Opgave 5: Goudkoers 2009 centrummaten

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 03 Jun 2010 16:55:30 +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/Jun/03/t12755841793lrqxows5vaai67.htm/, Retrieved Thu, 03 Jun 2010 18:56:21 +0200

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/Jun/03/t12755841793lrqxows5vaai67.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:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

23100 22650 22440 22910 22980 22535 22300 22780 22780 23300 23800 24510 24660 24730 25070 24690 24880 23920 23880 23990 24590 23610 23580 23360 23910 23940 23060 22800 23020 22890 22780 22530 22290 22820 22480 22110 22000 22230 22260 22590 22820 22420 22230 21600 21000 21360 21640 21450 21710 21620 21800 21490 21670 22130 22050 22050 22140 22390 22220 21790 21510 21670 21745 21850 22105 22050 21670 21680 21800 21920 21980 22270 21740 21950 22010 21890 21920 22110 22340 22210 22240 21960 22220 22060 22090 21960 21940 21790 21710 21690 21710 21670 21640 21500 21290 21250 21580 21670 21620 21510 21360 21420 21470 21370 21370 21340 21130 21130 20990 21240 21320 21430 21390 21530 21510 21630 21560 21610 21560 21310 21340 21410 21550 21380 21600 21530 21560 21670 21540 21540 21550 21590 21420 21420 21370 21380 21210 21505 21365 21385 21350 21360 21530 21380 21630 22145 22 etc...

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 22633.4716157205 82.3446519937536 274.862678604039 Geometric Mean 22600.3718788395 Harmonic Mean 22568.3217148684 Quadratic Mean 22667.5985426325 Winsorized Mean ( 1 / 76 ) 22633.1659388646 82.2796593610049 275.07607730315 Winsorized Mean ( 2 / 76 ) 22633.0786026201 81.9771381515666 276.090128454765 Winsorized Mean ( 3 / 76 ) 22630.1965065502 81.5134832160454 277.625192958208 Winsorized Mean ( 4 / 76 ) 22626.615720524 80.6630240345563 280.507902987008 Winsorized Mean ( 5 / 76 ) 22627.2707423581 80.6130768125288 280.689828958884 Winsorized Mean ( 6 / 76 ) 22627.1397379913 80.5377360453084 280.950779709798 Winsorized Mean ( 7 / 76 ) 22628.3624454148 80.4472769142922 281.281894345820 Winsorized Mean ( 8 / 76 ) 22627.8384279476 80.2247648184482 282.05552835406 Winsorized Mean ( 9 / 76 ) 22624.4978165939 79.6821422860684 283.93435677682 Winsorized Mean ( 10 / 76 ) 22624.4978165939 79.5016652120417 284.578917388099 Winsorized Mean ( 11 / 76 ) 22624.2576419214 79.469287229675 284.691840465799 Winsorized Mean ( 12 / 76 ) 22624.2576419214 79.3617734092586 285.077521204711 Winsorized Mean ( 13 / 76 ) 22621.9868995633 78.9433251745177 286.559843400991 Winsorized Mean ( 14 / 76 ) 22620.1528384279 78.7015669235781 287.416804043976 Winsorized Mean ( 15 / 76 ) 22619.4978165939 78.6157299115242 287.72228970017 Winsorized Mean ( 16 / 76 ) 22614.9563318777 77.9574585800447 290.093555431355 Winsorized Mean ( 17 / 76 ) 22614.2139737991 77.788838259473 290.712838497048 Winsorized Mean ( 18 / 76 ) 22611.0698689956 77.3894147899405 292.172643123989 Winsorized Mean ( 19 / 76 ) 22610.6550218341 77.3370465222238 292.365121744552 Winsorized Mean ( 20 / 76 ) 22603.2314410480 76.2422415098051 296.465987796819 Winsorized Mean ( 21 / 76 ) 22602.3144104804 76.1296754451508 296.892299596951 Winsorized Mean ( 22 / 76 ) 22599.4323144105 75.7778272107091 298.232783206757 Winsorized Mean ( 23 / 76 ) 22599.4323144105 75.6814120636 298.612720061548 Winsorized Mean ( 24 / 76 ) 22599.9563318777 75.6445907228875 298.765002439755 Winsorized Mean ( 25 / 76 ) 22601.5938864629 75.4260598259668 299.652320943350 Winsorized Mean ( 26 / 76 ) 22601.5938864629 75.2098615538231 300.513701521552 