TEC 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 14:52:55 +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/23/t12905239289dikr6whi798hf6.htm/, Retrieved Tue, 23 Nov 2010 15:52:08 +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/23/t12905239289dikr6whi798hf6.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:

beursspel test

Dataseries X:

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0.0077760889499117 3.5527136788005E-15 -0.0077760889498966 1.1546319456102E-14 -0.024497471600388 0.0322735605503 -0.0077760889498966 1.1546319456102E-14 0.0077760889499117 3.5527136788005E-15 3.5527136788005E-15 3.5527136788005E-15 3.5527136788005E-15 -0.0077760889498966 1.1546319456102E-14 1.1546319456102E-14 1.1546319456102E-14 0.0077760889499117 3.5527136788005E-15 0.0023210842142034 0 -0.0077579908108998 3.9523939676656E-14 3.9523939676656E-14 0.0077579908109393 0 0 0 0 -0.0077579908108998 3.9523939676656E-14 3.9523939676656E-14 3.9523939676656E-14 3.9523939676656E-14 3.9523939676656E-14 3.9523939676656E-14 -0.00038948393876082 -4.9293902293357E-14 -4.9293902293357E-14 -4.9293902293357E-14 -4.9293902293357E-14 -0.007036776495049 -2.3980817331903E-14 0.0070367764949757 -4.9293902293357E-14 -4.9293902293357E-14 -0.007036776495049 0.0070367764949757 -4.9293902293357E-14 -4.9293902293357E-14 -4.9293902293357E-14 -0.007036776495049 -2.3980817331903E-14 -2.3 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 -0.000199070878573121 0.00811822810863816 -0.0245214689596244 Geometric Mean NaN Harmonic Mean 0 Quadratic Mean 0.114521873571678 Winsorized Mean ( 1 / 66 ) 3.49757198224397e-05 0.000452053702790338 0.077370718581773 Winsorized Mean ( 2 / 66 ) -4.24361079389703e-05 0.000381423051816164 -0.111257323690607 Winsorized Mean ( 3 / 66 ) -4.29597005456553e-05 0.000381316965000244 -0.112661393247027 Winsorized Mean ( 4 / 66 ) -4.29597005456553e-05 0.000381316965000244 -0.112661393247027 Winsorized Mean ( 5 / 66 ) -4.29597005456553e-05 0.000381316965000244 -0.112661393247027 Winsorized Mean ( 6 / 66 ) 7.56954449059047e-05 0.000359827768753275 0.210365767956634 Winsorized Mean ( 7 / 66 ) -6.27355581201068e-05 0.00029460174243167 -0.212950397381501 Winsorized Mean ( 8 / 66 ) -6.27355581201068e-05 0.00029460174243167 -0.212950397381501 Winsorized Mean ( 9 / 66 ) -6.27355581201068e-05 0.00029460174243167 -0.212950397381501 Winsorized Mean ( 10 / 66 ) -6.27355581188868e-05 0.000294361871341725 -0.213123927473803 Winsorized Mean ( 11 / 66 ) -9.81190542981158e-05 0.000289785881454386 -0.338591562175745 Winsorized Mean ( 12 / 66 ) -5.95188766513537e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 13 / 66 ) -5.95188766513537e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 14 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 15 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 16 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 17 / 66 ) -5.95188766513537e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 18 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 19 / 66 ) -5.95188766513537e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 20 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 21 / 66 ) -5.95188766513537e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 22 / 66 ) -5.95188766513538e-05 0.000284818496920136 -0.208971247636501 Winsorized Mean ( 23 / 66 ) -6.49799154283834e-05 0.000284128450410484 -0.228699080766133 Winsorized Mean ( 24 / 66 ) -6.49799154283834e-05 0.000284128450410484 -0.228699080766133 Winsorized Mean ( 25 / 66 ) -6.28428447320835e-05 0.000282910028624659 -0.222130141648171 Winsorized Mean ( 26 / 66 ) -6.28428447320833e-05 0.000282910028624659 -0.22213014164817 Winsorized Mean ( 27 / 66 ) -5.87400964405813e-05 0.000282400092781488 -0.208003106026004 Winsorized Mean ( 28 / 66 ) -5.87400964405814e-05 0.000282400092781488 -0.208003106026005 Winsorized Mean ( 29 / 66 ) -5.87400964405814e-05 0.000282400092781488 -0.208003106026005 Winsorized Mean ( 30 / 66 ) -5.87400964405813e-05 0.000282400092781488 -0.208003106026004 Winsorized Mean ( 31 / 66 ) -0.000789672399960288 0.000205576990232402 -3.84124896014663 Winsorized Mean ( 32 / 66 ) -0.00109801652537552 0.00018743039380492 -5.85826291609005 Winsorized Mean ( 33 / 66 ) -6.62122695905518e-05 1.03711405995079e-05 -6.38428039377788 Winsorized Mean ( 34 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 35 