WS10

*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, 12 Dec 2010 16:35:27 +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/12/t1292171632yug4exq4lqkhl6y.htm/, Retrieved Sun, 12 Dec 2010 17:33:52 +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/12/t1292171632yug4exq4lqkhl6y.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 «

10 11 16 1 24 14 33 12 24 14 11 13 2 25 11 30 8 25 18 15 16 2 17 6 30 8 30 15 9 15 1 18 12 26 8 19 11 17 15 2 16 10 24 7 22 17 16 14 2 20 10 28 4 25 19 9 11 2 16 11 24 11 23 7 12 15 2 18 16 27 7 17 12 14 13 2 17 11 28 7 21 15 4 6 2 30 12 42 10 19 14 13 11 2 23 8 31 10 15 14 12 9 2 18 12 25 8 16 16 13 14 1 12 4 23 4 27 12 15 5 2 21 9 27 9 22 12 10 8 1 15 8 23 8 14 13 9 6 1 20 8 34 7 22 9 11 15 2 27 15 36 9 23 11 15 12 2 21 9 31 13 19 12 10 10 1 31 14 39 8 18 11 9 8 1 19 11 27 8 20 14 15 16 2 16 8 27 9 23 18 12 8 2 20 9 31 6 25 11 12 12 1 21 9 31 9 19 17 14 14 2 17 9 26 6 22 14 16 13 1 22 9 34 9 24 14 5 8 2 26 11 39 5 29 12 10 11 2 25 16 39 16 26 14 9 12 2 25 8 35 7 32 15 14 13 2 17 9 30 9 25 10 5 4 1 33 14 40 6 32 11 12 16 1 32 16 38 6 29 14 14 17 1 13 16 21 5 17 11 16 14 2 32 12 45 12 28 15 11 8 2 22 9 32 9 25 16 6 6 2 17 9 29 5 25 15 11 15 1 33 11 40 6 28 16 9 11 2 31 14 44 11 23 13 16 16 1 20 10 28 8 26 15 13 5 1 15 12 24 8 20 16 10 5 2 29 10 37 8 25 13 6 9 1 23 13 33 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 15.8129739767573 -0.067519832546916Popularity[t] + 0.0891906979933913KnowPeople[t] + 0.620779773881064Gender[t] -0.14400699076612CMistakes[t] -0.257376073460273DAction[t] + 0.122065094451775PExpectations[t] -0.117312080212603PCriticism[t] + 0.00324282956475924PStandards[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) 15.8129739767573 1.669852 9.4697 0 0 Popularity -0.067519832546916 0.077834 -0.8675 0.387215 0.193607 KnowPeople 0.0891906979933913 0.06621 1.3471 0.180206 0.090103 Gender 0.620779773881064 0.410795 1.5112 0.133083 0.066542 CMistakes -0.14400699076612 0.100025 -1.4397 0.152264 0.076132 DAction -0.257376073460273 0.078485 -3.2793 0.001324 0.000662 PExpectations 0.122065094451775 0.083353 1.4644 0.1454 0.0727 PCriticism -0.117312080212603 0.088174 -1.3305 0.185607 0.092804 PStandards 0.00324282956475924 0.0525 0.0618 0.950839 0.475419 Multiple Linear Regression - Regression Statistics Multiple R 0.39521657780064 R-squared 0.156196143368449 Adjusted R-squared 0.106192951864358 F-TEST (value) 3.12372347984363 F-TEST (DF numerator) 8 F-TEST (DF denominator) 135 p-value 0.00285720771111464 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.22343763349311 Sum Squared Residuals 667.396112854517 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 12.7568850175974 -2.75688501759745 2 14 13.8445097941729 0.155490205827120 3 18 16.2971529992195 1.70284700078049 4 15 13.7801065880793 1.21989341192066 5 11 14.5464042100412 -3.54640421004119 6 17 14.7986304886694 2.20136951133057 7 19 14.0064185136970 4.99358148630297 8 7 13.4017140860134 -6.40171408601343 9 12 14.6542167957111 -2.65421679571111 10 15 13.9261027043642 1.07389729563578 11 14 14.4462435733843 -0.446243573384301 12 14 13.5313890932133 0.468610906786741 13 16 16.4728827661904 -0.472882766190354 14 12 13.4584491377609 -1.45844913776091 15 12 14.1673677045390 -2.16736770453902 16 13 14.8224419429688 -1.82244194296875 17 9 13.3139657421549 -4.31396574215486 18 11 14.0920675919771 -3.09206759197706 19 12 12.4633936369937 -0.463393636993678 20 11 13.3944487088363 -2.39444870883629 21 14 15.4202006725438 -1.42020067254384 22 18 14.9225128272871 3.07748717271286 23 11 14.1430956365872 -3.14309563658715 24 17 15.1345843614989 1.86541563850114 25 14 14.2006094448059 -0.20060944480588 26 14 15.1231617176841 -1.12316171768412 27 12 12.6100999013616 -0.610099901361558 28 14 15.4128243410789 -1.41282434107893 29 15 15.1914462893690 -0.191446289369035 30 10 12.3799234990193 -2.37992349901928 31 11 12.3529692133593 -1.35296921335931 32 14 