Winsorized Mean ( 27 / 76 ) 22600.4148471616 75.0671238211838 301.069412236942 Winsorized Mean ( 28 / 76 ) 22599.192139738 74.919442109378 301.646562006488 Winsorized Mean ( 29 / 76 ) 22599.192139738 74.6795967149975 302.615347883896 Winsorized Mean ( 30 / 76 ) 22599.8471615721 74.2642374506088 304.316693167456 Winsorized Mean ( 31 / 76 ) 22602.5545851528 74.0815375675694 305.103745511993 Winsorized Mean ( 32 / 76 ) 22604.6506550218 73.8112035706667 306.249587616874 Winsorized Mean ( 33 / 76 ) 22603.9301310044 73.4561358458093 307.720109024683 Winsorized Mean ( 34 / 76 ) 22600.9606986900 72.962457727339 309.761504788529 Winsorized Mean ( 35 / 76 ) 22589.4978165939 71.4602665919232 316.112700021008 Winsorized Mean ( 36 / 76 ) 22586.3537117904 71.0903295030329 317.713448083354 Winsorized Mean ( 37 / 76 ) 22581.5065502183 70.5225398191403 320.20268425003 Winsorized Mean ( 38 / 76 ) 22582.3362445415 70.0119386440077 322.549791962864 Winsorized Mean ( 39 / 76 ) 22561.8995633188 67.6511474413645 333.503575573111 Winsorized Mean ( 40 / 76 ) 22514.7379912664 62.3969593081458 360.830691766211 Winsorized Mean ( 41 / 76 ) 22507.5764192140 61.3088992026503 367.117607915573 Winsorized Mean ( 42 / 76 ) 22504.8253275109 61.0146910113627 368.842732044974 Winsorized Mean ( 43 / 76 ) 22497.3144104804 59.8872320863664 375.661282492332 Winsorized Mean ( 44 / 76 ) 22493.4716157205 59.4821132205285 378.1552200798 Winsorized Mean ( 45 / 76 ) 22493.4716157205 59.1391705200643 380.348107995345 Winsorized Mean ( 46 / 76 ) 22487.4454148472 58.5080801673799 384.347689251042 Winsorized Mean ( 47 / 76 ) 22480.2620087336 57.760918656459 389.195022025845 Winsorized Mean ( 48 / 76 ) 22482.3580786026 57.252338986455 392.688901040735 Winsorized Mean ( 49 / 76 ) 22477.0087336245 56.330731451656 399.018584605361 Winsorized Mean ( 50 / 76 ) 22444.2576419214 52.6369452770961 426.397419602683 Winsorized Mean ( 51 / 76 ) 22444.2576419214 52.6369452770961 426.397419602683 Winsorized Mean ( 52 / 76 ) 22443.1222707424 52.1384965809199 430.452041053969 Winsorized Mean ( 53 / 76 ) 22441.9650655022 51.6317875637142 434.65404016486 Winsorized Mean ( 54 / 76 ) 22434.8908296943 50.9320209220545 440.486955426888 Winsorized Mean ( 55 / 76 ) 22384.4541484716 45.6736557649024 490.095521665529 Winsorized Mean ( 56 / 76 ) 22369.7816593886 44.310733974822 504.838887843732 Winsorized Mean ( 57 / 76 ) 22366.0480349345 43.5573523264657 513.48502239758 Winsorized Mean ( 58 / 76 ) 22336.9213973799 40.9387199364128 545.618461741703 Winsorized Mean ( 59 / 76 ) 22329.192139738 39.0120480130903 572.366570764128 Winsorized Mean ( 60 / 76 ) 22318.711790393 38.1108148706917 585.626727376977 Winsorized Mean ( 61 / 76 ) 22313.384279476 37.6581300890768 592.525019874745 Winsorized Mean ( 62 / 76 ) 22307.9694323144 37.2017187351436 599.648892330358 Winsorized Mean ( 63 / 76 ) 22307.9694323144 37.2017187351436 599.648892330358 Winsorized Mean ( 64 / 76 ) 22302.3799126638 36.7343119567106 607.126654201279 Winsorized Mean ( 65 / 76 ) 22299.5414847162 36.0487464537915 618.594089347891 Winsorized Mean ( 66 / 76 ) 22302.4235807860 35.8320963815082 622.414701705692 Winsorized Mean ( 67 / 76 ) 22289.2576419214 33.8326080140661 658.809915944242 Winsorized Mean ( 68 / 76 ) 22287.7729257642 33.7122929611849 661.117087212832 Winsorized Mean ( 69 / 76 ) 22286.2663755459 33.5904574244438 663.47016636124 Winsorized Mean ( 70 / 76 ) 22290.8515283843 32.5344959959268 685.145131222411 Winsorized Mean ( 71 / 76 ) 22289.3013100437 32.1693524585231 692.873794670935 Winsorized Mean ( 