WS10 MR

*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 20:19:00 +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/t1292185042tktfra56jvd9xs2.htm/, Retrieved Sun, 12 Dec 2010 21:17:25 +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/t1292185042tktfra56jvd9xs2.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 «

5 2 1 3 73 62 66 12 1 1 1 58 54 54 11 1 1 3 68 41 82 6 1 1 3 62 49 61 12 1 2 3 65 49 65 11 1 1 3 81 72 77 12 1 1 1 73 78 66 7 2 4 3 64 58 66 8 1 1 3 68 58 66 13 1 1 1 51 23 48 12 1 1 1 68 39 57 13 1 1 3 61 63 80 12 1 1 1 69 46 60 12 1 3 3 73 58 70 11 2 1 3 61 39 85 12 2 1 1 62 44 59 12 1 1 1 63 49 72 12 1 6 1 69 57 70 11 2 1 3 47 76 74 13 2 1 1 66 63 70 9 1 1 3 58 18 51 11 2 1 3 63 40 70 11 1 1 1 69 59 71 11 2 1 3 59 62 72 9 1 1 1 59 70 50 11 2 1 4 63 65 69 12 2 1 3 65 56 73 12 1 1 3 65 45 66 10 2 1 3 71 57 73 12 1 4 3 60 50 58 12 2 1 1 81 40 78 12 1 1 3 67 58 83 9 2 1 3 66 49 76 9 1 1 3 62 49 77 12 1 1 3 63 27 79 14 2 1 1 73 51 71 12 2 1 3 55 75 79 11 1 1 1 59 65 60 9 1 1 2 64 47 73 11 2 1 3 63 49 70 7 1 1 1 64 65 42 15 1 1 1 73 61 74 11 1 1 3 54 46 68 12 1 1 3 76 69 83 12 2 2 1 74 55 62 9 2 1 3 63 78 79 12 2 1 3 73 58 61 11 2 1 3 67 34 86 11 2 2 3 68 67 64 8 1 4 3 66 45 75 7 2 1 1 62 68 59 12 2 4 3 71 49 82 8 1 1 2 63 19 61 10 1 1 1 75 72 69 12 1 2 2 77 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 FF[t] = + 9.18224164145946 + 0.298990332610133Geslacht[t] + 0.163161952286726Opvoeding[t] -0.257077272602828Huwelijksstatus[t] -0.0100266770906813TotNV[t] -0.0148141827227416TotAngst[t] + 0.0473935603538839TotGroep[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) 9.18224164145946 1.557861 5.8941 0 0 Geslacht 0.298990332610133 0.307136 0.9735 0.332007 0.166004 Opvoeding 0.163161952286726 0.17309 0.9426 0.3475 0.17375 Huwelijksstatus -0.257077272602828 0.161041 -1.5964 0.112681 0.056341 TotNV -0.0100266770906813 0.021414 -0.4682 0.640346 0.320173 TotAngst -0.0148141827227416 0.011896 -1.2453 0.215131 0.107566 TotGroep 0.0473935603538839 0.016719 2.8346 0.005272 0.002636 Multiple Linear Regression - Regression Statistics Multiple R 0.274950342612271 R-squared 0.075597690902605 Adjusted R-squared 0.0356954329559548 F-TEST (value) 1.89457175590615 F-TEST (DF numerator) 6 F-TEST (DF denominator) 139 p-value 0.0858767171679528 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.76419276719941 Sum Squared Residuals 432.620280657581 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.6497006680846 -5.64970066808459 2 12 10.5650557745757 1.43494422542434 3 11 11.4702385237676 -0.470238523767581 4 6 10.4166203570982 -4.41662035709817 5 12 10.7392765195284 1.26072348047161 6 11 10.6436842554143 0.356315744585684 7 12 10.6278379571162 1.37216204288375 8 7 11.2886833496519 -4.28868334965187 9 8 10.4601004518188 -2.46010045181883 10 13 10.8101208164921 2.18987918350789 11 12 10.8291824255716 1.17081757442838 12 13 11.1197261227943 1.88027387720573 13 12 10.8576371504834 1.1423628495166 14 12 10.9258652123544 1.07413478764559 15 11 12.0112246425196 -1.01122464251962 16 12 11.2090490278199 0.790950972180097 17 12 11.4420773891059 0.557922610894129 18 12 11.9844265055057 0.0155734944942877 19 11 11.082144197155 -0.0821441971549943 