*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: Mon, 22 Nov 2010 15:01:17 +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/22/t1290437984mz0vzk54k47i28x.htm/, Retrieved Mon, 22 Nov 2010 15:59:46 +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/22/t1290437984mz0vzk54k47i28x.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:

» Textbox « » Textfile « » CSV «

43.31 43.31 44.05 44.05 44.05 43.31 44.05 44.05 44.05 44.05 44.05 44.05 43.31 43.31 43.31 43.31 44.05 44.61 44.61 43.89 43.89 43.89 44.61 44.61 44.61 43.89 43.89 44.61 44.61 44.61 43.89 44.61 44.61 44.61 43.89 43.89 43.89 43.89 44.61 44.61 44.61 44.61 43.89 44.61 44.61 43.89 44.61 44.61 43.89 43.89 43.89 43.89 43.89 43.89 44.61 44.61 44.61 44.61 44.61 44.61 44.61 43.89 43.89 43.89 43.89 43.89 43.89 43.89 44.28 44.28 44.28 44.28 44.28 43.58 43.58 44.28 44.28 43.58 43.58 43.58 44.28 44.28 44.28 43.58 43.58 44.28 43.58 44.28 44.28 44.28 43.58 44.28 44.28 44.28 44.28 44.28 44.28 44.28 44.28 44.28 43.58 43.58 43.58 43.58 44.28 44.28 44.28 44.28 43.58 43.58 43.58 43.58 43.58 43.58 43.58 43.58 43.33 43.33 44.03 44.03 44.03 43.33 44.03 44.03 44.03 44.03 43.33 43.33 44.03 44.03 44.03 43.33 43.33 44.03 44.03 43.33 43.17 43.17 43.17 43.17 43.17 43.9 43.9 43.17 43.17 43.17 43.9 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 2 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 43.8712437810945 0.0317181261855825 1383.16001154684 Geometric Mean 43.868949615132 Harmonic Mean 43.8666544923661 Quadratic Mean 43.8735368850742 Winsorized Mean ( 1 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 2 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 3 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 4 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 5 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 6 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 7 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 8 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 9 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 10 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 11 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 12 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 13 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 14 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 15 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 16 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 17 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 18 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 19 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 20 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 21 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 22 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 23 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 24 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 25 / 67 ) 43.8712437810945 0.0317181261855825 1383.16001154684 Winsorized Mean ( 26 / 67 ) 43.8285572139304 0.0274228368317628 1598.25030075537 Winsorized Mean ( 27 / 67 ) 43.8285572139304 0.0274228368317628 1598.25030075537 Winsorized Mean ( 28 / 67 ) 43.8285572139304 0.0274228368317628 1598.25030075537 Winsorized Mean ( 29 / 67 ) 43.8285572139304 0.0274228368317628 1598.25030075537 Winsorized Mean ( 30 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 31 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 32 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 33 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 34 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 35 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 36 / 67 ) 43.8494527363184 0.0250369079803953 1751.39249505785 Winsorized Mean ( 37 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 38 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 39 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 40 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 41 