herberekening vraag 6

*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: Fri, 24 Oct 2008 11:29:02 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/24/t1224869389g3a7bd0hbhbrtzn.htm/, Retrieved Fri, 24 Oct 2008 17:29:51 +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/Oct/24/t1224869389g3a7bd0hbhbrtzn.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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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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.000101522842639594 0.00620693590168776 0.0163563542861766 Geometric Mean NaN Harmonic Mean 0 Quadratic Mean 0.0868971619287364 Winsorized Mean ( 1 / 65 ) 0.000101522842639594 0.00619933722882035 0.0163764026527900 Winsorized Mean ( 2 / 65 ) 0.000101522842639594 0.00618161535707712 0.0164233516282057 Winsorized Mean ( 3 / 65 ) 8.62944162436549e-05 0.00615780920774306 0.0140138177933712 Winsorized Mean ( 4 / 65 ) 8.62944162436549e-05 0.00612990339713548 0.0140776143852415 Winsorized Mean ( 5 / 65 ) 8.62944162436555e-05 0.00609590368808812 0.0141561318319844 Winsorized Mean ( 6 / 65 ) 8.62944162436555e-05 0.00606400717988264 0.0142305926895894 Winsorized Mean ( 7 / 65 ) 8.62944162436543e-05 0.00602754376801593 0.0143166801544536 Winsorized Mean ( 8 / 65 ) 4.56852791878167e-05 0.0059913621583576 0.00762519072963875 Winsorized Mean ( 9 / 65 ) 4.56852791878167e-05 0.00595741950140562 0.00766863558576622 Winsorized Mean ( 10 / 65 ) -5.07614213198026e-06 0.00591363433415689 -0.00085837944065305 Winsorized Mean ( 11 / 65 ) -6.09137055837575e-05 0.00587959594954237 -0.0103601856499168 Winsorized Mean ( 12 / 65 ) -6.09137055837575e-05 0.00583630436968691 -0.0104370337332193 Winsorized Mean ( 13 / 65 ) -0.000126903553299492 0.00579714183902648 -0.0218907104265027 Winsorized Mean ( 14 / 65 ) -0.000197969543147207 0.00575548346804984 -0.0343966834838787 Winsorized Mean ( 15 / 65 ) -0.000350253807106599 0.00571942488375488 -0.0612393403577061 Winsorized Mean ( 16 / 65 ) -0.000431472081218274 0.00567287461466305 -0.0760588080164896 Winsorized Mean ( 17 / 65 ) -0.000431472081218274 0.00563424804040995 -0.0765802424961894 Winsorized Mean ( 18 / 65 ) -0.000614213197969541 0.00559238792085133 -0.109830220410754 Winsorized Mean ( 19 / 65 ) -0.000710659898477158 0.00553879480502891 -0.128305872214641 Winsorized Mean ( 20 / 65 ) -0.000812182741116752 0.00550500100080745 -0.147535439321014 Winsorized Mean ( 21 / 65 ) -0.000918781725888325 0.00544686219700034 -0.168680919887107 Winsorized Mean ( 22 / 65 ) -0.00103045685279188 0.00541039239166965 -0.190458801912125 Winsorized Mean ( 23 / 65 ) -0.00114720812182741 0.00534787143317351 -0.214516772918499 Winsorized Mean ( 24 / 65 ) -0.00114720812182741 0.00529671926064101 -0.216588432457071 Winsorized Mean ( 25 / 65 ) -0.00140101522842640 0.00524259038617744 -0.267237210086124 Winsorized Mean ( 26 / 65 ) -0.00153299492385787 0.00517394278286506 -0.296291433475996 Winsorized Mean ( 27 / 65 ) -0.00180710659898477 0.00511681796799026 -0.35316999945858 Winsorized Mean ( 28 / 65 ) -0.00180710659898477 0.0050592385082031 -0.357189445813774 Winsorized Mean ( 29 / 65 ) -0.00210152284263959 0.0049990674815252 -0.420382971505403 Winsorized