GRB 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 15:06:40 +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/t12905248029im4b4e1ox3wfw1.htm/, Retrieved Tue, 23 Nov 2010 16:06:44 +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/t12905248029im4b4e1ox3wfw1.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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4.2632564145606E-14 0.0051001932048425 2.2204460492503E-15 2.2204460492503E-15 2.2204460492503E-15 2.2204460492503E-15 2.2204460492503E-15 2.2204460492503E-15 -0.010226531783798 3.3750779948605E-14 3.3750779948605E-14 3.3750779948605E-14 3.3750779948605E-14 0.010226531783834 -0.6758556834499 0.66342407910779 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 0.0080616046710476 2.0872192862953E-14 2.0872192862953E-14 2.0872192862953E-14 2.0872192862953E-14 -0.0080616046709792 -0.018567187675552 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 -0.013585114590278 2.8865798640254E-14 2.8865798640254E-14 0.013585114590329 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 2.2204460492503E-14 -0.013585114590278 0.013585114590329 -0.0075244899785778 3.7747582837255E-14 3.77475828372 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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.000776844275730595 0.00477779709136716 -0.162594656255756 Geometric Mean NaN Harmonic Mean 3.12761694230772e-14 Quadratic Mean 0.0674035989487042 Winsorized Mean ( 1 / 66 ) -0.000833402204441895 0.000579714079363603 -1.43760904575025 Winsorized Mean ( 2 / 66 ) -0.000702262531285605 0.000542245375022732 -1.29510100709695 Winsorized Mean ( 3 / 66 ) -0.00068630701820517 0.00053855386802121 -1.27435166462891 Winsorized Mean ( 4 / 66 ) -0.00066627579138603 0.000534074524992314 -1.24753336886012 Winsorized Mean ( 5 / 66 ) -0.00052941839265728 0.00050670981124298 -1.04481575235063 Winsorized Mean ( 6 / 66 ) -0.00043672845445673 0.000491271356950133 -0.888976017588301 Winsorized Mean ( 7 / 66 ) -0.00043672845445673 0.000491271356950133 -0.888976017588301 Winsorized Mean ( 8 / 66 ) -0.00044018336440405 0.000490710097447482 -0.897033435206946 Winsorized Mean ( 9 / 66 ) -0.000492458119138315 0.000482462893523917 -1.02071708674090 Winsorized Mean ( 10 / 66 ) -0.000524676844819215 0.000477600768935843 -1.09856783938657 Winsorized Mean ( 11 / 66 ) -0.000524676844819215 0.000477600768935843 -1.09856783938657 Winsorized Mean ( 12 / 66 ) -0.000543091517071875 0.000474887210108236 -1.14362211807746 Winsorized Mean ( 13 / 66 ) -0.000537477288417165 0.000474001555094819 -1.13391460985745 Winsorized Mean ( 14 / 66 ) -0.000471905937805195 0.000463870486666921 -1.01732261777638 Winsorized Mean ( 15 / 66 ) -0.00045503636508682 0.0004613272404683 -0.986363529335284 Winsorized Mean ( 16 / 66 ) -0.0004034864039957 0.000453695915773728 -0.889332237667598 Winsorized Mean ( 17 / 66 ) -0.0004034864039957 0.000453695915773728 -0.889332237667598 Winsorized Mean ( 18 / 66 ) -0.00059304101214617 0.000418106969961237 -1.41839542211208 Winsorized Mean ( 19 / 66 ) -0.00065370783024796 0.000409882161896195 -1.59486772301527 Winsorized Mean ( 20 / 66 ) -0.0008702005415266 0.000382687603948824 -2.27391881144645 Winsorized Mean ( 21 / 66 ) -0.00109719820863011 0.000358162024349788 -3.06341301990903 Winsorized Mean ( 22 / 66 ) -0.00118514639008995 0.000349871307962865 -3.38737805334908 Winsorized Mean ( 23 / 66 ) -0.00177166860864137 0.000314106871104355 -5.64033700508504 Winsorized Mean ( 24 / 66 ) -0.00140546801637005 0.000247499896370137 -5.67866102969258 Winsorized Mean ( 25 / 66 ) -0.00135767829773729 0.000238955623720441 -5.68171728540551 Winsorized Mean ( 26 / 66 ) -0.00112593908044892 0.000197839198788936 -5.69118297759646 Winsorized Mean ( 27 / 66 ) -0.00105342859697473 0.000185081825071075 -5.6916912104697 Winsorized Mean ( 28 / 66 ) 2.11297646046662e-14 2.19527224866822e-15 9.6251226322770 Winsorized Mean ( 29 / 66 ) 2.11297646046662e-14 2.19527224866822e-15 9.6251226322770 Winsorized Mean ( 30 / 66 ) 2.11297646046662e-14 2.19527224866822e-15 9.6251226322770 Winsorized Mean ( 31 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 32 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 33 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 34 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 35 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 