.994736685703131 8.53712761933898 -0.116518896057003 Winsorized Mean ( 22 / 64 ) -1.01157040966145 8.47548455483973 -0.119352516439172 Winsorized Mean ( 23 / 64 ) -2.77960388882813 8.24320501987889 -0.337199412379648 Winsorized Mean ( 24 / 64 ) -3.21506388882813 8.17498013935353 -0.393280941852217 Winsorized Mean ( 25 / 64 ) -4.38361129768229 8.0425511241079 -0.545052338497697 Winsorized Mean ( 26 / 64 ) -3.85657224455729 7.93390877424922 -0.486087293702496 Winsorized Mean ( 27 / 64 ) -4.97438463361979 7.78044088983913 -0.63934482686143 Winsorized Mean ( 28 / 64 ) -5.47142847111979 7.70549918371714 -0.710068009958619 Winsorized Mean ( 29 / 64 ) -5.91446325341146 7.64892241045275 -0.77324137127198 Winsorized Mean ( 30 / 64 ) -6.70059055028645 7.54541021544002 -0.888035290192066 Winsorized Mean ( 31 / 64 ) -5.71889572528647 7.37680556624331 -0.775253688596113 Winsorized Mean ( 32 / 64 ) -6.22706670861979 7.2178171405957 -0.86273544858825 Winsorized Mean ( 33 / 64 ) -6.41993694455729 7.13245319004063 -0.900102219180595 Winsorized Mean ( 34 / 64 ) -7.96024804664063 6.93823462900092 -1.14730165125403 Winsorized Mean ( 35 / 64 ) -7.25617865861979 6.826758119697 -1.06290255658594 Winsorized Mean ( 36 / 64 ) -7.3219533773698 6.75061613251324 -1.08463482942021 Winsorized Mean ( 37 / 64 ) -7.3288117898698 6.73667398133189 -1.08789764951945 Winsorized Mean ( 38 / 64 ) -8.03388989507812 6.54335984850556 -1.22779276718412 Winsorized Mean ( 39 / 64 ) -8.47547549976562 6.45758888474338 -1.3124829794894 Winsorized Mean ( 40 / 64 ) -8.34599770809896 6.33932914709153 -1.31654273101249 Winsorized Mean ( 41 / 64 ) -8.38180296539062 6.28470994379208 -1.33368175148163 Winsorized Mean ( 42 / 64 ) -9.48999190476563 6.13437862059099 -1.54701763482792 Winsorized Mean ( 43 / 64 ) -8.84197473809896 5.89291733807751 -1.50044099226811 Winsorized Mean ( 44 / 64 ) -8.92710865601562 5.84557313781894 -1.52715712309887 Winsorized Mean ( 45 / 64 ) -9.15383368179687 5.8124302807431 -1.57487199667995 Winsorized Mean ( 46 / 64 ) -9.13834411377604 5.80142125581835 -1.57519058017224 Winsorized Mean ( 47 / 64 ) -7.81407211867188 5.65457334772996 -1.38190304345576 Winsorized Mean ( 48 / 64 ) -8.05252711367187 5.55494447381642 -1.44961433037323 Winsorized Mean ( 49 / 64 ) -8.71862968622396 5.48836172361314 -1.58856688485253 Winsorized Mean ( 50 / 64 ) -8.76540532945313 5.48143726057306 -1.59910711602977 Winsorized Mean ( 51 / 64 ) -7.91104225820312 5.20760440241907 -1.51913272339355 Winsorized Mean ( 52 / 64 ) -6.17215160632813 5.0422597930784 -1.22408441048610 Winsorized Mean ( 53 / 64 ) -6.99832114372396 4.93967985958686 -1.41675601307273 Winsorized Mean ( 54 / 64 ) -7.29493259122396 4.73135234065629 -1.54182822710939 Winsorized Mean ( 55 / 64 ) -6.30494060632813 4.62225515591229 -1.36403993151774 Winsorized Mean ( 56 / 64 ) -8.3232230642448 4.42817324509417 -1.87960646604462 Winsorized Mean ( 57 / 64 ) -9.01121064471354 4.34929759167664 -2.07187722954588 Winsorized Mean ( 58 / 64 ) -9.9614512582552 4.26188282477518 -2.33733578979396 Winsorized Mean ( 59 / 64 ) -10.0913346602865 4.1961407053366 -2.40490855024294 Winsorized Mean ( 60 / 64 ) -10.4004823571615 