Meervoudige regressie: Inclusief geslacht

*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: Sat, 11 Dec 2010 18:27:12 +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/11/t1292091956ucirci2hgxibtuz.htm/, Retrieved Sat, 11 Dec 2010 19:25:59 +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/11/t1292091956ucirci2hgxibtuz.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 «

13 13 14 13 3 2 2 12 12 8 13 5 1 1 15 10 12 16 6 0 1 12 9 7 12 6 3 1 10 10 10 11 5 3 2 12 12 7 12 3 1 2 15 13 16 18 8 3 1 9 12 11 11 4 1 1 12 12 14 14 4 4 1 11 6 6 9 4 0 1 11 5 16 14 6 3 2 11 12 11 12 6 2 1 15 11 16 11 5 4 1 7 14 12 12 4 3 2 11 14 7 13 6 1 2 11 12 13 11 4 1 1 10 12 11 12 6 2 1 14 11 15 16 6 3 2 10 11 7 9 4 1 1 6 7 9 11 4 1 2 11 9 7 13 2 2 1 15 11 14 15 7 3 2 11 11 15 10 5 4 1 12 12 7 11 4 2 2 14 12 15 13 6 1 1 15 11 17 16 6 2 2 9 11 15 15 7 2 2 13 8 14 14 5 4 1 13 9 14 14 6 2 2 16 12 8 14 4 3 1 13 10 8 8 4 3 1 12 10 14 13 7 3 2 14 12 14 15 7 4 1 11 8 8 13 4 2 2 9 12 11 11 4 2 1 16 11 16 15 6 4 2 12 12 10 15 6 3 1 10 7 8 9 5 4 2 13 11 14 13 6 2 1 16 11 16 16 7 5 1 14 12 13 13 6 3 2 15 9 5 11 3 1 1 5 15 8 12 3 1 1 8 11 10 12 4 1 2 11 11 8 12 6 2 1 16 11 13 14 7 3 2 17 11 15 14 5 9 1 9 15 6 8 4 0 2 9 11 12 13 5 0 1 13 12 16 16 6 2 1 10 12 5 13 6 2 1 6 9 15 11 6 3 2 12 12 12 14 5 1 2 8 12 8 13 4 2 2 14 13 13 13 5 0 2 12 11 14 13 5 5 1 11 9 1 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 1.20820716919088 + 0.13002278384503FindingFriends[t] + 0.22210132830252KnowingPeople[t] + 0.337203746175027Liked[t] + 0.569203382109655Celebrity[t] + 0.234237609027133SumFriends1[t] -0.826628007906952Gender[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) 1.20820716919088 1.484305 0.814 0.41695 0.208475 FindingFriends 0.13002278384503 0.094558 1.3751 0.171176 0.085588 KnowingPeople 0.22210132830252 0.0628 3.5367 0.00054 0.00027 Liked 0.337203746175027 0.094831 3.5558 0.000505 0.000253 Celebrity 0.569203382109655 0.15376 3.7019 0.000301 0.00015 SumFriends1 0.234237609027133 0.118517 1.9764 0.049955 0.024978 Gender -0.826628007906952 0.344691 -2.3982 0.017715 0.008857 Multiple Linear Regression - Regression Statistics Multiple R 0.72642302133931 R-squared 0.527690405931731 Adjusted R-squared 0.508671227647103 F-TEST (value) 27.7451737417185 F-TEST (DF numerator) 6 F-TEST (DF denominator) 149 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.05842445387573 Sum Squared Residuals 631.329573614728 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 10.9144000042562 2.08559999574375 2 12 11.1825664136952 0.817433586304789 3 15 13.1575031708228 1.84249682917716 4 12 11.2708715878465 0.729128412153506 5 10 10.3341632204075 -0.33416322040745 6 12 8.6582265670914 3.3417734329086 7 15 16.9515039192188 -1.95150391921877 8 9 10.6052595241431 -1.60525952414306 9 12 12.9858875746571 -0.985887574657101 10 11 7.8059710781831 3.1940289218169 11 11 12.5974718916322 -1.59747189163215 12 11 12.3151076435645 -1.31510764356453 13 15 12.8576595910017 2.14234040899832 14 7 11.066457376458 -4.06645737645798 15 11 10.9630860272855 0.036913972714543 16 11 11.0494621807481 -0.0494621807481023 17 10 12.3151076435645 -2.31510764356453 18 14 13.8299147587499 0.170085241250129 19 10 8.9124239347379 1.0875760652621 20 6 8.68431494040592 -2.68431494040592 21 11 9.09702419655577 1.90297580344423 22 15 13.839813066382 1.16018693361802 23 11 12.2983545165241 -1.29835451652414 24 12 9.12446381205316 2.87553618794684 25 14 13.3064790939225 0.693520906077494 26 15 14.0398798063278 0.960120193672223 27 9 13.8276767856574 -4.82767678565737 28 13 13.0349998213866 -0.0349998213866353 29 13 12.4391227613801 0.560877238619896 30 16 11.4190419958148 4.58095800418515 31 13 9.13577395107463 3.86422604892537 32 12 13.0353827901869 -1.03538279018689 33 14 15.0307014671611 -1.03070146716109 34 11 9.50088149732562 1.49911850267438 35 9 10.8394971331702 -1.8394971331702 36 16 13.9490499499045 2.0509500500955 37 12 13.3388551628142 -1.33885516281423 38 10 9.0597223289444 0.9402776710556 39 13 13.1885925908021 -0.188592590802089 40 16 15.9163226951233 0.0836773048767376 41 14 12.5041236474648 