Multiple Regression 2

*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, 25 Dec 2010 15:04:54 +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/25/t12932894348eq6r21qargr60m.htm/, Retrieved Sat, 25 Dec 2010 16:03:56 +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/25/t12932894348eq6r21qargr60m.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 «

12 2 53 10 7 6 0 9 11 2 86 12 5 6 0 9 14 4 66 11 7 11 0 9 12 3 67 10 3 7 1 9 21 4 76 12 7 12 0 9 12 3 78 12 7 8 0 9 22 3 53 14 7 7 0 9 11 4 80 14 1 11 0 9 10 3 74 11 4 8 0 9 13 4 76 11 5 9 0 9 10 3 79 13 6 9 1 9 8 2 54 11 4 6 0 9 15 3 67 10 7 9 1 9 10 3 87 14 6 5 0 9 14 3 58 14 2 9 1 9 14 2 75 12 2 4 1 9 11 3 88 11 6 9 0 9 10 2 64 10 7 6 1 9 13 4 57 12 5 8 0 9 7 5 66 10 2 12 1 9 12 3 54 14 7 7 0 9 14 3 56 12 4 8 0 9 11 1 86 13 5 3 1 9 9 4 80 13 5 9 0 9 11 3 76 12 5 7 1 9 15 4 69 14 3 9 0 9 13 3 67 11 5 9 1 9 9 3 80 12 1 7 0 9 15 1 54 13 1 5 1 9 10 4 71 11 3 8 0 9 11 4 84 11 2 7 0 9 13 2 74 14 3 6 1 9 8 2 71 12 2 6 1 9 20 1 63 13 5 4 1 9 12 3 71 11 2 8 1 9 10 4 76 13 3 8 0 9 10 1 69 13 4 3 1 9 9 3 74 13 6 8 1 9 14 3 75 12 2 9 0 9 8 2 54 14 7 6 1 9 14 4 52 14 6 9 1 9 11 3 69 8 5 5 0 9 13 3 68 13 3 8 0 9 11 2 75 11 3 6 0 9 11 3 75 13 4 9 1 9 10 2 72 10 5 8 0 9 14 1 67 10 2 5 1 9 18 3 63 13 7 9 1 9 14 3 62 12 6 8 0 9 11 5 63 16 5 11 1 9 12 1 76 13 6 7 0 9 13 3 74 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Depressie[t] = + 6.83819967635574 -0.114673066891991Leeftijd[t] -0.0937460780704117Sportgerelateerde_groep[t] + 0.466815574575993Stress[t] + 0.260747924507668Veranderingen_verleden[t] + 0.297659363826662Alcoholgebruik[t] -0.357602662543149Depressie_mannen[t] + 0.369370037808583Depressie_oktober[t] + 0.00235586693461173t + 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) 6.83819967635574 7.734283 0.8841 0.37818 0.18909 Leeftijd -0.114673066891991 0.411141 -0.2789 0.780734 0.390367 Sportgerelateerde_groep -0.0937460780704117 0.02383 -3.9339 0.000133 6.6e-05 Stress 0.466815574575993 0.165556 2.8197 0.005526 0.002763 Veranderingen_verleden 0.260747924507668 0.138261 1.8859 0.06144 0.03072 Alcoholgebruik 0.297659363826662 0.173245 1.7181 0.088046 0.044023 Depressie_mannen -0.357602662543149 0.537147 -0.6657 0.506702 0.253351 Depressie_oktober 0.369370037808583 0.820422 0.4502 0.653269 0.326634 t 0.00235586693461173 0.00982 0.2399 0.810772 0.405386 Multiple Linear Regression - Regression Statistics Multiple R 0.470096264194295 R-squared 0.220990497609432 Adjusted R-squared 0.175166409233517 F-TEST (value) 4.82258361140864 F-TEST (DF numerator) 8 F-TEST (DF denominator) 136 p-value 3.0413779014804e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.88249480486812 Sum Squared Residuals 1129.99357681247 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 13.2463450123254 -1.24634501232539 2 11 10.5672156030731 0.432784396926941 3 14 13.7581239912046 0.241876008795425 4 12 10.7233594565043 1.2766405434957 5 21 13.5898498827723 7.41015011722766 6 12 12.3287492051515 -0.328749205151467 7 22 15.3107288091717 6.6892711908283 8 11 12.2934174095738 -1.29341740957384 9 10 11.461741770138 -1.46174177013795 10 13 11.7203397023741 1.27966029762592 11 10 12.392906813106 -2.39290681310595 12 8 12.8630852715887 -4.86308527158869 13 15 12.3828726845998 2.61712731540019 14 10 11.283786571159 -1.28378657115899 15 14 13.7948217968684 0.205178203131633 16 14 9.89623943521267 4.10376056478732 17 11 10.9872988254711 0.0127011745289203 18 10 11.8975852288961 -1.8975852288961 19 13 13.6918741988727 -0.691874198872739 20 7 11.8530021663702 -4.85300216637016 21 12 15.2499648681858 -3.24996486818585 22 14 13.6466130201313 0.353386979868692 23 11 9.94759669614475 1.05240330385525 24 9 12.311968676329 -3.31196867632898 25 11 11.3842449576648 -0.384244957664766 26 15 13.2932069945334 1.70679300546661 27 13 12.361174547245 0.638825452754973 28 9 10.3309392106994 -1.33093921069943 29 15 12.5139334256283 2.48606657437175 30 10 11.4170322185764 -1.41703221857637 31 11 9.6422817822613 1.3577182177387 32 13 11.8173771855498 1.18262281445015 33 8 10.906592213036 -2.90659221303604 34 20 12.4273303918716 7.57266960812838 35 12 10.9251340330906 1.07486596690939 36 10 11.896068178984 -1.89606817898397 37 10 11.3135142359186 -1.31351423591865 38 9 12.6275862468659 -3.62758624686587 39 14 11.6816507894932 2.31834921050679 40 8 14.7541373804657 -6.75413738046565 41 14 15.3468694367294 -1.34686943672942 42 11 9.9755388786319 1.0244611213681 43 13 12.7772009389815 0.222799061018461 44 11 10.7090574495099 0.290942550490051 45 11 12.3264947521491 -1.32649475214906 46 10 11.6450064196831 -1.64500641968308 47 14 10.1979412163156 3.8020587836844 48 18 14.2407590633208 3.75924093667916 49 14 13.6692408079587 0.330759192041309 50 11 15.4903942657721 -4.49039426577205 51 12 12.7600097933755 -0.760009793375463 52 13 12.5882669112366 0.411733088763421 53 9 12.4784316373264 -3.47843163732641 54 10 11.8076262116177 -1.80762621161773 55 15 13.1533051212567 1.84669487874327 56 20 14.8855773294899 5.11442267051015 57 12 12.1672729992236 -0.167272999223579 58 12 13.1948022353324 -1.19480223533235 59 14 13.1105616481519 0.889438351848071 60 13 13.6529888580294 -0.652988858029378 61 11 10.7628984958227 0.237101504177274 62 17 14.4547889931032 2.54521100689677 63 12 13.4703397530705 -1.47033975307047 64 13 15.0054256346264 -2.00542563462636 65 14 14.27068866753 -0.270688667530026 66 13 11.9156152178381 1.08438478216186 67 15 15.4539639751776 -0.453963975177584 68 13 12.5082644842554 0.491735515744601 69 10 11.7490207486865 -1.74902074868649 70 11 10.4559197625219 0.54408023747809 71 13 14.0271846045951 -1.0271846045951 72 17 12.9289074759405 4.07109252405953 73 13 13.4341451784999 -0.434145178499948 74 9 13.4369904458717 -4.43699044587171 75 11 11.2378304697267 -0.237830469726695 76 10 15.0009559936661 -5.00095599366615 77 9 12.570208357538 -3.57020835753803 78 12 12.3387617252187 -0.338761725218668 79 12 12.0003312219935 -0.000331221993505235 80 13 12.1470442613497 0.852955738650269 81 13 12.368343568885 0.631656431114986 82 22 15.4304645114702 6.56953548852985 83 13 13.2158094852649 -0.215809485264872 84 15 15.4801836268806 -0.480183626880643 85 13 11.3165870300471 1.68341296995286 86 15 12.3244821089957 2.67551789100434 87 10 14.0610488439968 -4.06104884399675 88 11 11.400068552919 -0.400068552919048 89 16 12.9867260489492 3.01327395105079 