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 36 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 37 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 38 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 39 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 40 / 66 ) -9.59232693276122e-15 2.32387970583695e-15 -4.12772094384573 Winsorized Mean ( 41 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 42 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 43 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 44 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 45 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 46 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 47 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 48 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 49 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 50 / 66 ) -1.5054624213917e-14 1.81259663662844e-15 -8.30555674091932 Winsorized Mean ( 51 / 66 ) -1.3129497489217e-14 1.6362931073857e-15 -8.0239276386087 Winsorized Mean ( 52 / 66 ) -1.3129497489217e-14 1.6362931073857e-15 -8.0239276386087 Winsorized Mean ( 53 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 54 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 55 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 56 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 57 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 58 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 59 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 60 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 61 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 62 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 63 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 64 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 65 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Winsorized Mean ( 66 / 66 ) -9.598988270909e-15 1.35493635789385e-15 -7.084456930383 Trimmed Mean ( 1 / 66 ) -3.94416659305081e-06 0.000407958195240613 -0.00966806559854632 Trimmed Mean ( 2 / 66 ) -4.36583364047758e-05 0.000356214357943642 -0.122561978289722 Trimmed Mean ( 3 / 66 ) -4.42883510778715e-05 0.000342001504605545 -0.129497532851361 Trimmed Mean ( 4 / 66 ) -4.47496880682243e-05 0.000326536750042918 -0.137043343704323 Trimmed Mean ( 5 / 66 ) -4.52207374162688e-05 0.000309552317804111 -0.146084312135195 Trimmed Mean ( 6 / 66 ) -4.57018090908674e-05 0.000290742745317395 -0.15718985194618 Trimmed Mean ( 7 / 66 ) -6.74575893770272e-05 0.000275284553294547 -0.245046765500313 Trimmed Mean ( 8 / 66 ) -6.81908240442509e-05 0.00027175481235411 -0.250927751577017 Trimmed Mean ( 9 / 66 ) -6.89401737591059e-05 0.000267984668567832 -0.257254171022309 Trimmed Mean ( 10 / 66 ) -6.97061756898465e-05 0.000263953303396574 -0.264085255963314 Trimmed Mean ( 11 / 66 ) -7.04893911472577e-05 0.00025967155899306 -0.271455955440778 Trimmed Mean ( 12 / 66 ) -6.76350871027476e-05 0.000255695048767351 -0.264514653016558 Trimmed Mean ( 13 / 66 ) -6.84125018969423e-05 0.000252056641332412 -0.271417176454081 Trimmed Mean ( 14 / 66 ) -6.92079961049556e-05 0.000248154533958812 -0.278890717815547 Trimmed Mean ( 15 / 66 ) -7.00222078237457e-05 0.000243963989127958 -0.287018621371285 Trimmed Mean ( 16 / 66 ) -7.08558055358403e-05 0.000239456742416069 -0.295902319646212 Trimmed Mean ( 17 / 66 ) -7.17094899397926e-05 0.000234600257029543 -0.305666715150965 Trimmed Mean ( 18 / 66 ) -7.2583995914573e-05 0.000229356757511184 -0.316467658080812 Trimmed Mean ( 19 / 66 ) -7.34800946294714e-05 0.000223681955405694 -0.328502558448222 Trimmed Mean ( 20 / 66 ) -7.43985958122423e-05 0.000217523335309068 -0.342025813950182 Trimmed Mean ( 21 / 66 ) -7.53403501895137e-05 0.000210817796908727 -0.357371869425863 Trimmed Mean ( 22 / 66 ) -7.63062521149203e-05 0.000203488324618662 -0.374990812165359 Trimmed Mean ( 23 / 66 ) -7.72972424020258e-05 0.000195439136007182 -0.395505444719042 Trimmed Mean ( 24 / 66 ) -7.80018949748886e-05 0.000186627842395615 -0.417954223622966 Trimmed Mean ( 25 / 66 ) -7.87253382830278e-05 0.000176828769637203 -0.445206616799672 Trimmed Mean ( 26 / 66 ) -7.95838514479437e-05 0.000165982266289279 -0.479472013650195 Trimmed Mean ( 27 / 66 ) -8.04658855214875e-05 0.000153668912793231 -0.523631514395878 Trimmed Mean ( 28 / 66 ) -8.15834672643324e-05 0.000139524398997059 -0.584725451969529 Trimmed Mean ( 29 / 66 ) -8.15834672643324e-05 0.000122777863155367 -0.664480266781432 Trimmed Mean ( 30 / 66 ) -8.39144234708377e-05 0.000102078446224822 -0.822058197143998 Trimmed Mean ( 31 / 66 ) -8.51305745351012e-05 7.40705104597118e-05 -1.14931804852898 Trimmed Mean ( 32 / 66 ) -5.17082868014586e-05 5.19025162713803e-05 -0.996257802436666 Trimmed Mean ( 33 / 66 ) -2.90659656945957e-06 2.90659655782978e-06 -1.00000000400117 Trimmed Mean ( 34 / 66 ) -1.20173231635183e-14 2.13129066629166e-15 -5.63851911594387 Trimmed Mean ( 35 / 66 ) -1.21270514997516e-14 2.10770753523476e-15 -5.75366899677609 Trimmed Mean ( 36 / 66 ) -1.22402088464923e-14 2.08175558847248e-15 -5.87975308641957 Trimmed Mean ( 37 / 66 ) -1.23569584899548e-14 2.05317422685872e-15 -6.01846561694889 Trimmed Mean ( 38 / 66 ) -1.24774742509484e-14 2.02166249975175e-15 -6.17188786579392 Trimmed Mean ( 39 / 66 ) -1.26019413483681e-14 1.98687020216997e-15 -6.3426092628521 Trimmed Mean ( 40 / 66 ) -1.2730557349035e-14 1.9483862464037e-15 -6.53389817985673 Trimmed Mean ( 41 / 66 ) -1.28635332141314e-14 1.90572319073582e-15 -6.74994840628699 Trimmed Mean ( 42 / 66 ) -1.27713931384465e-14 1.89984520517594e-15 -6.72233353730723 Trimmed Mean ( 43 / 66 ) -1.26760200776499e-14 1.8928915587426e-15 -6.69664356582068 Trimmed Mean ( 44 / 66 ) -1.25772408361106e-14 1.88474412042817e-15 -6.67318215761478 Trimmed Mean ( 45 / 66 ) -1.24748696221517e-14 1.87526892366701e-15 -6.6523096846065 Trimmed Mean ( 46 / 66 ) -1.23687068817498e-14 1.86431343534475e-15 -6.6344567642203 Trimmed Mean ( 47 / 66 ) -1.22585380002007e-14 1.85170321771858e-15 -6.62014186879474 Trimmed Mean ( 48 / 66 ) -1.21441318539766e-14 1.83723781104973e-15 -6.60999451510195 Trimmed Mean ( 49 / 66 ) -1.20252391922143e-14 1.82068560335313e-15 -6.60478622452313 Trimmed Mean ( 50 / 66 ) -1.19015908239816e-14 1.80177736519905e-15 -6.60547249280532 Trimmed Mean ( 51 / 66 ) -1.17728955835761e-14 1.78019799740856e-15 -6.61325066128256 Trimmed Mean ( 52 / 66 ) -1.17174788390646e-14 1.77146084766935e-15 -6.61458527546961 Trimmed Mean ( 53 / 66 ) -1.16597039352122e-14 1.76107585235815e-15 -6.62078462980419 Trimmed Mean ( 54 / 66 ) -1.17442287778825e-14 1.76999819924857e-15 -6.63516425207008 Trimmed Mean ( 55 / 66 ) -1.18325102802271e-14 1.77875738947071e-15 -6.65212150362338 Trimmed Mean ( 56 / 66 ) -1.19248045781328e-14 1.7873084261692e-15 -6.67193440344913 Trimmed Mean ( 57 / 66 ) -1.19248045781328e-14 1.79559861298218e-15 -6.64113042409171 Trimmed Mean ( 58 / 66 ) -1.20213916340807e-14 1.80356611491359e-15 -6.66534569189143 Trimmed Mean ( 59 / 66 ) -1.22287004370906e-14 1.81113820589597e-15 -6.75194217497117 Trimmed Mean ( 60 / 66 ) -1.23401289187085e-14 1.818229122696e-15 -6.78689432738324 Trimmed Mean ( 61 / 66 ) -1.2457271681435e-14 1.82473742053829e-15 -6.82688453759015 Trimmed Mean ( 62 / 66 ) -1.2580579852726e-14 1.83054269292332e-15 -6.87259570692406 Trimmed Mean ( 63 / 66 ) -1.27105533305733e-14 1.83550147300261e-15 -6.9248396242258 Trimmed Mean ( 64 / 66 ) -1.28477475571899e-14 1.83944207120855e-15 -6.98458938081628 Trimmed Mean ( 65 / 66 ) -1.29927814538989e-14 1.84215801553727e-15 -7.05302224039098 Trimmed Mean ( 66 / 66 ) -1.31463467562966e-14 1.84339963447824e-15 -7.1315771742667 Median -2.3980817331903e-14 Midrange -0.0195166153646 Midmean - Weighted Average at Xnp -1.49036338825681e-14 Midmean - Weighted Average at X(n+1)p -1.49036338825681e-14 Midmean - Empirical Distribution Function -1.49036338825681e-14 Midmean - Empirical Distribution Function - Averaging -1.49036338825681e-14 Midmean - Empirical Distribution Function - Interpolation -1.49036338825681e-14 Midmean - Closest Observation -1.49036338825681e-14 Midmean - True Basic - Statistics Graphics Toolkit -1.49036338825681e-14 Midmean - MS Excel (old versions) -1.49036338825681e-14 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12905239289dikr6whi798hf6/1s15k1290523970.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905239289dikr6whi798hf6/1s15k1290523970.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905239289dikr6whi798hf6/2lbmn1290523970.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905239289dikr6whi798hf6/2lbmn1290523970.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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Software written by Ed van Stee & Patrick Wessa

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