Mean ( 30 / 65 ) -0.00225380710659898 0.00492284643386454 -0.45782600308125 Winsorized Mean ( 31 / 65 ) -0.00256852791878173 0.0048600115362864 -0.528502432474548 Winsorized Mean ( 32 / 65 ) -0.00273096446700508 0.0047803801220814 -0.571286047816633 Winsorized Mean ( 33 / 65 ) -0.00289847715736041 0.00473144406854487 -0.612598841996206 Winsorized Mean ( 34 / 65 ) -0.00307106598984771 0.00464836512988323 -0.660676582849433 Winsorized Mean ( 35 / 65 ) -0.003248730964467 0.00459745134812115 -0.706637377640694 Winsorized Mean ( 36 / 65 ) -0.00343147208121827 0.00451109204480248 -0.760674366015629 Winsorized Mean ( 37 / 65 ) -0.00361928934010152 0.00445830984140736 -0.811807494061251 Winsorized Mean ( 38 / 65 ) -0.00361928934010153 0.00438665408240198 -0.825068325907231 Winsorized Mean ( 39 / 65 ) -0.00381725888324873 0.00433192222024317 -0.88119284907993 Winsorized Mean ( 40 / 65 ) -0.00381725888324873 0.00425762921588183 -0.896569120911134 Winsorized Mean ( 41 / 65 ) -0.0040253807106599 0.00420103333243145 -0.958188234210012 Winsorized Mean ( 42 / 65 ) -0.00381218274111675 0.00414326362300802 -0.920091765328969 Winsorized Mean ( 43 / 65 ) -0.00403045685279188 0.00408467882326992 -0.986725524129548 Winsorized Mean ( 44 / 65 ) -0.00380710659898477 0.00402493609746749 -0.945880010711281 Winsorized Mean ( 45 / 65 ) -0.00380710659898477 0.00398448466523814 -0.955482808655063 Winsorized Mean ( 46 / 65 ) -0.00380710659898477 0.00390231077317308 -0.975603128576328 Winsorized Mean ( 47 / 65 ) -0.00380710659898477 0.0038606015099617 -0.986143374072954 Winsorized Mean ( 48 / 65 ) -0.00380710659898477 0.00381818571632821 -0.997098329372493 Winsorized Mean ( 49 / 65 ) -0.00355837563451777 0.0037535527515379 -0.948002031691133 Winsorized Mean ( 50 / 65 ) -0.00355837563451777 0.0037098294887781 -0.959174982376288 Winsorized Mean ( 51 / 65 ) -0.0032994923857868 0.00364334172561231 -0.90562253949217 Winsorized Mean ( 52 / 65 ) -0.0032994923857868 0.00359833710007374 -0.916949216825513 Winsorized Mean ( 53 / 65 ) -0.0032994923857868 0.00355265307642276 -0.92874038494891 Winsorized Mean ( 54 / 65 ) -0.00275126903553299 0.00350671172846499 -0.784572342573855 Winsorized Mean ( 55 / 65 ) -0.00275126903553299 0.00345968333660271 -0.795237242213804 Winsorized Mean ( 56 / 65 ) -0.00275126903553300 0.00341198403285727 -0.806354604546325 Winsorized Mean ( 57 / 65 ) -0.00246192893401015 0.00333987956610462 -0.737131050770659 Winsorized Mean ( 58 / 65 ) -0.00246192893401015 0.00329094770523417 -0.748091174494968 Winsorized Mean ( 59 / 65 ) -0.00246192893401015 0.00324135713163111 -0.759536463904323 Winsorized Mean ( 60 / 65 ) -0.00246192893401015 0.00319110959920328 -0.771496201391771 Winsorized Mean ( 61 / 65 ) -0.00215228426395939 0.00311535960002737 -0.690862224682018 Winsorized Mean ( 62 / 65 ) -0.00246700507614213 0.0030376919955146 -0.812131407589995 Winsorized Mean ( 63 / 65 ) -0.00246700507614213 0.00298568286556931 -0.826278338061778 Winsorized Mean ( 64 / 65 ) -0.00246700507614213 0.00293303412930928 -0.841110252175316 Winsorized Mean ( 65 / 65 ) -0.00246700507614213 0.00287974667964126 -0.856674336525133 Trimmed Mean ( 1 / 65 ) 0.000101522842639594 0.0061249153630881 0.0165753870251698 Trimmed