13.0465445905705 0.95345540942953 33 11 13.7021329052291 -2.70213290522907 34 15 14.4721475321158 0.527852467884224 35 16 15.4544532901893 0.545546709810738 36 15 13.7150590837869 1.28494091621313 37 16 13.5154873208599 2.48451267914009 38 13 13.8902266194895 -0.890226619489494 39 15 12.8092538909173 2.19074610908266 40 16 13.7343078143309 2.26569218566912 41 13 12.8553182107809 0.144681789219117 42 9 11.6529413034380 -2.65294130343796 43 14 14.0644107743148 -0.0644107743147942 44 15 15.5161267068968 -0.516126706896842 45 14 13.8343339658184 0.165666034181566 46 16 14.7356796048856 1.26432039511445 47 13 13.4577413726532 -0.45774137265324 48 17 12.7850852438922 4.21491475610781 49 16 13.6566154115553 2.34338458844467 50 15 14.7000595441445 0.299940455855537 51 16 13.6638951032945 2.33610489670551 52 15 14.3579389331082 0.642061066891804 53 13 14.3405534133744 -1.34055341337439 54 11 14.5464042100412 -3.5464042100412 55 16 13.0078172921444 2.99218270785565 56 17 14.5739216814113 2.42607831858873 57 10 14.2674664006857 -4.26746640068571 58 17 13.888088967128 3.11191103287199 59 11 14.4219320801474 -3.4219320801474 60 14 13.3167860887002 0.683213911299754 61 15 14.3749598210048 0.62504017899521 62 11 12.9540994051834 -1.95409940518335 63 15 14.3500890489767 0.649910951023294 64 16 14.1792618866486 1.82073811335139 65 16 15.0943216687061 0.90567833129387 66 15 13.4648946291656 1.53510537083444 67 14 15.1259533759282 -1.12595337592815 68 17 14.7157028830430 2.28429711695704 69 12 13.731396348594 -1.73139634859401 70 13 13.5615852146398 -0.561585214639848 71 12 13.0922558621654 -1.09225586216544 72 9 14.4315415747077 -5.43154157470775 73 17 15.5793871154115 1.42061288458853 74 11 13.3149125728471 -2.31491257284713 75 16 14.6326534129155 1.36734658708449 76 14 14.7112527896454 -0.711252789645417 77 9 12.7958266559079 -3.7958266559079 78 15 14.2428730974465 0.757126902553537 79 17 14.7986304886694 2.20136951133057 80 17 12.3060012723212 4.69399872767878 81 15 14.033169526392 0.966830473608008 82 18 13.1949313715317 4.80506862846828 83 13 13.9671527784902 -0.967152778490243 84 15 14.9336418283889 0.0663581716110736 85 12 13.2789398461791 -1.27893984617910 86 16 13.7236684919968 2.27633150800317 87 17 15.1989867051391 1.80101329486089 88 13 13.7778618697625 -0.777861869762505 89 15 12.3509891418084 2.64901085819162 90 12 14.1283816040723 -2.1283816040723 91 11 14.5311068587945 -3.53110685879453 92 15 14.3669710772301 0.633028922769895 93 15 14.9301796095864 0.0698203904135882 94 18 15.0290249740631 2.97097502593685 95 16 12.4974737679987 3.50252623200135 96 12 13.6382270909777 -1.63822709097771 97 16 14.4404840414667 1.55951595853335 98 15 14.6111048015635 0.388895198436529 99 15 13.5288490054556 1.47115099454436 100 17 15.3952968412283 1.60470315877167 101 16 14.9958827106776 1.00411728932239 102 13 15.2872315400232 -2.28723154002325 103 13 13.4244581860286 -0.424458186028593 104 13 12.6431365076289 0.356863492371148 105 16 13.8529645659302 2.14703543406976 106 11 13.2510894948278 -2.25108949482776 107 15 14.508789944592 0.491210055408002 108 15 14.8407039616459 0.159296038354094 109 9 14.1642409444759 -5.16424094447593 110 14 11.1022064037709 2.89779359622905 111 14 14.0535949468925 -0.0535949468924951 112 15 14.1362830973409 0.8637169026591 113 14 14.7225466449787 -0.722546644978747 114 15 13.2879553816439 1.71204461835614 115 14 14.1288391303254 -0.128839130325372 116 13 13.3082203413268 -0.308220341326841 117 15 14.9266232216534 0.0733767783465921 118 16 14.7494993366727 1.25050066332734 119 14 14.0850010888461 -0.0850010888460854 120 14 14.2438309804088 -0.243830980408849 121 14 13.8528027457148 0.147197254285215 122 15 13.7801065880793 1.21989341192066 123 15 14.7908801362229 0.209119863777135 124 13 13.1283370574974 -0.128337057497380 125 15 13.2155369706681 1.78446302933189 126 16 14.5023714827766 1.49762851722342 127 10 12.7823617273212 -2.78236172732121 128 8 