4.15038204969003 -2.50590963256942 Winsorized Mean ( 61 / 64 ) -10.2953297883073 4.11580888159069 -2.50141104324755 Winsorized Mean ( 62 / 64 ) -9.51945532028646 4.01521937281708 -2.37084314364812 Winsorized Mean ( 63 / 64 ) -9.61593325122396 3.87918120438278 -2.47885642474233 Winsorized Mean ( 64 / 64 ) -9.41303989789063 3.80076565429347 -2.47661675411567 Trimmed Mean ( 1 / 64 ) -0.42606354713158 10.3568008047625 -0.0411385286985201 Trimmed Mean ( 2 / 64 ) -0.858891380079788 10.0981272767792 -0.0850545211541172 Trimmed Mean ( 3 / 64 ) -1.10134282287634 9.8689617898274 -0.111596624480963 Trimmed Mean ( 4 / 64 ) -1.27268851442935 9.67916990269295 -0.131487361749406 Trimmed Mean ( 5 / 64 ) -1.57825562832418 9.509815142173 -0.165960705305944 Trimmed Mean ( 6 / 64 ) -1.87397828586111 9.33405392164412 -0.200767887307322 Trimmed Mean ( 7 / 64 ) -2.23837025985955 9.16768192563276 -0.244158804593895 Trimmed Mean ( 8 / 64 ) -2.45137686338068 9.0249192212105 -0.271623136262474 Trimmed Mean ( 9 / 64 ) -2.69928477158046 8.8844278037427 -0.303822016589897 Trimmed Mean ( 10 / 64 ) -2.99601816311047 8.74302226810227 -0.342675343975850 Trimmed Mean ( 11 / 64 ) -3.33700742620588 8.60010010613043 -0.388019602681968 Trimmed Mean ( 12 / 64 ) -3.65214742889881 8.46175253022228 -0.431606504191027 Trimmed Mean ( 13 / 64 ) -3.92707786599398 8.3336693208854 -0.471230344615683 Trimmed Mean ( 14 / 64 ) -4.19536238631098 8.2157944548438 -0.510645977010478 Trimmed Mean ( 15 / 64 ) -4.34709948799383 8.12189114744502 -0.535232424207179 Trimmed Mean ( 16 / 64 ) -4.47913479159375 8.03122431706607 -0.557715064946667 Trimmed Mean ( 17 / 64 ) -4.63859767313291 7.94349181354002 -0.583949449689899 Trimmed Mean ( 18 / 64 ) -4.81030686766026 7.85155426677624 -0.612656641502819 Trimmed Mean ( 19 / 64 ) -4.99936247373377 7.75738436168478 -0.644464969201034 Trimmed Mean ( 20 / 64 ) -5.18357665825658 7.65727337640403 -0.676948099336485 Trimmed Mean ( 21 / 64 ) -5.3808279857 7.5547790547428 -0.712241608485158 Trimmed Mean ( 22 / 64 ) -5.65178343280405 7.45428708999416 -0.758192348184497 Trimmed Mean ( 23 / 64 ) -5.92915606681507 7.35082475752667 -0.806597390414467 Trimmed Mean ( 24 / 64 ) -6.11173880177083 7.25954284274178 -0.841890313779395 Trimmed Mean ( 25 / 64 ) -6.27493175461268 7.16630380543269 -0.87561620676139 Trimmed Mean ( 26 / 64 ) -6.37868419110714 7.07639752907352 -0.901402749760763 Trimmed Mean ( 27 / 64 ) -6.5136467032971 6.98759367512287 -0.932173077906189 Trimmed Mean ( 28 / 64 ) -6.59413099452206 6.90372821218926 -0.955155068659775 Trimmed Mean ( 29 / 64 ) -6.65158272279851 6.81835340032509 -0.9755409161516 Trimmed Mean ( 30 / 64 ) -6.68855423223485 6.72966381584848 -0.993891287181863 Trimmed Mean ( 31 / 64 ) -6.68796167503846 6.64113613735975 -1.00705083237419 Trimmed Mean ( 32 / 64 ) -6.73485196292969 6.55793987881809 -1.02697677736922 Trimmed Mean ( 33 / 64 ) -6.75903221313492 6.47955309447262 -1.04313246833346 Trimmed Mean ( 34 / 64 ) -6.77494284157258 6.40020758527323 -1.05855048470015 Trimmed Mean ( 35 / 64 ) -6.72007818405738 6.32844883550935 -1.0618839400819 Trimmed Mean ( 36 / 64 ) -6.69557073379167 6.2581388790653 -1.06989807404078 Trimmed Mean ( 