GUn 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 17:52:58 +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/t129053469998gepkpixizfduu.htm/, Retrieved Tue, 23 Nov 2010 18:51:40 +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/t129053469998gepkpixizfduu.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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3.2862601528905E-14 0.073315265017433 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -0.07331526501745 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 0.073315265017433 -0.07331526501745 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 3.2862601528905E-14 0.073315265017433 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -4.9737991503207E-14 -0.03293710907405 3.5527136788005E-14 3.5527136788005E-14 0.071931219008635 -0.0085349024497958 0.0085349024498416 -0.0085349024497958 4.1744385725906E-14 -0.063396316558758 3.5527136788005E-14 3.5527136788005E-14 3.5527136788005E-14 3.5527136788005E-14 0.071931219008635 0.036848103398004 2.3536728122053E-14 2 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.000589795537474516 0.00153583770384743 0.384022046077537 Geometric Mean NaN Harmonic Mean -1.6407463296585e-13 Quadratic Mean 0.0216736833933537 Winsorized Mean ( 1 / 66 ) 0.000589795537474516 0.00153583770384743 0.384022046077537 Winsorized Mean ( 2 / 66 ) 0.000645201226022256 0.00152288254968505 0.423671035009029 Winsorized Mean ( 3 / 66 ) 0.000690116229949056 0.00150347159478650 0.459015143579787 Winsorized Mean ( 4 / 66 ) 0.00105038244416870 0.00143538971454673 0.731775094612815 Winsorized Mean ( 5 / 66 ) 0.000484451973246071 0.00120946840811575 0.400549505878212 Winsorized Mean ( 6 / 66 ) 0.000528460777056431 0.00120115370901221 0.439960991746029 Winsorized Mean ( 7 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 8 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 9 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 10 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 11 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 12 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 13 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 14 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 15 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 16 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 17 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 18 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 19 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 20 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 21 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 22 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 23 / 66 ) 0.000340232371272902 0.00117451511649747 0.289679005824557 Winsorized Mean ( 24 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 25 / 66 ) 0.000340232371272902 0.00117451511649747 0.289679005824557 Winsorized Mean ( 26 / 66 ) 0.000340232371272901 0.00117451511649747 0.289679005824556 Winsorized Mean ( 27 / 66 ) 0.00339980060609386 0.000843265127723 4.03171018736903 Winsorized Mean ( 28 / 66 ) 0.00339980060609386 0.000843265127723 4.03171018736903 Winsorized Mean ( 29 / 66 ) 0.00217036976103516 0.000360306251490161 6.02368055524685 Winsorized Mean ( 30 / 66 ) 0.00132290987972025 0.000218960727342201 6.04176783562082 Winsorized Mean ( 31 / 66 ) 1.41220368732318e-15 2.40142837448132e-15 0.58806821070739 Winsorized Mean ( 32 / 66 ) 1.41220368732318e-15 2.40142837448132e-15 0.58806821070739 Winsorized Mean ( 33 / 66 ) 1.26565424807269e-15 2.38886848302046e-15 0.529813280667677 Winsorized Mean ( 34 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 35 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 36 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 37 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 38 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 39 / 66 ) 2.08721928629515e-16 2.30306697362906e-15 0.0906278154389151 Winsorized Mean ( 40 / 66 ) -3.24185123190486e-16 2.262888795598e-15 -0.143261623735609 Winsorized Mean ( 41 / 66 ) -3.24185123190486e-16 2.262888795598e-15 -0.143261623735609 Winsorized Mean ( 42 / 66 ) -3.24185123190485e-16 2.262888795598e-15 -0.143261623735609 Winsorized Mean ( 43 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 44 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 45 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 46 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960449 Winsorized Mean ( 47 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960449 Winsorized Mean ( 48 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 49 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 50 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 51 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960449 Winsorized Mean ( 52 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 53 / 66 ) 7.12319092599509e-15 1.56197275274455e-15 4.56038103960450 Winsorized Mean ( 54 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 55 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 56 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 57 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 58 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551019 Winsorized Mean ( 59 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 60 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 61 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 62 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 63 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 64 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 65 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Winsorized Mean ( 66 / 66 ) 4.60520510614505e-15 1.36913434076863e-15 3.36358892551020 Trimmed Mean ( 1 / 66 ) 0.000595753068156163 0.00145985942913366 0.408089338101346 Trimmed Mean ( 2 / 66 ) 0.000601832181096619 0.00137612716220453 0.437337622296833 Trimmed Mean ( 3 / 66 ) 0.000579477003299898 0.00129112046518338 0.448817146754461 Trimmed Mean ( 4 / 66 ) 0.000541060605157829 0.00120480309598955 0.449086333658063 Trimmed Mean ( 5 / 66 ) 0.000407028542260233 0.001132077933503 0.359541097140513 Trimmed Mean ( 6 / 66 ) 0.000390555471837714 0.00111341234084570 0.350773435420218 Trimmed Mean ( 7 / 66 ) 0.000365841259432926 0.00109522030698796 0.334034401205592 Trimmed Mean ( 8 / 66 ) 0.000369817794861501 0.00108072963948279 0.342192701440563 Trimmed Mean ( 9 / 66 ) 0.000373881726673122 0.00106524956779363 0.350980406823834 Trimmed Mean ( 10 / 66 ) 0.000378035968080557 0.00104869334205005 0.360482853206210 Trimmed Mean ( 11 / 66 ) 0.000382283563227484 0.00103096273228473 0.370802504548636 Trimmed Mean ( 12 / 66 ) 0.000386627694627751 0.00101194579040657 0.382063642433273 Trimmed Mean ( 13 / 66 ) 0.000391071691117680 0.000991514004259884 0.394418726752725 Trimmed Mean ( 14 / 66 ) 0.000395619036363187 0.000969518624964618 0.408057180312163 Trimmed Mean ( 15 / 66 ) 0.000400273377967413 0.00094578584799554 0.423217770508764 Trimmed Mean ( 16 / 66 ) 0.000405038537228882 0.000920110369723391 0.440206469307206 Trimmed Mean ( 17 / 66 ) 0.000409918519605086 0.000892246583220154 0.459422907651462 Trimmed Mean ( 18 / 66 ) 0.000414917525941684 0.000861896243039312 0.481400782626175 Trimmed Mean ( 19 / 66 ) 0.000420039964533508 0.000828690667664874 0.506871841234942 Trimmed Mean ( 20 / 66 ) 0.000425290464090127 0.000792164148665933 0.536871638039098 Trimmed Mean ( 21 / 66 ) 0.000430673887686153 0.000751712509830338 0.572923667032436 Trimmed Mean ( 22 / 66 ) 0.000436195347784643 0.000706525068601817 0.617381275158225 Trimmed Mean ( 23 / 66 ) 0.000441860222431144 0.000655465292109085 0.674116887271597 Trimmed Mean ( 24 / 66 ) 0.000447674172726238 0.000596842425312969 0.750070963020917 Trimmed Mean ( 25 / 66 ) 0.000453643161695868 0.000527918490589341 0.859305309024968 Trimmed Mean ( 26 / 66 ) 0.000459773474691704 0.000443633072979855 1.03638232290355 Trimmed Mean ( 27 / 66 ) 0.000466071741468248 0.000332089454366831 1.40345239916417 Trimmed Mean ( 28 / 66 ) 0.000315159762835243 0.000251831715839671 1.25146970382353 Trimmed Mean ( 29 / 66 ) 0.000315159762835243 0.000116214510674247 2.71187961818852 Trimmed Mean ( 30 / 66 ) 6.09635889306755e-05 6.09635889274167e-05 1.00000000005345 Trimmed Mean ( 31 / 66 ) 3.64281873587151e-15 2.24226919169203e-15 