Gabriels Wim OPG5OEF2 centrummaten aantal kinderen

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Mon, 01 Jun 2009 09:26:33 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243870084rlyu7wqzhnquduj.htm/, Retrieved Mon, 01 Jun 2009 17:28:06 +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/2009/Jun/01/t1243870084rlyu7wqzhnquduj.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

374 572 402 589 507 628 698 451 694 0 488 526 343 494 447 0 470 366 517 483 485 530 308 481 437 468 502 408 479 436 410 451 344 411 0 427 454 365 499 416 430 470 325 452 442 488 446 523 594 439 588 503 444 525 375 472 436 458 514 0 472 360 450 549 361 466 387 457 470 396 471 422 404 414 342 459 379 0 410 319 411 371 365 429 333 392 469 432 534 379 436 448 358 492 387 529 475 439 459 361 0 0 394 425 341 455 403 471 523 389 531 468 398 446 355 435 353 0 400 332 389 355 384 406 356 336 351 278 265 229 387 435 317 490 472 440 429 350 489 494 436 436 375 429 0 434 472 362 440 433 400 442 316 432 401 434 488 377 484 377 0 0 300 389 337 376 377 331 339 356 280 249 196 268 379 401 404 397 419 421 407 296 468 475 422 456 339 446 419 346 327 326 403 359 358 421 322 367 394 356 418 344 372 358 373 379 0 348 369 341 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 383.708333333333 7.16683133149253 53.5394675255213 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 400.924778518743 Winsorized Mean ( 1 / 88 ) 383.693181818182 7.1643204850931 53.5561163988319 Winsorized Mean ( 2 / 88 ) 383.193181818182 7.09028482464299 54.0448220763087 Winsorized Mean ( 3 / 88 ) 382.806818181818 7.04288620167647 54.3536850120758 Winsorized Mean ( 4 / 88 ) 382.731060606061 7.03434399286395 54.4089201486771 Winsorized Mean ( 5 / 88 ) 382.712121212121 7.03223705573195 54.4225284470713 Winsorized Mean ( 6 / 88 ) 382.348484848485 6.9933036495997 54.6735139793867 Winsorized Mean ( 7 / 88 ) 381.738636363636 6.93387920349726 55.0541226866337 Winsorized Mean ( 8 / 88 ) 381.284090909091 6.89388576785733 55.3075730797316 Winsorized Mean ( 9 / 88 ) 381.181818181818 6.88534782298591 55.3613017063986 Winsorized Mean ( 10 / 88 ) 381.143939393939 6.8822233212805 55.3809316555313 Winsorized Mean ( 11 / 88 ) 381.102272727273 6.87880684559671 55.4023802792523 Winsorized Mean ( 12 / 88 ) 380.965909090909 6.86775804371676 55.4716556212185 Winsorized Mean ( 13 / 88 ) 380.916666666667 6.86381585305704 55.4963412220641 Winsorized Mean ( 14 / 88 ) 391.204545454545 4.92260200724577 79.4710896551695 Winsorized Mean ( 15 / 88 ) 393.079545454545 4.65516643229940 84.439418261655 Winsorized Mean ( 16 / 88 ) 393.92803030303 4.46056195810991 88.3135430025393 Winsorized Mean ( 17 / 88 ) 394.314393939394 4.37018941208602 90.2282159324476 Winsorized Mean ( 18 / 88 ) 394.314393939394 4.26553892826317 92.4418697310893 Winsorized Mean ( 19 / 88 ) 394.242424242424 4.21220047737842 93.595360989986 Winsorized Mean ( 20 / 88 ) 394.924242424242 4.12068152138956 95.839545078715 Winsorized Mean ( 21 / 88 ) 394.765151515152 4.08875322995726 96.5490283499643 Winsorized Mean ( 22 / 88 ) 394.431818181818 4.04005960900373 97.6301976591588 Winsorized Mean ( 23 / 88 ) 394.954545454545 