72 / 76 ) 22284.5851528384 29.6179888922526 752.400348109644 Winsorized Mean ( 73 / 76 ) 22284.5851528384 29.6179888922526 752.400348109644 Winsorized Mean ( 74 / 76 ) 22281.3537117904 28.8706582446605 771.764658878576 Winsorized Mean ( 75 / 76 ) 22274.8034934498 28.3566729246675 785.522460714103 Winsorized Mean ( 76 / 76 ) 22276.4628820961 28.2349774816963 788.966908032318 Trimmed Mean ( 1 / 76 ) 22626.0572687225 81.4390947402365 277.827956473387 Trimmed Mean ( 2 / 76 ) 22618.8222222222 80.5496510698709 280.805961562789 Trimmed Mean ( 3 / 76 ) 22611.5022421525 79.7722946304339 283.450568231818 Trimmed Mean ( 4 / 76 ) 22605.0452488688 79.1218124165026 285.699285171506 Trimmed Mean ( 5 / 76 ) 22599.4063926941 78.6740428559016 287.253655364919 Trimmed Mean ( 6 / 76 ) 22593.5253456221 78.2093775425334 288.885119093741 Trimmed Mean ( 7 / 76 ) 22587.5581395349 77.730131431703 290.589475709060 Trimmed Mean ( 8 / 76 ) 22581.2910798122 77.2354023607354 292.369695626678 Trimmed Mean ( 9 / 76 ) 22574.9763033175 76.743339573183 294.16202668363 Trimmed Mean ( 10 / 76 ) 22568.9473684211 76.2965495368176 295.805609892361 Trimmed Mean ( 11 / 76 ) 22562.8019323672 75.8430912904245 297.493173715294 Trimmed Mean ( 12 / 76 ) 22556.5609756098 75.3629826930772 299.305576419042 Trimmed Mean ( 13 / 76 ) 22550.197044335 74.8625025406345 301.221523179712 Trimmed Mean ( 14 / 76 ) 22543.9054726368 74.3735377303438 303.117293604807 Trimmed Mean ( 15 / 76 ) 22537.6381909548 73.8761181107196 305.073395399270 Trimmed Mean ( 16 / 76 ) 22531.2944162437 73.3527440711984 307.163620141792 Trimmed Mean ( 17 / 76 ) 22525.1538461538 72.856086871679 309.173259412453 Trimmed Mean ( 18 / 76 ) 22518.9378238342 72.3396284112209 311.294629491644 Trimmed Mean ( 19 / 76 ) 22512.8010471204 71.8223744411986 313.451082928924 Trimmed Mean ( 20 / 76 ) 22506.5608465608 71.272490787038 315.78187598088 Trimmed Mean ( 21 / 76 ) 22500.6417112299 70.7757789415754 317.914434114586 Trimmed Mean ( 22 / 76 ) 22494.6486486487 70.251740585502 320.200588073272 Trimmed Mean ( 23 / 76 ) 22488.6885245902 69.71761402153 322.56824677972 Trimmed Mean ( 24 / 76 ) 22482.5966850829 69.1509233581843 325.123593341317 Trimmed Mean ( 25 / 76 ) 22476.3407821229 68.5441334488375 327.910495781519 Trimmed Mean ( 26 / 76 ) 22469.8587570621 67.906826166174 330.892489395343 Trimmed Mean ( 27 / 76 ) 22463.2285714286 67.2367458709036 334.091548906287 Trimmed Mean ( 28 / 76 ) 22456.5028901734 66.5260939318726 337.559317899687 Trimmed Mean ( 29 / 76 ) 22456.5028901734 65.7716121455319 341.431541019312 Trimmed Mean ( 30 / 76 ) 22442.6923076923 64.975191420579 345.404019857712 Trimmed Mean ( 31 / 76 ) 22435.5089820359 64.1449905977942 349.762448679935 Trimmed Mean ( 32 / 76 ) 22428.0303030303 63.2587527717328 354.54430130738 Trimmed Mean ( 33 / 76 ) 22420.2760736196 62.3174642876964 359.775166237728 Trimmed Mean ( 34 / 76 ) 22412.3602484472 61.3237143904834 365.476234947786 Trimmed Mean ( 35 / 76 ) 22404.3710691824 60.2840175333341 371.646947000403 Trimmed Mean ( 36 / 76 ) 22396.6560509554 59.2754304471375 377.840462431209 Trimmed Mean ( 37 / 76 ) 22388.8709677419 58.2084423927303 384.632710435456 Trimmed Mean ( 38 / 76 ) 22381.0784313725 57.093034571764 392.010664685204 Trimmed Mean ( 39 / 76 ) 22373.0463576159 55.9137417804378 400.135023076624 Trimmed Mean ( 40 / 76 ) 22365.6040268456 54.8246572758935 407.947904066148 Trimmed Mean ( 41 / 76 ) 22359.7959183673 54.0613147402724 413.600668533329 Trimmed Mean ( 42 / 76 ) 22354.1034482759 53.3108489998613 419.316215510543 Trimmed Mean ( 43 / 