*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 14:42:55 +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/t1290436891drng6ccyw5ym0yv.htm/, Retrieved Mon, 22 Nov 2010 15:41:40 +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/22/t1290436891drng6ccyw5ym0yv.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 «

27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.02 27.02 27.02 27.02 27.02 27.76 27.02 27.02 27.02 27.02 27.02 27.02 27.02 27.02 27.76 27.02 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.02 27.02 27.02 27.02 27.02 27.02 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.02 27.02 27.02 27.02 27.02 27.02 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.02 27.02 27.02 27.02 27.02 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.76 27.02 27.76 27.59 27.59 27.59 28.33 27.59 27.59 28.33 28.33 28.33 28.33 28.33 27.59 27.59 27.59 27.59 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 27.59 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 28.33 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 27.59 28.33 27.59 27.59 28 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 27.7649751243781 0.0309993680226152 895.662618157974 Geometric Mean 27.761507524653 Harmonic Mean 27.7580335202364 Quadratic Mean 27.7684359628139 Winsorized Mean ( 1 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 2 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 3 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 4 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 5 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 6 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 7 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157975 Winsorized Mean ( 8 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 9 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 10 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 11 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 12 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 13 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 14 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 15 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 16 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 17 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 18 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 19 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 20 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 21 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 22 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 23 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 24 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 25 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 26 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 27 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 28 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 29 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 30 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 31 / 67 ) 27.7649751243781 0.0309993680226152 895.662618157974 Winsorized Mean ( 32 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 33 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 34 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 35 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 36 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 37 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 38 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 39 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 40 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 41 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 42 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 43 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 44 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 45 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 46 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 47 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 48 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 49 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 50 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 51 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 52 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 53 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 54 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 55 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 56 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 57 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 58 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 59 / 67 ) 27.8557213930348 0.0224137486669171 1242.79618759847 Winsorized Mean ( 60 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 61 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 62 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 63 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 64 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 65 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 66 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Winsorized Mean ( 67 / 67 ) 27.6855721393035 0.00596373619073979 4642.32005806903 Trimmed Mean ( 1 / 67 ) 27.7658793969849 0.0309553078214198 896.966670697167 Trimmed Mean ( 2 / 67 ) 27.7668020304569 0.0309060179493919 898.427033722836 Trimmed Mean ( 3 / 67 ) 27.7677435897436 0.0308511760161225 900.054622722728 Trimmed Mean ( 4 / 67 ) 27.7687046632124 0.030790436086357 901.861363227542 Trimmed Mean ( 5 / 67 ) 27.7696858638743 0.0307234265304895 903.86031116406 Trimmed Mean ( 6 / 67 ) 27.7706878306878 0.0306497476298953 906.06579101489 Trimmed Mean ( 7 / 67 ) 