3.98473194819293 99.1169671108384 Winsorized Mean ( 24 / 88 ) 395.227272727273 3.92100378802088 100.797472814165 Winsorized Mean ( 25 / 88 ) 395.511363636364 3.85613051736568 102.566902716394 Winsorized Mean ( 26 / 88 ) 395.412878787879 3.84698770242275 102.785064412568 Winsorized Mean ( 27 / 88 ) 395.310606060606 3.83756134709774 103.010889027108 Winsorized Mean ( 28 / 88 ) 395.522727272727 3.81682096041585 103.626219666755 Winsorized Mean ( 29 / 88 ) 395.742424242424 3.79561302803146 104.263111471000 Winsorized Mean ( 30 / 88 ) 395.742424242424 3.73189929033149 106.043168224744 Winsorized Mean ( 31 / 88 ) 395.977272727273 3.68824865293594 107.361870087600 Winsorized Mean ( 32 / 88 ) 395.977272727273 3.66606405958312 108.011553069343 Winsorized Mean ( 33 / 88 ) 395.852272727273 3.63212349739169 108.986457374465 Winsorized Mean ( 34 / 88 ) 395.981060606061 3.57403409146185 110.793867789911 Winsorized Mean ( 35 / 88 ) 396.113636363636 3.46859436812864 114.200045990776 Winsorized Mean ( 36 / 88 ) 396.25 3.45666283782798 114.633685317422 Winsorized Mean ( 37 / 88 ) 396.25 3.43237363773686 115.444890860212 Winsorized Mean ( 38 / 88 ) 396.106060606061 3.39528859615985 116.663443883405 Winsorized Mean ( 39 / 88 ) 396.549242424242 3.35744396844898 118.110457285586 Winsorized Mean ( 40 / 88 ) 396.549242424242 3.35744396844898 118.110457285586 Winsorized Mean ( 41 / 88 ) 396.859848484848 3.33141683814396 119.126446123731 Winsorized Mean ( 42 / 88 ) 396.859848484848 3.30461723653301 120.092531170481 Winsorized Mean ( 43 / 88 ) 396.859848484848 3.30461723653301 120.092531170481 Winsorized Mean ( 44 / 88 ) 396.859848484848 3.27669317256967 121.115962827127 Winsorized Mean ( 45 / 88 ) 397.030303030303 3.26272968013912 121.686545301961 Winsorized Mean ( 46 / 88 ) 397.204545454545 3.24856264701730 122.270859027220 Winsorized Mean ( 47 / 88 ) 397.204545454545 3.21904760197548 123.391945248274 Winsorized Mean ( 48 / 88 ) 397.386363636364 3.17454324862361 125.179067511100 Winsorized Mean ( 49 / 88 ) 397.57196969697 3.15984150696124 125.820225103413 Winsorized Mean ( 50 / 88 ) 397.761363636364 3.14495493290016 126.476013845312 Winsorized Mean ( 51 / 88 ) 397.761363636364 3.08220224901628 129.051026344333 Winsorized Mean ( 52 / 88 ) 396.579545454545 2.95340668336625 134.278678140773 Winsorized Mean ( 53 / 88 ) 396.780303030303 2.93781141957254 135.059827321400 Winsorized Mean ( 54 / 88 ) 396.575757575758 2.92139918144227 135.748568732046 Winsorized Mean ( 55 / 88 ) 396.784090909091 2.87267916610891 138.123357314051 Winsorized Mean ( 56 / 88 ) 396.57196969697 2.85583392832946 138.863806387001 Winsorized Mean ( 57 / 88 ) 396.57196969697 2.82229121385693 140.514192068442 Winsorized Mean ( 58 / 88 ) 396.57196969697 2.78834502266673 142.224856132651 Winsorized Mean ( 59 / 88 ) 396.125 2.7535992718193 143.857170523683 Winsorized Mean ( 60 / 88 ) 395.897727272727 2.73613000432542 144.692586480493 Winsorized Mean ( 61 / 88 ) 396.128787878788 2.71856972410117 145.712204607795 Winsorized Mean ( 62 / 88 ) 396.128787878788 2.68286078297356 147.651637532879 Winsorized Mean ( 63 / 88 ) 395.890151515152 2.6287117038033 150.602346747407 