1.49587635253522 42 15 8.3133798206832 6.6866201793168 43 5 10.097024254836 -5.09702425483596 44 8 9.76371115026359 -1.76371115026359 45 11 11.5187808748119 -0.518780874811944 46 16 13.2805079919044 2.71949200809557 47 17 14.8183575463599 2.18164245364009 48 9 7.81234437870639 1.18765562129361 49 9 11.7067113340331 -2.70671133403313 50 13 14.7744292697772 -1.77442926977724 51 10 11.3197034199244 -1.31970341992444 52 6 11.8838504601847 -5.88385046018467 53 12 11.5815474651734 0.418452534826633 54 8 10.0209726327057 -2.02097263270574 55 14 11.3622302221188 2.63776977788124 56 12 13.3221020357738 -1.32210203577383 57 11 11.0087338561127 -0.0087338561126512 58 16 14.8528361362964 1.14716386370359 59 8 10.1053908426236 -2.10539084262362 60 15 14.7550329576197 0.244967042380292 61 7 8.64609028636679 -1.64609028636679 62 16 13.7418689875745 2.2581310124255 63 14 13.4676123094225 0.532387690577533 64 16 13.628762474125 2.37123752587499 65 9 10.7138044864615 -1.71380448646145 66 14 12.5273106642349 1.47268933576506 67 11 12.8453601669219 -1.84536016692193 68 13 9.79663333131071 3.20336666868929 69 15 13.5649892643989 1.43501073560114 70 5 5.55657615356619 -0.556576153566193 71 15 13.3221020357738 1.67789796422617 72 13 12.9957858822892 0.00421411771079032 73 11 11.9321361801072 -0.932136180107215 74 11 13.5291408664244 -2.52914086642436 75 12 13.0828423684063 -1.08284236840628 76 12 13.7749543119768 -1.77495431197677 77 12 12.7764686392346 -0.7764686392346 78 12 12.241457137882 -0.241457137881956 79 14 11.1848043867877 2.81519561321228 80 6 8.35634611841115 -2.35634611841115 81 7 9.22742994920106 -2.22742994920106 82 14 12.6818864241808 1.31811357581921 83 14 14.4465777190789 -0.446577719078915 84 10 11.4858586937648 -1.48585869376482 85 13 8.68249912854063 4.31750087145937 86 12 11.8406037478051 0.159396252194869 87 9 8.87933861033563 0.120661389664373 88 12 12.1074015807548 -0.107401580754807 89 16 15.0014068473715 0.998593152628547 90 10 10.0100850400153 -0.0100850400153331 91 14 13.3117985161171 0.68820148388291 92 10 13.8751363638768 -3.87513636387676 93 16 15.8121078699412 0.18789213005884 94 15 13.7370100725892 1.26298992741077 95 12 11.02349107873 0.976508921269972 96 10 9.70471793020837 0.29528206979163 97 8 10.2132505633696 -2.21325056336964 98 8 8.34807946761351 -0.348079467613512 99 11 12.0771003416004 -1.07710034160045 100 13 12.8865489987667 0.113451001233307 101 16 15.9041864143986 0.0958135856013505 102 16 15.2528027954636 0.747197204536387 103 14 14.8701180411858 -0.87011804118579 104 11 9.35204770590541 1.64795229409459 105 4 6.72250374906121 -2.72250374906121 106 14 14.9444884570536 -0.944488457053594 107 9 10.9188932847477 -1.91889328474767 108 14 15.4577457847011 -1.45774578470111 109 8 10.5655204845659 -2.56552048456591 110 8 10.859757933013 -2.85975793301298 111 11 12.1481299053903 -1.14812990539026 112 12 13.355591340652 -1.35559134065204 113 11 11.2954308584752 -0.295430858475215 114 14 13.5366839296675 0.463316070332479 115 15 13.862836939797 1.137163060203 116 16 13.7600339460042 2.23996605399575 117 16 13.1873439027679 2.81265609723212 118 11 12.1709906354501 -1.17099063545013 119 14 13.7540052682991 0.245994731700887 120 14 10.4421514158958 3.55784858410424 121 12 11.9702435628499 0.0297564371500984 122 14 12.1339977202426 1.86600227975737 123 8 10.4785602580696 -2.4785602580696 124 13 13.649790443117 -0.64979044311701 125 16 13.519767659272 2.48023234072802 126 12 10.8413298942381 1.15867010576193 127 16 15.7028709864186 0.297129013581357 128 12 13.0722414848954 -1.07224148489537 129 11 11.6781192901223 -0.678119290122291 130 4 6.37999791499802 -2.37999791499802 131 16 16.4055838135 -0.405583813500041 132 15 12.9786275432242 2.02137245677582 133 10 11.6249742701106 -1.62497427011064 134 13 14.3936921020432 -1.39369210204317 135 15 12.6070897846127 2.39291021538733 136 12 10.8354643598881 1.16453564011192 137 14 13.1555072663998 0.844492733600183 138 7 10.8174625448135 -3.81746254481347 139 19 14.2151241952997 4.78487580470031 140 12 13.2950453890767 -1.2950453890767 141 12 11.4335228662537 0.566477133746267 142 13 