27.7717112299465 0.0305689689025822 908.493554965822 Trimmed Mean ( 8 / 67 ) 27.7727567567568 0.030480626108734 911.160966893614 Trimmed Mean ( 9 / 67 ) 27.773825136612 0.0303842178886086 914.087215884032 Trimmed Mean ( 10 / 67 ) 27.7749171270718 0.0302792019766392 917.293565018673 Trimmed Mean ( 11 / 67 ) 27.7760335195531 0.0301649909251081 920.803642491186 Trimmed Mean ( 12 / 67 ) 27.7771751412429 0.0300409472579344 924.643783791019 Trimmed Mean ( 13 / 67 ) 27.7783428571429 0.0299063779593254 928.843435835768 Trimmed Mean ( 14 / 67 ) 27.7795375722543 0.0297605281824651 933.43563669082 Trimmed Mean ( 15 / 67 ) 27.7795375722543 0.0296025740390054 938.416285545 Trimmed Mean ( 16 / 67 ) 27.7820118343195 0.0294316142994382 943.951342650268 Trimmed Mean ( 17 / 67 ) 27.7832934131737 0.0292466607955829 949.964633821368 Trimmed Mean ( 18 / 67 ) 27.7846060606061 0.0290466272668265 956.551884849578 Trimmed Mean ( 19 / 67 ) 27.7859509202454 0.0288303163278748 963.775444023845 Trimmed Mean ( 20 / 67 ) 27.7873291925466 0.028596404152719 971.707108493378 Trimmed Mean ( 21 / 67 ) 27.7887421383648 0.0283434223604408 980.430019528968 Trimmed Mean ( 22 / 67 ) 27.7901910828025 0.0280697364436185 990.041040770816 Trimmed Mean ( 23 / 67 ) 27.7916774193548 0.0277735198854467 1000.65377143348 Trimmed Mean ( 24 / 67 ) 27.7932026143791 0.027452722846725 1012.40240429174 Trimmed Mean ( 25 / 67 ) 27.7947682119205 0.0271050339381199 1025.44672238284 Trimmed Mean ( 26 / 67 ) 27.7963758389262 0.0267278330804145 1039.97865278853 Trimmed Mean ( 27 / 67 ) 27.7980272108844 0.0263181327245848 1056.23098347388 Trimmed Mean ( 28 / 67 ) 27.799724137931 0.0258725036420652 1074.48913806441 Trimmed Mean ( 29 / 67 ) 27.8014685314685 0.0253869799218308 1095.10735885372 Trimmed Mean ( 30 / 67 ) 27.8014685314685 0.0248569354233651 1118.45921703347 Trimmed Mean ( 31 / 67 ) 27.8051079136691 0.0242769202174459 1145.33094250101 Trimmed Mean ( 32 / 67 ) 27.8070072992701 0.0236404395875656 1176.24747189118 Trimmed Mean ( 33 / 67 ) 27.8047407407407 0.0236189179475955 1177.22330897768 Trimmed Mean ( 34 / 67 ) 27.8024060150376 0.0235889435755499 1178.62022629343 Trimmed Mean ( 35 / 67 ) 27.8 0.0235496502025478 1180.48462549955 Trimmed Mean ( 36 / 67 ) 27.797519379845 0.0235000698414948 1182.86964963662 Trimmed Mean ( 37 / 67 ) 27.7949606299213 0.0234391179690161 1185.83645795303 Trimmed Mean ( 38 / 67 ) 27.79232 0.0233655760026472 1189.45580442148 Trimmed Mean ( 39 / 67 ) 27.789593495935 0.0232780704575663 1193.81000872013 Trimmed Mean ( 40 / 67 ) 27.7867768595041 0.0231750479941711 1198.99543968552 Trimmed Mean ( 41 / 67 ) 27.7838655462185 0.0230547453356366 1205.12567550559 Trimmed Mean ( 42 / 67 ) 27.7808547008547 0.0229151527189245 1212.33556859134 Trimmed Mean ( 43 / 67 ) 27.7777391304348 0.0227539691073512 1220.78653615912 Trimmed Mean ( 44 / 67 ) 27.7745132743363 0.022568546782956 1230.67353611407 Trimmed Mean ( 45 / 67 ) 27.7711711711712 0.0223558220677147 1242.23439813815 Trimmed Mean ( 46 / 67 ) 27.7677064220183 0.0221122276600164 1255.76250611006 Trimmed Mean ( 47 / 67 ) 27.7641121495327 0.0218335801992525 1271.62434635815 Trimmed Mean ( 48 / 67 ) 27.760380952381 0.0215149338242953 1290.28428249373 Trimmed Mean ( 49 / 67 ) 27.7565048543689 0.0211503860475699 1312.34034177632 Trimmed Mean ( 50 / 67 ) 27.7524752475248 0.0207328151148371 1338.57727924579 Trimmed Mean ( 51 / 67 ) 27.7482828282828 0.0202535161029861 1370.04768392742 Trimmed Mean ( 52 / 67 ) 27.7439175257732 0.0197016823275112 1408.20043002276 Trimmed Mean ( 53 / 67 ) 27.7393684210526 0.0190636410007155 1455.09288703095 Trimmed Mean ( 54 / 67 ) 27.734623655914 0.018321679627193 1513.75988557022 Trimmed Mean ( 55 / 67 ) 27.734623655914 0.0174521502112241 1589.18089291237 Trimmed Mean ( 56 / 67 ) 27.7244943820225 0.0164222027917722 1688.23237257263 Trimmed Mean ( 57 / 67 ) 27.7190804597701 0.015183658846312 1825.58635835676 Trimmed Mean ( 58 / 67 ) 27.7134117647059 0.0136600922984284 2028.78656741539 Trimmed Mean ( 59 / 67 ) 27.7074698795181 0.0117144035975132 2365.24801701385 Trimmed Mean ( 60 / 67 ) 27.7074698795181 0.00903932204802692 3065.21548101785 Trimmed Mean ( 61 / 67 ) 27.7018987341772 0.00912973312418083 3034.25065742683 Trimmed Mean ( 62 / 67 ) 27.7025974025974 0.0092219584701243 3003.98201665551 Trimmed Mean ( 63 / 67 ) 27.7033333333333 0.00931594261397034 2973.75525819459 Trimmed Mean ( 64 / 67 ) 27.7041095890411 0.0094116002785707 2943.61306993888 Trimmed Mean ( 65 / 67 ) 27.7049295774648 0.00950880824197028 2913.60692869795 Trimmed Mean ( 66 / 67 ) 27.7057971014493 0.00960739493240001 2883.79912519409 Trimmed Mean ( 67 / 67 ) 27.7067164179104 0.00970712705597062 2854.26535144286 Median 27.76 Midrange 27.675 Midmean - Weighted Average at Xnp 27.9060355029586 Midmean - Weighted Average at X(n+1)p 27.9060355029586 Midmean - Empirical Distribution Function 27.9060355029586 Midmean - Empirical Distribution Function - Averaging 27.9060355029586 Midmean - Empirical Distribution Function - Interpolation 27.9060355029586 Midmean - Closest Observation 27.9060355029586 Midmean - True Basic - Statistics Graphics Toolkit 27.9060355029586 Midmean - MS Excel (old versions) 27.9060355029586 Number of observations 201

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436891drng6ccyw5ym0yv/1ftz71290436971.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436891drng6ccyw5ym0yv/1ftz71290436971.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436891drng6ccyw5ym0yv/282yr1290436971.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436891drng6ccyw5ym0yv/282yr1290436971.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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