Winsorized Mean ( 64 / 88 ) 395.647727272727 2.61050903865454 151.559608265768 Winsorized Mean ( 65 / 88 ) 395.647727272727 2.57367037858375 153.728981988146 Winsorized Mean ( 66 / 88 ) 395.897727272727 2.55503496854054 154.948066131113 Winsorized Mean ( 67 / 88 ) 395.897727272727 2.55503496854054 154.948066131113 Winsorized Mean ( 68 / 88 ) 395.897727272727 2.47876753505882 159.715552859754 Winsorized Mean ( 69 / 88 ) 396.159090909091 2.42136177184107 163.610037754859 Winsorized Mean ( 70 / 88 ) 396.159090909091 2.38274790684912 166.261436961227 Winsorized Mean ( 71 / 88 ) 396.69696969697 2.34430419307481 169.217361325732 Winsorized Mean ( 72 / 88 ) 396.424242424242 2.28519661875403 173.474894532439 Winsorized Mean ( 73 / 88 ) 396.700757575758 2.26576157979482 175.084952059114 Winsorized Mean ( 74 / 88 ) 396.700757575758 2.26576157979482 175.084952059114 Winsorized Mean ( 75 / 88 ) 396.416666666667 2.24519614517460 176.562153609007 Winsorized Mean ( 76 / 88 ) 396.704545454545 2.22508790108431 178.287134302077 Winsorized Mean ( 77 / 88 ) 396.121212121212 2.18323977352223 181.437337724087 Winsorized Mean ( 78 / 88 ) 395.825757575758 2.16228601900455 183.058926569753 Winsorized Mean ( 79 / 88 ) 395.825757575758 2.16228601900455 183.058926569753 Winsorized Mean ( 80 / 88 ) 395.825757575758 2.16228601900455 183.058926569753 Winsorized Mean ( 81 / 88 ) 396.439393939394 2.11963939359394 187.031527691705 Winsorized Mean ( 82 / 88 ) 396.439393939394 2.11963939359394 187.031527691705 Winsorized Mean ( 83 / 88 ) 396.125 2.09740509205426 188.864326448270 Winsorized Mean ( 84 / 88 ) 396.125 2.09740509205426 188.864326448270 Winsorized Mean ( 85 / 88 ) 396.125 2.09740509205426 188.864326448270 Winsorized Mean ( 86 / 88 ) 396.125 2.05203254898520 193.040310299121 Winsorized Mean ( 87 / 88 ) 396.125 2.05203254898520 193.040310299121 Winsorized Mean ( 88 / 88 ) 396.458333333333 2.02918113278033 195.378483925738 Trimmed Mean ( 1 / 88 ) 383.973282442748 6.96809016560146 55.1045226622157 Trimmed Mean ( 2 / 88 ) 384.257692307692 6.75932924538895 56.848494629822 Trimmed Mean ( 3 / 88 ) 384.802325581395 6.57852585020743 58.4937012247603 Trimmed Mean ( 4 / 88 ) 385.48828125 6.40442551116969 60.1909227576597 Trimmed Mean ( 5 / 88 ) 386.204724409449 6.22153266578101 62.0754957268986 Trimmed Mean ( 6 / 88 ) 386.936507936508 6.02673403919074 64.2033488487017 Trimmed Mean ( 7 / 88 ) 387.744 5.82651137432862 66.5482267327899 Trimmed Mean ( 8 / 88 ) 388.657258064516 5.62288641953087 69.1205955564976 Trimmed Mean ( 9 / 88 ) 389.646341463415 5.41048124589828 72.0169470615591 Trimmed Mean ( 10 / 88 ) 390.663934426230 5.18211230823455 75.3870065311883 Trimmed Mean ( 11 / 88 ) 391.702479338843 4.9342415973637 79.3845359230332 Trimmed Mean ( 12 / 88 ) 392.7625 4.66335207270957 84.2232140906727 Trimmed Mean ( 13 / 88 ) 393.852941176471 4.36578308267754 90.213584531763 Trimmed Mean ( 14 / 88 ) 394.966101694915 4.03395641301595 97.9103543163032 Trimmed Mean ( 15 / 88 ) 395.269230769231 3.94025676699533 100.315602292753 Trimmed Mean ( 16 / 88 ) 395.435344827586 3.86970829657417 102.187378097120 Trimmed Mean ( 17 / 88 ) 395.543478260870 