12.5559456377475 -4.55594563774745 129 14 13.0338252136312 0.966174786368766 130 12 12.8948242180844 -0.89482421808436 131 13 15.0496665663129 -2.04966656631293 132 15 14.7177843410943 0.28221565890569 133 14 14.8877475731738 -0.88774757317379 134 15 12.9729758195497 2.0270241804503 135 19 14.8653760431002 4.13462395689978 136 17 15.5285034677449 1.47149653225507 137 16 14.7758960165593 1.22410398344071 138 17 12.9645829121061 4.03541708789390 139 13 15.5175042474868 -2.51750424748675 140 16 14.8888030209434 1.11119697905656 141 14 14.6959285530744 -0.695928553074447 142 12 13.5282913549783 -1.52829135497834 143 12 13.4584491377609 -1.45844913776091 144 13 12.8354120265444 0.164587973455570 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.565829480129599 0.868341039740802 0.434170519870401 13 0.917036282055967 0.165927435888066 0.0829637179440328 14 0.90279133826553 0.194417323468939 0.0972086617344695 15 0.859526849521843 0.280946300956314 0.140473150478157 16 0.792663371815769 0.414673256368462 0.207336628184231 17 0.795570265467286 0.408859469065429 0.204429734532714 18 0.739766582814041 0.520466834371917 0.260233417185959 19 0.830806557923743 0.338386884152515 0.169193442076257 20 0.815751511993685 0.36849697601263 0.184248488006315 21 0.75686156927369 0.486276861452619 0.243138430726309 22 0.785133410488294 0.429733179023411 0.214866589511706 23 0.745863440530845 0.50827311893831 0.254136559469155 24 0.734773481216194 0.530453037567611 0.265226518783806 25 0.805943023431337 0.388113953137327 0.194056976568664 26 0.855934515212032 0.288130969575936 0.144065484787968 27 0.83154072534916 0.33691854930168 0.16845927465084 28 0.877409177789986 0.245181644420028 0.122590822210014 29 0.841026485265399 0.317947029469202 0.158973514734601 30 0.831586480961865 0.336827038076269 0.168413519038135 31 0.813697499614683 0.372605000770635 0.186302500385317 32 0.849171318008231 0.301657363983538 0.150828681991769 33 0.827173151072858 0.345653697854284 0.172826848927142 34 0.787300462864569 0.425399074270862 0.212699537135431 35 0.744108242266248 0.511783515467504 0.255891757733752 36 0.743196586579866 0.513606826840268 0.256803413420134 37 0.808786133875629 0.382427732248742 0.191213866124371 38 0.770108657325418 0.459782685349164 0.229891342674582 39 0.802098891018122 0.395802217963755 0.197901108981878 40 0.798579683766257 0.402840632467487 0.201420316233743 41 0.760913874541657 0.478172250916687 0.239086125458343 42 0.755978145727055 0.488043708545889 0.244021854272945 43 0.714964375245059 0.570071249509883 0.285035624754941 44 0.667645464508743 0.664709070982514 0.332354535491257 45 0.615644214585945 0.76871157082811 0.384355785414055 46 0.606250827795888 0.787498344408224 0.393749172204112 47 0.554364897784023 0.891270204431955 0.445635102215977 48 0.720851293379371 0.558297413241258 0.279148706620629 49 0.736352966887333 0.527294066225334 0.263647033112667 50 0.692132880718194 0.615734238563611 0.307867119281806 51 0.70729335099169 0.58541329801662 0.29270664900831 52 0.664997680204548 0.670004639590905 0.335002319795452 53 0.625619601019111 0.748760797961778 0.374380398980889 54 0.680476100446538 0.639047799106924 0.319523899553462 55 0.713323320795796 0.573353358408408 0.286676679204204 56 0.72240411380834 0.55519177238332 0.27759588619166 57 0.816518174095411 0.366963651809178 0.183481825904589 58 0.852750317640239 0.294499364719522 0.147249682359761 59 0.894976172830007 0.210047654339987 0.105023827169993 60 0.87161678176232 0.256766436475360 0.128383218237680 61 0.846191898625465 0.30761620274907 0.153808101374535 62 0.836333240178218 0.327333519643565 0.163666759821782 63 0.805579917264215 0.38884016547157 0.194420082735785 64 0.795140972618266 0.409718054763468 0.204859027381734 65 0.769563885537139 0.460872228925722 0.230436114462861 66 0.754223932246108 0.491552135507784 0.245776067753892 67 0.722068927752393 0.555862144495213 0.277931072247607 68 