*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 28 Dec 2010 11:42:46 +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/Dec/28/t1293536435uvar110iaqq80ur.htm/, Retrieved Tue, 28 Dec 2010 12:40:35 +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/Dec/28/t1293536435uvar110iaqq80ur.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

9 14 11 12 24 26 9 11 7 8 25 23 9 6 17 8 30 25 9 12 10 8 19 23 9 8 12 9 22 19 9 10 12 7 22 29 10 10 11 4 25 25 10 11 11 11 23 21 10 16 12 7 17 22 10 11 13 7 21 25 10 13 14 12 19 24 10 12 16 10 19 18 10 8 11 10 15 22 10 12 10 8 16 15 10 11 11 8 23 22 10 4 15 4 27 28 10 9 9 9 22 20 10 8 11 8 14 12 10 8 17 7 22 24 10 14 17 11 23 20 10 15 11 9 23 21 10 16 18 11 21 20 10 9 14 13 19 21 10 14 10 8 18 23 10 11 11 8 20 28 10 8 15 9 23 24 10 9 15 6 25 24 10 9 13 9 19 24 10 9 16 9 24 23 10 9 13 6 22 23 10 10 9 6 25 29 10 16 18 16 26 24 10 11 18 5 29 18 10 8 12 7 32 25 10 9 17 9 25 21 10 16 9 6 29 26 10 11 9 6 28 22 10 16 12 5 17 22 10 12 18 12 28 22 10 12 12 7 29 23 10 14 18 10 26 30 10 9 14 9 25 23 10 10 15 8 14 17 10 9 16 5 25 23 10 10 10 8 26 23 10 12 11 8 20 25 10 14 14 10 18 24 10 14 9 6 32 24 10 10 12 8 25 23 10 14 17 7 25 21 10 16 5 4 23 24 10 9 12 8 21 24 10 10 12 8 20 28 10 6 6 4 15 16 10 8 24 20 30 20 10 13 12 8 24 29 10 10 12 8 26 27 10 8 14 6 24 22 10 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation PersonalStandards[t] = + 19.3022138771629 -1.09313336363416Maand[t] -0.113100006049103DoubtsAboutActions[t] + 0.339616385684055ParentalExpectations[t] + 0.0926111824644223ParentalCriticism[t] + 0.440453149254567`Organization `[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 19.3022138771629 16.145395 1.1955 0.233732 0.116866 Maand -1.09313336363416 1.594907 -0.6854 0.494135 0.247068 DoubtsAboutActions -0.113100006049103 0.109722 -1.0308 0.304267 0.152134 ParentalExpectations 0.339616385684055 0.109455 3.1028 0.002284 0.001142 ParentalCriticism 0.0926111824644223 0.142598 0.6495 0.517016 0.258508 `Organization ` 0.440453149254567 0.079412 5.5465 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.474394730418847 R-squared 0.22505036024917 Adjusted R-squared 0.199725208623326 F-TEST (value) 8.88643683457784 F-TEST (DF numerator) 5 F-TEST (DF denominator) 153 p-value 1.99000035228103e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.77241020116043 Sum Squared Residuals 2177.35504505035 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 24.1795098324845 -0.179509832484548 2 25 21.4685401302742 3.53145986972578 3 30 26.3111103158694 3.68888968413058 4 19 22.3742892812773 -3.37428928127728 5 22 21.8367206622880 0.163279337712045 6 22 25.8298297778066 -3.82982977780658 7 25 22.3574338840768 2.64256611592317 8 23 21.1307995582604 1.86920044173959 9 17 20.9749243330958 -3.97492433309583 10 21 23.2014001967891 -2.20140019678911 11 19 23.3374193334425 -4.3374193334425 12 19 21.3018108504035 -2.30181085040347 13 15 21.8179415431979 -6.81794154319787 14 16 17.7575307236066 -1.75753072360659 15 23 21.2934191601217 1.70658083987829 16 27 25.7158589108714 1.28414108912863 17 22 20.0520912848071 1.94790871519290 18 14 17.2281876857233 -3.22818768572335 19 22 24.4587126084181 -2.45871260841807 20 23 22.3887447049629 0.611255295037128 21 23 20.4931771691352 2.50682283086484 22 21 22.5021610785487 -1.50216107854872 23 19 22.5610710923396 -3.56107109233963 24 18 21.0549559055449 -3.05495590554492 25 20 23.9361380556491 -3.93613805564912 26 23 23.9647022019788 -0.964702201978801 27 25 23.5737686485364 1.42623135146357 28 19 23.1723694245616 -4.17236942456159 29 24 23.7507654323592 0.249234567640813 30 22 22.4540827279138 -0.454082727913754 31 25 23.6252360746558 1.37476392534417 32 26 24.7270295878891 1.27297041211090 33 29 