90 11 11.6291838822481 -0.629183882248089 91 11 11.0473791268677 -0.0473791268676598 92 10 12.0399612331697 -2.03996123316967 93 10 12.5459946202631 -2.5459946202631 94 16 13.3917871218324 2.60821287816755 95 12 12.6597654048493 -0.659765404849253 96 11 13.1421716061762 -2.14217160617622 97 16 13.427708739196 2.57229126080403 98 19 13.2162003594773 5.78379964052272 99 11 12.4316266687934 -1.43162666879344 100 15 13.512073072357 1.48792692764305 101 24 17.97204969524 6.02795030476001 102 14 11.1539668201045 2.84603317989555 103 15 14.624307744054 0.375692255945983 104 11 11.0401334375755 -0.0401334375755472 105 15 15.9982709935626 -0.99827099356259 106 12 13.5293946437637 -1.52939464376369 107 10 10.2217790118711 -0.221779011871085 108 14 14.707677883219 -0.707677883218995 109 9 13.6289649847128 -4.62896498471277 110 15 11.3333949270364 3.66660507296362 111 15 12.8802587455208 2.11974125447916 112 14 11.1587104469871 2.84128955301294 113 11 11.8234596834956 -0.82345968349563 114 8 13.0445145653456 -5.0445145653456 115 11 12.0558375341057 -1.05583753410566 116 8 10.0018131730916 -2.00181317309158 117 10 12.4921710884259 -2.49217108842594 118 11 13.1844651193203 -2.18446511932031 119 13 14.548092788969 -1.54809278896896 120 11 13.6912733437775 -2.69127334377751 121 20 13.0824564637145 6.91754353628548 122 10 13.5649341504054 -3.5649341504054 123 12 12.6348589368471 -0.634858936847107 124 14 13.6399830474762 0.360016952523753 125 23 15.1137497087129 7.8862502912871 126 14 12.9218484739061 1.07815152609386 127 16 14.0931890859356 1.90681091406436 128 11 14.4631975637828 -3.46319756378277 129 12 13.0440987807509 -1.04409878075085 130 10 13.6525837163621 -3.65258371636212 131 14 12.8928553301592 1.10714466984081 132 12 9.29388566756919 2.70611433243082 133 12 12.5234513601797 -0.523451360179669 134 11 11.1860224477108 -0.186022447710815 135 12 13.1486680454118 -1.14866804541178 136 13 13.6472982457722 -0.647298245772176 137 17 13.1504611741158 3.84953882588421 138 9 13.0528194607799 -4.05281946077994 139 12 14.4667069745839 -2.46670697458386 140 19 13.2781436780198 5.72185632198019 141 15 15.6144680421535 -0.614468042153512 142 14 14.4204420064533 -0.420442006453278 143 11 13.2433617926856 -2.24336179268561 144 9 11.8843791574773 -2.88437915747732 145 18 13.4297121179846 4.57028788201542 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.917649485645883 0.164701028708235 0.0823505143541175 13 0.927366716135294 0.145266567729411 0.0726332838647056 14 0.886732299108813 0.226535401782375 0.113267700891187 15 0.823976131876344 0.352047736247312 0.176023868123656 16 0.95121353123354 0.0975729375329196 0.0487864687664598 17 0.9310423666437 0.137915266712601 0.0689576333563005 18 0.904777273174386 0.190445453651228 0.0952227268256138 19 0.868044463093756 0.263911073812488 0.131955536906244 20 0.888189538208106 0.223620923583788 0.111810461791894 21 0.877895680798445 0.244208638403109 0.122104319201554 22 0.883999155525234 0.232001688949533 0.116000844474766 23 0.85356472012137 0.29287055975726 0.14643527987863 24 0.824927545660767 0.350144908678466 0.175072454339233 25 0.774423310640117 0.451153378719767 0.225576689359883 26 0.784644793776288 