*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: Thu, 17 Dec 2009 21:22:55 +0100

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/17/t12610814072qh7z96lhywzzss.htm/, Retrieved Thu, 17 Dec 2009 21:23:27 +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/2009/Dec/17/t12610814072qh7z96lhywzzss.htm/}, year = {2009}, } @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 = {2009}, 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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-0.0192508842078761 -0.0243384070148590 0.0158952358252125 -0.0260722519603377 0.00884040098416066 -0.0212744425298722 0.0374645687523337 0.000287491611075654 -0.00339379103432857 0.0171818334669544 -0.033426965424278 0.00217934347065340 -0.0274115854113506 -0.0569170628639589 -0.000209124152662078 -0.00400646079770227 -0.00748529583959452 -0.00585685997080524 0.0752980051262616 0.00262906294324769 -0.00639835830921532 0.0257097451976159 0.0327139661508631 0.0363171189336884 -0.00129610688362125 -0.0219713395125436 -0.0176410998712386 0.0411130767250018 0.0124816435524092 0.020793906880278 -0.0484360434735618 0.0263730615102121 -0.0200050532987077 -0.0134631900868672 -0.013113310509618 0.00219519903716346 0.00349361484962575 0.0314382751789365 0.0140064877187480 -0.0167139079425391 0.0122645988561186 0.00114204009422627 -0.0409497518101819 -0.00376200146549827 0.000868011842158073 -0.0322586305360591 0.0682477804173895 -0.0118187390548836 -0.0359119197592944 -0 etc...

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.000693034527680564 0.00257859145711113 -0.268764765263354 Geometric Mean NaN Harmonic Mean -0.0410296023783424 Quadratic Mean 0.0339231552486104 Winsorized Mean ( 1 / 58 ) -0.000992447062667264 0.0024566386345667 -0.403985774994666 Winsorized Mean ( 2 / 58 ) -0.00124765202590218 0.00237361825059533 -0.525632976401851 Winsorized Mean ( 3 / 58 ) -0.00128842744984829 0.00236326703184951 -0.545189110026196 Winsorized Mean ( 4 / 58 ) -0.0013694916351735 0.00234364470641407 -0.584342682756256 Winsorized Mean ( 5 / 58 ) -0.00126827002683108 0.00231417778137105 -0.548043472303881 Winsorized Mean ( 6 / 58 ) -0.00118316271752062 0.00229644445599825 -0.515215037938424 Winsorized Mean ( 7 / 58 ) -0.00112764833855704 0.00226066069802968 -0.498813616541334 Winsorized Mean ( 8 / 58 ) -0.0012363848202876 0.00223590036467178 -0.552969550800667 Winsorized Mean ( 9 / 58 ) -0.00112464266403594 0.00219127559289298 -0.513236521998202 Winsorized Mean ( 10 / 58 ) -0.000968725006163092 0.00214993162429076 -0.450584100079305 Winsorized Mean ( 11 / 58 ) -0.00116396297132286 0.00209357737589012 -0.555968451286869 Winsorized Mean ( 12 / 58 ) -0.0010829166687962 0.00204313403058558 -0.53002722904372 Winsorized Mean ( 13 / 58 ) -0.00158703165840677 0.00196025674201282 -0.809603979107956 Winsorized Mean ( 14 / 58 ) -0.00166790440918306 0.00190578586307678 -0.87517933756226 Winsorized Mean ( 15 / 58 ) -0.00179091672886095 0.00188272707821201 -0.951235444364964 Winsorized Mean ( 16 / 58 ) -0.00180193054219734 0.00184213495399593 -0.978175099651965 Winsorized Mean ( 17 / 58 ) -0.00190802130007822 0.00182775116602942 -1.04391743008605 Winsorized Mean ( 18 / 58 ) -0.00190408304855669 0.00182095242346258 -1.04565227735936 Winsorized Mean ( 19 / 58 ) -0.00221426153168923 0.00177171331869595 -1.24978545249014 Winsorized Mean ( 20 / 58 ) -0.00212252235046545 0.00172773634030696 -1.22849899081728 Winsorized Mean ( 21 / 58 ) -0.00194597764969185 0.00169762153407333 -1.14629651582153 Winsorized Mean ( 22 / 58 ) -0.0019172964852568 0.00168314051111271 -1.13911849462247 Winsorized Mean ( 23 / 58 ) -0.00212750133104558 0.00165258995143029 -1.28737399692178 Winsorized Mean ( 24 / 58 ) -0.00215822111291691 0.00162226065520059 -1.33037875633497 Winsorized Mean ( 25 / 58 ) -0.00199158670523985 0.00160193410941102 -1.24323884081105 Winsorized Mean ( 26 / 58 ) -0.00187777394044766 0.00158505496614407 -1.18467433657249 Winsorized Mean ( 27 / 58 ) -0.00187600864370236 0.00158329975877962 -1.18487268964682 Winsorized Mean ( 28 / 58 ) -0.00189835621066694 0.00156130375593157 -1.21587884705643 Winsorized Mean ( 29 / 58 ) -0.00176766179500174 0.00153885450824253 -1.1486867572819 Winsorized Mean ( 30 / 58 ) -0.00163514766435907 0.00151824665615481 -1.07699737570991 Winsorized Mean ( 31 / 58 ) -0.00156145563132904 0.00149473595445209 -1.04463643005189 Winsorized Mean ( 32 / 58 ) -0.0015772290275505 0.00148934524014611 -1.05900833805046 Winsorized Mean ( 33 / 58 ) -0.00151499944264178 0.00148162013580838 -1.02252892359295 Winsorized Mean ( 34 / 58 ) -0.00170095512700067 0.00143749928888618 -1.18327371717772 Winsorized Mean ( 35 / 58 ) -0.00151034768256863 0.00141346577322359 -1.06854209785645 Winsorized Mean ( 36 / 58 ) -0.00166960933537507 0.00139344512578261 -1.19818807679086 Winsorized Mean ( 37 / 58 ) -0.0018203230438611 0.00137043054335739 -1.32828551777715 Winsorized Mean ( 38 / 58 ) -0.00164416951385666 0.00133449562487272 -1.23205313169421 Winsorized Mean ( 39 / 58 ) -0.00172996297440471 0.00131590418915031 -1.31465724379353 Winsorized Mean ( 40 / 58 ) -0.00173030609790439 0.00131120999710038 -1.31962546177256 Winsorized Mean ( 41 / 58 ) -0.00181267347909682 0.00128862261279447 -1.4066752058354 Winsorized Mean ( 42 / 58 ) -0.0018151427848985 0.00128131538308185 -1.41662451638774 Winsorized Mean ( 43 / 58 ) -0.00194433499670739 0.00126720968009238 -1.53434354807458 Winsorized Mean ( 44 / 58 ) -0.00196808052828619 0.00125048864829576 -1.57384917565498 Winsorized Mean ( 45 / 58 ) -0.00208029839335984 0.00123357845821904 -1.68639325654507 Winsorized Mean ( 46 / 58 ) -0.00203645510577394 0.00122605684346894 -1.66097935558363 Winsorized Mean ( 47 / 58 ) -0.00229869735634633 0.00118981813293306 -1.93197371322602 Winsorized Mean ( 48 / 58 ) -0.00219052483419599 0.001146630482355 -1.91040170997111 Winsorized Mean ( 49 / 58 ) -0.00208754385975168 0.00111734465481274 -1.86830791265703 Winsorized Mean ( 50 / 58 ) -0.00215174987549886 0.0011102452432217 -1.9380852011173 Winsorized Mean ( 51 / 58 ) -0.00252119454610299 0.00104245264389347 -2.41852189725045 Winsorized Mean ( 52 / 58 ) -0.00232208368326501 0.00100893805289567 -2.30151264153492 Winsorized Mean ( 53 / 58 ) -0.00234165838527939 0.000976296680257413 -2.39851105983684 Winsorized Mean ( 54 / 58 ) -0.00246460297200938 0.000953843592889194 -2.58386489187823 Winsorized