Paper Multiple Regression zonder outlier

*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, 18 Dec 2010 15:07:16 +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/18/t1292684989n3qg08shhrdkffw.htm/, Retrieved Sat, 18 Dec 2010 16:10:01 +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/18/t1292684989n3qg08shhrdkffw.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 «

41 25 15 9 3 38 25 15 9 4 37 19 14 9 4 42 18 10 8 4 40 23 18 15 3 43 25 14 9 4 40 23 11 11 4 45 30 17 6 5 45 32 21 10 4 44 25 7 11 4 42 26 18 16 4 32 25 13 11 5 32 25 13 11 5 41 35 18 7 4 38 20 12 10 4 38 21 9 9 4 24 23 11 15 3 46 17 11 6 5 42 27 16 12 4 46 25 12 10 4 43 18 14 14 5 38 22 13 9 4 39 23 17 14 4 40 25 13 14 3 37 19 13 9 2 41 20 12 8 4 46 26 12 10 4 26 16 12 9 3 37 22 9 9 3 39 25 17 9 4 44 29 18 11 5 38 22 12 10 2 38 32 12 8 0 38 23 9 14 4 33 18 13 10 3 43 26 11 14 4 41 14 13 15 2 49 20 6 8 4 45 25 11 10 5 31 21 18 13 3 30 21 18 13 3 38 23 15 10 4 39 24 11 11 4 40 21 14 10 4 36 17 12 16 2 49 29 8 6 5 41 25 11 11 4 42 25 17 14 3 41 25 16 9 5 43 21 13 11 4 46 23 15 8 3 41 25 16 8 5 39 25 7 11 4 42 24 16 16 4 35 21 13 12 5 36 22 15 14 3 48 14 12 8 4 41 20 12 10 4 47 21 24 14 3 41 22 15 10 3 31 19 8 5 5 36 28 18 12 4 46 25 17 9 4 44 21 15 8 4 43 27 11 16 2 40 19 12 13 5 40 20 14 8 3 46 17 11 14 3 39 22 10 8 4 44 26 11 7 4 38 17 12 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation StudyForCareer[t] = + 34.1121269448891 + 0.146658179226907PersonalStandards[t] + 0.0321645865737117ParentalExpectations[t] -0.216148635234074Doubts[t] + 1.20809176779421LeaderPreference[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) 34.1121269448891 3.129847 10.899 0 0 PersonalStandards 0.146658179226907 0.09936 1.476 0.142182 0.071091 ParentalExpectations 0.0321645865737117 0.122176 0.2633 0.792734 0.396367 Doubts -0.216148635234074 0.147914 -1.4613 0.146171 0.073085 LeaderPreference 1.20809176779421 0.462908 2.6098 0.010045 0.005023 Multiple Linear Regression - Regression Statistics Multiple R 0.338860725617068 R-squared 0.114826591365726 Adjusted R-squared 0.0895359225476039 F-TEST (value) 4.5402749999022 F-TEST (DF numerator) 4 F-TEST (DF denominator) 140 p-value 0.00177031601377786 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.75523002302489 Sum Squared Residuals 3165.70976006283 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 41 39.9399878104432 1.06001218955680 2 38 41.1480795782376 -3.14807957823759 3 37 40.2359659163024 -3.23596591630244 4 42 40.1767980260148 1.82320197398524 5 40 38.4462734003063 1.55372659969373 6 43 41.1159149916639 1.88408500833611 7 40 40.2938076030208 -0.293807603020789 8 45 43.802237321016 1.19776267898401 9 45 42.1515257170341 2.84847428296585 10 44 40.4584656151798 3.54153438482024 11 42 39.8781910705471 2.12180892945288 12 32 41.8595449024162 -9.85954490241624 13 32 41.8595449024162 -9.85954490241624 14 41 43.143452400696 -2.14345240069595 15 38 40.1021462871479 -2.10214628714785 16 38 40.3684593418877 -2.3684593418877 17 24 38.2211212942903 -14.2211212942903 18 46 41.7026934716239 4.29730652837608 19 42 40.8251146175629 1.17488538243710 20 46 40.8354371832824 5.16456281671761 21 43 40.2166563286994 2.78334367130062 22 38 40.6437758674095 -2.64377586740945 23 39 39.8383492167608 -0.838349216760837 24 40 38.7949154611256 1.20508453887440 25 37 37.7876177941403 -0.78761779414031 26 41 40.534443557616 0.465556442383999 27 46 40.9820953625093 5.0179046374907 28 26 38.5235704376801 -12.5235704376801 29 37 39.3070257533204 -2.30702575332040 30 39 41.212408751385 -2.21240875138502 31 44 42.6070005521924 1.39299944780758 32 38 37.9792791100132 0.0207208899867524 33 38 37.4619746371621 0.538025362837949 34 38 39.5810325241711 -1.58103252417114 35 33 38.6329027474735 -5.63290274747354 36 43 40.0853362349993 2.91466376500071 37 41 35.7574350866013 5.24256491339867 38 49 40.3414560381737 8.65854396182627 39 45 42.0113643645029 2.98863563549711 40 31 38.5852543123206 -7.5852543123206 41 30 38.5852543123206 -8.5852543123206 42 38 40.6386145845497 -2.63861458454971 43 39 40.4404657822477 -1.44046578224770 44 40 40.3131336395222 -0.313133639522183 45 36 35.9490964024743 0.0509035975257332 46 49 43.3660978626257 5.63390213737432 47 41 40.5871239614746 0.412876038525396 48 42 38.9235738074204 3.07642619257956 49 41 42.3883359326055 -1.38833593260552 50 43 40.0648204177144 2.9351795822856 51 46 39.8628200872236 6.13717991277635 52 41 42.6044845678396 -1.60448456783959 53 39 40.4584656151798 -1.45846561517976 54 42 39.5205455389459 2.47945446105412 55 35 41.0567635502745 -6.05676355027453 56 36 38.4192700965923 -2.41927009659230 57 48 39.6544944822546 8.34550551774544 58 41 40.1021462871479 0.897853712852147 59 47 38.5620931965288 8.4379068034712 60 41 39.2838646375286 1.71613536247141 61 31 42.1156647055907 -11.1156647055907 62 36 41.0361019699372 -5.03610196993723 63 46 41.212408751385 4.78759124861498 64 44 40.7775954965640 3.22240450343596 65 43 37.3835136081696 5.61648639183037 66 40 40.5151339700129 -0.515133970012933 67 40 39.3906809629692 0.609319037030786 68 46 37.5573208541629 8.44267914583709 69 39 40.7634307429224 -1.76343074292239 70 44 41.5983766816378 2.40162331836219 71 38 37.0298395786446 0.970160421355363 72 39 40.0404624125073 -1.04046241250734 73 41 42.6494877044856 -1.64948770448562 74 39 38.8270800476993 0.172919952300693 75 40 39.6518491837476 0.348150816252356 76 44 40.369785545457 3.63021445454297 77 42 39.7689880749078 2.23101192509225 78 46 42.2943581572372 3.70564184276278 79 44 40.0403330983533 3.95966690164674 80 37 40.5047949553951 -3.50479495539512 81 39 37.6113110126925 1.38868898730748 82 40 38.5389249721052 1.46107502789476 83 42 38.9235738074204 3.07642619257956 84 37 39.2246967472409 -2.22469674724091 85 33 38.2970827878282 -5.2970827878282 86 35 40.1446334394411 -5.14463343944105 87 42 36.4355295945244 5.56447040547557 88 36 36.4637226790219 -0.463722679021899 89 44 39.5810325241711 4.41896747582886 90 45 39.7920198765455 5.20798012345453 91 47 40.9009561372133 6.09904386278669 92 40 41.0335859855844 -1.03358598558440 93 49 38.8619063816783 10.1380936183217 94 48 43.5950921565638 4.40490784343618 95 29 40.7389434235613 -11.7389434235613 96 45 41.501899370815 3.498100629185 97 29 36.9551878397777 -7.95518783977773 98 41 40.4649531016088 0.535046898391166 99 34 37.49549121647 -3.49549121646999 100 38 36.6038933718197 1.39610662818027 101 37 38.2507698965112 -1.25076989651116 102 48 43.730230880656 4.26976911934401 103 39 40.9177661893619 -1.91776618936187 104 34 40.3104883410153 -6.31048834101527 105 35 37.0993300346518 -2.09933003465180 106 41 40.1689749957498 0.83102500425022 107 43 39.6223298956808 3.37767010431916 108 41 38.8168867961339 2.1831132038661 109 39 36.5910548217474 2.40894517825259 110 36 40.9885828489384 -4.98858284893837 111 32 41.4027438636886 -9.40274386368862 112 46 40.045494381213 5.954505618787 113 42 40.9049276392896 1.09507236071044 114 42 