Mean ( 2 / 65 ) -2.56410256410258e-05 0.006045709898918 -0.0042411935189968 Trimmed Mean ( 3 / 65 ) -0.000287958115183246 0.00597111436266155 -0.0482251884143938 Trimmed Mean ( 4 / 65 ) -0.000287958115183246 0.0059006416851397 -0.0488011525777689 Trimmed Mean ( 5 / 65 ) -0.000550802139037433 0.00583366379222762 -0.0944178750532873 Trimmed Mean ( 6 / 65 ) -0.000686486486486487 0.00577038565891833 -0.118967176037099 Trimmed Mean ( 7 / 65 ) -0.000825136612021858 0.00570940118452837 -0.144522443834890 Trimmed Mean ( 8 / 65 ) -0.000825136612021858 0.00565086031115067 -0.146019644193582 Trimmed Mean ( 9 / 65 ) -0.00110614525139665 0.0055941701162409 -0.197731786558529 Trimmed Mean ( 10 / 65 ) -0.00124858757062147 0.00553855800257703 -0.225435496033537 Trimmed Mean ( 11 / 65 ) -0.00138857142857143 0.0054850253740931 -0.253156792150850 Trimmed Mean ( 12 / 65 ) -0.00152601156069364 0.00543201865069089 -0.280928998743322 Trimmed Mean ( 13 / 65 ) -0.00166666666666667 0.00538037816628593 -0.309767569333731 Trimmed Mean ( 14 / 65 ) -0.00180473372781065 0.00532941537355663 -0.338636342133386 Trimmed Mean ( 15 / 65 ) -0.00194011976047904 0.00527919512849571 -0.367502945668135 Trimmed Mean ( 16 / 65 ) -0.00194011976047904 0.00522896400331062 -0.37103329823091 Trimmed Mean ( 17 / 65 ) -0.00219018404907975 0.00517957869499455 -0.422849845142098 Trimmed Mean ( 18 / 65 ) -0.00231677018633540 0.00513011161811368 -0.451602296167441 Trimmed Mean ( 19 / 65 ) -0.00243396226415094 0.00508073132235836 -0.479057464314242 Trimmed Mean ( 20 / 65 ) -0.00254777070063694 0.00503235039873762 -0.506278478000271 Trimmed Mean ( 21 / 65 ) -0.00265806451612903 0.00498310037793705 -0.533415808338471 Trimmed Mean ( 22 / 65 ) -0.00276470588235294 0.00493493758175667 -0.560231175481008 Trimmed Mean ( 23 / 65 ) -0.00286754966887417 0.00488592767313237 -0.586899737514081 Trimmed Mean ( 24 / 65 ) -0.00296644295302013 0.00483810469487587 -0.613141537875762 Trimmed Mean ( 25 / 65 ) -0.00306802721088435 0.0047904525215757 -0.640446220282171 Trimmed Mean ( 26 / 65 ) -0.00315862068965517 0.00474315069662029 -0.665933024625558 Trimmed Mean ( 27 / 65 ) -0.00324475524475524 0.00469726913401457 -0.690774820897282 Trimmed Mean ( 28 / 65 ) -0.00331914893617021 0.00465188699332609 -0.713505925860213 Trimmed Mean ( 29 / 65 ) -0.00339568345323741 0.00460694260947199 -0.737079608123574 Trimmed Mean ( 30 / 65 ) -0.00345985401459854 0.00456261026968201 -0.75830584031883 Trimmed Mean ( 31 / 65 ) -0.00351851851851852 0.00452007195836228 -0.778420908102834 Trimmed Mean ( 32 / 65 ) -0.00351851851851852 0.00447833896282618 -0.785674900387188 Trimmed Mean ( 33 / 65 ) -0.0036030534351145 0.00443865579030595 -0.811744276946095 Trimmed Mean ( 34 / 65 ) -0.00363565891472868 0.00439874423236513 -0.826522007798996 Trimmed Mean ( 35 / 65 ) -0.00366141732283465 0.0043611093943699 -0.839560990504264 Trimmed Mean ( 36 / 65 ) -0.00368 0.00432340367706263 -0.851181216207928 Trimmed Mean ( 37 / 65 ) -0.00369105691056911 0.00428823138828402 -0.860741078630581 Trimmed Mean ( 38 / 65 ) -0.00369421487603306 0.00425317646608759 -0.868577851280948 Trimmed Mean ( 39 / 65 ) -0.00369747899159664 0.0042196081009431 -0.87626123164619 Trimmed Mean ( 40 / 65 ) -0.00369230769230769 0.00418630142131528 -0.881997572727962 Trimmed Mean ( 41 / 65 ) -0.00368695652173913 0.00415470041065198 -0.887418142662313 Trimmed Mean ( 42 / 65 ) -0.00367256637168142 0.00412353568381489 -0.890635283234349 Trimmed Mean ( 43 / 65 ) -0.00366666666666667 0.0040928881675088 -0.895862900866514 Trimmed Mean ( 44 / 65 ) -0.00365137614678899 0.00406280075907608 -0.898733746328074 Trimmed Mean ( 45 / 65 ) -0.00364485981308411 0.00403337015728572 -0.903676000701344 Trimmed Mean ( 46 / 65 ) -0.00363809523809524 0.00400309229851901 -0.90882122289341 Trimmed Mean ( 47 / 65 ) -0.00363106796116505 0.00397512708117501 -0.913447013646602 Trimmed Mean ( 48 / 65 ) -0.00362376237623762 0.0039464196453065 -0.918240507075162 Trimmed Mean ( 49 / 65 ) -0.00361616161616162 0.00391690401972181 -0.923219358440764 Trimmed Mean ( 50 / 65 ) -0.00361855670103093 0.00388827106967745 -0.930633856587346 Trimmed Mean ( 51 / 65 ) -0.00362105263157895 0.00385882386236459 -0.938382460753224 Trimmed Mean ( 52 / 65 ) -0.00363440860215054 0.00383034270436398 -0.948846848092675 Trimmed Mean ( 53 / 65 ) -0.00363440860215054 0.00380104649899405 -0.956159995178272 Trimmed Mean ( 54 / 65 ) -0.00366292134831461 0.00377085429843107 -0.971377056344666 Trimmed Mean ( 55 / 65 ) -0.00370114942528736 0.00373958912838191 -0.989720875268672 Trimmed Mean ( 56 / 65 ) -0.00374117647058824 0.00370721731324351 -1.00916028235610 Trimmed Mean ( 57 / 65 ) -0.00374117647058824 0.00367361665899757 -1.0183905447579 Trimmed Mean ( 58 / 65 ) -0.00383950617283951 0.00364087972640362 -1.05455452016046 Trimmed Mean ( 59 / 65 ) -0.00389873417721519 0.00360683809686959 -1.08092852312915 Trimmed Mean ( 60 / 65 ) -0.00396103896103896 0.00357133349777139 -1.10912043456898 Trimmed Mean ( 61 / 65 ) -0.00402666666666667 0.00353418177812642 -1.13934905430963 Trimmed Mean ( 62 / 65 ) -0.00410958904109589 0.00349774670039878 -1.17492471385288 Trimmed Mean ( 63 / 65 ) -0.0041830985915493 0.00346249290052399 -1.20811759380539 Trimmed Mean ( 64 / 65 ) -0.0041830985915493 0.00342553548212011 -1.22115173332267 Trimmed Mean ( 65 / 65 ) -0.00434328358208955 0.00338663082021653 -1.28247919913865 Median -0.006 Midrange 0.0125 Midmean - Weighted Average at Xnp -0.00426530612244898 Midmean - Weighted Average at X(n+1)p -0.00361616161616162 Midmean - Empirical Distribution Function -0.00361616161616162 Midmean - Empirical Distribution Function - Averaging -0.00361616161616162 Midmean - Empirical Distribution Function - Interpolation -0.00361616161616162 Midmean - Closest Observation -0.00427 Midmean - True Basic - Statistics Graphics Toolkit -0.00361616161616162 Midmean - MS Excel (old versions) -0.00361616161616162 Number of observations 197

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/24/t1224869389g3a7bd0hbhbrtzn/1uuax1224869336.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/24/t1224869389g3a7bd0hbhbrtzn/1uuax1224869336.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/24/t1224869389g3a7bd0hbhbrtzn/2o5u61224869336.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Oct/24/t1224869389g3a7bd0hbhbrtzn/2o5u61224869336.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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