12.8291911129152 0.170808887084805 143 15 12.7233446308986 2.27665536910138 144 8 8.54741632547693 -0.547416325476926 145 12 11.2918032972177 0.70819670278227 146 10 10.9933872664398 -0.993387266439819 147 8 10.6931421519633 -2.69314215196329 148 10 14.2470397569821 -4.2470397569821 149 15 13.3776085947023 1.62239140529766 150 16 14.2187517565572 1.78124824344282 151 13 13.5478309253338 -0.547830925333838 152 16 15.3557689326115 0.644231067388492 153 9 10.1340002208409 -1.13400022084089 154 14 12.4621466347951 1.53785336520488 155 14 12.7274736638015 1.27252633619849 156 12 10.5826788236309 1.41732117636906 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0962270946751361 0.192454189350272 0.903772905324864 11 0.158513422504586 0.317026845009173 0.841486577495414 12 0.0848783075361489 0.169756615072298 0.915121692463851 13 0.547274797878723 0.905450404242554 0.452725202121277 14 0.731376472127616 0.537247055744767 0.268623527872384 15 0.661440236129214 0.677119527741571 0.338559763870786 16 0.571096652931859 0.857806694136283 0.428903347068141 17 0.525732208776657 0.948535582446685 0.474267791223343 18 0.456898807746363 0.913797615492726 0.543101192253637 19 0.371762263003001 0.743524526006003 0.628237736996999 20 0.580526452614637 0.838947094770726 0.419473547385363 21 0.530711683936478 0.938576632127044 0.469288316063522 22 0.56071401883134 0.878571962337321 0.43928598116866 23 0.487329389452165 0.97465877890433 0.512670610547835 24 0.520411320415281 0.959177359169438 0.479588679584719 25 0.472525185968624 0.945050371937249 0.527474814031376 26 0.427849488651726 0.855698977303452 0.572150511348274 27 0.62045344990518 0.75909310018964 0.37954655009482 28 0.554310535433592 0.891378929132816 0.445689464566408 29 0.505566927658783 0.988866144682434 0.494433072341217 30 0.608792035935091 0.782415928129819 0.391207964064909 31 0.709810508697153 0.580378982605693 0.290189491302847 32 0.661795882070729 0.676408235858542 0.338204117929271 33 0.609387996685509 0.781224006628982 0.390612003314491 34 0.559097539316249 0.881804921367502 0.440902460683751 35 0.576787094893256 0.846425810213487 0.423212905106744 36 0.617372086536679 0.765255826926643 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0.980376815717004 0.0392463685659923 0.0196231842829962 72 0.973946546652014 0.0521069066959713 0.0260534533479856 73 0.967635729473137 0.0647285410537269 0.0323642705268634 74 0.972811061506486 0.0543778769870289 0.0271889384935145 75 0.96675911651519 0.0664817669696205 0.0332408834848103 76 0.964634896063897 0.0707302078722064 0.0353651039361032 77 0.956048282660142 0.087903434679716 0.043951717339858 78 0.944249072463948 0.111501855072104 0.0557509275360518 79 0.957677623668074 0.0846447526638524 0.0423223763319262 80 0.961409191412147 0.0771816171757063 0.0385908085878532 81 0.962985254042566 0.0740294919148676 0.0370147459574338 82 0.96045991615534 0.0790801676893211 0.0395400838446606 83 0.949462513812665 0.10107497237467 0.0505374861873351 84 0.942638331885743 0.114723336228515 0.0573616681142574 85 0.9836002246263 0.0327995507473996 0.0163997753736998 86 0.97832894348899 0.0433421130220202 0.0216710565110101 87 0.971685250357934 0.0566294992841315 0.0283147496420657 88 0.964098806830494 0.0718023863390119 0.035901193169506 89 0.955566506355347 0.0888669872893066 0.0444334936446533 90 0.943841690483464 0.112316619033073 0.0561583095165364 91 0.932578471524869 0.134843056950262 0.067421528475131 92 0.966749959730395 0.0665000805392107 0.0332500402696053 93 0.957052284028588 0.0858954319428232 0.0429477159714116 94 0.948725396600532 0.102549206798936 0.0512746033994682 95 0.939577001085103 0.120845997829793 0.0604229989148967 96 0.924125647721338 0.151748704557324 0.0758743522786622 97 0.92063762443319 0.158724751133619 0.0793623755668096 98 0.90163133884764 0.19673732230472 0.09836866115236 99 0.885807511039503 0.228384977920995 0.114192488960497 100 0.859948553064329 0.280102893871342 0.140051446935671 101 0.83188215266148 0.33623569467704 0.16811784733852 102 0.80104957815426 0.397900843691481 0.19895042184574 103 0.803477604496927 