36 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 37 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 38 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 39 / 66 ) 2.14050999147732e-14 2.13341506619728e-15 10.0332561881298 Winsorized Mean ( 40 / 66 ) 2.76223488526741e-14 1.38194314046385e-15 19.9880501909815 Winsorized Mean ( 41 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 42 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 43 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 44 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 45 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 46 / 66 ) 2.85327317328667e-14 1.28563962113428e-15 22.1934135070395 Winsorized Mean ( 47 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 48 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 49 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 50 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 51 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 52 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 53 / 66 ) 3.09330339121064e-14 1.05468913427784e-15 29.3290533738992 Winsorized Mean ( 54 / 66 ) 3.20121706920419e-14 9.63100949662166e-16 33.2386451319262 Winsorized Mean ( 55 / 66 ) 3.20121706920419e-14 9.63100949662166e-16 33.2386451319262 Winsorized Mean ( 56 / 66 ) 3.20121706920419e-14 9.63100949662166e-16 33.2386451319262 Winsorized Mean ( 57 / 66 ) 3.20121706920419e-14 9.63100949662166e-16 33.2386451319262 Winsorized Mean ( 58 / 66 ) 3.20121706920419e-14 9.63100949662166e-16 33.2386451319262 Winsorized Mean ( 59 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 60 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 61 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 62 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 63 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 64 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 65 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Winsorized Mean ( 66 / 66 ) 3.25361959596652e-14 9.21305166430538e-16 35.3153299744556 Trimmed Mean ( 1 / 66 ) -0.000721905307090954 0.000546120114828394 -1.32188009100855 Trimmed Mean ( 2 / 66 ) -0.000608132962855301 0.00050859470143939 -1.19571234449397 Trimmed Mean ( 3 / 66 ) -0.000559612566757206 0.000489648425515406 -1.14288648261894 Trimmed Mean ( 4 / 66 ) -0.000515621437782219 0.000470553285462745 -1.09577693687794 Trimmed Mean ( 5 / 66 ) -0.0004759755552549 0.000451193016221923 -1.05492669022339 Trimmed Mean ( 6 / 66 ) -0.000464604738786308 0.00043748292295251 -1.06199514177778 Trimmed Mean ( 7 / 66 ) -0.000469600488666161 0.000426150732738414 -1.10195865591628 Trimmed Mean ( 8 / 66 ) -0.000474704841804272 0.000413935778824467 -1.1468079496592 Trimmed Mean ( 9 / 66 ) -0.000479446802985621 0.000400830967451938 -1.19613214027209 Trimmed Mean ( 10 / 66 ) -0.000477840467658128 0.000388032735012260 -1.23144370189033 Trimmed Mean ( 11 / 66 ) -0.000472577953370365 0.000374917586163059 -1.26048489271141 Trimmed Mean ( 12 / 66 ) -0.000467195836485153 0.000360636696638229 -1.29547503301867 Trimmed Mean ( 13 / 66 ) -0.000459926135279529 0.000345416986463718 -1.33150989471631 Trimmed Mean ( 14 / 66 ) -0.000452989538397808 0.000328808063858233 -1.37767162119575 Trimmed Mean ( 15 / 66 ) -0.000451399925002229 0.000311921431810514 -1.44715905663207 Trimmed Mean ( 16 / 66 ) -0.000451111318646309 0.000293508502952488 -1.53696166928198 Trimmed Mean ( 17 / 66 ) -0.000454697532098915 0.000273974488872753 -1.65963456659681 Trimmed Mean ( 18 / 66 ) -0.000458371214172317 0.000251803765927703 -1.82035090890548 Trimmed Mean ( 19 / 66 ) -0.000449134602514302 0.00023187808340059 -1.93694287932500 Trimmed Mean ( 20 / 66 ) -0.000435675837531825 0.000210175379218453 -2.07291567238706 Trimmed Mean ( 21 / 66 ) -0.000408174273987852 0.000189045827077827 -2.15912871655090 Trimmed Mean ( 22 / 66 ) -0.000366109320712988 0.000167798675259817 -2.18183677639952 Trimmed Mean ( 23 / 66 ) -0.000317760025000063 0.000143302358655030 -2.21740959452735 Trimmed Mean ( 24 / 66 ) -0.00023458447673683 0.000117101143362506 -2.0032637598647 Trimmed Mean ( 25 / 66 ) -0.000169535391201651 9.78984160397437e-05 -1.73174805129457 Trimmed Mean ( 26 / 66 ) -0.000105311450307833 7.42573778611239e-05 -1.41819511193604 Trimmed Mean ( 27 / 66 ) -5.15376025659426e-05 