3.81431821588216 103.699653745169 Trimmed Mean ( 18 / 88 ) 395.627192982456 3.76421055158422 105.102301680747 Trimmed Mean ( 19 / 88 ) 395.712389380531 3.72051186809872 106.359663242453 Trimmed Mean ( 20 / 88 ) 395.803571428571 3.67886665160669 107.588452888258 Trimmed Mean ( 21 / 88 ) 395.855855855856 3.64236647594019 108.680951922520 Trimmed Mean ( 22 / 88 ) 395.918181818182 3.60630682067730 109.784941078259 Trimmed Mean ( 23 / 88 ) 396 3.57187584397503 110.866115536453 Trimmed Mean ( 24 / 88 ) 396.055555555556 3.53961015938062 111.892422533011 Trimmed Mean ( 25 / 88 ) 396.098130841121 3.51006919321162 112.846245768364 Trimmed Mean ( 26 / 88 ) 396.127358490566 3.48332970437780 113.720891247452 Trimmed Mean ( 27 / 88 ) 396.161904761905 3.45563456336829 114.642303026323 Trimmed Mean ( 28 / 88 ) 396.201923076923 3.42692439851231 115.614433527895 Trimmed Mean ( 29 / 88 ) 396.233009708738 3.39788213744758 116.611758054204 Trimmed Mean ( 30 / 88 ) 396.254901960784 3.36848345630111 117.635994684655 Trimmed Mean ( 31 / 88 ) 396.277227722772 3.34138899721448 118.596556118765 Trimmed Mean ( 32 / 88 ) 396.29 3.31537904182233 119.530827395885 Trimmed Mean ( 33 / 88 ) 396.30303030303 3.28912313229273 120.488961453621 Trimmed Mean ( 34 / 88 ) 396.321428571429 3.26329647324882 121.448183399918 Trimmed Mean ( 35 / 88 ) 396.335051546392 3.23934906146413 122.350214202373 Trimmed Mean ( 36 / 88 ) 396.34375 3.22009039086284 123.084665922622 Trimmed Mean ( 37 / 88 ) 396.347368421053 3.20022048149258 123.850019307481 Trimmed Mean ( 38 / 88 ) 396.351063829787 3.18039934218069 124.623049241993 Trimmed Mean ( 39 / 88 ) 396.360215053763 3.1613349274373 125.377482662069 Trimmed Mean ( 40 / 88 ) 396.353260869565 3.14307985885707 126.103464966905 Trimmed Mean ( 41 / 88 ) 396.346153846154 3.12351326935219 126.891138172867 Trimmed Mean ( 42 / 88 ) 396.327777777778 3.10400944691335 127.682529501284 Trimmed Mean ( 43 / 88 ) 396.308988764045 3.08460034231674 128.479849829230 Trimmed Mean ( 44 / 88 ) 396.289772727273 3.06376085410291 129.347488788680 Trimmed Mean ( 45 / 88 ) 396.270114942529 3.04296905018974 130.224825953396 Trimmed Mean ( 46 / 88 ) 396.244186046512 3.02140998061822 131.145454800356 Trimmed Mean ( 47 / 88 ) 396.211764705882 2.99902743529061 132.113417851241 Trimmed Mean ( 48 / 88 ) 396.178571428571 2.97663797605187 133.095987693489 Trimmed Mean ( 49 / 88 ) 396.138554216867 2.95505960408751 134.05433638933 Trimmed Mean ( 50 / 88 ) 396.091463414634 2.93261665798578 135.064179744066 Trimmed Mean ( 51 / 88 ) 396.037037037037 2.90924095161755 136.130710251703 Trimmed Mean ( 52 / 88 ) 395.98125 2.88762911000797 137.130232074336 Trimmed Mean ( 53 / 88 ) 395.962025316456 2.87165158345051 137.886513669836 Trimmed Mean ( 54 / 88 ) 395.935897435897 2.85512728588854 138.675392649851 Trimmed Mean ( 55 / 88 ) 395.915584415584 2.83807331760007 139.501535059136 Trimmed Mean ( 56 / 88 ) 395.888157894737 2.82223639012238 140.274627341748 Trimmed Mean ( 57 / 88 ) 395.866666666667 2.80590088762960 141.083624304738 Trimmed Mean ( 58 / 88 ) 395.844594594595 2.78995507383424 141.882067674511 Trimmed Mean ( 59 / 88 ) 395.821917808219 2.77443087707858 