W7 Multiple Regression + depression

*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, 23 Nov 2010 17:23:55 +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/23/t1290533999ffoaestihubyjj0.htm/, Retrieved Tue, 23 Nov 2010 18:40:03 +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/23/t1290533999ffoaestihubyjj0.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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14 26 9 15 6 25 25 11 12 18 20 9 15 6 25 24 12 11 11 21 9 14 13 19 21 15 14 12 31 14 10 8 18 23 10 12 16 21 8 10 7 18 17 12 21 18 18 8 12 9 22 19 11 12 14 26 11 18 5 29 18 5 22 14 22 10 12 8 26 27 16 11 15 22 9 14 9 25 23 11 10 15 29 15 18 11 23 23 15 13 17 15 14 9 8 23 29 12 10 19 16 11 11 11 23 21 9 8 10 24 14 11 12 24 26 11 15 18 17 6 17 8 30 25 15 10 14 19 20 8 7 19 25 12 14 14 22 9 16 9 24 23 16 14 17 31 10 21 12 32 26 14 11 14 28 8 24 20 30 20 11 10 16 38 11 21 7 29 29 10 13 18 26 14 14 8 17 24 7 7 14 25 11 7 8 25 23 11 12 12 25 16 18 16 26 24 10 14 17 29 14 18 10 26 30 11 11 9 28 11 13 6 25 22 16 9 16 15 11 11 8 23 22 14 11 14 18 12 13 9 21 13 12 15 11 21 9 13 9 19 24 12 13 16 25 7 18 11 35 17 11 9 13 23 13 14 12 19 24 6 15 17 23 10 12 8 20 21 14 10 15 19 9 9 7 21 23 9 11 14 18 9 12 8 21 24 15 13 16 18 13 8 9 24 24 12 8 9 26 16 5 4 23 24 12 20 15 18 12 10 8 19 23 9 12 17 18 6 11 8 17 26 13 10 13 28 14 11 8 24 24 15 10 15 17 14 12 6 15 21 11 9 16 29 10 12 8 25 23 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 9 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Happines[t] = + 21.7347429204207 -0.0180014326985808Concern_over_Mistakes[t] -0.147556651387398Doubts_about_actions[t] + 0.0992260314758162Parental_Expectations[t] -0.0802257256507926Parental_Criticism[t] + 0.0089687156303007Personal_Standards[t] -0.0573688170520963Organization[t] -0.0282446698882794Popularity[t] -0.376605608154422Depression[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) 21.7347429204207 1.87791 11.5739 0 0 Concern_over_Mistakes -0.0180014326985808 0.037028 -0.4862 0.627636 0.313818 Doubts_about_actions -0.147556651387398 0.071178 -2.0731 0.040055 0.020027 Parental_Expectations 0.0992260314758162 0.060301 1.6455 0.102172 0.051086 Parental_Criticism -0.0802257256507926 0.076655 -1.0466 0.29715 0.148575 Personal_Standards 0.0089687156303007 0.049532 0.1811 0.856583 0.428292 Organization -0.0573688170520963 0.050067 -1.1458 0.25387 0.126935 Popularity -0.0282446698882794 0.056806 -0.4972 0.619845 0.309922 Depression -0.376605608154422 0.057887 -6.5058 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.592473962328227 R-squared 0.35102539603691 Adjusted R-squared 0.312850419333199 F-TEST (value) 9.19516988212817 F-TEST (DF numerator) 8 F-TEST (DF denominator) 136 p-value 4.56960691508357e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.96922665656298 Sum Squared Residuals 527.388092988874 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 14.9057707238344 -0.905770723834429 2 18 15.4195090753442 2.58049092465579 3 11 13.6444448548606 -2.64444485486064 4 12 13.5015999893043 -1.50159998930429 5 16 11.5454530394115 4.4545469605885 6 18 14.9762903208524 3.02370967914762 7 14 11.8294297608093 2.17057023919074 8 14 14.5017035977515 -0.50170359775151 9 15 15.5058220966137 -0.505822096613653 10 15 13.4701918987239 1.5298081012761 11 17 14.087749433377 2.91225056662302 12 19 15.7670886032303 3.2329113967697 13 10 12.129577495341 -2.12957749534098 14 18 16.2335302988412 1.76646970115882 15 14 11.7985814615027 2.20141853849726 16 14 14.0476596618759 -0.0476596618758974 17 17 15.0794925347539 1.92050746524608 18 14 15.8720975137168 -1.8720975137168 19 16 14.3678093479275 1.6321906520725 20 18 15.889935796455 2.11006420354499 21 14 13.7891367847543 0.210863215245652 22 12 12.7276674210026 -0.727667421002637 23 17 14.1894885991503 2.81051140084973 24 9 15.5369024188891 -6.53690241888905 25 16 14.6973582219245 1.3026417780755 26 14 13.6624724391093 0.337527560890664 27 11 14.155354892651 -3.15535489265097 28 16 16.7938894426644 -0.79388944266439 29 13 12.8039310792485 