0.393044791006147 0.196522395503073 104 0.849391900444286 0.301216199111428 0.150608099555714 105 0.844105445813626 0.311789108372748 0.155894554186374 106 0.816222072873568 0.367555854252865 0.183777927126432 107 0.797073209347 0.405853581306002 0.202926790653001 108 0.799330084731244 0.401339830537511 0.200669915268756 109 0.80527849876542 0.389443002469159 0.19472150123458 110 0.837045964018044 0.325908071963912 0.162954035981956 111 0.826735592272983 0.346528815454034 0.173264407727017 112 0.881759003450685 0.236481993098629 0.118240996549314 113 0.851938913286024 0.296122173427952 0.148061086713976 114 0.82029664001168 0.35940671997664 0.17970335998832 115 0.786758563580733 0.426482872838533 0.213241436419267 116 0.77362091751375 0.452758164972498 0.226379082486249 117 0.777939507822761 0.444120984354478 0.222060492177239 118 0.748450541615614 0.503098916768772 0.251549458384386 119 0.699105183690561 0.601789632618878 0.300894816309439 120 0.79933743904695 0.401325121906102 0.200662560953051 121 0.758779670269408 0.482440659461185 0.241220329730593 122 0.736827236628351 0.526345526743297 0.263172763371649 123 0.718018407167582 0.563963185664837 0.281981592832418 124 0.666346677710072 0.667306644579856 0.333653322289928 125 0.681648392005328 0.636703215989344 0.318351607994672 126 0.665750582347491 0.668498835305018 0.334249417652509 127 0.609522734727258 0.780954530545483 0.390477265272742 128 0.605045705885128 0.789908588229744 0.394954294114872 129 0.622744273738877 0.754511452522247 0.377255726261123 130 0.664947101562755 0.670105796874489 0.335052898437245 131 0.611114409446637 0.777771181106726 0.388885590553363 132 0.566465870478368 0.867068259043264 0.433534129521632 133 0.525330257776233 0.949339484447533 0.474669742223767 134 0.453143846118363 0.906287692236726 0.546856153881637 135 0.478494562169608 0.956989124339217 0.521505437830392 136 0.456953122147726 0.913906244295453 0.543046877852274 137 0.375401186531926 0.750802373063852 0.624598813468074 138 0.595048600382258 0.809902799235485 0.404951399617742 139 0.81917890474313 0.36164219051374 0.18082109525687 140 0.932127224604827 0.135745550790346 0.0678727753951728 141 0.887937693564784 0.224124612870433 0.112062306435216 142 0.825663205307197 0.348673589385606 0.174336794692803 143 0.788988434294927 0.422023131410145 0.211011565705073 144 0.674416781361919 0.651166437276163 0.325583218638081 145 0.586104121587404 0.827791756825192 0.413895878412596 146 0.670704586498716 0.658590827002568 0.329295413501284 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 22 0.160583941605839 NOK 10% type I error level 37 0.27007299270073 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/10gj0w1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/10gj0w1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/1ril21292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/1ril21292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/2ril21292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/2ril21292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/3j9kn1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/3j9kn1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/4j9kn1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/4j9kn1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/5j9kn1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/5j9kn1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/6ui181292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/6ui181292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/7n90t1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/7n90t1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/8n90t1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/8n90t1292092021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/9n90t1292092021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292091956ucirci2hgxibtuz/9n90t1292092021.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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Software written by Ed van Stee & Patrick Wessa

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