20 13 11.4088020116178 1.59119798838219 21 9 10.442031126327 -1.44203112632705 22 11 11.2654537003073 -0.265453700307255 23 11 11.1863819389805 -0.186381938980484 24 11 11.0744355094774 -0.0744355094774334 25 9 10.1284279325056 -1.12842793250558 26 11 10.590628299282 0.409371700717996 27 12 11.1505541036237 0.84944589637632 28 12 10.6827648584865 1.31723514151348 29 10 11.0755798583568 -1.07557985835685 30 12 10.7691647043553 1.23083529564468 31 12 11.9782765407117 0.0217234592882804 32 12 11.2758176549255 0.724182345074461 33 9 11.3864073866538 -2.38640738665384 34 9 11.1749173227603 -2.17491732276032 35 12 11.5855897862777 0.414410213722283 36 14 11.5637790250098 2.43622097499018 37 12 11.2537127649217 0.746287235078295 38 11 10.6764344496581 0.323565550341876 39 9 11.2519953652117 -2.25199536521173 40 11 11.1321260558026 -0.132126055802581 41 7 9.7732169778348 -2.77321697783481 42 15 11.2588275462339 3.74117245376607 43 11 10.873031244469 0.126968755530964 44 12 11.0226215511593 0.97737844884075 45 12 11.2311155261299 0.768884473870052 46 9 11.129056800028 -2.12905680002803 47 12 10.4719895972061 1.52801040279386 48 11 12.0725290539431 -1.07252905394312 49 11 10.6941379715032 0.305862028496751 50 8 11.588766081441 -3.58876608144097 51 7 10.8535086424741 -3.85350864247411 52 12 12.1101212201839 -0.110121220183915 53 8 11.1080964342926 -3.10809643429257 54 10 10.838850380333 -0.838850380332988 55 12 10.4909240380462 1.50907596195379 56 15 10.9915900217422 4.00840997825778 57 12 11.1404721600285 0.859527839971514 58 12 11.5916247740538 0.408375225946216 59 12 10.6691204610075 1.33087953899246 60 12 10.9357697397321 1.06423026026791 61 8 9.6418800470631 -1.64188004706309 62 10 11.0944927189487 -1.09449271894873 63 14 11.6249115428489 2.37508845715108 64 10 11.3479000098012 -1.34790000980115 65 12 10.7436137487935 1.25638625120647 66 14 10.408244961218 3.59175503878202 67 6 11.7654569157029 -5.76545691570287 68 11 10.3993361196706 0.600663880329379 69 10 10.9995203860051 -0.99952038600505 70 14 11.8285154174088 2.17148458259116 71 12 11.1918322975252 0.808167702474758 72 13 11.5284891139334 1.47151088606655 73 11 10.820336074058 0.179663925941977 74 11 10.9147339675377 0.0852660324622618 75 12 11.3345237662079 0.6654762337921 76 13 11.8253181432473 1.17468185675268 77 12 10.6982950056316 1.3017049943684 78 8 9.9023635413064 -1.90236354130641 79 12 11.256752729042 0.743247270958031 80 11 11.1911163331029 -0.191116333102898 81 10 10.9644416620148 -0.96444166201481 82 12 10.5211936761562 1.4788063238438 83 11 10.1307002609877 0.869299739012338 84 12 11.5676665157928 0.432333484207174 85 12 10.8917296011707 1.10827039882928 86 10 10.6013341139498 -0.60133411394983 87 12 11.5814060117639 0.418593988236091 88 12 11.5328542453932 0.467145754606766 89 11 11.4155542561326 -0.415554256132604 90 10 10.4119212649148 -0.411921264914847 91 12 11.3495491360168 0.650450863983222 92 11 10.9701894660996 0.0298105339004298 93 12 10.470652012422 1.52934798757803 94 12 10.0745952736475 1.92540472635252 95 10 10.2382404230222 -0.238240423022224 96 11 11.2920061249348 -0.292006124934809 97 10 11.1059305101711 -1.10593051017114 98 11 11.2652395080787 -0.265239508078719 99 11 