37 / 64 ) -6.66725965385593 6.18640467605322 -1.07772769532262 Trimmed Mean ( 38 / 64 ) -6.63766552288793 6.1079457145493 -1.08672634517311 Trimmed Mean ( 39 / 64 ) -6.57578300223684 6.03669984155524 -1.08930097152929 Trimmed Mean ( 40 / 64 ) -6.4922800353125 5.96435237096559 -1.08851382874641 Trimmed Mean ( 41 / 64 ) -6.41139053686364 5.89344672190226 -1.08788470302726 Trimmed Mean ( 42 / 64 ) -6.32595259958333 5.81870513468505 -1.08717531704341 Trimmed Mean ( 43 / 64 ) -6.18949807429245 5.7475044153222 -1.07690183896022 Trimmed Mean ( 44 / 64 ) -6.07561714418269 5.68799044760653 -1.06814826785430 Trimmed Mean ( 45 / 64 ) -5.9536282025 5.62470607192731 -1.0584781011428 Trimmed Mean ( 46 / 64 ) -5.81708610205 5.55589541015832 -1.04701144867021 Trimmed Mean ( 47 / 64 ) -5.67563057005102 5.47892242158904 -1.03590270738036 Trimmed Mean ( 48 / 64 ) -5.58463305734375 5.40529132329462 -1.03317892104654 Trimmed Mean ( 49 / 64 ) -5.47961628898936 5.33065271827507 -1.02794471495087 Trimmed Mean ( 50 / 64 ) -5.34166363320652 5.25160726304147 -1.01714834443139 Trimmed Mean ( 51 / 64 ) -5.1955839875 5.16190388584821 -1.00652474404727 Trimmed Mean ( 52 / 64 ) -5.07941464971591 5.08655658556653 -0.998595919315853 Trimmed Mean ( 53 / 64 ) -5.03249928843023 5.01650879994209 -1.00318757309632 Trimmed Mean ( 54 / 64 ) -4.94771990922619 4.94565767157857 -1.00041697945644 Trimmed Mean ( 55 / 64 ) -4.84594374957317 4.88449892656826 -0.992106625966201 Trimmed Mean ( 56 / 64 ) -4.7822784321875 4.82436660032699 -0.991275918348196 Trimmed Mean ( 57 / 64 ) -4.62663251429487 4.77317384662577 -0.969298974426735 Trimmed Mean ( 58 / 64 ) -4.43230218164474 4.72021827301802 -0.939003648831435 Trimmed Mean ( 59 / 64 ) -4.18495907290541 4.66576487368917 -0.896950272077557 Trimmed Mean ( 60 / 64 ) -3.91800424409722 4.60761724721257 -0.850331968539154 Trimmed Mean ( 61 / 64 ) -3.6216623875 4.54260523161166 -0.797265490361591 Trimmed Mean ( 62 / 64 ) -3.31275589257353 4.46849340063723 -0.74135857336191 Trimmed Mean ( 63 / 64 ) -3.02153245901515 4.39267562391628 -0.687856950457295 Trimmed Mean ( 64 / 64 ) -2.70751337367188 4.3184790699083 -0.626959938867869 Median 4.709752322 Midrange 40.4760369 Midmean - Weighted Average at Xnp -6.63164039798968 Midmean - Weighted Average at X(n+1)p -5.58463305734374 Midmean - Empirical Distribution Function -6.63164039798968 Midmean - Empirical Distribution Function - Averaging -5.58463305734374 Midmean - Empirical Distribution Function - Interpolation -5.58463305734374 Midmean - Closest Observation -6.63164039798968 Midmean - True Basic - Statistics Graphics Toolkit -5.58463305734374 Midmean - MS Excel (old versions) -5.67563057005101 Number of observations 192

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597875m9jt3vbfler2vz8/1ixmo1227597825.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597875m9jt3vbfler2vz8/1ixmo1227597825.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597875m9jt3vbfler2vz8/2u9x41227597825.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597875m9jt3vbfler2vz8/2u9x41227597825.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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