TIN central tendency

*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, 23 Nov 2010 15:02:42 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290524555f0jv77y59jvbduc.htm/, Retrieved Tue, 23 Nov 2010 16:02:36 +0100

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/2010/Nov/23/t1290524555f0jv77y59jvbduc.htm/}, year = {2010}, } @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 = {2010}, 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:

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0.0017356093451131 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -0.0028943580263334 3.1086244689504E-14 0.0028943580263312 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -0.00057820180853341 8.8817841970013E-16 -0.0023161562177991 3.1086244689504E-14 3.1086244689504E-14 0.0023161562178311 -0.0023161562177991 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 0.0028943580263312 -3.3306690738755E-14 -0.0028943580263334 3.1086244689504E-14 0.0028943580263312 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -3.3306690738755E-14 -0.0028943580263334 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 3.1086244689504E-14 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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.000704603455272898 0.00468453174745085 -0.150410647906553 Geometric Mean NaN Harmonic Mean -1.69238476316236e-14 Quadratic Mean 0.0660872087995196 Winsorized Mean ( 1 / 66 ) -0.000761908980055467 0.000770672373452211 -0.9886288989996 Winsorized Mean ( 2 / 66 ) -0.000896363322633277 0.000657674900165395 -1.36292767506842 Winsorized Mean ( 3 / 66 ) -0.000676291815543547 0.000581929130442927 -1.16215494321241 Winsorized Mean ( 4 / 66 ) -0.000760643225945707 0.000470238440379553 -1.61756921729273 Winsorized Mean ( 5 / 66 ) -0.000760643225945707 0.000470238440379553 -1.61756921729273 Winsorized Mean ( 6 / 66 ) -0.000538167972496087 0.000416503944825799 -1.29210774395228 Winsorized Mean ( 7 / 66 ) -0.000291878777357002 0.000371528592210896 -0.785615921563632 Winsorized Mean ( 8 / 66 ) -0.000291878777357002 0.000371528592210896 -0.785615921563632 Winsorized Mean ( 9 / 66 ) -0.000291878777357002 0.000371528592210896 -0.785615921563632 Winsorized Mean ( 10 / 66 ) -0.000314992729551502 0.000367982917906633 -0.855998238568847 Winsorized Mean ( 11 / 66 ) -0.000314992729551502 0.000367982917906633 -0.855998238568847 Winsorized Mean ( 12 / 66 ) -0.000314992729551502 0.000367982917906633 -0.855998238568847 Winsorized Mean ( 13 / 66 ) -0.000314992729551503 0.000367982917906633 -0.855998238568848 Winsorized Mean ( 14 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 15 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 16 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 17 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 18 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 19 / 66 ) -0.000282633196479272 0.000363230503016839 -0.778109751609076 Winsorized Mean ( 20 / 66 ) -0.00106847657147085 0.000269645778681107 -3.9625191860855 Winsorized Mean ( 21 / 66 ) -0.00106847657147085 0.000269645778681107 -3.9625191860855 Winsorized Mean ( 22 / 66 ) -0.00106847657147085 0.000269645778681107 -3.9625191860855 Winsorized Mean ( 23 / 66 ) -0.00106847657147085 0.000269645778681107 -3.9625191860855 Winsorized Mean ( 24 / 66 ) -0.000545809195257465 0.000178618903258674 -3.05571910531233 Winsorized Mean ( 25 / 66 ) -0.000563367283350689 0.000162839859509987 -3.45963994961649 Winsorized Mean ( 26 / 66 ) -0.000240370803705447 9.24998419689664e-05 -2.59860772287688 Winsorized Mean ( 27 / 66 ) -0.000474678065289301 7.53044379579287e-05 -6.30345406142591 Winsorized Mean ( 28 / 66 ) -0.000474678065289301 7.53044379579287e-05 -6.30345406142591 Winsorized Mean ( 29 / 66 ) -0.000474678065289301 7.53044379579287e-05 -6.30345406142591 Winsorized Mean ( 30 / 66 ) -0.000474678065289301 7.53044379579287e-05 -6.30345406142591 Winsorized Mean ( 31 / 66 ) -0.000385056784966485 6.0920843986538e-05 -6.32060818217773 Winsorized Mean ( 32 / 66 ) -0.000385056784966485 6.0920843986538e-05 -6.32060818217773 Winsorized Mean ( 33 / 66 ) -9.82943074376457e-05 1.53963017588193e-05 -6.38428039261708 Winsorized Mean ( 34 / 66 ) 6.99440505513846e-15 2.39139153631166e-15 2.92482638201782 Winsorized Mean ( 35 / 66 ) 6.99440505513846e-15 2.39139153631166e-15 