ws 7 -vierde regressiemodel ) lineaire trend zonder maand

*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: Sun, 21 Nov 2010 16:18:22 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/21/t1290357713kb7q94alqml1yyg.htm/, Retrieved Sun, 21 Nov 2010 17:41:53 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/21/t1290357713kb7q94alqml1yyg.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:

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24 14 11 12 24 26 25 11 7 8 25 23 17 6 17 8 30 25 18 12 10 8 19 23 18 8 12 9 22 19 16 10 12 7 22 29 20 10 11 4 25 25 16 11 11 11 23 21 18 16 12 7 17 22 17 11 13 7 21 25 23 13 14 12 19 24 30 12 16 10 19 18 23 8 11 10 15 22 18 12 10 8 16 15 15 11 11 8 23 22 12 4 15 4 27 28 21 9 9 9 22 20 15 8 11 8 14 12 20 8 17 7 22 24 31 14 17 11 23 20 27 15 11 9 23 21 34 16 18 11 21 20 21 9 14 13 19 21 31 14 10 8 18 23 19 11 11 8 20 28 16 8 15 9 23 24 20 9 15 6 25 24 21 9 13 9 19 24 22 9 16 9 24 23 17 9 13 6 22 23 24 10 9 6 25 29 25 16 18 16 26 24 26 11 18 5 29 18 25 8 12 7 32 25 17 9 17 9 25 21 32 16 9 6 29 26 33 11 9 6 28 22 13 16 12 5 17 22 32 12 18 12 28 22 25 12 12 7 29 23 29 14 18 10 26 30 22 9 14 9 25 23 18 10 15 8 14 17 17 9 16 5 25 23 20 10 10 8 26 23 15 12 11 8 20 25 20 14 14 10 18 24 33 14 9 6 32 24 29 10 12 8 25 23 23 14 17 7 25 21 26 16 5 4 23 24 18 9 12 8 21 24 20 10 12 8 20 28 11 6 6 4 15 16 28 8 24 20 30 20 26 13 12 8 24 29 22 10 12 8 26 27 17 8 14 6 24 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 10 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Concernovermistakes[t] = -2.70676782746938 + 0.80482569659637Doubtsaboutactions[t] + 0.246618734551963Parentalexpectations[t] + 0.190858166207396Parentalcritism[t] + 0.5682129740418Personalstandards[t] -0.100756792404192Organization[t] + 0.00564436253658327t + 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) -2.70676782746938 3.230753 -0.8378 0.403451 0.201726 Doubtsaboutactions 0.80482569659637 0.130765 6.1547 0 0 Parentalexpectations 0.246618734551963 0.133141 1.8523 0.06592 0.03296 Parentalcritism 0.190858166207396 0.168568 1.1322 0.25932 0.12966 Personalstandards 0.5682129740418 0.096019 5.9177 0 0 Organization -0.100756792404192 0.105352 -0.9564 0.340397 0.170199 t 0.00564436253658327 0.008004 0.7052 0.481742 0.240871 Multiple Linear Regression - Regression Statistics Multiple R 0.639616097712746 R-squared 0.409108752453280 Adjusted R-squared 0.385784097944857 F-TEST (value) 17.5397561539675 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 2.22044604925031e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.48508352276095 Sum Squared Residuals 3057.62807933355 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 24.5869751364709 -0.586975136470905 2 25 21.2987181574354 3.70128184256465 3 17 22.3859726679103 -5.38597266791033 4 18 19.4454109385100 -1.44541093850998 5 18 19.0235142417146 -1.02351424171457 6 16 19.2495257409872 -3.24952574098718 7 20 20.5436429620918 -0.543642962091785 8 16 21.9567214062097 -5.95672140620968 9 18 21.9596456847955 -3.9596456847955 10 17 20.1583618178568 -3.15836181785682 11 23 21.9388979834957 1.06110201650432 12 30 21.8557785405502 8.14422145944982 13 23 14.7331473781575 8.2668526218425 14 18 18.6032699809840 -0.603269980983952 15 15 21.3229006529394 -6.32290065293938 16 12 17.5861185544217 -5.58611855442169 17 21 19.0554592926899 1.94454070731014 18 15 14.8190078084257 0.180992191574258 19 20 19.4501286955508 0.549871304449199 20 31 26.0194000461538 4.98059995384624 21 27 24.8676845731560 