0.430710412447424 0.215355206223712 27 0.75125963312238 0.497480733755241 0.248740366877621 28 0.709221513850829 0.581556972298342 0.290778486149171 29 0.699837500649168 0.600324998701664 0.300162499350832 30 0.652733187253302 0.694533625493396 0.347266812746698 31 0.66596374648759 0.66807250702482 0.33403625351241 32 0.609049423529702 0.781901152940597 0.390950576470298 33 0.588484439294483 0.823031121411035 0.411515560705517 34 0.817528045409456 0.364943909181089 0.182471954590544 35 0.7856507764692 0.428698447061601 0.214349223530801 36 0.747872315180165 0.50425536963967 0.252127684819835 37 0.725086483987319 0.549827032025363 0.274913516012681 38 0.752029574087591 0.495940851824817 0.247970425912409 39 0.74685917447981 0.506281651040381 0.253140825520191 40 0.880038144041163 0.239923711917674 0.119961855958837 41 0.855864914188765 0.28827017162247 0.144135085811235 42 0.843104842616482 0.313790314767037 0.156895157383518 43 0.80858848373895 0.3828230325221 0.19141151626105 44 0.768425949139489 0.463148101721023 0.231574050860511 45 0.728606355806658 0.542787288386684 0.271393644193342 46 0.693015372063081 0.613969255873837 0.306984627936919 47 0.715285034301454 0.569429931397091 0.284714965698546 48 0.767626294777383 0.464747410445234 0.232373705222617 49 0.72693242221893 0.546135155562142 0.273067577781071 50 0.75471318788873 0.49057362422254 0.24528681211127 51 0.722394696004117 0.555210607991766 0.277605303995883 52 0.679592718326016 0.640814563347968 0.320407281673984 53 0.68928553101205 0.621428937975899 0.31071446898795 54 0.663306507844625 0.67338698431075 0.336693492155375 55 0.653644228579005 0.69271154284199 0.346355771420995 56 0.739175264181259 0.521649471637483 0.260824735818741 57 0.696944431466534 0.606111137066933 0.303055568533466 58 0.66135480804699 0.67729038390602 0.33864519195301 59 0.615697807965653 0.768604384068694 0.384302192034347 60 0.572686995486979 0.854626009026042 0.427313004513021 61 0.529971904203603 0.940056191592794 0.470028095796397 62 0.512493630719147 0.975012738561707 0.487506369280853 63 0.48598693775739 0.97197387551478 0.51401306224261 64 0.45412272675907 0.90824545351814 0.54587727324093 65 0.408664553287729 0.817329106575458 0.591335446712271 66 0.370956396515709 0.741912793031417 0.629043603484291 67 0.330641093124901 0.661282186249802 0.669358906875099 68 0.289877270043013 0.579754540086026 0.710122729956987 69 0.259184477156696 0.518368954313391 0.740815522843304 70 0.221991859146661 0.443983718293321 0.77800814085334 71 0.190378477380521 0.380756954761043 0.809621522619479 72 0.227212644067668 0.454425288135336 0.772787355932332 73 0.191350743179003 0.382701486358006 0.808649256820997 74 0.232542408063483 0.465084816126966 0.767457591936517 75 0.196933791232931 0.393867582465863 0.803066208767068 76 0.273524735676698 0.547049471353396 0.726475264323302 77 0.291241832365296 0.582483664730591 0.708758167634704 78 0.268693064189314 0.537386128378629 0.731306935810686 79 0.23238650259227 0.46477300518454 0.76761349740773 80 0.200688228446674 0.401376456893348 0.799311771553326 81 0.174182720252634 0.348365440505268 0.825817279747366 82 0.320831286015797 0.641662572031595 