142.667788582649 Trimmed Mean ( 60 / 88 ) 395.8125 2.75940450172804 143.441275011376 Trimmed Mean ( 61 / 88 ) 395.80985915493 2.74388620522084 144.251557663658 Trimmed Mean ( 62 / 88 ) 395.8 2.72782472846574 145.097298909896 Trimmed Mean ( 63 / 88 ) 395.789855072464 2.71223729286734 145.927443779833 Trimmed Mean ( 64 / 88 ) 395.786764705882 2.69822842081965 146.683935893631 Trimmed Mean ( 65 / 88 ) 395.791044776119 2.68375986445971 147.476326037017 Trimmed Mean ( 66 / 88 ) 395.795454545455 2.66989753321848 148.243687115713 Trimmed Mean ( 67 / 88 ) 395.792307692308 2.65557538746367 149.042015361623 Trimmed Mean ( 68 / 88 ) 395.7890625 2.63959786944353 149.942939067245 Trimmed Mean ( 69 / 88 ) 395.785714285714 2.62654597602485 150.686764251779 Trimmed Mean ( 70 / 88 ) 395.774193548387 2.61541454110629 151.323695470844 Trimmed Mean ( 71 / 88 ) 395.762295081967 2.60516649453697 151.914396224533 Trimmed Mean ( 72 / 88 ) 395.733333333333 2.59580391128548 152.451166135026 Trimmed Mean ( 73 / 88 ) 395.71186440678 2.58871568370294 152.860303237607 Trimmed Mean ( 74 / 88 ) 395.681034482759 2.58152236903319 153.274300168449 Trimmed Mean ( 75 / 88 ) 395.649122807018 2.57298320626961 153.770581107151 Trimmed Mean ( 76 / 88 ) 395.625 2.5643307723785 154.280018888922 Trimmed Mean ( 77 / 88 ) 395.590909090909 2.55544996289335 154.802838965788 Trimmed Mean ( 78 / 88 ) 395.574074074074 2.54785298084376 155.257810026022 Trimmed Mean ( 79 / 88 ) 395.566037735849 2.54017207883703 155.724110595276 Trimmed Mean ( 80 / 88 ) 395.557692307692 2.53090778135762 156.290835731482 Trimmed Mean ( 81 / 88 ) 395.549019607843 2.51987354006638 156.971773907917 Trimmed Mean ( 82 / 88 ) 395.52 2.50995915295348 157.580253660539 Trimmed Mean ( 83 / 88 ) 395.489795918367 2.49810552658539 158.315888464069 Trimmed Mean ( 84 / 88 ) 395.46875 2.48583500765980 159.088897204123 Trimmed Mean ( 85 / 88 ) 395.446808510638 2.47127709555588 160.017186750030 Trimmed Mean ( 86 / 88 ) 395.423913043478 2.45412928031975 161.125950541594 Trimmed Mean ( 87 / 88 ) 395.4 2.43793367408774 162.186528781574 Trimmed Mean ( 88 / 88 ) 395.375 2.41881300392618 163.458274516564 Median 395 Midrange 349 Midmean - Weighted Average at Xnp 395.791044776119 Midmean - Weighted Average at X(n+1)p 396.172932330827 Midmean - Empirical Distribution Function 395.791044776119 Midmean - Empirical Distribution Function - Averaging 396.172932330827 Midmean - Empirical Distribution Function - Interpolation 396.172932330827 Midmean - Closest Observation 395.791044776119 Midmean - True Basic - Statistics Graphics Toolkit 396.172932330827 Midmean - MS Excel (old versions) 395.791044776119 Number of observations 264

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243870084rlyu7wqzhnquduj/1kx621243869990.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243870084rlyu7wqzhnquduj/1kx621243869990.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243870084rlyu7wqzhnquduj/287pd1243869990.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243870084rlyu7wqzhnquduj/287pd1243869990.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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