2.13231542684405 22 34 26.7505329508880 7.24946704911197 23 21 19.2804560909692 1.71954390903083 24 31 20.5997366083926 10.4002633916074 25 19 19.0701646017547 -0.0701646017546607 26 16 19.9463310706596 -3.94633107065955 27 20 21.3206525792539 -1.32065257925392 28 21 17.9963561270580 3.00364387294204 29 22 21.6836783558636 0.316321644136373 30 17 19.2404660680385 -2.24046606803853 31 24 20.1645593566639 3.83544064333612 32 25 30.1993051078831 -5.19930510788308 33 26 26.390560835707 -0.390560835707007 34 25 23.8830734088535 1.11692659114645 35 17 22.7338898244853 -5.73388982448527 36 32 27.5968576223048 4.4031423776952 37 33 23.4131876974345 9.5868123025655 38 13 21.7416158659416 -8.74161586594163 39 32 27.5940197273161 4.40598027268392 40 25 25.6331170331415 -0.633117033141512 41 29 26.8907632258501 2.10923677414994 42 22 21.8320305737771 0.167969426222916 43 18 17.0524592412200 0.947540758780044 44 17 21.5731241031246 -4.57312410312459 45 20 22.0446692276098 -2.04466922760976 46 15 20.2957922888319 -5.29579228883186 47 20 21.9969914249525 -1.99699142495246 48 33 27.9610910864848 5.03890891351516 49 29 21.9922711728182 7.00772882718179 50 23 26.4609674131011 -3.46096741310109 51 26 23.1055675302885 2.89443246971151 52 18 18.8307698752602 -0.8307698752602 53 20 18.6699997907346 1.33000020926542 54 11 11.5812129333856 -0.58121293338563 55 28 28.8095440113789 -0.809544011378862 56 26 23.2735050718965 2.72649492810354 57 22 22.2026118775359 -0.202611877535911 58 17 20.0774839975062 -3.07748399750625 59 12 15.4292884866592 -3.42928848665917 60 14 20.780061210624 -6.78006121062399 61 17 20.6763094340181 -3.67630943401805 62 21 21.2615805214850 -0.261580521484976 63 19 22.9113260083569 -3.91132600835689 64 18 23.0268271622096 -5.02682716220963 65 10 17.8541098959786 -7.8541098959786 66 29 24.2846006276799 4.71539937232012 67 31 18.4322038562024 12.5677961437976 68 19 22.8792466318609 -3.87924663186088 69 9 20.0338902827611 -11.0338902827611 70 20 22.4674961633225 -2.46749616332249 71 28 17.5756295114181 10.4243704885819 72 19 18.1136995485328 0.886300451467227 73 30 23.0877801178341 6.91221988216585 74 29 27.073369677065 1.92663032293498 75 26 21.4908251886057 4.50917481139428 76 23 19.5051176759053 3.49488232409465 77 13 22.7601530313896 -9.76015303138962 78 21 22.6443090003536 -1.6443090003536 79 19 21.5370492704958 -2.53704927049577 80 28 22.9377313025900 5.06226869740997 81 23 25.6175014577243 -2.61750145772435 82 18 13.8646394460410 4.13536055395896 83 21 20.7319285749601 0.268071425039858 84 20 21.8476069466243 -1.84760694662434 85 23 20.0434165598231 2.95658344017692 86 21 20.8597333926554 0.140266607344571 87 21 21.8550198976718 -0.855019897671836 88 15 22.9508811919658 -7.9508811919658 89 28 27.1965037434143 0.80349625658569 90 19 17.6886416630847 1.31135833691528 91 26 21.2954140701055 4.70458592989447 92 10 13.3326243716318 -3.3326243716318 93 16 17.1873143264118 -1.18731432641177 94 22 21.2020388132927 0.797961186707265 95 19 18.9063046849835 0.093695315016467 96 31 28.9157779289149 2.08422207108509 97 31 25.3018849084882 5.69811509151183 98 29 24.8551287876825 4.14487121231755 99 19 17.5028282033594 1.49717179664061 100 22 18.9522887480601 3.04771125193991 101 23 22.472438197516 0.52756180248399 102 15 16.2874647056108 -1.28746470561081 103 20 21.4645212040962 -1.46452120409622 104 18 19.6425371514452 -1.64253715144523 105 23 22.2171502344810 0.782849765518966 106 25 20.9744599375139 4.02554006248607 107 21 16.6529471075685 4.34705289243147 108 24 