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 42 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 43 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 44 / 67 ) 43.8531343283582 0.0246431877163698 1779.5236084343 Winsorized Mean ( 45 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 46 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 47 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 48 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 49 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 50 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 51 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 52 / 67 ) 43.9091044776119 0.0192038286540571 2286.47658071743 Winsorized Mean ( 53 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 54 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 55 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 56 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 57 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 58 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 59 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 60 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 61 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 62 / 67 ) 43.8484577114428 0.0139715612814202 3138.40785780712 Winsorized Mean ( 63 / 67 ) 43.8421890547264 0.0135277831080675 3240.89976195584 Winsorized Mean ( 64 / 67 ) 43.8421890547264 0.0135277831080675 3240.89976195584 Winsorized Mean ( 65 / 67 ) 43.8421890547264 0.0135277831080675 3240.89976195584 Winsorized Mean ( 66 / 67 ) 43.9439800995025 0.00469946519768254 9350.84701152222 Winsorized Mean ( 67 / 67 ) 43.9439800995025 0.00469946519768254 9350.84701152222 Trimmed Mean ( 1 / 67 ) 43.8710552763819 0.0316240921402374 1387.26686862141 Trimmed Mean ( 2 / 67 ) 43.8708629441624 0.0315226003337414 1391.72728390695 Trimmed Mean ( 3 / 67 ) 43.8706666666667 0.0314131917470522 1396.56826405688 Trimmed Mean ( 4 / 67 ) 43.8704663212435 0.0312953720840426 1401.81961100929 Trimmed Mean ( 5 / 67 ) 43.8702617801047 0.0311686082683951 1407.51429779395 Trimmed Mean ( 6 / 67 ) 43.8700529100529 0.0310323244946280 1413.68890743738 Trimmed Mean ( 7 / 67 ) 43.8698395721925 0.0308858977618901 1420.38414782047 Trimmed Mean ( 8 / 67 ) 43.8696216216216 0.0307286528051133 1427.64545845373 Trimmed Mean ( 9 / 67 ) 43.8693989071038 0.0305598563207435 1435.52372912585 Trimmed Mean ( 10 / 67 ) 43.8691712707182 0.0303787103626329 1444.07615553948 Trimmed Mean ( 11 / 67 ) 43.868938547486 0.0301843447565638 1453.36726376830 Trimmed Mean ( 12 / 67 ) 43.8687005649718 0.0299758083476022 1463.47014419983 Trimmed Mean ( 13 / 67 ) 43.8684571428571 0.0297520588508495 1474.46794733685 Trimmed Mean ( 14 / 67 ) 43.8682080924855 0.0295119510201126 1486.45570950524 Trimmed Mean ( 15 / 67 ) 43.8682080924855 0.0292542227763573 1499.55131017663 Trimmed Mean ( 16 / 67 ) 43.8676923076923 0.028977478842696 1513.85469197744 Trimmed Mean ( 17 / 67 ) 43.8674251497006 0.0286801713068027 1529.53846336669 Trimmed Mean ( 18 / 67 ) 43.8671515151515 0.0283605763631959 1546.76516278699 Trimmed Mean ( 19 / 67 ) 43.8668711656442 0.0280167662595318 1565.73641509109 Trimmed Mean ( 20 / 67 ) 43.8665838509317 0.0276465751574271 1586.69142926903 Trimmed Mean ( 21 / 67 ) 43.8662893081761 0.0272475571810751 1609.91640522709 Trimmed Mean ( 22 / 67 ) 43.8659872611465 0.0268169343073132 1635.75697201092 Trimmed Mean ( 23 / 67 ) 43.8656774193548 0.0263515308569965 1664.63488050859 Trimmed Mean ( 24 / 67 ) 43.8653594771242 0.0258476900324180 1697.07077971411 Trimmed Mean ( 25 / 67 ) 43.8650331125828 0.0253011659674588 1733.71587574264 Trimmed Mean ( 26 / 67 ) 43.8646979865772 0.0247069817076531 1775.39686982445 Trimmed Mean ( 27 / 67 ) 43.8665986394558 0.0244267987818561 1795.83903037018 Trimmed Mean ( 28 / 67 ) 43.8685517241379 0.0241215352087519 1818.64675463199 Trimmed Mean ( 29 / 67 ) 43.8705594405594 0.0237886422456065 1844.18089051139 Trimmed Mean ( 30 / 67 ) 43.8705594405594 