*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 14:37:03 +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/t1290436507cm3qfztfoqvmawh.htm/, Retrieved Mon, 22 Nov 2010 15:35:08 +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/t1290436507cm3qfztfoqvmawh.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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26.33 26.33 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 26.33 26.33 26.33 25.84 25.84 25.84 25.84 25.84 25.84 25.84 25.84 26.33 25.84 25.84 26.33 25.84 26.33 26.33 26.33 25.84 25.84 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 25.84 25.84 25.84 25.84 26.33 26.33 26.33 26.33 26.33 25.84 26.33 25.84 25.84 25.84 26.33 25.84 25.84 25.84 25.84 26.33 26.33 26.33 26.33 25.84 25.84 26.33 26.33 26.33 26.33 26.33 25.84 26.33 26.33 25.84 25.84 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 25.84 25.84 25.84 25.84 25.84 26.33 26.33 25.84 25.84 25.84 25.84 25.84 25.84 26.33 26.33 25.84 25.84 25.84 25.84 25.84 25.84 26.33 26.33 25.84 25.84 25.84 25.84 25.84 25.84 25.84 26.33 26.33 26.33 26.33 26.33 26.33 25.84 25.84 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.33 26.72 26.72 26.72 26.72 26.72 26.72 26.72 26.72 26 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 26.3471641791045 0.0355132529329943 741.896672456782 Geometric Mean 26.3424289492536 Harmonic Mean 26.3377450055197 Quadratic Mean 26.3519505636198 Winsorized Mean ( 1 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 2 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 3 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 4 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 5 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 6 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 7 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 8 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 9 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 10 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 11 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 12 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 13 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 14 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 15 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 16 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 17 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 18 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 19 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 20 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 21 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 22 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 23 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 24 / 67 ) 26.3471641791045 0.0355132529329943 741.896672456782 Winsorized Mean ( 25 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 26 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 27 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 28 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 29 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 30 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 31 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 32 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 33 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 34 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 35 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 36 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 37 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 38 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 39 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 40 / 67 ) 26.2613432835821 0.02466397927076 1064.76505657446 Winsorized Mean ( 41 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 42 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 43 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 44 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 45 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 46 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 47 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 48 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 49 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 50 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 51 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 52 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 53 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 54 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 55 / 67 ) 26.2593034825871 0.0244752400460648 1072.89258177507 Winsorized Mean ( 56 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 57 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 58 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 59 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 60 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 61 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 62 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 63 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 64 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 65 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 66 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Winsorized Mean ( 67 / 67 ) 26.1534328358209 0.0163399301405209 1600.58412801679 Trimmed Mean ( 1 / 67 ) 26.3443718592965 0.035376507694304 744.685486960553 Trimmed Mean ( 2 / 67 ) 26.3415228426396 0.035228730909413 747.728406974809 Trimmed Mean ( 3 / 67 ) 26.3386153846154 0.0350691798290051 751.047373022143 Trimmed Mean ( 4 / 67 ) 26.3356476683938 0.0348970482058977 754.666913746103 Trimmed Mean ( 5 / 67 ) 26.3326178010471 0.034711459176573 758.614544755849 Trimmed