0.196068920751512 30 17 15.2071977215147 1.79280227848532 31 15 14.8681565577328 0.131843442267169 32 14 14.1235623065175 -0.123562306517452 33 16 15.0508740467417 0.949125953258345 34 9 10.039407151334 -1.03940715133396 35 15 14.0679453026792 0.93205469732078 36 17 15.5026998968183 1.49730010318174 37 13 14.263261662473 -1.26326166247301 38 15 15.3019272031259 -0.301927203125921 39 16 13.6358513163059 2.36414868369406 40 16 17.3517895627986 -1.35178956279863 41 12 14.3099442716745 -2.30994427167452 42 11 13.417359988719 -2.41735998871901 43 15 15.5469053708189 -0.546905370818936 44 17 13.7426122082553 3.25738779174473 45 13 14.5687296684054 -1.56872966840542 46 16 14.9539109481875 1.04608905181246 47 14 13.7556336769984 0.244366323001562 48 11 12.7453902986786 -1.74539029867862 49 12 12.8064432791371 -0.80644327913712 50 12 15.7459765854881 -3.74597658548807 51 15 14.8010711509746 0.198928849025394 52 16 14.3335886148203 1.66641138517972 53 15 15.9062291570902 -0.906229157090205 54 12 15.4890483980866 -3.48904839808659 55 12 13.1768869558733 -1.17688695587331 56 8 10.4902385518076 -2.49023855180762 57 13 15.366514318652 -2.36651431865196 58 11 14.1344751449257 -3.13447514492569 59 14 14.059484120722 -0.0594841207220411 60 15 13.4399729786774 1.56002702132262 61 10 14.326319825877 -4.32631982587699 62 11 12.81323080134 -1.81323080134 63 12 13.7698505000775 -1.76985050007754 64 15 13.0887402983902 1.91125970160982 65 15 13.7107590750262 1.28924092497382 66 14 13.6102864051806 0.389713594819377 67 16 13.1653911507121 2.83460884928786 68 15 13.8238809857649 1.17611901423512 69 15 15.4867245816992 -0.486724581699238 70 13 14.9335515331232 -1.93355153312318 71 17 14.1119410364411 2.88805896355893 72 13 12.3037019159621 0.696298084037921 73 15 13.7116981932075 1.2883018067925 74 13 15.4980525759228 -2.49805257592281 75 15 14.3790336423782 0.620966357621809 76 16 15.0295511234547 0.970448876545318 77 15 15.7196914497976 -0.719691449797592 78 16 14.7653364652836 1.23466353471641 79 15 13.9819942278108 1.01800577218919 80 14 14.0359836981033 -0.0359836981032603 81 15 13.030483321526 1.96951667847402 82 7 9.9450377803145 -2.94503778031449 83 17 14.8066382723383 2.19336172766168 84 13 13.5513051875617 -0.551305187561724 85 15 13.7564601620036 1.24353983799638 86 14 13.285438858077 0.714561141923016 87 13 15.2381705771465 -2.23817057714647 88 16 15.8798019669483 0.120198033051667 89 12 12.8669612219408 -0.866961221940764 90 14 15.2957228396481 -1.29572283964811 91 17 14.8977669225046 2.10223307749542 92 15 16.0018512127134 -1.00185121271337 93 17 14.6055018665187 2.39449813348131 94 12 13.0524454276766 -1.05244542767659 95 16 14.9970279173381 1.00297208266189 96 11 14.3954567913681 -3.39545679136809 97 15 12.3962729503771 2.60372704962293 98 9 12.4940741600249 -3.49407416002494 99 16 14.4265253523148 1.57347464768523 100 10 12.6064222704831 -2.60642227048315 101 10 10.3639885615379 -0.363988561537923 102 15 14.2804571855256 0.719542814474413 103 11 13.099388338146 -2.09938833814603 104 13 15.3531336073664 -2.35313360736644 105 14 12.7752463124134 1.22475368758662 106 18 14.9279335131776 3.07206648682239 107 16 15.4572548557537 0.542745144246334 108 14 12.8005501119041 1.19944988809589 109 14 13.7505076329222 0.249492367077826 110 14 15.6094601110529 -1.60946011105287 111 14 13.9021218642478 0.097878135752231 112 12 13.1002087709175 -1.1002087709175 113 14 14.1496355785078 -0.149635578507765 114 15 15.2207136391422 -0.220713639142172 115 15 16.0919227057247 -1.09192270572475 116 13 13.9673315269833 -0.967331526983318 117 17 16.2121723586471 0.787827641352853 118 17 15.6710936454105 1.32890635458947 119 19 15.0877687454447 3.91223125455526 120 15 14.4884901437482 0.511509856251822 121 13 14.6104007923928 -1.61040079239276 122 9 10.5109744868568 -1.51097448685678 123 15 15.9527440704162 -0.952744070416216 124 15 14.5228224749835 0.477177525016505 125 16 13.7722005440992 2.22779945590084 126 11 10.3675495758058 0.632450424194224 127 14 14.1670521938402 -0.167052193840206 128 11 12.5774290076569 -1.57742900765693 129 15 15.0915058907059 -0.0915058907058622 130 13 13.8910403831401 -0.891040383140057 131 16 12.3919206641532 3.60807933584677 132 14 14.4154545998879 -0.415454599887913 133 15 14.6474561196134 0.352543880386627 134 16 14.7677577376598 1.23224226234017 135 16 15.3445753919422 0.655424608057795 136 11 12.7431651465027 -1.74316514650273 137 13 12.620912093499 0.37908790650104 138 16 15.4505694382333 0.549430561766656 139 12 14.1024440454568 -2.1024440454568 140 9 10.9401466598227 -1.94014665982271 141 13 11.6742167003584 1.32578329964165 142 13 13.5513051875617 -0.551305187561724 143 14 13.3229156030485 0.677084396951531 144 19 15.0877687454447 3.91223125455526 145 13 16.2381814740127 -3.23818147401273 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.139168285998357 0.278336571996715 0.860831714001643 13 0.164198717605387 0.328397435210773 0.835801282394613 14 0.103345073717282 0.206690147434564 0.896654926282718 15 0.473050963724171 0.946101927448342 0.526949036275829 16 0.377914042667246 0.755828085334492 0.622085957332754 17 0.674452787953793 0.651094424092415 0.325547212046207 18 0.586832122882077 0.826335754235847 0.413167877117923 19 0.744370426524023 0.511259146951953 0.255629573475977 20 0.682609663989938 0.634780672020125 0.317390336010062 21 0.60058101907667 0.79883796184666 0.39941898092333 22 0.518899521946069 0.962200956107862 0.481100478053931 23 0.586317920334128 0.827364159331745 0.413682079665872 24 0.930027885875566 0.139944228248867 0.0699721141244337 25 0.904865206787798 0.190269586424404 0.095134793212202 26 0.871728763564512 0.256542472870975 0.128271236435488 27 0.960728092735173 0.0785438145296539 0.0392719072648269 28 0.944181985748541 0.111636028502917 0.0558180142514587 29 0.938278192795853 0.123443614408294 0.0617218072041472 30 0.943915900192261 0.112168199615477 0.0560840998077385 31 0.927646180743817 0.144707638512365 0.0723538192561827 32 0.90599430453182 0.18801139093636 0.0940056954681798 33 0.881361345422646 0.237277309154708 0.118638654577354 34 0.855212584006721 0.289574831986557 0.144787415993279 35 0.825713963739965 0.34857207252007 0.174286036260035 36 0.79541257417351 0.40917485165298 0.20458742582649 37 0.757108959110435 0.48578208177913 0.242891040889565 38 0.75989365592049 0.48021268815902 0.24010634407951 39 0.790265394844777 0.419469210310445 0.209734605155223 40 0.852204737672384 0.295590524655233 0.147795262327616 41 0.840782545253967 0.318434909492066 0.159217454746033 42 0.868507824382723 0.262984351234553 0.131492175617277 43 0.841314046533706 0.317371906932588 0.158685953466294 44 0.875639633382936 0.248720733234128 0.124360366617064 45 0.858488718841917 0.283022562316165 0.141511281158083 46 0.835537567631776 0.328924864736447 0.164462432368224 47 0.809149989063216 0.381700021873568 0.190850010936784 48 0.848540639109853 0.302918721780295 0.151459360890147 49 0.824360894174092 0.351278211651816 0.175639105825908 50 0.89704596317864 0.205908073642719 0.10295403682136 51 0.888776024039593 0.222447951920814 0.111223975960407 52 0.886005722116173 0.227988555767654 0.113994277883827 53 0.867490596436987 0.265018807126027 0.132509403563013 54 0.913693818525366 0.172612362949269 0.0863061814746345 55 0.898997136015782 0.202005727968437 0.101002863984218 56 0.926508834956506 0.146982330086988 0.0734911650434939 57 0.939381124358478 0.121237751283044 0.0606188756415222 58 0.956849331351442 0.0863013372971163 0.0431506686485582 59 0.945591333477849 0.108817333044303 0.0544086665221513 60 0.9404504235812 0.119099152837602 0.0595495764188008 61 0.978665698590004 0.0426686028199923 0.0213343014099961 62 0.977596334438246 0.0448073311235082 0.0224036655617541 63 0.97620523495864 0.0475895300827212 0.0237947650413606 64 0.975417664235786 0.0491646715284281 0.024582335764214 65 0.971355216569371 0.0572895668612582 0.0286447834306291 66 0.96234689102015 0.0753062179597007 0.0376531089798504 67 0.970909599294794 0.058180801410412 0.029090400705206 68 0.966448555087243 0.0671028898255137 0.0335514449127569 69 