36.8265294934829 5.17347050651712 115 45 39.6749938506411 5.32500614935888 116 39 40.7209435906292 -1.72094359062919 117 45 40.9627693260079 4.0372306739921 118 48 42.2326742825967 5.7673257174033 119 28 38.504260850077 -10.5042608500770 120 35 38.0166049794467 -3.0166049794467 121 38 39.0162418281177 -1.01624182811774 122 42 38.7307320510306 3.26926794896942 123 36 38.4295926623118 -2.42959266231178 124 37 40.7955953294961 -3.79559532949610 125 38 39.3958422458290 -1.39584224582896 126 43 40.5909661493968 2.40903385060323 127 35 35.2491598736969 -0.249159873696940 128 36 39.8538330653401 -3.85383306534007 129 33 36.9025074359191 -3.90250743591912 130 39 38.3652799380627 0.634720061937313 131 32 40.9897726297219 -8.98977262972187 132 45 39.4730099690487 5.5269900309513 133 35 39.9233235213472 -4.92332352134723 134 38 38.1414375867177 -0.141437586717712 135 36 37.7541434528956 -1.75414345289559 136 42 38.4540799816729 3.54592001832708 137 41 39.5553554240265 1.44464457597349 138 47 38.985386996215 8.01461300378497 139 35 38.8065642304144 -3.80656423041442 140 43 37.9972953918436 5.00270460815636 141 40 40.0286843290644 -0.0286843290644402 142 46 40.7261048734889 5.27389512651106 143 44 41.501899370815 2.498100629185 144 35 38.9957095619345 -3.99570956193452 145 29 39.6273618643865 -10.6273618643865 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.235969161968699 0.471938323937397 0.764030838031301 9 0.129584160166089 0.259168320332179 0.87041583983391 10 0.0612430334934687 0.122486066986937 0.938756966506531 11 0.0270902216581776 0.0541804433163552 0.972909778341822 12 0.235178261627283 0.470356523254566 0.764821738372717 13 0.285096061926052 0.570192123852103 0.714903938073948 14 0.316108114972573 0.632216229945147 0.683891885027427 15 0.232999111010344 0.465998222020687 0.767000888989656 16 0.172124856780170 0.344249713560341 0.82787514321983 17 0.76859571291641 0.462808574167181 0.231404287083590 18 0.757375986999797 0.485248026000406 0.242624013000203 19 0.720078882267012 0.559842235465976 0.279921117732988 20 0.771143481057775 0.45771303788445 0.228856518942225 21 0.773220856543257 0.453558286913486 0.226779143456743 22 0.732164883400089 0.535670233199822 0.267835116599911 23 0.670110352589067 0.659779294821865 0.329889647410933 24 0.636011051398859 0.727977897202282 0.363988948601141 25 0.581699650522363 0.836600698955275 0.418300349477637 26 0.514110052056124 0.971779895887752 0.485889947943876 27 0.557439651215152 0.885120697569696 0.442560348784848 28 0.818581646090324 0.362836707819353 0.181418353909676 29 0.77647400441042 0.447051991179161 0.223525995589580 30 0.737056140797277 0.525887718405446 0.262943859202723 31 0.685976265605559 0.628047468788882 0.314023734394441 32 0.639408960024617 0.721182079950766 0.360591039975383 33 0.583815883329398 0.832368233341205 0.416184116670602 34 0.527605406921895 0.94478918615621 0.472394593078105 35 0.512523735674534 0.974952528650932 0.487476264325466 36 0.489041451412791 0.978082902825582 0.510958548587209 37 0.568710034477465 0.86257993104507 0.431289965522535 38 0.707869198482367 0.584261603035266 0.292130801517633 39 0.675072460598168 0.649855078803664 0.324927539401832 40 0.708178837885376 0.583642324229248 0.291821162114624 41 0.757564590162706 0.484870819674588 0.242435409837294 42 0.721500409308793 0.556999181382414 0.278499590691207 43 0.678962549625583 0.642074900748834 0.321037450374417 44 0.631462563397841 0.737074873204319 0.368537436602159 