tumbler 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 14:43:11 +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/t1290523341udg2trlu8lib6it.htm/, Retrieved Tue, 23 Nov 2010 15:42:23 +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/t1290523341udg2trlu8lib6it.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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-0.0042361726044575 -4.3964831775156E-14 0.0042361726044557 -0.0042361726044575 0.0042361726044557 4.2188474935756E-14 4.2188474935756E-14 4.2188474935756E-14 4.2188474935756E-14 -0.0042361726044575 0.0042361726044557 4.2188474935756E-14 4.2188474935756E-14 -0.0042361726044575 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 0.0042361726044557 4.2188474935756E-14 4.2188474935756E-14 -0.0042361726044575 -4.3964831775156E-14 0.0042361726044557 -0.0042361726044575 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 0.0042361726044557 -0.0042361726044575 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 -4.3964831775156E-14 0.0042361726044557 4.2188474935756E-14 -0.0042361726044575 -4.3964831775156E-14 -4.3964831775156E-14 0.0052924177514559 -0.0052924177515092 0.0052924177514559 -9.3258734068513E-15 -9.3258734068513E-15 -0.0052924177515092 -4.3964831775156E-14 -4.3964831775156E-14 -4.39 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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.000113565336218700 0.00652288324015706 -0.0174102972623447 Geometric Mean NaN Harmonic Mean -7.5120887745592e-14 Quadratic Mean 0.0920166617752744 Winsorized Mean ( 1 / 66 ) -0.000113565336218571 0.000385002446787769 -0.294973024629044 Winsorized Mean ( 2 / 66 ) -3.48781848637607e-05 0.000360788560779757 -0.0966720917879989 Winsorized Mean ( 3 / 66 ) -0.000134103500639456 0.000280085712752149 -0.478794506587793 Winsorized Mean ( 4 / 66 ) -0.000240227771237878 0.000223921736140317 -1.07282024236961 Winsorized Mean ( 5 / 66 ) -0.000138914785085120 0.000207031157554434 -0.670984921912519 Winsorized Mean ( 6 / 66 ) -0.000138914785085120 0.000207031157554434 -0.670984921912519 Winsorized Mean ( 7 / 66 ) -0.000139603575754145 0.000206940163316215 -0.67460841586765 Winsorized Mean ( 8 / 66 ) -0.000139603575754145 0.000206940163316215 -0.67460841586765 Winsorized Mean ( 9 / 66 ) -0.000139603575754145 0.000206940163316215 -0.67460841586765 Winsorized Mean ( 10 / 66 ) -0.000138619589082935 0.000206816757601243 -0.670253178179125 Winsorized Mean ( 11 / 66 ) -0.000138619589082935 0.000206816757601243 -0.670253178179125 Winsorized Mean ( 12 / 66 ) -0.000138619589082935 0.000206816757601243 -0.670253178179125 Winsorized Mean ( 13 / 66 ) -0.000138619589082935 0.000206816757601243 -0.670253178179125 Winsorized Mean ( 14 / 66 ) -0.000166696844694858 0.000203207414198801 -0.820328556180413 Winsorized Mean ( 15 / 66 ) -0.000166696844694858 0.000203207414198801 -0.820328556180413 Winsorized Mean ( 16 / 66 ) -0.000166696844694858 0.000203207414198801 -0.820328556180413 Winsorized Mean ( 17 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 18 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 19 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 20 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 21 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 22 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 23 / 66 ) -0.000132603034306724 0.000198996975235542 -0.666357034571851 Winsorized Mean ( 24 / 66 ) -0.00014371318030682 0.00019759396621263 -0.727315631450866 Winsorized Mean ( 25 / 66 ) -0.000214032798481883 0.000189008844158704 -1.13239567933746 Winsorized Mean ( 26 / 66 ) -0.000214032798481883 0.000189008844158704 -1.13239567933746 Winsorized Mean ( 27 / 66 ) -0.000214032798481883 0.000189008844158704 -1.13239567933746 Winsorized Mean ( 28 / 66 ) -0.000201070961490899 0.000187403898747918 -1.07292838000860 Winsorized Mean ( 29 / 66 ) -0.000119500204394704 0.000177618096541663 -0.672792957032242 Winsorized Mean ( 30 / 66 ) -0.000119500204394704 0.000177618096541663 -0.672792957032242 Winsorized Mean ( 31 / 66 ) -0.000119500204394704 0.000177618096541663 -0.672792957032242 Winsorized Mean ( 32 / 66 ) -0.00054278087455464 0.000133212519682471 -4.07454851727470 Winsorized Mean ( 33 / 66 ) -0.000585680252554107 0.000129894343021474 -4.50889730014865 Winsorized Mean ( 34 / 66 ) -0.000811894524108506 0.000117350720531348 -6.91853037145711 Winsorized Mean ( 35 / 66 ) -0.000811894524108506 