19.5455744278297 4.45442557217025 109 25 25.3595410763598 -0.359541076359756 110 17 19.6935475859109 -2.69354758591087 111 13 14.6943965458432 -1.69439654584321 112 28 18.4098746117709 9.59012538822914 113 21 20.3106346801039 0.689365319896122 114 25 28.2836350486841 -3.28363504868414 115 9 21.0044477704288 -12.0044477704288 116 16 18.0612276169349 -2.06122761693493 117 19 21.2966898433179 -2.29668984331794 118 17 19.6269476477759 -2.62694764777592 119 25 24.7319979920982 0.268002007901781 120 20 15.6369512420149 4.36304875798512 121 29 21.8759344009052 7.12406559909483 122 14 19.1778709228727 -5.17787092287271 123 22 27.0980959242537 -5.09809592425374 124 15 15.9019397751253 -0.901939775125262 125 19 25.6813751580878 -6.68137515808782 126 20 22.1919545613241 -2.19195456132411 127 15 17.7608142090159 -2.76081420901586 128 20 22.1258994239983 -2.12589942399832 129 18 20.5527935376764 -2.55279353767642 130 33 25.7888479793484 7.2111520206516 131 22 24.0146988185677 -2.01469881856773 132 16 16.7435243851904 -0.743524385190438 133 17 19.4036823708245 -2.40368237082451 134 16 15.3816109084570 0.618389091543024 135 21 17.3637695845882 3.63623041541183 136 26 27.9214437785666 -1.92144377856656 137 18 21.4147283663736 -3.41472836637364 138 18 23.2764637604010 -5.27646376040095 139 17 18.7078618855540 -1.70786188555397 140 22 24.9760915015815 -2.97609150158152 141 30 24.952242458675 5.047757541325 142 30 27.6220295065553 2.37797049344472 143 24 30.0288532518421 -6.02885325184214 144 21 22.3733372368221 -1.37333723682208 145 21 25.7186060818219 -4.71860608182192 146 29 27.7760626071577 1.22393739284233 147 31 23.567572013391 7.43242798660901 148 20 19.3381789476296 0.661821052370395 149 16 14.4271132095025 1.57288679049751 150 22 19.2888597043427 2.71114029565732 151 20 20.766728259862 -0.766728259862021 152 28 27.7200998654264 0.279900134573624 153 38 27.0811321597216 10.9188678402784 154 22 19.5906715514475 2.40932844855246 155 20 26.0566974089159 -6.05669740891587 156 17 18.448530410331 -1.44853041033101 157 28 24.9055766121567 3.09442338784326 158 22 24.5396741854444 -2.53967418544438 159 31 26.4518682878217 4.54813171217832 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0986137661024837 0.197227532204967 0.901386233897516 11 0.354975571887273 0.709951143774547 0.645024428112727 12 0.749205672924374 0.501588654151252 0.250794327075626 13 0.743994274618463 0.512011450763075 0.256005725381537 14 0.727142351575679 0.545715296848642 0.272857648424321 15 0.689806032322999 0.620387935354002 0.310193967677001 16 0.607520523040911 0.784958953918178 0.392479476959089 17 0.552963977005474 0.894072045989052 0.447036022994526 18 0.524489297213624 0.951021405572752 0.475510702786376 19 0.469705119785148 0.939410239570297 0.530294880214852 20 0.498689321613758 0.997378643227515 0.501310678386242 21 0.431176180699654 0.862352361399307 0.568823819300346 22 0.419451092528738 0.838902185057477 0.580548907471262 23 0.376937483513752 0.753874967027504 0.623062516486248 24 0.530934349054127 0.938131301891747 0.469065650945873 25 0.488778271623547 0.977556543247095 0.511221728376453 26 0.505724304437271 0.988551391125458 0.494275695562729 27 0.438393462196286 0.876786924392572 0.561606537803714 28 0.379359317119068 0.758718634238137 0.620640682880932 29 0.317823247581353 0.635646495162706 0.682176752418647 30 0.278075108364166 0.556150216728331 0.721924891635834 31 0.269604109958724 0.539208219917449 0.730395890041276 32 0.399006591421525 0.79801318284305 0.600993408578475 