11.0234811955348 -0.023481195534839 100 12 11.0769357059841 0.923064294015935 101 11 10.8903447735509 0.109655226449124 102 11 11.2232680918122 -0.223268091812199 103 7 9.33954315309279 -2.33954315309279 104 12 10.0910079377327 1.90899206226727 105 8 10.8097328025085 -2.80973280250851 106 10 10.8283482175604 -0.828348217560387 107 12 11.0725998661786 0.927400133821434 108 11 10.7868566815778 0.213143318422178 109 13 11.4573130958534 1.54268690414665 110 9 10.8733652051926 -1.87336520519258 111 11 10.6817918647056 0.318208135294363 112 13 10.2285724682609 2.77142753173914 113 8 10.4117737296717 -2.41177372967167 114 12 11.4707493113886 0.529250688611413 115 11 10.246588682243 0.753411317757005 116 11 11.1895994047568 -0.189599404756792 117 12 10.5282102192668 1.47178978073322 118 13 10.8376827500841 2.16231724991592 119 11 11.2586754809422 -0.258675480942236 120 10 10.8364394474226 -0.836439447422622 121 10 11.1884198955491 -1.18841989554912 122 10 11.0403170839399 -1.04031708393989 123 12 10.8552036109621 1.14479638903787 124 12 10.9723322451242 1.02766775487578 125 13 10.5974936272484 2.40250637275158 126 11 11.2364171361871 -0.236417136187077 127 11 10.6826327577603 0.317367242239749 128 12 11.0666956022001 0.933304397799921 129 9 11.0407747600513 -2.04077476005125 130 11 11.4205236571965 -0.420523657196514 131 12 11.1177375640562 0.882262435943825 132 12 11.0090395922788 0.990960407721214 133 13 11.1809337354306 1.81906626456941 134 6 10.34688528329 -4.34688528328997 135 11 11.3282099079976 -0.32820990799762 136 10 11.5994346551206 -1.59943465512056 137 12 12.2015349545338 -0.20153495453375 138 11 10.6872527635837 0.312747236416293 139 12 11.2903630715429 0.709636928457118 140 12 11.2210974105929 0.778902589407103 141 7 11.5838094094834 -4.58380940948336 142 12 10.940959654971 1.059040345029 143 12 11.760584851912 0.239415148087993 144 9 11.0533246033564 -2.05332460335639 145 12 10.5920134385635 1.40798656143649 146 12 10.8889897760712 1.11101022392881 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.509763784674013 0.980472430651975 0.490236215325988 11 0.738081857196782 0.523836285606436 0.261918142803218 12 0.759033655001324 0.481932689997353 0.240966344998676 13 0.663976874446344 0.672046251107313 0.336023125553656 14 0.572167143821781 0.855665712356437 0.427832856178219 15 0.51547594035565 0.9690481192887 0.48452405964435 16 0.578439957197578 0.843120085604845 0.421560042802422 17 0.761829952553621 0.476340094892757 0.238170047446379 18 0.807383291126714 0.385233417746572 0.192616708873286 19 0.76751930842009 0.46496138315982 0.23248069157991 20 0.743132555216358 0.513734889567284 0.256867444783642 21 0.688089058791997 0.623821882416006 0.311910941208003 22 0.695692687649495 0.60861462470101 0.304307312350505 23 0.714239699878759 0.571520600242483 0.285760300121241 24 0.68073310804226 0.63853378391548 0.31926689195774 25 0.664703585888567 0.670592828222865 0.335296414111433 26 0.762739444603977 0.474521110792045 0.237260555396023 27 0.759314711910985 0.48137057617803 0.240685288089015 28 0.753518548312015 0.492962903375969 0.246481451687985 29 0.705364708863755 0.58927058227249 0.294635291136245 30 0.729482505507425 0.54103498898515 