Home » date » 2007 » Oct » 21 » attachments

workshop2Q8

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

Title produced by software: Central Tendency

Date of computation: Sun, 21 Oct 2007 10:03:11 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Oct/21/w6xciup0dowzg941192985923.htm/, Retrieved Sun, 21 Oct 2007 18:58:45 +0200

User-defined keywords:

werkloosheid boven 25

Dataseries X:

» Textbox « » Textfile « » CSV «

358 361 360 361 360 362 375 377 380 382 382 382 384 383 381 378 379 377 391 397 393 393 394 392 405 399 384 369 360 347 352 351 348 346 344 347 351 346 342 338 336 334 338 338 337 344 341 348 352 345 343 339 336 334 344 340 338 340 338 341 342 337 332 326 321 316 327 324 316 315 313 316 316 313 312 303 299 295 306 309 300 299 298 301 302 298 294 289 285 284 298 300 295 297 299 306 309 310 305 303 301 301 316 322 317 322 323 331 336 334 334 335 332 336 353 360 357 360 365 377 384 388 388 390 391 395 415 425 425 423 427 434 439 438 435 434 434 435 449 456 452 448 445 452 457 454 446 444 440 441 458 466 464 464 457 465 472 461 451 445 435 426 444 451 440 432 429 434 438 435 431 427 420 424 440 447 436 427 421 427 427 424 417 410 402 400 417 424 414 398 390 395 398 393 386 378 374 373 392 402 387 379 370 373 377 370 358 357 349 348 369 381 368 361 351 351 358 354 347 345 343 340 362 370 373 371 354 357 363 364 363 358 357 357 380 378 376 380 379 384 392 394 392 396 392 396 419 421 420 418 410 418 426 428 430 424 423 427 441 449 452 462 455 461 461 463 462 456 455 456 472 472 471 465 459 465 468 467 463 460 462 461 476 476 471 453 443 442 444 438 427 424 416 406 431 434 418

Text written by user:

Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 385.350168350168 2.99109452332891 128.832494374433 Geometric Mean 381.859995432486 Harmonic Mean 378.333368340314 Quadratic Mean 388.771088940471 Winsorized Mean ( 1 / 99 ) 385.353535353535 2.99071096322667 128.850143023443 Winsorized Mean ( 2 / 99 ) 385.353535353535 2.98501558313020 129.095987817068 Winsorized Mean ( 3 / 99 ) 385.404040404040 2.97964458671338 129.345641464290 Winsorized Mean ( 4 / 99 ) 385.417508417508 2.97825602901867 129.410468630698 Winsorized Mean ( 5 / 99 ) 385.400673400673 2.9766115170919 129.476309282443 Winsorized Mean ( 6 / 99 ) 385.441077441077 2.97250806563789 129.668639724402 Winsorized Mean ( 7 / 99 ) 385.393939393939 2.96338199909191 130.052061972449 Winsorized Mean ( 8 / 99 ) 385.367003367003 2.96085918330937 130.153776153676 Winsorized Mean ( 9 / 99 ) 385.336700336700 2.95805206232734 130.267044736706 Winsorized Mean ( 10 / 99 ) 385.336700336700 2.95162319164328 130.550776748088 Winsorized Mean ( 11 / 99 ) 385.336700336700 2.95162319164328 130.550776748088 Winsorized Mean ( 12 / 99 ) 385.336700336700 2.95162319164328 130.550776748088 Winsorized Mean ( 13 / 99 ) 385.336700336700 2.94334514908593 130.917945677002 Winsorized Mean ( 14 / 99 ) 385.336700336700 2.94334514908593 130.917945677002 Winsorized Mean ( 15 / 99 ) 385.336700336700 2.93388085324266 131.340268951552 Winsorized Mean ( 16 / 99 ) 385.336700336700 2.93388085324266 131.340268951552 Winsorized Mean ( 17 / 99 ) 385.279461279461 2.92878863293724 131.549083790683 Winsorized Mean ( 18 / 99 ) 385.340067340067 2.92292366024455 131.833777454122 Winsorized Mean ( 19 / 99 ) 385.404040404040 2.91678954374664 132.132961471394 Winsorized Mean ( 20 / 99 ) 385.336700336700 2.91084562050089 132.379641717444 Winsorized Mean ( 21 / 99 ) 385.478114478114 2.89745360115164 133.040306262333 Winsorized Mean ( 22 / 99 ) 385.552188552189 2.89053450895256 133.384392180082 Winsorized Mean ( 23 / 99 ) 385.552188552189 2.89053450895256 133.384392180082 Winsorized Mean ( 24 / 99 ) 385.713804713805 2.86117639202182 134.809516040094 Winsorized Mean ( 25 / 99 ) 385.629629629630 2.85382908885843 135.127093327052 Winsorized Mean ( 26 / 99 ) 385.629629629630 2.83834579998297 135.864216978757 Winsorized Mean ( 27 / 99 ) 385.720538720539 2.81431162968309 137.056797354021 Winsorized Mean ( 28 / 99 ) 385.814814814815 2.80600755383818 137.496000068525 Winsorized Mean ( 29 / 99 ) 385.717171717172 2.79767967869578 137.870384038027 Winsorized Mean ( 30 / 99 ) 385.919191919192 2.78010425494686 138.814647412052 Winsorized Mean ( 31 / 99 ) 386.023569023569 2.77115132708342 139.300789982426 Winsorized Mean ( 32 / 99 ) 385.915824915825 2.76200318000758 139.723164589103 Winsorized Mean ( 33 / 99 ) 385.915824915825 2.76200318000758 139.723164589103 Winsorized Mean ( 34 / 99 ) 385.801346801347 2.75237486172204 140.170349673942 Winsorized Mean ( 35 / 99 ) 385.683501683502 2.74255635416858 140.629198410920 Winsorized Mean ( 36 / 99 ) 385.683501683502 2.72217399230952 141.682163878248 Winsorized Mean ( 37 / 99 ) 386.181818181818 2.68046054868386 144.072934918381 Winsorized Mean ( 38 / 99 ) 386.309764309764 2.66999924354779 144.685345976522 Winsorized Mean ( 39 / 99 ) 386.178451178451 2.65913474879394 145.227108687743 Winsorized Mean ( 40 / 99 ) 386.313131313131 2.64820484181285 145.8773600945 Winsorized Mean ( 41 / 99 ) 386.175084175084 2.61448417919060 147.706032130069 Winsorized Mean ( 42 / 99 ) 386.457912457912 2.59197831352409 149.097664298155 Winsorized Mean ( 43 / 99 ) 386.457912457912 2.56885284873084 150.439879282632 Winsorized Mean ( 44 / 99 ) 386.902356902357 2.51140851359469 154.057914038273 Winsorized Mean ( 45 / 99 ) 386.902356902357 2.48785878908440 155.516204778145 Winsorized Mean ( 46 / 99 ) 386.747474747475 2.47548738987533 156.230840168793 Winsorized Mean ( 47 / 99 ) 387.063973063973 2.45209304708893 157.850442716065 Winsorized Mean ( 48 / 99 ) 386.902356902357 2.43925231678378 158.615143763601 Winsorized Mean ( 49 / 99 ) 386.902356902357 2.43925231678378 158.615143763601 Winsorized Mean ( 50 / 99 ) 386.902356902357 2.43925231678378 158.615143763601 Winsorized Mean ( 51 / 99 ) 386.902356902357 2.41318969177055 160.328198906936 Winsorized Mean ( 52 / 99 ) 386.902356902357 2.38681866967026 162.099602210589 Winsorized Mean ( 53 / 99 ) 386.723905723906 2.37296536089943 162.970733621381 Winsorized Mean ( 54 / 99 ) 386.723905723906 2.37296536089943 162.970733621381 Winsorized Mean ( 55 / 99 ) 386.538720538721 2.35872027637464 163.876456403229 Winsorized Mean ( 56 / 99 ) 386.727272727273 2.34514217918678 164.905682972866 Winsorized Mean ( 57 / 99 ) 386.727272727273 2.34514217918678 164.905682972866 Winsorized Mean ( 58 / 99 ) 386.727272727273 2.31626913505572 166.961285661637 Winsorized Mean ( 59 / 99 ) 386.528619528620 2.3011904249924 167.968984804852 Winsorized Mean ( 60 / 99 ) 386.528619528620 2.3011904249924 167.968984804852 Winsorized Mean ( 61 / 99 ) 386.528619528620 2.3011904249924 167.968984804852 Winsorized Mean ( 62 / 99 ) 386.111111111111 2.26991378011924 170.099461262721 Winsorized Mean ( 63 / 99 ) 386.111111111111 2.23908087842977 172.441788427886 Winsorized Mean ( 64 / 99 ) 386.326599326599 2.22383912613491 173.720569436174 Winsorized Mean ( 65 / 99 ) 386.326599326599 2.22383912613491 173.720569436174 Winsorized Mean ( 66 / 99 ) 386.326599326599 2.22383912613491 173.720569436174 Winsorized Mean ( 67 / 99 ) 386.326599326599 2.19138787430451 176.293117186847 Winsorized Mean ( 68 / 99 ) 386.326599326599 2.19138787430451 176.293117186847 Winsorized Mean ( 69 / 99 ) 386.558922558923 2.17523144656148 177.709329814066 Winsorized Mean ( 70 / 99 ) 386.558922558923 2.17523144656148 177.709329814066 Winsorized Mean ( 71 / 99 ) 386.79797979798 2.15876643745558 179.175464787139 Winsorized Mean ( 72 / 99 ) 386.313131313131 2.12323347263849 181.9456674414 Winsorized Mean ( 73 / 99 ) 386.313131313131 2.08853399138878 184.968563071483 Winsorized Mean ( 74 / 99 ) 386.313131313131 2.08853399138878 184.968563071483 Winsorized Mean ( 75 / 99 ) 386.060606060606 2.07035380485413 186.470836605537 Winsorized Mean ( 76 / 99 ) 386.060606060606 2.03455148136114 189.752193344514 Winsorized Mean ( 77 / 99 ) 385.801346801347 2.01614219952399 191.356218272915 Winsorized Mean ( 78 / 99 ) 385.801346801347 1.97972731031127 194.876003776848 Winsorized Mean ( 79 / 99 ) 385.801346801347 1.97972731031127 194.876003776848 Winsorized Mean ( 80 / 99 ) 386.070707070707 1.96151639870664 196.822574272267 Winsorized Mean ( 81 / 99 ) 386.070707070707 1.96151639870664 196.822574272267 Winsorized Mean ( 82 / 99 ) 386.070707070707 1.96151639870664 196.822574272267 Winsorized Mean ( 83 / 99 ) 386.350168350168 1.94279477178967 198.863088350948 Winsorized Mean ( 84 / 99 ) 386.350168350168 1.94279477178967 198.863088350948 Winsorized Mean ( 85 / 99 ) 386.063973063973 1.92263751012939 200.799147540813 Winsorized Mean ( 86 / 99 ) 386.353535353535 1.90335434009799 202.985606628372 Winsorized Mean ( 87 / 99 ) 386.646464646465 1.84420213704833 209.65514402087 Winsorized Mean ( 88 / 99 ) 386.646464646465 1.84420213704833 209.65514402087 Winsorized Mean ( 89 / 99 ) 386.346801346801 1.82322081164246 211.903461654082 Winsorized Mean ( 90 / 99 ) 386.346801346801 1.82322081164246 211.903461654082 Winsorized Mean ( 91 / 99 ) 386.653198653199 1.80324022402656 214.421347472950 Winsorized Mean ( 92 / 99 ) 386.653198653199 1.80324022402656 214.421347472950 Winsorized Mean ( 93 / 99 ) 386.96632996633 1.78299869577014 217.031190703808 Winsorized Mean ( 94 / 99 ) 386.96632996633 1.74051406182854 222.328758182967 Winsorized Mean ( 95 / 99 ) 386.96632996633 1.74051406182854 222.328758182967 Winsorized Mean ( 96 / 99 ) 387.289562289562 1.63396493399695 237.024402563027 Winsorized Mean ( 97 / 99 ) 387.289562289562 1.63396493399695 237.024402563027 Winsorized Mean ( 98 / 99 ) 386.959595959596 1.61103408724934 240.19330132256 Winsorized Mean ( 99 / 99 ) 386.959595959596 1.61103408724934 240.19330132256 Trimmed Mean ( 1 / 99 ) 385.386440677966 2.97580067651582 129.50680592263 Trimmed Mean ( 2 / 99 ) 385.419795221843 2.96026043828851 130.197934694110 Trimmed Mean ( 3 / 99 ) 385.453608247423 2.94707055118222 130.792121041282 Trimmed Mean ( 4 / 99 ) 385.470588235294 2.93523091867469 131.325472821519 Trimmed Mean ( 5 / 99 ) 385.484320557491 2.92324360541678 131.868695391375 Trimmed Mean ( 6 / 99 ) 385.501754385965 2.91108505945979 132.425451854541 Trimmed Mean ( 7 / 99 ) 385.512367491166 2.89915352819299 132.974112527063 Trimmed Mean ( 8 / 99 ) 385.53024911032 2.88817600951782 133.485718266417 Trimmed Mean ( 9 / 99 ) 385.551971326165 2.87705448607916 134.009269964016 Trimmed Mean ( 10 / 99 ) 385.57761732852 2.86578142049382 134.545368523633 Trimmed Mean ( 11 / 99 ) 385.603636363636 2.85475376566116 135.074219360678 Trimmed Mean ( 12 / 99 ) 385.630036630037 2.84320000630479 135.632398626513 Trimmed Mean ( 13 / 99 ) 385.656826568266 2.83109572031714 136.221754637481 Trimmed Mean ( 14 / 99 ) 385.684014869888 2.81922694666777 136.80488380893 Trimmed Mean ( 15 / 99 ) 385.711610486891 2.8067862353316 137.421085236768 Trimmed Mean ( 16 / 99 ) 385.739622641509 2.79458001204375 138.031339585589 Trimmed Mean ( 17 / 99 ) 385.768060836502 2.78177883684125 138.676754502362 Trimmed Mean ( 18 / 99 ) 385.800766283525 2.76876189623808 139.340535857458 Trimmed Mean ( 19 / 99 ) 385.830115830116 2.75556452834461 140.018537712089 Trimmed Mean ( 20 / 99 ) 385.856031128405 2.74217823664213 140.711506630910 Trimmed Mean ( 21 / 99 ) 385.886274509804 2.72855570613501 141.425103999951 Trimmed Mean ( 22 / 99 ) 385.909090909091 2.71519955642342 142.129181627235 Trimmed Mean ( 23 / 99 ) 385.92828685259 2.70164391201067 142.849427763915 Trimmed Mean ( 24 / 99 ) 385.947791164659 2.68740013141611 143.613817180728 Trimmed Mean ( 25 / 99 ) 385.959514170041 2.67434010168040 144.319532855050 Trimmed Mean ( 26 / 99 ) 385.975510204082 2.66106410652019 145.045551235822 Trimmed Mean ( 27 / 99 ) 385.991769547325 2.64805123598816 145.764464184654 Trimmed Mean ( 28 / 99 ) 386.004149377593 2.63580318584017 146.446499287675 Trimmed Mean ( 29 / 99 ) 386.012552301255 2.62338691320325 147.142821502422 Trimmed Mean ( 30 / 99 ) 386.025316455696 2.6107789869656 147.858289952133 Trimmed Mean ( 31 / 99 ) 386.029787234043 2.59849016651389 148.559264225324 Trimmed Mean ( 32 / 99 ) 386.030042918455 2.58602472118562 149.275465062635 Trimmed Mean ( 33 / 99 ) 386.034632034632 2.57337173824796 150.011219248665 Trimmed Mean ( 34 / 99 ) 386.039301310044 2.56000779231875 150.796143069699 Trimmed Mean ( 35 / 99 ) 386.04845814978 2.54641703899212 151.604569180302 Trimmed Mean ( 36 / 99 ) 386.062222222222 2.53258576410698 152.437965850429 Trimmed Mean ( 37 / 99 ) 386.076233183857 2.51905718350415 153.262194964071 Trimmed Mean ( 38 / 99 ) 386.072398190045 2.50694978735137 154.000850012212 Trimmed Mean ( 39 / 99 ) 386.063926940639 2.49466400936115 154.755881149504 Trimmed Mean ( 40 / 99 ) 386.059907834101 2.48220420416496 155.531082892504 Trimmed Mean ( 41 / 99 ) 386.051162790698 2.46955131628093 156.324414174182 Trimmed Mean ( 42 / 99 ) 386.046948356807 2.45784519018735 157.067235112306 Trimmed Mean ( 43 / 99 ) 386.03317535545 2.44653397658601 157.787784289894 Trimmed Mean ( 44 / 99 ) 386.019138755981 2.43565484150022 158.486798777374 Trimmed Mean ( 45 / 99 ) 385.990338164251 2.42686748127874 159.048790732022 Trimmed Mean ( 46 / 99 ) 385.960975609756 2.41860163208654 159.58021796123 Trimmed Mean ( 47 / 99 ) 385.935960591133 2.41034006836711 160.116809099301 Trimmed Mean ( 48 / 99 ) 385.900497512438 2.4025691573764 160.619933177632 Trimmed Mean ( 49 / 99 ) 385.869346733668 2.39482909854225 161.126047352836 Trimmed Mean ( 50 / 99 ) 385.837563451777 2.38649296015670 161.675550648363 Trimmed Mean ( 51 / 99 ) 385.805128205128 2.37752381239467 162.271825078606 Trimmed Mean ( 52 / 99 ) 385.772020725389 2.36912376923106 162.833206831823 Trimmed Mean ( 53 / 99 ) 385.738219895288 2.36131978146851 163.357044193056 Trimmed Mean ( 54 / 99 ) 385.708994708995 2.3535513482409 163.883823906065 Trimmed Mean ( 55 / 99 ) 385.679144385027 2.34514504218958 164.458546250483 Trimmed Mean ( 56 / 99 ) 385.654054054054 2.33674889564042 165.038723148026 Trimmed Mean ( 57 / 99 ) 385.654054054054 2.32829867230492 165.637707327416 Trimmed Mean ( 58 / 99 ) 385.591160220994 2.31914752399907 166.264179501653 Trimmed Mean ( 59 / 99 ) 385.558659217877 2.31063384192189 166.862724947015 Trimmed Mean ( 60 / 99 ) 385.531073446328 2.30213439557659 167.466796980707 Trimmed Mean ( 61 / 99 ) 385.502857142857 2.29290150809556 168.128834047935 Trimmed Mean ( 62 / 99 ) 385.473988439306 2.28288379911168 168.853968208677 Trimmed Mean ( 63 / 99 ) 385.456140350877 2.27358578696118 169.536659914676 Trimmed Mean ( 64 / 99 ) 385.437869822485 2.26498638913207 170.172267556179 Trimmed Mean ( 65 / 99 ) 385.413173652695 2.25633874545278 170.813524533681 Trimmed Mean ( 66 / 99 ) 385.387878787879 2.24689303227949 171.520349768008 Trimmed Mean ( 67 / 99 ) 385.361963190184 2.23658992127392 172.298891059426 Trimmed Mean ( 68 / 99 ) 385.335403726708 2.22700367070435 173.028634301638 Trimmed Mean ( 69 / 99 ) 385.308176100629 