33 0.343222070528927 0.686444141057854 0.656777929471073 34 0.313641715491668 0.627283430983335 0.686358284508332 35 0.356773406558775 0.71354681311755 0.643226593441225 36 0.323998275685865 0.64799655137173 0.676001724314135 37 0.438555192615726 0.877110385231451 0.561444807384274 38 0.745624437903073 0.508751124193854 0.254375562096927 39 0.7299817348089 0.5400365303822 0.2700182651911 40 0.693934084025518 0.612131831948964 0.306065915974482 41 0.656722991738823 0.686554016522354 0.343277008261177 42 0.60963548605611 0.78072902788778 0.39036451394389 43 0.559432351693984 0.881135296612032 0.440567648306016 44 0.55279891173134 0.894402176537321 0.447201088268660 45 0.540193248509558 0.919613502980883 0.459806751490442 46 0.584175010022566 0.831649979954867 0.415824989977434 47 0.547117591212123 0.905764817575755 0.452882408787877 48 0.537011624774243 0.925976750451514 0.462988375225757 49 0.589669637022597 0.820660725954807 0.410330362977403 50 0.568422033221753 0.863155933556493 0.431577966778247 51 0.538178226534436 0.923643546931127 0.461821773465564 52 0.491057984345914 0.982115968691829 0.508942015654086 53 0.445536972695211 0.891073945390421 0.554463027304789 54 0.404935211723214 0.809870423446429 0.595064788276786 55 0.366544629337198 0.733089258674396 0.633455370662802 56 0.33576260610805 0.6715252122161 0.66423739389195 57 0.292733820431060 0.585467640862121 0.70726617956894 58 0.267371541827075 0.534743083654151 0.732628458172925 59 0.249510500844237 0.499021001688474 0.750489499155763 60 0.308670878298286 0.617341756596572 0.691329121701714 61 0.294097713731882 0.588195427463764 0.705902286268118 62 0.25509957966582 0.51019915933164 0.74490042033418 63 0.239430065579774 0.478860131159548 0.760569934420226 64 0.265644227426384 0.531288454852768 0.734355772573616 65 0.330364327637465 0.660728655274929 0.669635672362535 66 0.338966844575648 0.677933689151295 0.661033155424352 67 0.683800498206362 0.632399003587276 0.316199501793638 68 0.673315481045092 0.653369037909816 0.326684518954908 69 0.841190161183828 0.317619677632344 0.158809838816172 70 0.819138327521376 0.361723344957248 0.180861672478624 71 0.930851738061275 0.138296523877451 0.0691482619387253 72 0.914656901731175 0.170686196537649 0.0853430982688247 73 0.938282933785067 0.123434132429866 0.0617170662149329 74 0.926386103955744 0.147227792088511 0.0736138960442556 75 0.927628103388979 0.144743793222043 0.0723718966110213 76 0.921532903260819 0.156934193478362 0.0784670967391811 77 0.969328781764506 0.0613424364709884 0.0306712182354942 78 0.961667340439293 0.076665319121414 0.038332659560707 79 0.954107486897141 0.0917850262057179 0.0458925131028589 80 0.956601951586903 0.0867960968261945 0.0433980484130972 81 0.94866640143641 0.102667197127180 0.0513335985635901 82 0.947674557240555 0.104650885518891 0.0523254427594455 83 0.933876863951651 0.132246272096698 0.0661231360483489 84 0.920778464987114 0.158443070025772 0.079221535012886 85 0.911255843490232 0.177488313019536 0.088744156509768 86 0.893685955156079 0.212628089687843 0.106314044843921 87 0.871647292220026 0.256705415559949 0.128352707779974 88 0.919379775763868 0.161240448472264 0.0806202242361318 89 0.902611562542193 0.194776874915614 0.097388437457807 90 0.88176524363347 0.236469512733059 0.118234756366529 91 0.88139741398992 0.237205172020159 0.118602586010080 92 0.871398039668186 0.257203920663629 0.128601960331815 93 0.849132483499532 0.301735033000937 0.150867516500468 94 0.820422351999574 0.359155296000851 