5.15376025931852e-05 -0.999999999471404 Trimmed Mean ( 28 / 66 ) 2.71017776122387e-14 1.91958766407778e-15 14.1185412468563 Trimmed Mean ( 29 / 66 ) 2.71017776122387e-14 1.88933050691146e-15 14.3446461659811 Trimmed Mean ( 30 / 66 ) 2.77111666946441e-14 1.85601634310356e-15 14.9304540326981 Trimmed Mean ( 31 / 66 ) 2.80291088245947e-14 1.81927066657777e-15 15.4067832453542 Trimmed Mean ( 32 / 66 ) 2.8343340746313e-14 1.78429017335022e-15 15.8849391033158 Trimmed Mean ( 33 / 66 ) 2.86669527253960e-14 1.7455626328735e-15 16.4227580182587 Trimmed Mean ( 34 / 66 ) 2.90003711280876e-14 1.70257491826783e-15 17.0332423066541 Trimmed Mean ( 35 / 66 ) 2.93440485585543e-14 1.65470498852715e-15 17.7337040511816 Trimmed Mean ( 36 / 66 ) 2.96984659087231e-14 1.6011864169947e-15 18.5477878112811 Trimmed Mean ( 37 / 66 ) 3.00641346033417e-14 1.54105604390469e-15 19.5087873164989 Trimmed Mean ( 38 / 66 ) 3.04415990623029e-14 1.47307352349254e-15 20.6653629821058 Trimmed Mean ( 39 / 66 ) 3.08314394051644e-14 1.39559133885427e-15 22.0920254710636 Trimmed Mean ( 40 / 66 ) 3.12342744261213e-14 1.30633110633704e-15 23.9099216688656 Trimmed Mean ( 41 / 66 ) 3.13873221199114e-14 1.29099003087640e-15 24.3125983696433 Trimmed Mean ( 42 / 66 ) 3.15073637678107e-14 1.28201561820341e-15 24.5764274010674 Trimmed Mean ( 43 / 66 ) 3.16316174033556e-14 1.27182043977657e-15 24.8711346461082 Trimmed Mean ( 44 / 66 ) 3.17603086687413e-14 1.26025974238086e-15 25.2013990455177 Trimmed Mean ( 45 / 66 ) 3.18936796165047e-14 1.24716634194624e-15 25.5729156118291 Trimmed Mean ( 46 / 66 ) 3.20319902290001e-14 1.23234591411721e-15 25.9926939847455 Trimmed Mean ( 47 / 66 ) 3.21755201098915e-14 1.21557095300745e-15 26.4694710171265 Trimmed Mean ( 48 / 66 ) 3.22263583340235e-14 1.21629078454730e-15 26.4956034720086 Trimmed Mean ( 49 / 66 ) 3.22791902140038e-14 1.21657366106015e-15 26.5328695229806 Trimmed Mean ( 50 / 66 ) 3.23341353691833e-14 1.21636355654986e-15 26.5826242450877 Trimmed Mean ( 51 / 66 ) 3.23913231837578e-14 1.21559659218963e-15 26.6464412551635 Trimmed Mean ( 52 / 66 ) 3.24508938239397e-14 1.21419969589906e-15 26.7261587476442 Trimmed Mean ( 53 / 66 ) 3.25129993849803e-14 1.21208897941326e-15 26.8239377943351 Trimmed Mean ( 54 / 66 ) 3.25778051878053e-14 1.2091677594685e-15 26.9423369360469 Trimmed Mean ( 55 / 66 ) 3.26010823275487e-14 1.21283115333456e-15 26.8801491765077 Trimmed Mean ( 56 / 66 ) 3.26254175190986e-14 1.21618730579977e-15 26.8259809681569 Trimmed Mean ( 57 / 66 ) 3.26254175190986e-14 1.21918444675051e-15 26.7600342229225 Trimmed Mean ( 58 / 66 ) 3.26508845800228e-14 1.22176250711863e-15 26.7244119784177 Trimmed Mean ( 59 / 66 ) 3.27055455888359e-14 1.22385156145633e-15 26.7234578267955 Trimmed Mean ( 60 / 66 ) 3.27127214205804e-14 1.22889820890144e-15 26.6195533394289 Trimmed Mean ( 61 / 66 ) 3.27202652436965e-14 1.23363962625534e-15 26.5233578326415 Trimmed Mean ( 62 / 66 ) 3.27282061101344e-14 1.23801254703287e-15 26.4360859577503 Trimmed Mean ( 63 / 66 ) 3.27365762125960e-14 1.24194212089441e-15 26.3591802402354 Trimmed Mean ( 64 / 66 ) 3.27454113207499e-14 1.24533946186097e-15 26.2943657722184 Trimmed Mean ( 65 / 66 ) 3.27547512922269e-14 1.24809857391671e-15 26.2437214309428 Trimmed Mean ( 66 / 66 ) 3.27646406737908e-14 1.25009246325011e-15 26.2097737863375 Median 3.352873534368e-14 Midrange -0.006215802171055 Midmean - Weighted Average at Xnp 3.40711065917756e-14 Midmean - Weighted Average at X(n+1)p 3.40711065917756e-14 Midmean - Empirical Distribution Function 3.40711065917756e-14 Midmean - Empirical Distribution Function - Averaging 3.40711065917756e-14 Midmean - Empirical Distribution Function - Interpolation 3.40711065917756e-14 Midmean - Closest Observation 3.40711065917756e-14 Midmean - True Basic - Statistics Graphics Toolkit 3.40711065917756e-14 Midmean - MS Excel (old versions) 3.40711065917756e-14 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12905248029im4b4e1ox3wfw1/1smvh1290524796.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905248029im4b4e1ox3wfw1/1smvh1290524796.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905248029im4b4e1ox3wfw1/23dc21290524796.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905248029im4b4e1ox3wfw1/23dc21290524796.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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