0.000117350720531348 -6.91853037145712 Winsorized Mean ( 36 / 66 ) -0.000811894524108506 0.000117350720531348 -6.91853037145712 Winsorized Mean ( 37 / 66 ) -0.000579021783874364 8.34112220817724e-05 -6.94177317419853 Winsorized Mean ( 38 / 66 ) -0.000519307896801038 7.47961119540682e-05 -6.94297983189208 Winsorized Mean ( 39 / 66 ) -1.80278014738633e-14 2.56064286940515e-15 -7.0403419739869 Winsorized Mean ( 40 / 66 ) -1.80278014738633e-14 2.56064286940515e-15 -7.0403419739869 Winsorized Mean ( 41 / 66 ) -1.80278014738633e-14 2.56064286940515e-15 -7.0403419739869 Winsorized Mean ( 42 / 66 ) -1.80278014738633e-14 2.56064286940515e-15 -7.0403419739869 Winsorized Mean ( 43 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 44 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 45 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 46 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 47 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 48 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 49 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 50 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 51 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 52 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 53 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 54 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 55 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 56 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 57 / 66 ) -2.26108021195159e-14 1.8666267243681e-15 -12.1131889007805 Winsorized Mean ( 58 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 59 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 60 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 61 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024816 Winsorized Mean ( 62 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 63 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 64 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Winsorized Mean ( 65 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024816 Winsorized Mean ( 66 / 66 ) -2.99515967583374e-14 1.14001119418022e-15 -26.2730725024815 Trimmed Mean ( 1 / 66 ) -0.000114712460826667 0.000338077647700491 -0.339308030586787 Trimmed Mean ( 2 / 66 ) -0.000115882996141052 0.000280961742562339 -0.412451158240307 Trimmed Mean ( 3 / 66 ) -0.00015763805350048 0.000227288514253410 -0.693559258892974 Trimmed Mean ( 4 / 66 ) -0.000165809773243891 0.000204805321097137 -0.809596998533301 Trimmed Mean ( 5 / 66 ) -0.000146226089561263 0.000199101063977862 -0.734431482382846 Trimmed Mean ( 6 / 66 ) -0.000147781686258315 0.000197192486247494 -0.7494285866087 Trimmed Mean ( 7 / 66 ) -0.000149370736647777 0.000195157580201175 -0.765385267094419 Trimmed Mean ( 8 / 66 ) -0.000150887376538092 0.000193003097083513 -0.78178733304369 Trimmed Mean ( 9 / 66 ) -0.000152437349173250 0.000190702831146554 -0.799344971738266 Trimmed Mean ( 10 / 66 ) -0.000154021765644744 0.000188244816374946 -0.818199239749386 Trimmed Mean ( 11 / 66 ) -0.000155752347280902 0.000185632969971918 -0.839033859688096 Trimmed Mean ( 12 / 66 ) -0.000157522260317883 0.000182836214308496 -0.861548467920576 Trimmed Mean ( 13 / 66 ) -0.000159332861010885 0.000179837639166902 -0.885981720784341 Trimmed Mean ( 14 / 66 ) -0.000161185568696748 0.000176617988933318 -0.912622602432668 Trimmed Mean ( 15 / 66 ) -0.000160722436259933 0.000173578264939473 -0.92593641442364 Trimmed Mean ( 16 / 66 ) -0.000160248276860335 0.000170308164536054 -0.940931265960601 Trimmed Mean ( 17 / 66 ) -0.000159762691933037 0.000166783804007843 -0.957902914395235 Trimmed Mean ( 18 / 66 ) -0.00016171101744425 0.000163430784256379 -0.989477093804853 Trimmed Mean ( 19 / 66 ) -0.000163707449758209 0.000159808126892898 -1.02440002859132 Trimmed Mean ( 20 / 66 ) -0.000165753792880018 0.000155884895915609 -1.06330887227042 Trimmed Mean ( 21 / 66 ) -0.000167851942156808 0.000151624412943502 -1.10702451470894 Trimmed Mean ( 22 / 66 ) -0.000170003890133004 0.000146982614341551 -1.15662584241397 Trimmed Mean ( 23 / 66 ) -0.000172211732861828 0.000141905732089377 -1.21356431714374 Trimmed Mean ( 24 / 66 ) -0.000174477676715095 0.000136326917401377 -1.27984759019669 Trimmed Mean ( 25 / 66 ) -0.000176186815404444 0.000130320856476009 -1.35194642030977 Trimmed Mean ( 26 / 66 ) -0.000174141086589447 0.000124621109450412 -1.39736427766869 Trimmed Mean ( 27 / 66 ) -0.000172039310409656 