0.726256012299356 0.547487975401289 0.273743987700644 69 0.706861290685484 0.586277418629031 0.293138709314516 70 0.664035904502583 0.671928190994833 0.335964095497417 71 0.631123361937738 0.737753276124524 0.368876638062262 72 0.833710112872962 0.332579774254077 0.166289887127038 73 0.815651148921586 0.368697702156828 0.184348851078414 74 0.82029991503954 0.359400169920919 0.179700084960460 75 0.802165476099749 0.395669047800502 0.197834523900251 76 0.773803333913999 0.452393332172003 0.226196666086001 77 0.858144916601112 0.283710166797775 0.141855083398888 78 0.83188959426866 0.336220811462681 0.168110405731340 79 0.830329093703452 0.339341812593096 0.169670906296548 80 0.921321391919797 0.157357216160406 0.0786786080802028 81 0.90574654101522 0.188506917969561 0.0942534589847803 82 0.958527808544538 0.0829443829109233 0.0414721914554617 83 0.949014949050039 0.101970101899922 0.0509850509499612 84 0.933873808776189 0.132252382447623 0.0661261912238114 85 0.92288444094443 0.154231118111140 0.0771155590555699 86 0.928693450286246 0.142613099427507 0.0713065497137536 87 0.918094114899731 0.163811770200537 0.0819058851002685 88 0.899892437647958 0.200215124704085 0.100107562352042 89 0.907018954482157 0.185962091035686 0.0929810455178432 90 0.905098042429922 0.189803915140155 0.0949019575700776 91 0.941006080027036 0.117987839945928 0.0589939199729639 92 0.924135142422802 0.151729715154396 0.075864857577198 93 0.903771943579731 0.192456112840537 0.0962280564202686 94 0.93605403446412 0.127891931071759 0.0639459655358797 95 0.958053842859672 0.0838923142806569 0.0419461571403285 96 0.964791640261943 0.0704167194761143 0.0352083597380572 97 0.960559807327372 0.0788803853452562 0.0394401926726281 98 0.94685950280266 0.106280994394678 0.0531404971973392 99 0.938805840925091 0.122388318149818 0.0611941590749089 100 0.93541991502081 0.129160169958379 0.0645800849791896 101 0.929628101441514 0.140743797116971 0.0703718985584857 102 0.926362064984372 0.147275870031255 0.0736379350156276 103 0.909901373891936 0.180197252216127 0.0900986261080637 104 0.884856822487481 0.230286355025037 0.115143177512519 105 0.895960847739957 0.208078304520085 0.104039152260043 106 0.918033910231793 0.163932179536414 0.0819660897682071 107 0.892637071446688 0.214725857106624 0.107362928553312 108 0.863009225136684 0.273981549726632 0.136990774863316 109 0.92098022204696 0.158039555906078 0.0790197779530392 110 0.935078817811449 0.129842364377102 0.0649211821885512 111 0.922647765318825 0.154704469362351 0.0773522346811753 112 0.895737017900774 0.208525964198452 0.104262982099226 113 0.867712422340477 0.264575155319047 0.132287577659523 114 0.833103778190638 0.333792443618724 0.166896221809362 115 0.829260601178077 0.341478797643846 0.170739398821923 116 0.780627488709206 0.438745022581588 0.219372511290794 117 0.721982882952782 0.556034234094437 0.278017117047218 118 0.690793772708573 0.618412454582853 0.309206227291426 119 0.699879697284893 0.600240605430214 0.300120302715107 120 0.627882758362237 0.744234483275526 0.372117241637763 121 0.548657132612549 0.902685734774902 0.451342867387451 122 0.674118099420731 0.651763801158537 0.325881900579269 123 0.594955808782876 0.810088382434249 0.405044191217124 124 0.534336608902179 0.931326782195642 0.465663391097821 125 0.453909594530365 0.90781918906073 0.546090405469635 126 0.362851458120892 0.725702916241785 0.637148541879108 127 0.284885078421694 0.569770156843388 0.715114921578306 128 0.353315756897445 0.70663151379489 0.646684243102555 129 0.506609384800181 0.986781230399637 0.493390615199819 130 0.473524452264806 0.947048904529612 0.526475547735194 131 0.601963855619594 0.796072288760813 0.398036144380406 132 0.452211060533723 0.904422121067446 0.547788939466277 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 0.0330578512396694 OK

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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