HAR central tendency

*The author of this computation has been verified*

R Software Module: /rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Tue, 23 Nov 2010 15:36:06 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1.htm/, Retrieved Tue, 23 Nov 2010 16:34:54 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1.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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0.24320322098874 -5.7731597280508E-15 -5.7731597280508E-15 -5.7731597280508E-15 -5.7731597280508E-15 -0.24320322098871 3.9523939676656E-14 3.9523939676656E-14 0.01889076246954 -0.018890762469514 3.9523939676656E-14 0.01889076246954 -0.018890762469514 0.01889076246954 -1.4210854715202E-14 -1.4210854715202E-14 -1.4210854715202E-14 -0.077171589615714 -2.0872192862953E-14 -2.0872192862953E-14 -0.010797183330221 4.3076653355456E-14 0.00063836580524335 -0.00063836580522958 4.3076653355456E-14 4.3076653355456E-14 4.3076653355456E-14 4.3076653355456E-14 0.010797183330243 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -0.010797183330221 0.010797183330243 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -0.010797183330221 0.010797183330243 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E-14 -2.0872192862953E 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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.00335959984214654 0.00341247095938507 -0.984506500460283 Geometric Mean NaN Harmonic Mean 1.10625107460305e-13 Quadratic Mean 0.0482559173861611 Winsorized Mean ( 1 / 66 ) -0.00289986012940814 0.00320414047703873 -0.905035266146693 Winsorized Mean ( 2 / 66 ) -0.00417799031144734 0.00280595166549255 -1.48897444058929 Winsorized Mean ( 3 / 66 ) -0.00223150026881629 0.00211797934111286 -1.05359869451974 Winsorized Mean ( 4 / 66 ) -0.00257317143055421 0.00203078679656463 -1.26708103229108 Winsorized Mean ( 5 / 66 ) -0.00293628763817946 0.00181533335917567 -1.61749224919924 Winsorized Mean ( 6 / 66 ) -0.00334734756061179 0.00147809322872446 -2.26463899269766 Winsorized Mean ( 7 / 66 ) -0.0032584461288312 0.00145488403682747 -2.23966037591324 Winsorized Mean ( 8 / 66 ) -0.00332538947830280 0.00135954428593396 -2.44595892366859 Winsorized Mean ( 9 / 66 ) -0.00343354866922014 0.00130773266527397 -2.62557383507798 Winsorized Mean ( 10 / 66 ) -0.00179423594688549 0.000943063480871296 -1.90256115657007 Winsorized Mean ( 11 / 66 ) -0.00171287656891073 0.000928406241533082 -1.8449645126063 Winsorized Mean ( 12 / 66 ) -0.00170332166802449 0.000926709766034606 -1.83803142089784 Winsorized Mean ( 13 / 66 ) -0.000895857868115488 0.000800355047503298 -1.11932556795901 Winsorized Mean ( 14 / 66 ) -0.000895857868115487 0.000800355047503298 -1.11932556795901 Winsorized Mean ( 15 / 66 ) -0.00100197744719446 0.000785410437786042 -1.27573737117486 Winsorized Mean ( 16 / 66 ) -0.00111006481821230 0.000770790169672294 -1.44016473210115 Winsorized Mean ( 17 / 66 ) -0.00113427936104506 0.000767595833968522 -1.4777039046457 Winsorized Mean ( 18 / 66 ) -0.00113221173748300 0.000766416502960192 -1.47727995562460 Winsorized Mean ( 19 / 66 ) -0.00100351460004715 0.00074920031562533 -1.33944764720174 Winsorized Mean ( 20 / 66 ) -0.00100351460004715 0.00074920031562533 -1.33944764720174 Winsorized Mean ( 21 / 66 ) -0.000876987399830008 0.00072886188970018 -1.20322850216625 Winsorized Mean ( 22 / 66 ) -0.00123654811791273 0.00068234132031044 -1.81221344964151 Winsorized Mean ( 23 / 66 ) -0.00129696602537079 0.000666654382064673 -1.94548488731748 Winsorized Mean ( 24 / 66 ) -0.00129696602537079 0.000666654382064673 -1.94548488731748 Winsorized Mean ( 25 / 66 ) -0.00157270852387842 0.000633560236072703 -2.48233464528532 Winsorized Mean ( 26 / 66 ) -0.00175115741943634 0.000614304197351372 -2.85063560852524 Winsorized Mean ( 27 / 66 ) -0.00176554594529404 0.000607981428188992 -2.90394716587497 Winsorized Mean ( 28 / 66 ) -0.00176554594529404 0.000607981428188992 -2.90394716587497 Winsorized Mean ( 29 / 66 ) -0.00156311826731958 0.000579924474589371 -2.69538247791039 Winsorized Mean ( 30 / 66 ) -0.00263483682337352 0.000402238722755006 -6.55043056353958 Winsorized Mean ( 31 / 66 ) -0.00263483682337352 0.000402238722755006 -6.55043056353958 Winsorized Mean ( 32 / 66 ) -0.00268781221845955 0.00039993879730412 -6.72055883694549 Winsorized Mean ( 33 / 66 ) -0.00219652382567331 0.0003075291389888 -7.14249008369026 Winsorized Mean ( 34 / 66 ) -0.00219652382567331 0.0003075291389888 -7.14249008369026 Winsorized Mean ( 35 / 66 ) -0.00215230957580628 0.000301141804699425 -7.14716303820567 Winsorized Mean ( 36 / 66 ) -0.00215230957580628 0.000301141804699425 -7.14716303820567 Winsorized Mean ( 37 / 66 ) -0.00215230957580628 0.000301141804699425 -7.14716303820567 Winsorized Mean ( 38 / 66 ) -0.00130610478827248 0.000180293054082233 -7.24434335488464 Winsorized Mean ( 39 / 66 ) -0.00117918729713712 0.000162524807734600 -7.25542957763539 Winsorized Mean ( 40 / 66 ) -0.00115177826740770 0.000158701324742643 -7.25752144334948 Winsorized Mean ( 41 / 66 ) -0.000211626164290982 2.82950262639897e-05 -7.4792708201269 Winsorized Mean ( 42 / 66 ) -0.000211626164290982 2.82950262639897e-05 -7.4792708201269 Winsorized Mean ( 43 / 66 ) -0.000140440477144215 1.87457024438441e-05 -7.49187594142859 Winsorized Mean ( 44 / 66 ) -6.11510841963527e-15 2.29758864405825e-15 -2.66153318412738 Winsorized Mean ( 45 / 66 ) -5.51558798633777e-15 2.24224085145754e-15 -2.45985527502651 Winsorized Mean ( 46 / 66 ) -5.51558798633777e-15 2.24224085145754e-15 -2.45985527502651 Winsorized Mean ( 47 / 66 ) -5.41122702202278e-15 2.23274844872398e-15 -2.42357217854762 Winsorized Mean ( 48 / 66 ) -5.41122702202278e-15 2.23274844872398e-15 -2.42357217854762 Winsorized Mean ( 49 / 66 ) -5.41122702202279e-15 2.23274844872398e-15 -2.42357217854762 Winsorized Mean ( 50 / 66 ) -5.41122702202278e-15 2.23274844872398e-15 -2.42357217854762 Winsorized Mean ( 51 / 66 ) -5.63771251904654e-15 2.21377960772928e-15 -2.54664579046749 Winsorized Mean ( 52 / 66 ) -5.63771251904654e-15 2.21377960772928e-15 -2.54664579046749 Winsorized Mean ( 53 / 66 ) -5.63771251904654e-15 2.21377960772928e-15 -2.54664579046749 Winsorized Mean ( 54 / 66 ) -5.63771251904654e-15 2.21377960772928e-15 -2.54664579046749 Winsorized Mean ( 55 / 66 ) -5.63771251904654e-15 2.21377960772928e-15 -2.54664579046749 Winsorized Mean ( 56 / 66 ) -1.16129328375798e-15 1.83181048196817e-15 -0.633959296111376 Winsorized Mean ( 57 / 66 ) -1.16129328375798e-15 1.83181048196817e-15 -0.633959296111376 Winsorized Mean ( 58 / 66 ) -1.16129328375798e-15 1.83181048196817e-15 -0.633959296111376 Winsorized Mean ( 59 / 66 ) -1.16129328375798e-15 1.83181048196817e-15 -0.633959296111376 Winsorized Mean ( 60 / 66 ) 1.50324197534232e-15 1.6349573928177e-15 0.91943801223567 Winsorized Mean ( 61 / 66 ) 1.50324197534232e-15 1.6349573928177e-15 0.919438012235671 Winsorized Mean ( 62 / 66 ) 1.50324197534232e-15 1.6349573928177e-15 0.919438012235671 Winsorized Mean ( 63 / 66 ) 1.50324197534232e-15 