21.6310877154986 7.36891228450143 34 32 23.2010838292524 8.79891617074764 35 25 23.2094755195341 1.79052448046589 36 29 21.6252765905975 7.37472340940249 37 28 20.4289640238248 7.57103597617524 38 17 20.789701968167 -3.78970196816699 39 28 23.9280785837187 4.07192141628131 40 29 21.8677775065468 7.13222249345319 41 26 27.0402814007282 -1.04028140072818 42 25 23.0715326609911 1.92846733900892 43 14 20.5627189626342 -6.5627189626342 44 25 23.3803207025015 1.61967929749850 45 26 21.5073559297413 4.49264407025867 46 20 22.5016786018363 -2.50167860183631 47 18 23.0390969624646 -5.03909696246455 48 32 20.9705703041866 11.0294296958134 49 25 22.1865887011094 2.81341129889056 50 25 22.4587531243598 2.54124687564025 51 23 19.2006823844233 3.79931761557669 52 21 22.7401418564131 -1.74014185641311 53 20 24.3888544473823 -4.38885444738228 54 15 17.1476736365619 -2.14767363656186 55 30 26.2781600832257 3.72183991677432 56 24 24.4900075784895 -0.490007578489536 57 26 23.9484012981277 2.05159870187229 58 24 22.4663459703923 1.53365402960766 59 22 22.6596278072516 -0.659627807251618 60 14 18.877533011766 -4.87753301176601 61 24 23.2732061881321 0.7267938118679 62 24 22.7814358711192 1.21856412888078 63 24 24.7492760241878 -0.74927602418776 64 24 21.0218874719449 2.9781125280551 65 19 21.5691387680321 -2.56913876803212 66 31 23.8599780353969 7.14002196460313 67 22 22.5136254767782 -0.513625476778157 68 27 21.7583987943335 5.24160120566654 69 19 21.8637398451743 -2.86373984517431 70 25 22.6147786078854 2.38522139211458 71 20 21.8797243814887 -1.87972438148866 72 21 21.1882283676420 -0.188228367641955 73 27 24.7325084944388 2.26749150556117 74 23 23.9366205323615 -0.936620532361526 75 25 24.014221797791 0.985778202209001 76 20 21.3056824026003 -1.30568240260031 77 21 22.7694889961774 -1.76948899617738 78 22 23.1726857920983 -1.17268579209834 79 23 23.9238748131705 -0.923874813170527 80 25 21.787429566561 3.21257043343902 81 25 24.0208400245442 0.979159975455801 82 17 23.6207317873855 -6.6207317873855 83 19 21.8595519254407 -2.85955192544072 84 25 24.3969297701273 0.603070229872721 85 19 21.8597021838018 -2.85970218380181 86 20 23.7797962045867 -3.77979620458670 87 26 22.0095919172867 3.99040808271331 88 23 23.3580584153883 -0.358058415388266 89 27 23.4340681772794 3.56593182272058 90 17 21.2130712201359 -4.21307122013588 91 17 22.8538745975357 -5.85387459753571 92 19 21.6488539729445 -2.64885397294445 93 17 20.7768059906149 -3.77680599061490 94 22 21.6988560454691 0.30114395453094 95 21 23.9071072834216 -2.9071072834216 96 32 26.9349403498873 5.06505965011267 97 21 23.1607389171565 -2.16073891715649 98 21 23.374525428415 -2.37452542841502 99 18 21.326171226185 -3.32617122618499 100 18 20.7643924897752 -2.76439248977523 101 23 22.5097539245813 0.490246075418683 102 19 21.4597600558188 -2.45976005581877 103 20 21.7790537270938 -1.77905372709380 104 21 23.1764070859341 -2.17640708593409 105 20 24.2225135516852 -4.22251355168522 106 17 17.5931135497992 -0.593113549799169 107 18 18.9982258638477 -0.998225863847709 108 19 19.3009022637349 -0.300902263734905 109 22 22.1499650828493 -0.149965082849328 110 15 20.6680600134750 -5.66806001347505 111 14 20.5583649337250 -6.55836493372495 112 18 24.5821521350561 -6.58215213505612 113 24 20.3504136557850 3.64958634421496 114 35 22.1987016852269 12.8012983147731 115 29 21.5583567299629 7.4416432700371 116 21 22.915024700753 -1.915024700753 117 25 20.3729923104350 4.62700768956498 118 20 19.1076045760611 0.892395423938942 119 22 23.2861021656842 -1.28610216568419 120 13 15.9400469299208 -2.94004692992075 121 26 19.5645088875278 6.4354911124722 122 17 18.8977054678139 -1.89770546781394 123 25 20.9294423986561 4.07055760134386 124 20 21.0707743326836 -1.07077433268360 125 19 20.4854182139269 -1.48541821392690 126 21 23.7715706234806 -2.77157062348062 127 22 21.8035643612364 0.196435638763587 128 24 23.05523175714 0.944768242860016 129 21 24.2797921027057 -3.27979210270568 130 26 22.2754785897381 3.72452141026189 131 24 20.4886728818648 3.51132711813518 132 16 21.4187824086494 -5.41878240864941 133 23 23.0035982218449 -0.00359822184492236 134 18 21.0234948262978 -3.02349482629779 135 16 22.1865887011094 -6.18658870110944 136 26 24.3089889841089 1.69101101589115 137 19 20.3407068702696 -1.34070687026959 138 21 17.9880847646140 3.01191523538596 139 21 22.9458530449266 -1.94585304492664 140 22 18.7867194012477 3.21328059875229 141 23 16.8438723590342 6.15612764096582 142 29 22.7240070617377 6.27599293826232 143 21 21.1950285543854 -0.195028554385388 144 21 20.0856263443049 0.91437365569505 145 23 23.3827510095211 -0.382751009521109 146 27 21.4236030634565 5.57639693654351 147 25 22.3285692210250 2.67143077897503 148 21 20.5461016912464 0.453898308753636 149 10 17.9877683970773 -7.9877683970773 150 20 21.9558844017409 -1.9558844017409 151 26 21.8384303667825 4.16156963321745 152 24 23.3069073568056 0.693092643194387 153 29 27.6801438792798 1.31985612072018 154 19 17.9487144717976 1.05128552820243 155 24 23.9321162450912 0.0678837549088088 156 19 21.5731764294046 -2.57317642940462 157 24 21.8350254404835 2.16497455951646 158 22 22.6109070556886 -0.610907055688576 159 17 23.3048333765549 -6.30483337655489 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.519481478592316 0.961037042815368 0.480518521407684 10 0.423875242886899 0.847750485773798 0.576124757113101 11 0.362764797226805 0.72552959445361 0.637235202773195 12 0.243981598593986 0.487963197187971 0.756018401406014 13 0.607228287268034 0.785543425463931 0.392771712731966 14 0.504807301689689 0.990385396620623 0.495192698310311 15 0.472291065004565 0.94458213000913 0.527708934995435 16 0.375898477609547 0.751796955219093 0.624101522390453 17 0.316039568252978 0.632079136505956 0.683960431747022 18 0.301894066432194 0.603788132864388 0.698105933567806 19 0.236881047696959 0.473762095393917 0.763118952303041 20 0.246953383417576 0.493906766835151 0.753046616582424 21 0.262791723713174 0.525583447426349 0.737208276286826 22 0.204245148458703 0.408490296917406 0.795754851541297 23 0.164455508860500 0.328911017720999 0.8355444911395 24 0.137096295558383 0.274192591116767 0.862903704441617 25 0.120340969839388 0.240681939678777 0.879659030160612 26 0.0880016302333591 0.176003260466718 0.911998369766641 27 0.0694700805872943 0.138940161174589 0.930529919412706 28 0.061990685621128 0.123981371242256 0.938009314378872 29 0.046571606388222 0.093143212776444 0.953428393611778 30 0.0320122948684836 0.0640245897369673 0.967987705131516 31 0.0260345249066769 0.0520690498133537 0.973965475093323 32 0.0299179078770837 0.0598358157541674 0.970082092122916 33 0.0672234318439303 0.134446863687861 0.93277656815607 34 0.274074227831138 0.548148455662275 0.725925772168862 35 0.234729703971435 0.469459407942871 0.765270296028565 36 0.360169024191119 0.720338048382238 0.639830975808881 37 0.492619323597905 0.98523864719581 0.507380676402095 38 0.560061061311944 0.879877877376112 0.439938938688056 39 0.590593132772742 0.818813734454517 0.409406867227258 40 0.690760897322879 0.618478205354242 0.309239102677121 41 0.64637460489372 0.70725079021256 0.35362539510628 42 0.60769578131244 0.78460843737512 0.39230421868756 43 0.707380550235706 0.585238899528588 0.292619449764294 44 0.66555740014247 0.66888519971506 0.33444259985753 45 0.676353932092491 0.647292135815018 0.323646067907509 46 0.655757217493276 0.688485565013449 0.344242782506724 47 0.684269922104256 0.631460155791489 0.315730077895744 