0.179577648000426 95 0.787017743228047 0.425964513543906 0.212982256771953 96 0.755715717519414 0.488568564961172 0.244284282480586 97 0.773221498701841 0.453557002596318 0.226778501298159 98 0.769991622825097 0.460016754349807 0.230008377174903 99 0.734360026106277 0.531279947787446 0.265639973893723 100 0.712285342532018 0.575429314935964 0.287714657467982 101 0.673804180886969 0.652391638226063 0.326195819113031 102 0.632880695737512 0.734238608524976 0.367119304262488 103 0.591795492302649 0.816409015394702 0.408204507697351 104 0.548631219337819 0.902737561324361 0.451368780662181 105 0.506340488687242 0.987319022625516 0.493659511312758 106 0.492691634388543 0.985383268777087 0.507308365611457 107 0.500002089027185 0.99999582194563 0.499997910972815 108 0.539303621396917 0.921392757206165 0.460696378603083 109 0.501865343762256 0.996269312475488 0.498134656237744 110 0.460704289802822 0.921408579605645 0.539295710197178 111 0.413743913231613 0.827487826463226 0.586256086768387 112 0.7406188406695 0.518762318661001 0.259381159330500 113 0.775579162293001 0.448841675413997 0.224420837706999 114 0.751192493702792 0.497615012594416 0.248807506297208 115 0.875459864023444 0.249080271953112 0.124540135976556 116 0.847093187395818 0.305813625208364 0.152906812604182 117 0.817630525758243 0.364738948483514 0.182369474241757 118 0.784169778505269 0.431660442989463 0.215830221494731 119 0.75623961941945 0.4875207611611 0.24376038058055 120 0.804970576588137 0.390058846823727 0.195029423411863 121 0.884059519069933 0.231880961860133 0.115940480930067 122 0.865249738023542 0.269500523952917 0.134750261976459 123 0.844910324733052 0.310179350533897 0.155089675266948 124 0.806900129855564 0.386199740288873 0.193099870144436 125 0.811564180355852 0.376871639288295 0.188435819644148 126 0.768212231461183 0.463575537077634 0.231787768538817 127 0.736867409455641 0.526265181088718 0.263132590544359 128 0.690330756079321 0.619338487841358 0.309669243920679 129 0.645015922560204 0.709968154879591 0.354984077439796 130 0.765893935019667 0.468212129960667 0.234106064980334 131 0.713580282724145 0.572839434551711 0.286419717275856 132 0.653407430680831 0.693185138638338 0.346592569319169 133 0.615380189803281 0.769239620393437 0.384619810196718 134 0.54569034141003 0.90861931717994 0.45430965858997 135 0.571000707376548 0.857998585246904 0.428999292623452 136 0.500535746389736 0.998928507220527 0.499464253610264 137 0.453408493907197 0.906816987814394 0.546591506092803 138 0.470186630788955 0.94037326157791 0.529813369211045 139 0.396281266677954 0.792562533355908 0.603718733322046 140 0.322837797121087 0.645675594242173 0.677162202878913 141 0.425940533040582 0.851881066081163 0.574059466959418 142 0.3759469279344 0.7518938558688 0.6240530720656 143 0.303350492215239 0.606700984430479 0.696649507784761 144 0.227185895699065 0.45437179139813 0.772814104300935 145 0.333525544777945 0.667051089555889 0.666474455222055 146 0.254150574218036 0.508301148436071 0.745849425781964 147 0.252870623285587 0.505741246571175 0.747129376714413 148 0.159612301285357 0.319224602570713 0.840387698714643 149 0.0863434141037003 0.172686828207401 0.9136565858963 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 4 0.0285714285714286 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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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Software written by Ed van Stee & Patrick Wessa

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