0.000118280988655179 -1.45449672314794 Trimmed Mean ( 28 / 66 ) -0.000169879151558204 0.000111165692005307 -1.52816168814110 Trimmed Mean ( 29 / 66 ) -0.000169879151558204 0.000103295065450317 -1.64460084145948 Trimmed Mean ( 30 / 66 ) -0.000170714577706334 9.5554950054413e-05 -1.78655922701149 Trimmed Mean ( 31 / 66 ) -0.000173188702020905 8.65841903482088e-05 -2.00023470017339 Trimmed Mean ( 32 / 66 ) -0.00017573559469767 7.59054744009821e-05 -2.31518999234917 Trimmed Mean ( 33 / 66 ) -0.00015861594545061 6.95211529097003e-05 -2.28154941067553 Trimmed Mean ( 34 / 66 ) -0.000139007850542277 6.23587583936738e-05 -2.2291632181756 Trimmed Mean ( 35 / 66 ) -0.000108560489747425 5.4775156281506e-05 -1.98192934748556 Trimmed Mean ( 36 / 66 ) -7.71616489277336e-05 4.51513257526187e-05 -1.70895644018290 Trimmed Mean ( 37 / 66 ) -4.4766019510592e-05 3.15771745545389e-05 -1.41767020457368 Trimmed Mean ( 38 / 66 ) -2.14767534092942e-05 2.14767533824358e-05 -1.00000000125058 Trimmed Mean ( 39 / 66 ) -2.74243287459866e-14 2.20431281446409e-15 -12.4412145889802 Trimmed Mean ( 40 / 66 ) -2.78258897405218e-14 2.15642061279393e-15 -12.9037394539044 Trimmed Mean ( 41 / 66 ) -2.82410629721599e-14 2.10267697482246e-15 -13.4310040535563 Trimmed Mean ( 42 / 66 ) -2.86705525221303e-14 2.04215957725923e-15 -14.0393301490224 Trimmed Mean ( 43 / 66 ) -2.91151118808716e-14 1.97371745334887e-15 -14.7514082278956 Trimmed Mean ( 44 / 66 ) -2.93852244124894e-14 1.96329957309381e-15 -14.9672647084538 Trimmed Mean ( 45 / 66 ) -2.96651592179842e-14 1.95102383711846e-15 -15.2049189013486 Trimmed Mean ( 46 / 66 ) -2.9955461979238e-14 1.93665123882062e-15 -15.4676595242210 Trimmed Mean ( 47 / 66 ) -3.02567195616712e-14 1.91990402026076e-15 -15.759495913531 Trimmed Mean ( 48 / 66 ) -3.05695639741980e-14 1.90045732307619e-15 -16.0853725064009 Trimmed Mean ( 49 / 66 ) -3.08946767950592e-14 1.87792844042274e-15 -16.4514664829852 Trimmed Mean ( 50 / 66 ) -3.12327941287549e-14 1.85186276223678e-15 -16.8656094639703 Trimmed Mean ( 51 / 66 ) -3.15847121699483e-14 1.82171506237056e-15 -17.3378992260446 Trimmed Mean ( 52 / 66 ) -3.19512934628581e-14 1.78682405725653e-15 -17.8816114172516 Trimmed Mean ( 53 / 66 ) -3.23334739597216e-14 1.74637695822223e-15 -18.5146017917210 Trimmed Mean ( 54 / 66 ) -3.27322709999269e-14 1.69935862432072e-15 -19.261544050486 Trimmed Mean ( 55 / 66 ) -3.31487923530302e-14 1.64447602954720e-15 -20.1576622324849 Trimmed Mean ( 56 / 66 ) -3.35842464949110e-14 1.58004115739238e-15 -21.2552985330691 Trimmed Mean ( 57 / 66 ) -3.35842464949110e-14 1.50377948568677e-15 -22.3332255922970 Trimmed Mean ( 58 / 66 ) -3.40399543178094e-14 1.41249459211189e-15 -24.0991749688149 Trimmed Mean ( 59 / 66 ) -3.47093627552323e-14 1.40883959286373e-15 -24.6368450539348 Trimmed Mean ( 60 / 66 ) -3.4910963009338e-14 1.40354420029756e-15 -24.8734332712405 Trimmed Mean ( 61 / 66 ) -3.51229017380133e-14 1.39631815705544e-15 -25.1539389934459 Trimmed Mean ( 62 / 66 ) -3.53459951366188e-14 1.38680977032597e-15 -25.4872700588999 Trimmed Mean ( 63 / 66 ) -3.55811476378516e-14 1.37458907037539e-15 -25.8849341993784 Trimmed Mean ( 64 / 66 ) -3.58293641669307e-14 1.35912488734380e-15 -26.3620837941932 Trimmed Mean ( 65 / 66 ) -3.60917644976715e-14 1.33975294819518e-15 -26.9391192953087 Trimmed Mean ( 66 / 66 ) -3.63696001419853e-14 1.31563028350131e-15 -27.644240633618 Median -4.3076653355456e-14 Midrange -2.99760216648792e-14 Midmean - Weighted Average at Xnp -2.91151118808717e-14 Midmean - Weighted Average at X(n+1)p -2.91151118808717e-14 Midmean - Empirical Distribution Function -2.91151118808717e-14 Midmean - Empirical Distribution Function - Averaging -2.91151118808717e-14 Midmean - Empirical Distribution Function - Interpolation -2.91151118808717e-14 Midmean - Closest Observation -2.91151118808717e-14 Midmean - True Basic - Statistics Graphics Toolkit -2.91151118808717e-14 Midmean - MS Excel (old versions) -2.91151118808717e-14 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290523341udg2trlu8lib6it/1ibw01290523387.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290523341udg2trlu8lib6it/1ibw01290523387.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290523341udg2trlu8lib6it/2s2v31290523387.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290523341udg2trlu8lib6it/2s2v31290523387.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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