48 0.890486260624858 0.219027478750285 0.109513739375142 49 0.87635082392922 0.247298352141559 0.123649176070779 50 0.858835701710217 0.282328596579566 0.141164298289783 51 0.845145657935586 0.309708684128828 0.154854342064414 52 0.822598384266034 0.354803231467932 0.177401615733966 53 0.838600644732247 0.322798710535506 0.161399355267753 54 0.826512828704134 0.346974342591733 0.173487171295866 55 0.87031347775618 0.259373044487642 0.129686522243821 56 0.844623031689564 0.310753936620872 0.155376968310436 57 0.821271779147472 0.357456441705057 0.178728220852529 58 0.792584033800938 0.414831932398124 0.207415966199062 59 0.759965555173892 0.480068889652216 0.240034444826108 60 0.784739952810327 0.430520094379345 0.215260047189673 61 0.748985529472131 0.502028941055738 0.251014470527869 62 0.713535381159233 0.572929237681533 0.286464618840767 63 0.677398778547436 0.645202442905129 0.322601221452564 64 0.658660727377545 0.68267854524491 0.341339272622455 65 0.639387114545898 0.721225770908203 0.360612885454102 66 0.733653177296643 0.532693645406715 0.266346822703357 67 0.694593100199404 0.610813799601192 0.305406899800596 68 0.732061552941342 0.535876894117316 0.267938447058658 69 0.7201293350521 0.559741329895801 0.279870664947901 70 0.69571780402744 0.60856439194512 0.30428219597256 71 0.664487898205595 0.671024203588809 0.335512101794405 72 0.621425454281524 0.757149091436952 0.378574545718476 73 0.594006236278303 0.811987527443393 0.405993763721697 74 0.55057366963802 0.89885266072396 0.44942633036198 75 0.51150855919044 0.97698288161912 0.48849144080956 76 0.4698163359518 0.9396326719036 0.5301836640482 77 0.437107983616315 0.87421596723263 0.562892016383685 78 0.395798819208501 0.791597638417003 0.604201180791499 79 0.356877964394845 0.71375592878969 0.643122035605155 80 0.348180192709430 0.696360385418859 0.65181980729057 81 0.309934077575164 0.619868155150328 0.690065922424836 82 0.393343308113856 0.786686616227713 0.606656691886144 83 0.372434995182491 0.744869990364983 0.627565004817509 84 0.330883889319077 0.661767778638154 0.669116110680923 85 0.314190839496031 0.628381678992063 0.685809160503969 86 0.311078400304828 0.622156800609657 0.688921599695172 87 0.321737423514639 0.643474847029278 0.678262576485361 88 0.281931917589138 0.563863835178275 0.718068082410862 89 0.278879598864285 0.55775919772857 0.721120401135715 90 0.283464270243134 0.566928540486269 0.716535729756866 91 0.332204529924989 0.664409059849978 0.667795470075011 92 0.307346823545459 0.614693647090918 0.692653176454541 93 0.301908465949577 0.603816931899153 0.698091534050423 94 0.262822654684781 0.525645309369562 0.737177345315219 95 0.245549797908868 0.491099595817736 0.754450202091132 96 0.281638347674941 0.563276695349881 0.71836165232506 97 0.252925597509071 0.505851195018142 0.747074402490929 98 0.227925611034910 0.455851222069821 0.77207438896509 99 0.215710338794078 0.431420677588156 0.784289661205922 100 0.197226782160499 0.394453564320998 0.802773217839501 101 0.166114759078432 0.332229518156864 0.833885240921568 102 0.146768060461600 0.293536120923199 0.8532319395384 103 0.124638653021134 0.249277306042269 0.875361346978866 104 0.107679249363385 0.21535849872677 0.892320750636615 105 0.112845013202984 0.225690026405967 0.887154986797016 106 0.0912096802584884 0.182419360516977 0.908790319741512 107 0.0742996080015193 0.148599216003039 0.92570039199848 108 0.0595120769132105 0.119024153826421 0.94048792308679 109 0.0462917349239522 0.0925834698479043 0.953708265076048 110 0.0604981063925755 0.120996212785151 0.939501893607424 111 0.0953094284634912 0.190618856926982 0.904690571536509 112 0.151574925107430 0.303149850214860 0.84842507489257 113 0.141696807568731 0.283393615137462 