0.679168713984203 83 0.277725874404447 0.555451748808893 0.722274125595553 84 0.24131851295014 0.48263702590028 0.75868148704986 85 0.213471537170618 0.426943074341236 0.786528462829382 86 0.201763135045775 0.40352627009155 0.798236864954225 87 0.236848441738193 0.473696883476385 0.763151558261807 88 0.199772357437238 0.399544714874476 0.800227642562762 89 0.200980356889255 0.40196071377851 0.799019643110745 90 0.1689037025308 0.3378074050616 0.8310962974692 91 0.138523009590804 0.277046019181608 0.861476990409196 92 0.126600704886749 0.253201409773497 0.873399295113251 93 0.119565420976921 0.239130841953843 0.880434579023079 94 0.110477268557874 0.220954537115748 0.889522731442126 95 0.0898362915004407 0.179672583000881 0.91016370849956 96 0.0838512483655847 0.167702496731169 0.916148751634415 97 0.0758443070556683 0.151688614111337 0.924155692944332 98 0.122657487007097 0.245314974014195 0.877342512992903 99 0.101666546828429 0.203333093656858 0.89833345317157 100 0.0829278554228184 0.165855710845637 0.917072144577182 101 0.174296414244879 0.348592828489757 0.825703585755121 102 0.171385474721833 0.342770949443667 0.828614525278167 103 0.154123912218134 0.308247824436267 0.845876087781866 104 0.125243629344186 0.250487258688373 0.874756370655814 105 0.099327039348248 0.198654078696496 0.900672960651752 106 0.07913635821617 0.15827271643234 0.92086364178383 107 0.0599741464699506 0.119948292939901 0.94002585353005 108 0.0452971495524328 0.0905942991048656 0.954702850447567 109 0.0583955071377346 0.116791014275469 0.941604492862265 110 0.0853454123941815 0.170690824788363 0.914654587605819 111 0.0705034790257009 0.141006958051402 0.9294965209743 112 0.0817262045155717 0.163452409031143 0.918273795484428 113 0.0664078185995473 0.132815637199095 0.933592181400453 114 0.0762212079813115 0.152442415962623 0.923778792018689 115 0.0563637278722062 0.112727455744412 0.943636272127794 116 0.0448964610653967 0.0897929221307934 0.955103538934603 117 0.0366579273631597 0.0733158547263194 0.96334207263684 118 0.0261647820786883 0.0523295641573767 0.973835217921312 119 0.0178422650946325 0.035684530189265 0.982157734905368 120 0.0200466101641333 0.0400932203282666 0.979953389835867 121 0.0548820207132489 0.109764041426498 0.945117979286751 122 0.064914152894404 0.129828305788808 0.935085847105596 123 0.048165935367135 0.09633187073427 0.951834064632865 124 0.0349130801768101 0.0698261603536202 0.96508691982319 125 0.226365254674916 0.452730509349832 0.773634745325084 126 0.466266540036117 0.932533080072235 0.533733459963883 127 0.56381414252386 0.87237171495228 0.43618585747614 128 0.474254968707204 0.948509937414408 0.525745031292796 129 0.389341055859414 0.778682111718828 0.610658944140586 130 0.312198528802398 0.624397057604795 0.687801471197602 131 0.30882381577784 0.61764763155568 0.69117618422216 132 0.207671708667236 0.415343417334472 0.792328291332764 133 0.221780588543262 0.443561177086524 0.778219411456738 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 2 0.0163934426229508 OK 10% type I error level 9 0.0737704918032787 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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