76 ) 22348.3566433566 52.5140105552706 425.569412944288 Trimmed Mean ( 44 / 76 ) 22342.7304964539 51.7295220626486 431.914496897826 Trimmed Mean ( 45 / 76 ) 22337.0863309353 50.9028562035708 438.817936691111 Trimmed Mean ( 46 / 76 ) 22331.2773722628 50.0214377935692 446.434136188178 Trimmed Mean ( 47 / 76 ) 22325.5185185185 49.1059164296636 454.640095160351 Trimmed Mean ( 48 / 76 ) 22319.8496240601 48.1622550699668 463.430327164611 Trimmed Mean ( 49 / 76 ) 22313.9312977099 47.1596658168576 473.157112358791 Trimmed Mean ( 50 / 76 ) 22308.0232558140 46.1307656229215 483.582332844039 Trimmed Mean ( 51 / 76 ) 22303.1102362205 45.3153461144139 492.175656783218 Trimmed Mean ( 52 / 76 ) 22298.04 44.4137037846538 502.053152516063 Trimmed Mean ( 53 / 76 ) 22292.8455284553 43.45578107731 513.000686578276 Trimmed Mean ( 54 / 76 ) 22287.5206611570 42.4348116150027 525.217853289052 Trimmed Mean ( 55 / 76 ) 22282.2689075630 41.36520451925 538.671793516543 Trimmed Mean ( 56 / 76 ) 22278.6324786325 40.6606955188692 547.915676166773 Trimmed Mean ( 57 / 76 ) 22275.3913043478 39.997480270851 556.91986478912 Trimmed Mean ( 58 / 76 ) 22275.3913043478 39.3222888357448 566.482571688426 Trimmed Mean ( 59 / 76 ) 22269.8648648649 38.799314579942 573.975728849026 Trimmed Mean ( 60 / 76 ) 22267.7522935780 38.3699071103298 580.344180389823 Trimmed Mean ( 61 / 76 ) 22265.9345794393 37.9630426391717 586.516070144084 Trimmed Mean ( 62 / 76 ) 22264.2380952381 37.542880303775 593.034895433939 Trimmed Mean ( 63 / 76 ) 22264.2380952381 37.1080516634604 599.98402225901 Trimmed Mean ( 64 / 76 ) 22261.0396039604 36.6165112375053 607.950862919977 Trimmed Mean ( 65 / 76 ) 22259.5454545455 36.1034419189304 616.549123059481 Trimmed Mean ( 66 / 76 ) 22258.0927835052 35.5839572840813 625.50920365067 Trimmed Mean ( 67 / 76 ) 22256.4736842105 35.0122258029773 635.677200571405 Trimmed Mean ( 68 / 76 ) 22255.2688172043 34.5519445519351 644.110457625688 Trimmed Mean ( 69 / 76 ) 22254.0659340659 34.0371584716595 653.816797092373 Trimmed Mean ( 70 / 76 ) 22252.8651685393 33.4600183371495 665.058367401812 Trimmed Mean ( 71 / 76 ) 22251.4367816092 32.901531060076 676.303991476249 Trimmed Mean ( 72 / 76 ) 22250 32.2967681431594 688.923421110562 Trimmed Mean ( 73 / 76 ) 22248.6746987952 31.8656686268477 698.202035530177 Trimmed Mean ( 74 / 76 ) 22247.2839506173 31.3630731330353 709.346429677003 Trimmed Mean ( 75 / 76 ) 22245.9493670886 30.8599662973694 720.867584647523 Trimmed Mean ( 76 / 76 ) 22244.8051948052 30.3322963517257 733.370297351048 Median 22210 Midrange 23475 Midmean - Weighted Average at Xnp 22266.6228070175 Midmean - Weighted Average at X(n+1)p 22275.3913043478 Midmean - Empirical Distribution Function 22275.3913043478 Midmean - Empirical Distribution Function - Averaging 22275.3913043478 Midmean - Empirical Distribution Function - Interpolation 22275.3913043478 Midmean - Closest Observation 22264.3589743590 Midmean - True Basic - Statistics Graphics Toolkit 22275.3913043478 Midmean - MS Excel (old versions) 22275.3913043478 Number of observations 229

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/03/t12755841793lrqxows5vaai67/19dvw1275584126.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t12755841793lrqxows5vaai67/19dvw1275584126.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t12755841793lrqxows5vaai67/2kndz1275584126.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/03/t12755841793lrqxows5vaai67/2kndz1275584126.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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