1.6349573928177e-15 0.91943801223567 Winsorized Mean ( 64 / 66 ) 1.50324197534232e-15 1.6349573928177e-15 0.91943801223567 Winsorized Mean ( 65 / 66 ) 6.37268016134822e-16 1.55674143770227e-15 0.409360219173853 Winsorized Mean ( 66 / 66 ) 6.37268016134822e-16 1.55674143770227e-15 0.409360219173853 Trimmed Mean ( 1 / 66 ) -0.00291928808708802 0.00273255536404864 -1.06833629996895 Trimmed Mean ( 2 / 66 ) -0.00293911253370015 0.00213304893856650 -1.37789268711076 Trimmed Mean ( 3 / 66 ) -0.00230051574104695 0.00166608590088251 -1.38079059418748 Trimmed Mean ( 4 / 66 ) -0.00232447944668260 0.00146833321476540 -1.58307353079524 Trimmed Mean ( 5 / 66 ) -0.00225903418776901 0.00126860972874270 -1.78071643042490 Trimmed Mean ( 6 / 66 ) -0.00211493770895828 0.00110719526705924 -1.91017589388333 Trimmed Mean ( 7 / 66 ) -0.00189407572837521 0.00101897135261550 -1.85881155884656 Trimmed Mean ( 8 / 66 ) -0.00168221697054043 0.000923796224915193 -1.82098272884246 Trimmed Mean ( 9 / 66 ) -0.00145650646123241 0.0008365848886944 -1.74101454725710 Trimmed Mean ( 10 / 66 ) -0.00121242717629566 0.000746534347020346 -1.62407420520548 Trimmed Mean ( 11 / 66 ) -0.00114705540431927 0.000717181472519117 -1.59939352628591 Trimmed Mean ( 12 / 66 ) -0.00108860280467139 0.000687391230706485 -1.58367281402842 Trimmed Mean ( 13 / 66 ) -0.00102972168749198 0.000654904603550298 -1.57232317792510 Trimmed Mean ( 14 / 66 ) -0.0010416951954863 0.00063721296109435 -1.63476774498951 Trimmed Mean ( 15 / 66 ) -0.00105395043308049 0.000618040606960448 -1.70530936189430 Trimmed Mean ( 16 / 66 ) -0.00105807527323017 0.000599071583678159 -1.76619172409053 Trimmed Mean ( 17 / 66 ) -0.00105416039785501 0.0005801993641679 -1.81689340416091 Trimmed Mean ( 18 / 66 ) -0.00104841298299202 0.00056000128963563 -1.87216172961705 Trimmed Mean ( 19 / 66 ) -0.00104266546896658 0.000538027473797217 -1.93794094120844 Trimmed Mean ( 20 / 66 ) -0.00104524118402707 0.000515926578604421 -2.02594948074673 Trimmed Mean ( 21 / 66 ) -0.00104788210706377 0.00049153106257259 -2.13187362275607 Trimmed Mean ( 22 / 66 ) -0.00105831523937352 0.000466914814531638 -2.26661310893534 Trimmed Mean ( 23 / 66 ) -0.00104779381797215 0.000445124712665632 -2.35393315212142 Trimmed Mean ( 24 / 66 ) -0.00103353911503173 0.000422745261001346 -2.44482720535674 Trimmed Mean ( 25 / 66 ) -0.00101890428667956 0.000397707242180968 -2.56194551824612 Trimmed Mean ( 26 / 66 ) -0.000988968922506646 0.000373494105130940 -2.64788361829682 Trimmed Mean ( 27 / 66 ) -0.000948811467452394 0.000348373145073709 -2.72354939199359 Trimmed Mean ( 28 / 66 ) -0.000906798377028441 0.000320434943211708 -2.82989853709347 Trimmed Mean ( 29 / 66 ) -0.000906798377028441 0.000287845043372932 -3.15030047557078 Trimmed Mean ( 30 / 66 ) -0.000829142880940968 0.000253548466008031 -3.27015538289515 Trimmed Mean ( 31 / 66 ) -0.000741911289519106 0.000236257315796042 -3.14026800405872 Trimmed Mean ( 32 / 66 ) -0.000652114063055424 0.000216098863937479 -3.01766539246635 Trimmed Mean ( 33 / 66 ) -0.000557165455060829 0.000192167103364365 -2.89937999431878 Trimmed Mean ( 34 / 66 ) -0.000481896475693864 0.000177853767007655 -2.70950952460356 Trimmed Mean ( 35 / 66 ) -0.000404311527730994 0.000160923800640986 -2.51244083299396 Trimmed Mean ( 36 / 66 ) -0.000326275900584775 0.000141466821890270 -2.30637753944776 Trimmed Mean ( 37 / 66 ) -0.000245762951941852 0.000116772998391521 -2.10462140500878 Trimmed Mean ( 38 / 66 ) -0.000162652811407221 8.19848558855581e-05 -1.98393727293083 Trimmed Mean ( 39 / 66 ) -0.000113323563052378 6.58393916070103e-05 -1.72121218447456 Trimmed Mean ( 40 / 66 ) -6.77738308265345e-05 4.77801229842132e-05 -1.41845241480285 Trimmed Mean ( 41 / 66 ) -2.18414394459767e-05 1.27118650095225e-05 -1.71819315494738 Trimmed Mean ( 42 / 66 ) -1.38605847426544e-05 9.96651847852916e-06 -1.39071479900571 Trimmed Mean ( 43 / 66 ) -5.59970004974176e-06 5.59970004583928e-06 -1.00000000069691 Trimmed Mean ( 44 / 66 ) -4.29021897373005e-15 2.29600140287384e-15 -1.86856112908298 Trimmed Mean ( 45 / 66 ) -4.21481031894058e-15 2.27829265696614e-15 -1.8499863509867 Trimmed Mean ( 46 / 66 ) -4.1612803737802e-15 2.26268993219395e-15 -1.83908555678477 Trimmed Mean ( 47 / 66 ) -4.10573043068924e-15 2.24492800087252e-15 -1.82889180815309 Trimmed Mean ( 48 / 66 ) -4.05231403988182e-15 2.22552858182807e-15 -1.82083217127376 Trimmed Mean ( 49 / 66 ) -3.99680288865057e-15 2.20349196663945e-15 -1.81384953935008 Trimmed Mean ( 50 / 66 ) -3.93907129137007e-15 2.17848145018448e-15 -1.80817297803316 Trimmed Mean ( 51 / 66 ) -3.87898330236383e-15 2.15010277759491e-15 -1.80409203819682 Trimmed Mean ( 52 / 66 ) -3.80713978861046e-15 2.11951509142193e-15 -1.79623150786643 Trimmed Mean ( 53 / 66 ) -3.73223910405906e-15 2.08473110455157e-15 -1.79027362133682 Trimmed Mean ( 54 / 66 ) -3.65408186800543e-15 2.04511930474435e-15 -1.78673286175948 Trimmed Mean ( 55 / 66 ) -3.57245097701609e-15 1.99991210862904e-15 -1.78630398886131 Trimmed Mean ( 56 / 66 ) -3.48710959098177e-15 1.94816298427139e-15 -1.78994756554516 Trimmed Mean ( 57 / 66 ) -3.48710959098177e-15 1.92794440668357e-15 -1.80871895418408 Trimmed Mean ( 58 / 66 ) -3.5836966469296e-15 1.90431235920303e-15 -1.88188488595926 Trimmed Mean ( 59 / 66 ) -3.79100544993958e-15 1.87672422403070e-15 -2.02001199824529 Trimmed Mean ( 60 / 66 ) -3.90243393155745e-15 1.84452042280589e-15 -2.11569028095717 Trimmed Mean ( 61 / 66 ) -4.1334457224506e-15 1.82418687139138e-15 -2.26591134234941 Trimmed Mean ( 62 / 66 ) -4.37661602865392e-15 1.79964938748112e-15 -2.43192705151288 Trimmed Mean ( 63 / 66 ) -4.63293067573309e-15 1.77012027955593e-15 -2.61729710079099 Trimmed Mean ( 64 / 66 ) -4.90348502542777e-15 1.73461166822399e-15 -2.82684886493834 Trimmed Mean ( 65 / 66 ) -5.18949962367644e-15 1.69186311807981e-15 -3.06732830110175 Trimmed Mean ( 66 / 66 ) -5.45315426801179e-15 1.65274459365296e-15 -3.29945370201394 Median -4.4408920985006e-15 Midrange -0.04695046359294 Midmean - Weighted Average at Xnp -3.11269868188482e-15 Midmean - Weighted Average at X(n+1)p -3.11269868188482e-15 Midmean - Empirical Distribution Function -3.11269868188482e-15 Midmean - Empirical Distribution Function - Averaging -3.11269868188482e-15 Midmean - Empirical Distribution Function - Interpolation -3.11269868188482e-15 Midmean - Closest Observation -3.11269868188482e-15 Midmean - True Basic - Statistics Graphics Toolkit -3.11269868188482e-15 Midmean - MS Excel (old versions) -3.11269868188482e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1/1ryb71290526562.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1/1ryb71290526562.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1/22paa1290526562.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526491g35mhlqlhn5ilk1/22paa1290526562.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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Software written by Ed van Stee & Patrick Wessa

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