45 0.591969245507722 0.816061508984555 0.408030754492278 46 0.585167109874154 0.829665780251692 0.414832890125846 47 0.533161935622487 0.933676128755026 0.466838064377513 48 0.522927633295182 0.954144733409636 0.477072366704818 49 0.473393842284094 0.946787684568189 0.526606157715906 50 0.449821025346854 0.899642050693708 0.550178974653146 51 0.502464280781618 0.995071438436763 0.497535719218382 52 0.455574493974331 0.911148987948662 0.544425506025669 53 0.41788733761543 0.83577467523086 0.58211266238457 54 0.391214742486412 0.782429484972824 0.608785257513588 55 0.414420295125471 0.828840590250943 0.585579704874529 56 0.37579494740451 0.75158989480902 0.62420505259549 57 0.500628129654834 0.998743740690332 0.499371870345166 58 0.452830286606567 0.905660573213134 0.547169713393433 59 0.579968620507354 0.840062758985293 0.420031379492646 60 0.536872920851007 0.926254158297985 0.463127079148992 61 0.731722940984756 0.536554118030488 0.268277059015244 62 0.735476273317674 0.529047453364652 0.264523726682326 63 0.733284442544372 0.533431114911256 0.266715557455628 64 0.70961201362297 0.58077597275406 0.29038798637703 65 0.729593409405464 0.540813181189071 0.270406590594536 66 0.691057019301732 0.617885961396536 0.308942980698268 67 0.647572561443995 0.70485487711201 0.352427438556005 68 0.735417904475474 0.529164191049052 0.264582095524526 69 0.700118270875988 0.599763458248024 0.299881729124012 70 0.667276404776358 0.665447190447283 0.332723595223642 71 0.6243301313455 0.751339737308999 0.375669868654500 72 0.581835016494961 0.836329967010079 0.418164983505039 73 0.540211587174076 0.919576825651847 0.459788412825924 74 0.492114841525803 0.984229683051605 0.507885158474197 75 0.44399508770545 0.8879901754109 0.55600491229455 76 0.421312379805032 0.842624759610065 0.578687620194968 77 0.381771444070293 0.763542888140585 0.618228555929707 78 0.363155731172606 0.726311462345213 0.636844268827394 79 0.347610902675602 0.695221805351203 0.652389097324398 80 0.32617276678076 0.65234553356152 0.67382723321924 81 0.289839315207301 0.579678630414603 0.710160684792698 82 0.253547655095353 0.507095310190706 0.746452344904647 83 0.23749541416332 0.47499082832664 0.76250458583668 84 0.208311974581025 0.41662394916205 0.791688025418975 85 0.210093533389816 0.420187066779632 0.789906466610184 86 0.225063544382458 0.450127088764916 0.774936455617542 87 0.235675308884116 0.471350617768232 0.764324691115884 88 0.199964169243416 0.399928338486833 0.800035830756584 89 0.191254554340106 0.382509108680211 0.808745445659894 90 0.196368705061104 0.392737410122207 0.803631294938896 91 0.225923217977658 0.451846435955316 0.774076782022342 92 0.190553352678516 0.381106705357033 0.809446647321484 93 0.337461627937091 0.674923255874183 0.662538372062909 94 0.372455238751933 0.744910477503866 0.627544761248067 95 0.652710304504312 0.694579390991376 0.347289695495688 96 0.627342821328384 0.745314357343232 0.372657178671616 97 0.681905017197621 0.636189965604757 0.318094982802379 98 0.633507757060326 0.732984485879347 0.366492242939674 99 0.613561849564111 0.772876300871778 0.386438150435889 100 0.563456206612094 0.873087586775812 0.436543793387906 101 0.514498111651238 0.971003776697523 0.485501888348762 102 0.507707758222523 0.984584483554953 0.492292241777477 103 0.46243283350731 0.92486566701462 0.53756716649269 104 0.452528772981515 0.90505754596303 0.547471227018485 105 0.413883879929053 0.827767759858105 0.586116120070947 106 0.362891710353883 