0.270517494492575 31 0.674362018274269 0.651275963451462 0.325637981725731 32 0.617444663788137 0.765110672423726 0.382555336211863 33 0.622173637216103 0.755652725567794 0.377826362783897 34 0.683202017132298 0.633595965735404 0.316797982867702 35 0.628054705240088 0.743890589519824 0.371945294759912 36 0.670049445367978 0.659901109264044 0.329950554632022 37 0.626004966640925 0.74799006671815 0.373995033359075 38 0.582075287903469 0.835849424193062 0.417924712096531 39 0.65896594040937 0.682068119181259 0.341034059590629 40 0.613599761556146 0.772800476887709 0.386400238443854 41 0.661282428549529 0.677435142900942 0.338717571450471 42 0.762543302180735 0.474913395638531 0.237456697819265 43 0.718055968053583 0.563888063892834 0.281944031946417 44 0.684934138006917 0.630131723986166 0.315065861993083 45 0.650726160961848 0.698547678076305 0.349273839038152 46 0.661392180710877 0.677215638578245 0.338607819289123 47 0.729482950526268 0.541034098947465 0.270517049473733 48 0.703521895272535 0.59295620945493 0.296478104727465 49 0.680126526392213 0.639746947215575 0.319873473607787 50 0.78796165089011 0.424076698219781 0.212038349109891 51 0.906710772826686 0.186578454346628 0.093289227173314 52 0.888614305010157 0.222771389979685 0.111385694989843 53 0.927143784951365 0.145712430097271 0.0728562150486355 54 0.918716263269471 0.162567473461058 0.0812837367305291 55 0.916875188300469 0.166249623399063 0.0831248116995314 56 0.979642843803924 0.0407143123921515 0.0203571561960757 57 0.974430939718472 0.0511381205630555 0.0255690602815278 58 0.966294136906728 0.0674117261865432 0.0337058630932716 59 0.964709611115986 0.0705807777680276 0.0352903888840138 60 0.957534535803035 0.0849309283939292 0.0424654641969646 61 0.955288353440698 0.0894232931186038 0.0447116465593019 62 0.94701519870626 0.105969602587479 0.0529848012937395 63 0.955252588329453 0.0894948233410943 0.0447474116705471 64 0.950048641681865 0.0999027166362697 0.0499513583181349 65 0.945429516653971 0.109140966692057 0.0545704833460286 66 0.977953907669126 0.0440921846617478 0.0220460923308739 67 0.99941012931895 0.00117974136209937 0.000589870681049683 68 0.999141163922553 0.00171767215489323 0.000858836077446615 69 0.998917401898932 0.00216519620213657 0.00108259810106829 70 0.999081131735156 0.00183773652968834 0.000918868264844172 71 0.99874309336239 0.0025138132752215 0.00125690663761075 72 0.99856961413416 0.00286077173167961 0.0014303858658398 73 0.997870243801895 0.00425951239621016 0.00212975619810508 74 0.996879839934404 0.00624032013119236 0.00312016006559618 75 0.995779616436747 0.00844076712650541 0.00422038356325271 76 0.994750785728069 0.010498428543862 0.00524921427193098 77 0.994095692839998 0.0118086143200036 0.0059043071600018 78 0.995130513236337 0.00973897352732596 0.00486948676366298 79 0.993334213596015 0.0133315728079704 0.0066657864039852 80 0.990632628243145 0.0187347435137093 0.00936737175685467 81 0.989122176397687 0.0217556472046258 0.0108778236023129 82 0.987735387199287 0.0245292256014269 0.0122646128007134 83 0.98528852903918 0.0294229419216385 0.0147114709608192 84 0.981036209626199 0.0379275807476029 0.0189637903738015 85 0.976576018226778 0.0468479635464447 