1.6246125796888 Trimmed Mean ( 32 / 66 ) 3.74863538902845e-15 2.22243226519151e-15 1.68672649679401 Trimmed Mean ( 33 / 66 ) 3.85761074824978e-15 2.20064971987547e-15 1.75294173961864 Trimmed Mean ( 34 / 66 ) 3.97661701547554e-15 2.17762674013526e-15 1.82612425820438 Trimmed Mean ( 35 / 66 ) 4.14711000583056e-15 2.15824744822414e-15 1.92151739099374 Trimmed Mean ( 36 / 66 ) 4.32293090213418e-15 2.13679177285307e-15 2.02309413441917 Trimmed Mean ( 37 / 66 ) 4.50433341419347e-15 2.11303585514931e-15 2.13168811272972 Trimmed Mean ( 38 / 66 ) 4.69158762019015e-15 2.08672301492456e-15 2.24830396110802 Trimmed Mean ( 39 / 66 ) 4.88498130835066e-15 2.05755706012610e-15 2.37416565645634 Trimmed Mean ( 40 / 66 ) 5.08482145278319e-15 2.02519373286816e-15 2.51078273167568 Trimmed Mean ( 41 / 66 ) 5.31401664668038e-15 1.99203079476475e-15 2.66763779990055 Trimmed Mean ( 42 / 66 ) 5.55111512312575e-15 1.95505373367258e-15 2.83936703504202 Trimmed Mean ( 43 / 66 ) 5.79653284435868e-15 1.91373798204841e-15 3.0289062027991 Trimmed Mean ( 44 / 66 ) 5.74143907020434e-15 1.92376861327467e-15 2.98447486386171 Trimmed Mean ( 45 / 66 ) 5.68434188608075e-15 1.93383398408239e-15 2.93941565453355 Trimmed Mean ( 46 / 66 ) 5.62512999143407e-15 1.94392184646277e-15 2.89370172040083 Trimmed Mean ( 47 / 66 ) 5.56368368566865e-15 1.95401798264929e-15 2.84730423930148 Trimmed Mean ( 48 / 66 ) 5.49987406045071e-15 1.96410590531946e-15 2.80019221242358 Trimmed Mean ( 49 / 66 ) 5.43356209698893e-15 1.97416650720518e-15 2.75233222585728 Trimmed Mean ( 50 / 66 ) 5.36459765498868e-15 1.98417765036895e-15 2.70368817731172 Trimmed Mean ( 51 / 66 ) 5.29281833780475e-15 1.99411368325226e-15 2.65422096155146 Trimmed Mean ( 52 / 66 ) 5.21804821573815e-15 2.00394487090854e-15 2.60388810664857 Trimmed Mean ( 53 / 66 ) 5.14009638634956e-15 2.01363672043936e-15 2.5526433512933 Trimmed Mean ( 54 / 66 ) 5.05875534698757e-15 2.02314917935154e-15 2.50043615103509 Trimmed Mean ( 55 / 66 ) 5.07741996595228e-15 2.04566784041872e-15 2.48203538503738 Trimmed Mean ( 56 / 66 ) 5.09693297668811e-15 2.06895353042056e-15 2.46353187819161 Trimmed Mean ( 57 / 66 ) 5.09693297668811e-15 2.09305068320062e-15 2.43516940014757 Trimmed Mean ( 58 / 66 ) 5.11735356931864e-15 2.11800741111379e-15 2.41611693257843 Trimmed Mean ( 59 / 66 ) 5.16118313398904e-15 2.14387590482064e-15 2.40740759406073 Trimmed Mean ( 60 / 66 ) 5.18474152499937e-15 2.17071288732342e-15 2.38849714085974 Trimmed Mean ( 61 / 66 ) 5.20950803862563e-15 2.19858013112887e-15 2.36948745459224 Trimmed Mean ( 62 / 66 ) 5.23557805296905e-15 2.22754504913878e-15 2.35038032339380 Trimmed Mean ( 63 / 66 ) 5.26305725727699e-15 2.25768137196894e-15 2.33117804957882 Trimmed Mean ( 64 / 66 ) 5.29206308404647e-15 2.28906992697870e-15 2.31188353910681 Trimmed Mean ( 65 / 66 ) 5.32272638663136e-15 2.32179953748265e-15 2.29250040785277 Trimmed Mean ( 66 / 66 ) 5.3551934128977e-15 2.35596806457763e-15 2.27303310830649 Median 2.3536728122053e-14 Midrange -8.50014503228635e-15 Midmean - Weighted Average at Xnp 6.49053460550089e-15 Midmean - Weighted Average at X(n+1)p 6.49053460550089e-15 Midmean - Empirical Distribution Function 6.49053460550089e-15 Midmean - Empirical Distribution Function - Averaging 6.49053460550089e-15 Midmean - Empirical Distribution Function - Interpolation 6.49053460550089e-15 Midmean - Closest Observation 6.49053460550089e-15 Midmean - True Basic - Statistics Graphics Toolkit 6.49053460550089e-15 Midmean - MS Excel (old versions) 6.49053460550089e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t129053469998gepkpixizfduu/1lzm01290534770.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129053469998gepkpixizfduu/1lzm01290534770.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129053469998gepkpixizfduu/2e8l31290534770.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t129053469998gepkpixizfduu/2e8l31290534770.ps (open in new window)

Parameters (Session):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

Parameters (R input):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

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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