0.725783420707766 0.637108289646117 107 0.328450685104745 0.656901370209489 0.671549314895255 108 0.286418256217206 0.572836512434412 0.713581743782794 109 0.245553552620882 0.491107105241764 0.754446447379118 110 0.232295405813771 0.464590811627543 0.767704594186229 111 0.407121274333605 0.814242548667211 0.592878725666395 112 0.428866378831995 0.85773275766399 0.571133621168005 113 0.371625707222279 0.743251414444558 0.628374292777721 114 0.377062474294110 0.754124948588219 0.622937525705890 115 0.354970437654553 0.709940875309107 0.645029562345447 116 0.305039747878960 0.610079495757921 0.69496025212104 117 0.276598148698552 0.553196297397104 0.723401851301448 118 0.294971380062037 0.589942760124075 0.705028619937963 119 0.498342489993742 0.996684979987483 0.501657510006258 120 0.44556692187416 0.89113384374832 0.55443307812584 121 0.378500394846854 0.757000789693708 0.621499605153146 122 0.341353928244679 0.682707856489357 0.658646071755321 123 0.28818641881514 0.57637283763028 0.71181358118486 124 0.254035401540862 0.508070803081725 0.745964598459138 125 0.205421986846095 0.410843973692189 0.794578013153905 126 0.194855942449745 0.389711884899490 0.805144057550255 127 0.145794681562637 0.291589363125273 0.854205318437363 128 0.125817342405241 0.251634684810482 0.874182657594759 129 0.124531146520927 0.249062293041854 0.875468853479073 130 0.0954188810098606 0.190837762019721 0.90458111899014 131 0.223248842213542 0.446497684427084 0.776751157786458 132 0.163388194614782 0.326776389229565 0.836611805385218 133 0.161892253086985 0.323784506173969 0.838107746913015 134 0.1096973016991 0.2193946033982 0.8903026983009 135 0.077337320548919 0.154674641097838 0.922662679451081 136 0.0420418892633935 0.084083778526787 0.957958110736607 137 0.0238221106538288 0.0476442213076577 0.976177889346171 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 1 0.00769230769230769 OK 10% type I error level 3 0.0230769230769231 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/10lib81292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/10lib81292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/179eh1292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/179eh1292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/279eh1292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/279eh1292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/379eh1292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/379eh1292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/4hiv21292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/4hiv21292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/5hiv21292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/5hiv21292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/6hiv21292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/6hiv21292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/7a9c51292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/7a9c51292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/8lib81292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/8lib81292684824.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/9lib81292684824.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292684989n3qg08shhrdkffw/9lib81292684824.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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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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