Mean ( 55 / 58 ) -0.00248329121653248 0.000943280792777682 -2.63261081487721 Winsorized Mean ( 56 / 58 ) -0.00243011719468659 0.000926065162393284 -2.62413196540759 Winsorized Mean ( 57 / 58 ) -0.00267166373957442 0.000862317613890265 -3.09823630706261 Winsorized Mean ( 58 / 58 ) -0.00295860243313869 0.000828123081170265 -3.57266027286395 Trimmed Mean ( 1 / 58 ) -0.00116703499190564 0.0023694315992769 -0.49253795393874 Trimmed Mean ( 2 / 58 ) -0.00134573087242022 0.00227405001542647 -0.591777165537778 Trimmed Mean ( 3 / 58 ) -0.00139652170365277 0.00221828599565732 -0.629549889593456 Trimmed Mean ( 4 / 58 ) -0.00143428957546398 0.0021620705542086 -0.663387035484132 Trimmed Mean ( 5 / 58 ) -0.00145147683401664 0.0021073035813367 -0.688783925995106 Trimmed Mean ( 6 / 58 ) -0.00149083237037502 0.00205559079909755 -0.725257366898861 Trimmed Mean ( 7 / 58 ) -0.00154659749495488 0.00200348698558015 -0.771952853243531 Trimmed Mean ( 8 / 58 ) -0.00161250812172994 0.00195409082238847 -0.825196097978182 Trimmed Mean ( 9 / 58 ) -0.0016649483897195 0.00190496584993952 -0.874004323895025 Trimmed Mean ( 10 / 58 ) -0.00173277897865813 0.00185902785808302 -0.932088764094651 Trimmed Mean ( 11 / 58 ) -0.00182024305182532 0.00181546518559926 -1.00263175866107 Trimmed Mean ( 12 / 58 ) -0.00188945076940559 0.00177634087276722 -1.06367578338844 Trimmed Mean ( 13 / 58 ) -0.00196846931304637 0.00174065380266499 -1.13087927652965 Trimmed Mean ( 14 / 58 ) -0.00200343778085843 0.00171256104749951 -1.16984897197307 Trimmed Mean ( 15 / 58 ) -0.00203239750638993 0.00168856231218193 -1.20362600285903 Trimmed Mean ( 16 / 58 ) -0.00205212410511765 0.00166510991191529 -1.23242561372853 Trimmed Mean ( 17 / 58 ) -0.00207155878366593 0.00164403155798094 -1.26004806514179 Trimmed Mean ( 18 / 58 ) -0.00208368816224404 0.00162258817198289 -1.28417561413483 Trimmed Mean ( 19 / 58 ) -0.00209645421199143 0.00159989297244293 -1.31037153615987 Trimmed Mean ( 20 / 58 ) -0.00208840296547634 0.00158008267771039 -1.32170486705325 Trimmed Mean ( 21 / 58 ) -0.00208615418782933 0.00156269672408977 -1.33497060284968 Trimmed Mean ( 22 / 58 ) -0.0020950885166337 0.00154647848350935 -1.35474792502733 Trimmed Mean ( 23 / 58 ) -0.0021060742458452 0.00152999814238276 -1.37652078620519 Trimmed Mean ( 24 / 58 ) -0.00210478773348327 0.00151466319413376 -1.38960776338597 Trimmed Mean ( 25 / 58 ) -0.0021016636044438 0.00150046817764349 -1.40067189411808 Trimmed Mean ( 26 / 58 ) -0.00210794340131642 0.00148656020361012 -1.4180006946219 Trimmed Mean ( 27 / 58 ) -0.00212077977509564 0.00147261438769284 -1.44014603742823 Trimmed Mean ( 28 / 58 ) -0.00213414768999283 0.00145727708352829 -1.46447625788894 Trimmed Mean ( 29 / 58 ) -0.00214677937638528 0.00144217069454661 -1.48857509343593 Trimmed Mean ( 30 / 58 ) -0.00216673293330021 0.00142730077378449 -1.51806330739604 Trimmed Mean ( 31 / 58 ) -0.00219426145615609 0.00141248294880197 -1.55347819102327 Trimmed Mean ( 32 / 58 ) -0.00222655125484228 0.00139794347956431 -1.5927333882885 Trimmed Mean ( 33 / 58 ) -0.00225924282531356 0.00138211163183355 -1.63463122173163 Trimmed Mean ( 34 / 58 ) -0.00229626350815659 0.00136501303990109 -1.68222825792417 Trimmed