0.85830319243127 114 0.584365803569272 0.831268392861457 0.415634196430728 115 0.724327232308885 0.551345535382229 0.275672767691115 116 0.686486144429657 0.627027711140685 0.313513855570343 117 0.736185254734636 0.527629490530728 0.263814745265364 118 0.690330183772102 0.619339632455796 0.309669816227898 119 0.650943159801919 0.698113680396163 0.349056840198081 120 0.65284862240579 0.69430275518842 0.34715137759421 121 0.727167620167448 0.545664759665104 0.272832379832552 122 0.697168350085919 0.605663299828161 0.302831649914081 123 0.692824428588809 0.614351142822382 0.307175571411191 124 0.639762498392043 0.720475003215914 0.360237501607957 125 0.666550476138439 0.666899047723122 0.333449523861561 126 0.636636060336398 0.726727879327204 0.363363939663602 127 0.579223944977798 0.841552110044404 0.420776055022202 128 0.522232412404737 0.955535175190526 0.477767587595263 129 0.48839031786165 0.9767806357233 0.51160968213835 130 0.456553528266257 0.913107056532515 0.543446471733743 131 0.454568477651943 0.909136955303886 0.545431522348057 132 0.492384657294839 0.984769314589678 0.507615342705161 133 0.425037108970965 0.85007421794193 0.574962891029035 134 0.399065597889604 0.798131195779209 0.600934402110396 135 0.52686396307558 0.94627207384884 0.47313603692442 136 0.464632508783107 0.929265017566213 0.535367491216894 137 0.43737437571738 0.87474875143476 0.56262562428262 138 0.386123278124523 0.772246556249045 0.613876721875477 139 0.33790665543857 0.67581331087714 0.66209334456143 140 0.304442418916989 0.608884837833978 0.695557581083011 141 0.416017095276141 0.832034190552282 0.583982904723859 142 0.540947880171497 0.918104239657006 0.459052119828503 143 0.672766681928406 0.654466636143188 0.327233318071594 144 0.609086881259463 0.781826237481075 0.390913118740537 145 0.5073214181892 0.9853571636216 0.4926785818108 146 0.513062301677755 0.97387539664449 0.486937698322245 147 0.49534768405517 0.99069536811034 0.50465231594483 148 0.368535894545484 0.737071789090969 0.631464105454516 149 0.562338170860515 0.875323658278971 0.437661829139486 150 0.467320178667622 0.934640357335245 0.532679821332378 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 5 0.0352112676056338 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/10vsot1293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/10vsot1293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/1h1821293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/1h1821293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/2h1821293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/2h1821293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/3h1821293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/3h1821293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/49s751293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/49s751293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/59s751293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/59s751293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/69s751293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/69s751293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/7kjp81293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/7kjp81293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/8vsot1293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/8vsot1293536554.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/9vsot1293536554.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293536435uvar110iaqq80ur/9vsot1293536554.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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

Software written by Ed van Stee & Patrick Wessa

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