0.0234239817732223 86 0.970686395247058 0.0586272095058839 0.029313604752942 87 0.96229077894201 0.075418442115979 0.0377092210579895 88 0.951130609329567 0.097738781340867 0.0488693906704335 89 0.938733275098535 0.122533449802931 0.0612667249014653 90 0.926296803619757 0.147406392760487 0.0737031963802434 91 0.915892915865865 0.168214168268271 0.0841070841341353 92 0.895241925155123 0.209516149689754 0.104758074844877 93 0.88958088778442 0.220838224431161 0.11041911221558 94 0.896961241216731 0.206077517566537 0.103038758783269 95 0.872789402318018 0.254421195363964 0.127210597681982 96 0.843086900515421 0.313826198969158 0.156913099484579 97 0.818930856621051 0.362138286757897 0.181069143378948 98 0.787380090436898 0.425239819126203 0.212619909563102 99 0.745720104382255 0.508559791235491 0.254279895617745 100 0.731233339855963 0.537533320288074 0.268766660144037 101 0.684280128728105 0.63143974254379 0.315719871271895 102 0.634588462128754 0.730823075742491 0.365411537871246 103 0.705499542512415 0.589000914975171 0.294500457487585 104 0.68433602274022 0.631327954519559 0.31566397725978 105 0.731460241973286 0.537079516053428 0.268539758026714 106 0.696790700049492 0.606418599901015 0.303209299950508 107 0.679910636230626 0.640178727538749 0.320089363769374 108 0.625989345033752 0.748021309932497 0.374010654966248 109 0.603748296969169 0.792503406061662 0.396251703030831 110 0.593697165109518 0.812605669780963 0.406302834890482 111 0.53437104292918 0.93125791414164 0.46562895707082 112 0.604437197668766 0.791125604662468 0.395562802331234 113 0.692544644910065 0.61491071017987 0.307455355089935 114 0.646294795896851 0.707410408206298 0.353705204103149 115 0.587328426612175 0.82534314677565 0.412671573387825 116 0.52276026250964 0.95447947498072 0.47723973749036 117 0.47853547656582 0.95707095313164 0.52146452343418 118 0.504699435772859 0.990601128454282 0.495300564227141 119 0.447967073751068 0.895934147502136 0.552032926248932 120 0.38384431714124 0.767688634282481 0.61615568285876 121 0.364879652442438 0.729759304884877 0.635120347557562 122 0.31406850250295 0.628137005005901 0.68593149749705 123 0.259633634604744 0.519267269209487 0.740366365395256 124 0.209107683016187 0.418215366032374 0.790892316983813 125 0.235723077120564 0.471446154241127 0.764276922879436 126 0.17899568969105 0.3579913793821 0.82100431030895 127 0.135454745191564 0.270909490383128 0.864545254808436 128 0.100283409689315 0.20056681937863 0.899716590310685 129 0.0976976424066733 0.195395284813347 0.902302357593327 130 0.0643889269938809 0.128777853987762 0.935611073006119 131 0.042540618337811 0.085081236675622 0.95745938166219 132 0.0272205312341473 0.0544410624682946 0.972779468765853 133 0.0374151067477274 0.0748302134954549 0.962584893252273 134 0.222635436954388 0.445270873908776 0.777364563045612 135 0.137853481750201 0.275706963500402 0.862146518249799 136 0.0834432562196476 0.166886512439295 0.916556743780352 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.078740157480315 NOK 5% type I error level 21 0.165354330708661 NOK 10% type I error level 34 0.267716535433071 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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