0.0234252017040211 1872.79324186262 Trimmed Mean ( 31 / 67 ) 43.8737410071942 0.0232279985447185 1888.83002221344 Trimmed Mean ( 32 / 67 ) 43.8748905109489 0.0230117758764039 1906.62775209530 Trimmed Mean ( 33 / 67 ) 43.8760740740741 0.0227746662024597 1926.52984171225 Trimmed Mean ( 34 / 67 ) 43.8772932330827 0.0225145526011136 1948.84144537309 Trimmed Mean ( 35 / 67 ) 43.8785496183206 0.0222290225555370 1973.93068042891 Trimmed Mean ( 36 / 67 ) 43.8798449612403 0.0219153101726338 2002.24612910267 Trimmed Mean ( 37 / 67 ) 43.8811811023622 0.0215702229439249 2034.34063785238 Trimmed Mean ( 38 / 67 ) 43.8824 0.0212242709514216 2067.55747231265 Trimmed Mean ( 39 / 67 ) 43.8836585365854 0.0208426600344466 2105.47302810961 Trimmed Mean ( 40 / 67 ) 43.884958677686 0.0204208894717490 2149.02287867520 Trimmed Mean ( 41 / 67 ) 43.8863025210084 0.0199536086180896 2199.41682534666 Trimmed Mean ( 42 / 67 ) 43.8876923076923 0.0194343774848986 2258.25048123075 Trimmed Mean ( 43 / 67 ) 43.8891304347826 0.0188553307272193 2327.67757138435 Trimmed Mean ( 44 / 67 ) 43.8906194690265 0.0182066915020990 2410.68617348553 Trimmed Mean ( 45 / 67 ) 43.8921621621622 0.0174760420562720 2511.56194410791 Trimmed Mean ( 46 / 67 ) 43.8914678899083 0.0171963860438061 2552.3658155905 Trimmed Mean ( 47 / 67 ) 43.8907476635514 0.0168836929892121 2599.59403973975 Trimmed Mean ( 48 / 67 ) 43.89 0.0165333495805191 2654.6344880843 Trimmed Mean ( 49 / 67 ) 43.8892233009709 0.0161397908376958 2719.31797272515 Trimmed Mean ( 50 / 67 ) 43.8884158415842 0.0156962079504923 2796.11585040243 Trimmed Mean ( 51 / 67 ) 43.8875757575758 0.0151941268456516 2888.45658611412 Trimmed Mean ( 52 / 67 ) 43.8867010309278 0.0146227780124587 3001.25605364016 Trimmed Mean ( 53 / 67 ) 43.8857894736842 0.0139681119381567 3141.85551118054 Trimmed Mean ( 54 / 67 ) 43.887311827957 0.0137670288830631 3187.85645041752 Trimmed Mean ( 55 / 67 ) 43.887311827957 0.0135355098030185 3242.3833654326 Trimmed Mean ( 56 / 67 ) 43.8905617977528 0.0132686159149442 3307.84778752392 Trimmed Mean ( 57 / 67 ) 43.8922988505747 0.0129602657517788 3386.68200878137 Trimmed Mean ( 58 / 67 ) 43.8941176470588 0.0126028476147781 3482.8729973366 Trimmed Mean ( 59 / 67 ) 43.8960240963855 0.0121866420744006 3601.97861136779 Trimmed Mean ( 60 / 67 ) 43.8960240963855 0.0116989233348213 3752.14221344037 Trimmed Mean ( 61 / 67 ) 43.9001265822785 0.0111224830431569 3946.97176987722 Trimmed Mean ( 62 / 67 ) 43.9023376623377 0.0104330304251230 4208.01395888002 Trimmed Mean ( 63 / 67 ) 43.9046666666667 0.0095941673973331 4576.18309629149 Trimmed Mean ( 64 / 67 ) 43.907397260274 0.00860521636881939 5102.41641562558 Trimmed Mean ( 65 / 67 ) 43.9102816901408 0.00732123460189698 5997.66078780803 Trimmed Mean ( 66 / 67 ) 43.9133333333333 0.00550775113225095 7973.00609248596 Trimmed Mean ( 67 / 67 ) 43.9119402985075 0.00538116892623446 8160.2976788977 Median 43.9 Midrange 43.89 Midmean - Weighted Average at Xnp 43.9488461538462 Midmean - Weighted Average at X(n+1)p 43.9488461538462 Midmean - Empirical Distribution Function 43.9488461538462 Midmean - Empirical Distribution Function - Averaging 43.9488461538462 Midmean - Empirical Distribution Function - Interpolation 43.9488461538462 Midmean - Closest Observation 43.9488461538462 Midmean - True Basic - Statistics Graphics Toolkit 43.9488461538462 Midmean - MS Excel (old versions) 43.9488461538462 Number of observations 201

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437984mz0vzk54k47i28x/1byre1290438074.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437984mz0vzk54k47i28x/1byre1290438074.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437984mz0vzk54k47i28x/2byre1290438074.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437984mz0vzk54k47i28x/2byre1290438074.ps (open in new window)

Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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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