2.21652444817815 173.834390330019 Trimmed Mean ( 70 / 99 ) 385.273885350318 2.2058933185354 174.656626461936 Trimmed Mean ( 71 / 99 ) 385.238709677419 2.19427376557058 175.565472149390 Trimmed Mean ( 72 / 99 ) 385.196078431373 2.18242776624930 176.498890083940 Trimmed Mean ( 73 / 99 ) 385.165562913907 2.17142653517607 177.379043994540 Trimmed Mean ( 74 / 99 ) 385.134228187919 2.16121211644597 178.202882196153 Trimmed Mean ( 75 / 99 ) 385.102040816327 2.14997154917813 179.119598565636 Trimmed Mean ( 76 / 99 ) 385.075862068966 2.1386357138005 180.056780864591 Trimmed Mean ( 77 / 99 ) 385.048951048951 2.12813065478837 180.932947036207 Trimmed Mean ( 78 / 99 ) 385.028368794326 2.11756640114627 181.825877377873 Trimmed Mean ( 79 / 99 ) 385.007194244604 2.10790886506748 182.648880425995 Trimmed Mean ( 80 / 99 ) 384.985401459854 2.09717206698913 183.573588223770 Trimmed Mean ( 81 / 99 ) 384.955555555556 2.08627796322049 184.51786499308 Trimmed Mean ( 82 / 99 ) 384.924812030075 2.07417213060011 185.579974945814 Trimmed Mean ( 83 / 99 ) 384.893129770992 2.06074093215936 186.774146989782 Trimmed Mean ( 84 / 99 ) 384.852713178295 2.04696258171684 188.011601489808 Trimmed Mean ( 85 / 99 ) 384.852713178295 2.03167432746035 189.426379994362 Trimmed Mean ( 86 / 99 ) 384.776 2.01606016295277 190.855415463618 Trimmed Mean ( 87 / 99 ) 384.731707317073 1.999935797008 192.372029088459 Trimmed Mean ( 88 / 99 ) 384.677685950413 1.98583880716654 193.710428339994 Trimmed Mean ( 89 / 99 ) 384.621848739496 1.97007926375072 195.231661901378 Trimmed Mean ( 90 / 99 ) 384.572649572650 1.9539842027411 196.814615508744 Trimmed Mean ( 91 / 99 ) 384.521739130435 1.93598339480774 198.618304352048 Trimmed Mean ( 92 / 99 ) 384.460176991150 1.91721999829028 200.530026462273 Trimmed Mean ( 93 / 99 ) 384.396396396396 1.89621284184268 202.717958614209 Trimmed Mean ( 94 / 99 ) 384.321100917431 1.87413501325303 205.065856088108 Trimmed Mean ( 95 / 99 ) 384.242990654206 1.85270709024543 207.395433782954 Trimmed Mean ( 96 / 99 ) 384.161904761905 1.82860481501878 210.084705895276 Trimmed Mean ( 97 / 99 ) 384.067961165049 1.80999520588612 212.192805768798 Trimmed Mean ( 98 / 99 ) 383.970297029703 1.78887958992117 214.642896700846 Trimmed Mean ( 99 / 99 ) 383.878787878788 1.76706117619829 217.241368351874 Median 382 Midrange 380 Midmean - Weighted Average at Xnp 384.55033557047 Midmean - Weighted Average at X(n+1)p 385.165562913907 Midmean - Empirical Distribution Function 385.165562913907 Midmean - Empirical Distribution Function - Averaging 385.165562913907 Midmean - Empirical Distribution Function - Interpolation 385.165562913907 Midmean - Closest Observation 385.165562913907 Midmean - True Basic - Statistics Graphics Toolkit 385.165562913907 Midmean - MS Excel (old versions) 385.165562913907 Number of observations 297

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/w6xciup0dowzg941192985923/1r9c81192986188.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/w6xciup0dowzg941192985923/1r9c81192986188.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/w6xciup0dowzg941192985923/277k81192986188.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/w6xciup0dowzg941192985923/277k81192986188.ps (open in new window)

Parameters:

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by