Mean ( 6 / 67 ) 26.3295238095238 0.0345114571061232 762.921244633634 Trimmed Mean ( 7 / 67 ) 26.3263636363636 0.0342959982047765 767.622026312594 Trimmed Mean ( 8 / 67 ) 26.3231351351351 0.034063939680168 772.756627163135 Trimmed Mean ( 9 / 67 ) 26.3198360655738 0.0338140271333128 778.370347956694 Trimmed Mean ( 10 / 67 ) 26.3164640883978 0.0335448798337757 784.515080060006 Trimmed Mean ( 11 / 67 ) 26.3130167597765 0.0332549734152492 791.250572695108 Trimmed Mean ( 12 / 67 ) 26.3094915254237 0.0329426194088047 798.646009260328 Trimmed Mean ( 13 / 67 ) 26.3058857142857 0.0326059408663436 806.781985593278 Trimmed Mean ( 14 / 67 ) 26.3021965317919 0.0322428431052054 815.753016753835 Trimmed Mean ( 15 / 67 ) 26.3021965317919 0.0318509783030018 825.78928287779 Trimmed Mean ( 16 / 67 ) 26.2945562130177 0.0314277022546116 836.668108918442 Trimmed Mean ( 17 / 67 ) 26.2905988023952 0.030970021017957 848.904777531518 Trimmed Mean ( 18 / 67 ) 26.2865454545455 0.0304745243399006 862.574429755026 Trimmed Mean ( 19 / 67 ) 26.2823926380368 0.0299373015391821 877.91455097041 Trimmed Mean ( 20 / 67 ) 26.2781366459627 0.0293538337207716 895.219919003887 Trimmed Mean ( 21 / 67 ) 26.2737735849057 0.0287188534580996 914.861508076523 Trimmed Mean ( 22 / 67 ) 26.2692993630573 0.0280261588116722 937.313584054796 Trimmed Mean ( 23 / 67 ) 26.2647096774194 0.0272683617013132 963.193534144536 Trimmed Mean ( 24 / 67 ) 26.26 0.0264365392756905 993.322148793777 Trimmed Mean ( 25 / 67 ) 26.2551655629139 0.025519737295913 1028.81801871521 Trimmed Mean ( 26 / 67 ) 26.2548322147651 0.0255206272008168 1028.76908189485 Trimmed Mean ( 27 / 67 ) 26.2544897959184 0.0255167785769082 1028.91082888017 Trimmed Mean ( 28 / 67 ) 26.2541379310345 0.0255077905248709 1029.2595866128 Trimmed Mean ( 29 / 67 ) 26.2537762237762 0.0254932246501145 1029.83347866345 Trimmed Mean ( 30 / 67 ) 26.2537762237762 0.0254726008088676 1030.66728131808 Trimmed Mean ( 31 / 67 ) 26.2530215827338 0.0254453922636155 1031.73970794992 Trimmed Mean ( 32 / 67 ) 26.2526277372263 0.0254110201480691 1033.1197875667 Trimmed Mean ( 33 / 67 ) 26.2522222222222 0.0253688471215174 1034.82125523771 Trimmed Mean ( 34 / 67 ) 26.2518045112782 0.0253181700671418 1036.87606338296 Trimmed Mean ( 35 / 67 ) 26.2513740458015 0.025258211657239 1039.32037636076 Trimmed Mean ( 36 / 67 ) 26.2509302325581 0.0251881105684323 1042.19529135535 Trimmed Mean ( 37 / 67 ) 26.2504724409449 0.0251069100792937 1045.5477140771 Trimmed Mean ( 38 / 67 ) 26.25 0.0250135447179091 1049.43142989269 Trimmed Mean ( 39 / 67 ) 26.249512195122 0.0249068245430236 1053.9084237647 Trimmed Mean ( 40 / 67 ) 26.2490082644628 0.0247854165328941 1059.05051987431 Trimmed Mean ( 41 / 67 ) 26.248487394958 0.0246478224115061 1064.94143607204 Trimmed Mean ( 42 / 67 ) 26.2480341880342 0.0245063971860245 1071.068667858 Trimmed Mean ( 43 / 67 ) 26.2475652173913 0.024346404012083 1078.08796750291 Trimmed Mean ( 44 / 67 ) 26.2470796460177 0.0241658177606167 1086.12420676253 Trimmed Mean ( 45 / 67 ) 26.2465765765766 0.0239623289855019 1095.32661004933 Trimmed Mean ( 46 / 67 ) 26.2460550458716 0.023733290650347 1105.87509471586 Trimmed Mean ( 47 / 67 ) 26.2455140186916 0.023475651607343 1117.9887339307 Trimmed Mean ( 48 / 67 ) 26.2449523809524 0.0231858725338537 1131.93723214997 Trimmed Mean ( 49 / 67 ) 26.2443689320388 0.0228598182570746 1148.05676217119 Trimmed Mean ( 50 / 67 ) 26.2437623762376 0.0224926177042837 1166.77225929286 Trimmed Mean ( 51 / 67 ) 26.2431313131313 0.0220784785224467 1188.62951930544 Trimmed Mean ( 52 / 67 ) 26.2424742268041 0.0216104366808256 1214.34261668985 Trimmed Mean ( 53 / 67 ) 26.2417894736842 0.0210800101927939 1244.86607139568 Trimmed Mean ( 54 / 67 ) 26.2410752688172 0.0204767067778807 1281.50857232391 Trimmed Mean ( 55 / 67 ) 26.2410752688172 0.0197873003200243 1326.15742645104 Trimmed Mean ( 56 / 67 ) 26.2395505617978 0.0189947240889815 1381.4125669253 Trimmed Mean ( 57 / 67 ) 26.2431034482759 0.0188441109395778 1392.64216456921 Trimmed Mean ( 58 / 67 ) 26.2468235294118 0.0186582202014071 1406.71635590582 Trimmed Mean ( 59 / 67 ) 26.2507228915663 0.018430786517146 1424.28663405902 Trimmed Mean ( 60 / 67 ) 26.2507228915663 0.0181540995532647 1445.99421274218 Trimmed Mean ( 61 / 67 ) 26.2591139240506 0.0178185432273919 1473.69589022762 Trimmed Mean ( 62 / 67 ) 26.2636363636364 0.0174119296347623 1508.37023319931 Trimmed Mean ( 63 / 67 ) 26.2684 0.0169185009878439 1552.64346521445 Trimmed Mean ( 64 / 67 ) 26.2734246575342 0.0163173661331157 1610.15107727545 Trimmed Mean ( 65 / 67 ) 26.2787323943662 0.0155799096469212 1686.70634104475 Trimmed Mean ( 66 / 67 ) 26.284347826087 0.0146651721621967 1792.29725606916 Trimmed Mean ( 67 / 67 ) 26.2902985074627 0.0135107544634481 1945.87937917074 Median 26.33 Midrange 26.625 Midmean - Weighted Average at Xnp 26.1438125 Midmean - Weighted Average at X(n+1)p 26.1438125 Midmean - Empirical Distribution Function 26.1438125 Midmean - Empirical Distribution Function - Averaging 26.1438125 Midmean - Empirical Distribution Function - Interpolation 26.1438125 Midmean - Closest Observation 26.1438125 Midmean - True Basic - Statistics Graphics Toolkit 26.1438125 Midmean - MS Excel (old versions) 26.1438125 Number of observations 201

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436507cm3qfztfoqvmawh/1k8291290436618.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436507cm3qfztfoqvmawh/1k8291290436618.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436507cm3qfztfoqvmawh/2vzjc1290436618.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436507cm3qfztfoqvmawh/2vzjc1290436618.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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