Mean ( 35 / 58 ) -0.00232555752917501 0.00134979302911627 -1.72289934753744 Trimmed Mean ( 36 / 58 ) -0.00236529044606843 0.00133470948899899 -1.77213878043405 Trimmed Mean ( 37 / 58 ) -0.00239891503308527 0.001319483187087 -1.8180716939496 Trimmed Mean ( 38 / 58 ) -0.00242667978932493 0.00130433679263555 -1.8604702428286 Trimmed Mean ( 39 / 58 ) -0.0024640034702272 0.00129033276830871 -1.9095876123931 Trimmed Mean ( 40 / 58 ) -0.00249884336282761 0.00127605390823065 -1.95825846126866 Trimmed Mean ( 41 / 58 ) -0.002535181809593 0.00126012651728537 -2.01184704457646 Trimmed Mean ( 42 / 58 ) -0.00256925130810421 0.00124405279508468 -2.06522690858094 Trimmed Mean ( 43 / 58 ) -0.00260475317039798 0.00122628132057895 -2.12410735341564 Trimmed Mean ( 44 / 58 ) -0.00263582747062031 0.00120728380716367 -2.18327078933725 Trimmed Mean ( 45 / 58 ) -0.00266726360913928 0.00118710555803196 -2.24686304523854 Trimmed Mean ( 46 / 58 ) -0.00269494164370449 0.00116560751760708 -2.31204895558416 Trimmed Mean ( 47 / 58 ) -0.00272607660500882 0.0011414833167041 -2.38818786496158 Trimmed Mean ( 48 / 58 ) -0.00274636138113192 0.00111767441300235 -2.45721056971727 Trimmed Mean ( 49 / 58 ) -0.002772873321693 0.00109490720890093 -2.53251901088167 Trimmed Mean ( 50 / 58 ) -0.00280576006366151 0.00107166646637391 -2.61812807594425 Trimmed Mean ( 51 / 58 ) -0.00280576006366151 0.00104518873811999 -2.68445301918227 Trimmed Mean ( 52 / 58 ) -0.00285278081539963 0.00102274107468674 -2.78934804322141 Trimmed Mean ( 53 / 58 ) -0.00287889543672073 0.00100033642749161 -2.87792722288411 Trimmed Mean ( 54 / 58 ) -0.00290561906363634 0.000977930748676207 -2.97119102510027 Trimmed Mean ( 55 / 58 ) -0.00292782299880506 0.000954168074078533 -3.06845625874932 Trimmed Mean ( 56 / 58 ) -0.00295050585221134 0.00092700011859532 -3.18285380230828 Trimmed Mean ( 57 / 58 ) -0.00297745455054744 0.000896658056662252 -3.32061316844775 Trimmed Mean ( 58 / 58 ) -0.00299354880375655 0.000870133931888778 -3.44033107323896 Median -0.0032998699566584 Midrange 0.0400710053956762 Midmean - Weighted Average at Xnp -0.00285771252830667 Midmean - Weighted Average at X(n+1)p -0.00260475317039798 Midmean - Empirical Distribution Function -0.00260475317039798 Midmean - Empirical Distribution Function - Averaging -0.00260475317039798 Midmean - Empirical Distribution Function - Interpolation -0.00263582747062031 Midmean - Closest Observation -0.00260475317039798 Midmean - True Basic - Statistics Graphics Toolkit -0.00260475317039798 Midmean - MS Excel (old versions) -0.00260475317039798 Number of observations 174

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t12610814072qh7z96lhywzzss/1xh3q1261081372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610814072qh7z96lhywzzss/1xh3q1261081372.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610814072qh7z96lhywzzss/2460w1261081372.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t12610814072qh7z96lhywzzss/2460w1261081372.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 0.0 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 1 ;

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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