2.92482638201782 Winsorized Mean ( 36 / 66 ) 6.99440505513846e-15 2.39139153631166e-15 2.92482638201782 Winsorized Mean ( 37 / 66 ) 7.32303107042746e-15 2.36235162497129e-15 3.09989037746084 Winsorized Mean ( 38 / 66 ) 7.32303107042746e-15 2.36235162497129e-15 3.09989037746084 Winsorized Mean ( 39 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 40 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 41 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 42 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 43 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 44 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 45 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 46 / 66 ) 7.4096284663482e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 47 / 66 ) 7.40962846634821e-15 2.35480639935634e-15 3.14659772810774 Winsorized Mean ( 48 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 49 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 50 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 51 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 52 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 53 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 54 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 55 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 56 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 57 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 58 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 59 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 60 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 61 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 62 / 66 ) 4.95825602797573e-15 2.1569891731028e-15 2.29869305317066 Winsorized Mean ( 63 / 66 ) 1.08335562742922e-14 1.66443316383873e-15 6.5088562939388 Winsorized Mean ( 64 / 66 ) 1.08335562742922e-14 1.66443316383873e-15 6.5088562939388 Winsorized Mean ( 65 / 66 ) 1.08335562742922e-14 1.66443316383873e-15 6.5088562939388 Winsorized Mean ( 66 / 66 ) 1.08335562742922e-14 1.66443316383873e-15 6.5088562939388 Trimmed Mean ( 1 / 66 ) -0.000711720661891664 0.000664978362736428 -1.07029145875196 Trimmed Mean ( 2 / 66 ) -0.000660508092336763 0.000532977006984401 -1.23928065128726 Trimmed Mean ( 3 / 66 ) -0.000538933231359178 0.0004525894603156 -1.19077724652131 Trimmed Mean ( 4 / 66 ) -0.000491239278517383 0.000395342705728987 -1.24256568136642 Trimmed Mean ( 5 / 66 ) -0.00042034350287835 0.000371642863459873 -1.13104150303087 Trimmed Mean ( 6 / 66 ) -0.00034793930648104 0.000345065527222543 -1.00832821314151 Trimmed Mean ( 7 / 66 ) -0.000313848147696981 0.000329308506735206 -0.953052050821582 Trimmed Mean ( 8 / 66 ) -0.000317259540606916 0.000321471952852659 -0.986896485965996 Trimmed Mean ( 9 / 66 ) -0.0003207459091852 0.000313036489185612 -1.0246278637345 Trimmed Mean ( 10 / 66 ) -0.00032430975262078 0.000303930574251861 -1.0670520839145 Trimmed Mean ( 11 / 66 ) -0.000325356609145418 0.000294631960012838 -1.10428145382206 Trimmed Mean ( 12 / 66 ) -0.000326427257863798 0.000284540021982447 -1.14721034879211 Trimmed Mean ( 13 / 66 ) -0.000327522519196394 0.00027353959141815 -1.19734959571436 Trimmed Mean ( 14 / 66 ) -0.00032864325172277 0.000261485584165899 -1.25683124280482 Trimmed Mean ( 15 / 66 ) -0.000332509642919703 0.000248880079500717 -1.33602353224395 Trimmed Mean ( 16 / 66 ) -0.000336468091049896 0.00023490229937405 -1.43237461679383 Trimmed Mean ( 17 / 66 ) -0.000340521923472382 0.000219248138100581 -1.55313484722122 Trimmed Mean ( 18 / 66 ) -0.000344674629856393 0.000201478760322364 -1.71072439251124 Trimmed Mean ( 19 / 66 ) -0.000348929872200503 0.00018091445961717 -1.92870085088206 Trimmed Mean ( 20 / 66 ) -0.000353291495603216 0.000156385738235815 -2.25910303323495 Trimmed Mean ( 21 / 66 ) -0.000308026617383745 0.000142420192065727 -2.16280158674123 Trimmed Mean ( 22 / 66 ) -0.000261601101261211 0.00012593211745914 -2.07731837230553 Trimmed Mean ( 23 / 66 ) -0.000213969727577052 0.000105669359651214 -2.02489849738192 Trimmed Mean ( 24 / 66 ) -0.000165084896690679 7.86568975446469e-05 -2.0987974588875 Trimmed Mean ( 25 / 66 ) -0.000143933546770303 6.47607772361519e-05 -2.22254199089435 Trimmed Mean ( 26 / 66 ) -0.000121261452901092 4.97216539855155e-05 -2.43880569492755 Trimmed Mean ( 27 / 66 ) -0.000114985933891379 4.48807438992396e-05 -2.56203270938492 Trimmed Mean ( 28 / 66 ) -9.64832522351076e-05 4.11417419031469e-05 -2.34514261603804 Trimmed Mean ( 29 / 66 ) -9.64832522351076e-05 3.66990938607894e-05 -2.62903636261695 Trimmed Mean ( 30 / 66 ) -5.78919447805981e-05 3.12234085412926e-05 -1.85411995311264 Trimmed Mean ( 31 / 66 ) -3.77573495869409e-05 2.39745154705907e-05 -1.57489521042699 Trimmed Mean ( 32 / 66 ) -2.12820443032814e-05 1.75226263639478e-05 -1.21454648756698 Trimmed Mean ( 33 / 66 ) -4.31493886190064e-06 4.3149388697106e-06 -0.999999998190019 Trimmed Mean ( 34 / 66 ) 7.50914482110094e-15 2.52519567731304e-15 2.97368829218459 Trimmed Mean ( 35 / 66 ) 7.53243621322594e-15 2.52372796236203e-15 2.98464665192207 Trimmed Mean ( 36 / 66 ) 7.55645546135484e-15 2.52154254047483e-15 2.99675906317722 Trimmed Mean ( 37 / 66 ) 7.58123722529736e-15 2.51857051639968e-15 3.01013498567227 Trimmed Mean ( 38 / 66 ) 7.5924929425977e-15 2.51669093487822e-15 3.01685552142107 Trimmed Mean ( 39 / 66 ) 7.60411769980954e-15 2.51400099480233e-15 3.02470751424958 Trimmed Mean ( 40 / 66 ) 7.61242920551301e-15 2.51092881441102e-15 3.03171844690413 Trimmed Mean ( 41 / 66 ) 7.62102245717254e-15 2.50691093951472e-15 3.04000526586229 Trimmed Mean ( 42 / 66 ) 7.6299120278548e-15 2.50184942271455e-15 3.04970873090205 Trimmed Mean ( 43 / 66 ) 7.63911351329786e-15 2.49563450123428e-15 3.06099050542848 Trimmed Mean ( 44 / 66 ) 7.64864362322102e-15 2.48814281664279e-15 3.07403721846691 Trimmed Mean ( 45 / 66 ) 7.65852028259593e-15 2.47923529763659e-15 3.08906552350906 Trimmed Mean ( 46 / 66 ) 7.6687627441699e-15 2.46875462632609e-15 3.10632845500011 Trimmed Mean ( 47 / 66 ) 7.67939171372781e-15 2.45652218529355e-15 3.12612349267676 Trimmed Mean ( 48 / 66 ) 7.69042948980717e-15 2.44233435129293e-15 3.14880290069048 Trimmed Mean ( 49 / 66 ) 7.80203788285584e-15 2.44008142417336e-15 3.19744980866734 Trimmed Mean ( 50 / 66 ) 7.91811061162646e-15 2.43659951032693e-15 3.24965616141163 Trimmed Mean ( 51 / 66 ) 8.03892100279587e-15 2.43173264570537e-15 3.3058408032615 Trimmed Mean ( 52 / 66 ) 8.16476516026402e-15 2.425302015114e-15 3.36649419716919 Trimmed Mean ( 53 / 66 ) 8.29596438826272e-15 2.41710176904247e-15 3.43219490983582 Trimmed Mean ( 54 / 66 ) 8.43286793052224e-15 2.40689387496736e-15 3.50363097360768 Trimmed Mean ( 55 / 66 ) 8.57585607465995e-15 2.39440172325139e-15 3.58162792458013 Trimmed Mean ( 56 / 66 ) 8.72534367989484e-15 2.37930210673678e-15 3.66718612789431 Trimmed Mean ( 57 / 66 ) 8.72534367989484e-15 2.36121504716361e-15 3.6952770101885 Trimmed Mean ( 58 / 66 ) 8.88178419700111e-15 2.33969072627544e-15 3.79613600090643 Trimmed Mean ( 59 / 66 ) 9.21755896542434e-15 2.31419245459361e-15 3.98305635606396 Trimmed Mean ( 60 / 66 ) 9.39803790345182e-15 2.2840741075929e-15 4.11459412468715 Trimmed Mean ( 61 / 66 ) 9.58777217163456e-15 2.24854965600105e-15 4.26398062682148 Trimmed Mean ( 62 / 66 ) 9.78749245393218e-15 2.20665109125398e-15 4.43545084799527 Trimmed Mean ( 63 / 66 ) 9.9980084271648e-15 2.15716877049068e-15 4.63478266695406 Trimmed Mean ( 64 / 66 ) 9.96116769316448e-15 2.15698848810414e-15 4.61809033664327 Trimmed Mean ( 65 / 66 ) 9.92222177436414e-15 2.15498967929431e-15 4.60430129652099 Trimmed Mean ( 66 / 66 ) 9.88098491916377e-15 2.15083845191551e-15 4.59401537589394 Median -2.6645352591004e-15 Midrange -1.50435219836709e-14 Midmean - Weighted Average at Xnp 4.42517212647037e-15 Midmean - Weighted Average at X(n+1)p 4.42517212647037e-15 Midmean - Empirical Distribution Function 4.42517212647037e-15 Midmean - Empirical Distribution Function - Averaging 4.42517212647037e-15 Midmean - Empirical Distribution Function - Interpolation 4.42517212647037e-15 Midmean - Closest Observation 4.42517212647037e-15 Midmean - True Basic - Statistics Graphics Toolkit 4.42517212647037e-15 Midmean - MS Excel (old versions) 4.42517212647037e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290524555f0jv77y59jvbduc/1wto81290524557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290524555f0jv77y59jvbduc/1wto81290524557.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290524555f0jv77y59jvbduc/2pk6t1290524557.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290524555f0jv77y59jvbduc/2pk6t1290524557.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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