0.956344616584224 0.0873107668315516 0.0436553834157758 70 0.95635317964945 0.0872936407010996 0.0436468203505498 71 0.968465429507377 0.0630691409852462 0.0315345704926231 72 0.95908851926356 0.0818229614728815 0.0409114807364408 73 0.952574760272053 0.0948504794558945 0.0474252397279473 74 0.966679303560938 0.0666413928781245 0.0333206964390622 75 0.956838060062507 0.0863238798749868 0.0431619399374934 76 0.947171939441762 0.105656121116476 0.0528280605582382 77 0.93755363842385 0.124892723152301 0.0624463615761505 78 0.927497837878431 0.145004324243137 0.0725021621215686 79 0.91293316326829 0.174133673463419 0.0870668367317093 80 0.89109047247206 0.21781905505588 0.10890952752794 81 0.887436792336195 0.225126415327611 0.112563207663805 82 0.909406320719842 0.181187358560316 0.0905936792801582 83 0.915994245868436 0.168011508263129 0.0840057541315643 84 0.903350905420177 0.193298189159646 0.0966490945798228 85 0.887403898656728 0.225192202686545 0.112596101343272 86 0.86607303959461 0.267853920810781 0.133926960405391 87 0.872961011239348 0.254077977521305 0.127038988760652 88 0.84957886689095 0.300842266218099 0.15042113310905 89 0.82185345688977 0.356293086220459 0.178146543110229 90 0.810749501626236 0.378500996747528 0.189250498373764 91 0.81723912742567 0.36552174514866 0.18276087257433 92 0.788777613543907 0.422444772912186 0.211222386456093 93 0.82596132245925 0.348077355081502 0.174038677540751 94 0.803678689012297 0.392642621975405 0.196321310987703 95 0.769793253923199 0.460413492153602 0.230206746076801 96 0.849545069601254 0.300909860797492 0.150454930398746 97 0.87262984330353 0.254740313392941 0.127370156696471 98 0.922029253167551 0.155941493664897 0.0779707468324487 99 0.90817256931527 0.183654861369463 0.0918274306847313 100 0.916047010462538 0.167905979074924 0.083952989537462 101 0.891970655333469 0.216058689333063 0.108029344666531 102 0.867778428054088 0.264443143891824 0.132221571945912 103 0.89001063877599 0.219978722448021 0.10998936122401 104 0.93148889391631 0.137022212167382 0.0685111060836908 105 0.946589844583045 0.10682031083391 0.0534101554169551 106 0.957083730647014 0.085832538705972 0.042916269352986 107 0.942942411917655 0.11411517616469 0.0570575880823448 108 0.94470016434159 0.110599671316821 0.0552998356584106 109 0.924790329178846 0.150419341642309 0.0752096708211543 110 0.904515853122752 0.190968293754495 0.0954841468772476 111 0.877364091453779 0.245271817092443 0.122635908546221 112 0.843126080439889 0.313747839120223 0.156873919560111 113 0.808061127963097 0.383877744073805 0.191938872036903 114 0.757864312181657 0.484271375636686 0.242135687818343 115 0.757884352144897 0.484231295710206 0.242115647855103 116 0.720321077479232 0.559357845041536 0.279678922520768 117 0.716317064991421 0.567365870017158 0.283682935008579 118 0.717600260227467 0.564799479545065 0.282399739772533 119 0.768103388940915 0.46379322211817 0.231896611059085 120 0.778204446739362 0.443591106521276 0.221795553260638 121 0.718749056925178 0.562501886149644 0.281250943074822 122 0.651945750463972 0.696108499072057 0.348054249536028 123 0.574108332977032 0.851783334045936 0.425891667022968 124 0.495177178608147 0.990354357216295 0.504822821391853 125 0.420943413965219 0.841886827930437 0.579056586034781 126 0.425998050406391 0.851996100812782 0.574001949593609 127 0.412863509188115 0.82572701837623 0.587136490811885 128 0.331104507482855 0.66220901496571 0.668895492517145 129 0.254845733264243 0.509691466528486 0.745154266735757 130 0.194010536691998 0.388021073383997 0.805989463308001 131 0.504153795267943 0.991692409464114 0.495846204732057 132 0.477154313942914 0.954308627885828 0.522845686057086 133 0.854510030178875 0.290979939642251 0.145489969821125 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 4 0.0327868852459016 OK 10% type I error level 18 0.147540983606557 NOK

Charts produced by software:

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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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