*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: Wed, 01 Dec 2010 15:05:12 +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/01/t1291215806v5qz9c6ybn1nsly.htm/, Retrieved Wed, 01 Dec 2010 16:03:37 +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/01/t1291215806v5qz9c6ybn1nsly.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 «

2 24 48 14 28 11 22 12 24 24 48 26 52 1 25 25 11 11 7 7 8 8 25 25 23 23 1 17 17 6 6 17 17 8 8 30 30 25 25 1 18 18 12 12 10 10 8 8 19 19 23 23 1 18 18 8 8 12 12 9 9 22 22 19 19 1 16 16 10 10 12 12 7 7 22 22 29 29 2 20 40 10 20 11 22 4 8 25 50 25 50 2 16 32 11 22 11 22 11 22 23 46 21 42 1 18 18 16 16 12 12 7 7 17 17 22 22 1 17 17 11 11 13 13 7 7 21 21 25 25 1 23 23 13 13 14 14 12 12 19 19 24 24 2 30 60 12 24 16 32 10 20 19 38 18 36 2 23 46 8 16 11 22 10 20 15 30 22 44 2 18 36 12 24 10 20 8 16 16 32 15 30 1 15 15 11 11 11 11 8 8 23 23 22 22 2 12 24 4 8 15 30 4 8 27 54 28 56 1 21 21 9 9 9 9 9 9 22 22 20 20 1 15 15 8 8 11 11 8 8 14 14 12 12 2 20 40 8 16 17 34 7 14 22 44 24 48 2 31 62 14 28 17 34 11 22 23 46 20 40 1 27 27 15 15 11 11 9 9 23 23 21 21 2 34 68 16 32 18 36 11 22 21 42 20 40 2 21 42 9 18 14 28 13 26 19 38 21 42 1 31 31 14 14 10 10 8 8 18 18 23 23 2 19 38 11 22 11 22 8 16 20 40 28 56 2 16 32 8 16 15 30 9 18 23 46 24 48 2 20 40 9 18 15 30 6 12 25 50 24 48 2 21 42 9 18 13 26 9 18 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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation ParentalExpectations[t] = + 7.56408311336285 -4.02282154930859Gender[t] -0.0497128514879975ConcernoverMistakes[t] + 0.0139238377293544`C*G`[t] -0.0584300461850058Doubtsaboutactions[t] + 0.0411688955628657`D*G`[t] + 0.554368493058405`PE*G`[t] + 0.762594333796532ParentalCriticism[t] -0.413361375350402`PC*G`[t] + 0.229602388869078PersonalStandards[t] -0.11794509827969`PS*G`[t] -0.205802809085591Organization[t] + 0.101854058940112`O*G`[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) 7.56408311336285 1.563469 4.838 3e-06 2e-06 Gender -4.02282154930859 0.950195 -4.2337 4e-05 2e-05 ConcernoverMistakes -0.0497128514879975 0.04408 -1.1278 0.261255 0.130628 `C*G` 0.0139238377293544 0.027093 0.5139 0.608083 0.304042 Doubtsaboutactions -0.0584300461850058 0.075433 -0.7746 0.439833 0.219917 `D*G` 0.0411688955628657 0.047329 0.8699 0.385808 0.192904 `PE*G` 0.554368493058405 0.012882 43.0342 0 0 ParentalCriticism 0.762594333796532 0.078568 9.7061 0 0 `PC*G` -0.413361375350402 0.047198 -8.7579 0 0 PersonalStandards 0.229602388869078 0.057857 3.9684 0.000113 5.7e-05 `PS*G` -0.11794509827969 0.034576 -3.4111 0.000837 0.000418 Organization -0.205802809085591 0.054165 -3.7995 0.000212 0.000106 `O*G` 0.101854058940112 0.033274 3.0611 0.002626 0.001313 Multiple Linear Regression - Regression Statistics Multiple R 0.979951191393236 R-squared 0.960304337513023 Adjusted R-squared 0.957041680322313 F-TEST (value) 294.331975865330 F-TEST (DF numerator) 12 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.714124047186037 Sum Squared Residuals 74.4560805963274 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11 10.5495807077397 0.450419292260260 2 7 9.5317176936113 -2.5317176936113 3 17 15.7984094400311 1.20159055996895 4 10 10.7581413749384 -0.758141374938443 5 12 13.0359227943400 -1.03592279434002 6 12 11.3350251022660 0.664974897734024 7 11 11.0502446508432 -0.0502446508432135 8 11 10.7336685617733 0.266331438226719 9 12 11.3292349690873 0.67076503091273 10 13 12.1404811409361 0.859518859063858 11 14 14.0723922113957 -0.0723922113957462 12 16 16.0907124941696 -0.0907124941696067 13 11 10.6212252819674 0.378774718032598 14 10 9.85407702031575 0.145922979684245 15 11 11.9877159723979 -0.987715972397948 16 15 15.4978078449037 -0.49780784490372 17 9 11.1442403731213 -2.14424037312126 18 11 12.0740713104147 -1.07407131041468 19 17 17.4834239412261 -0.483423941226097 20 17 17.1319307640624 -0.131930764062352 21 11 11.9423849133973 -0.942384913397281 22 18 18.2354633273534 -0.235463327353353 23 14 13.7996325470485 0.200367452951524 24 10 10.1467046042424 -0.146704604242418 25 11 10.8646588690316 0.135341130968438 26 15 15.2188660316108 -0.218866031610784 27 15 15.3351227077666 -0.335122707766567 28 13 12.9411251549327 0.0588748450673461 29 16 14.9004991415138 1.09950085848621 30 13 13.2042023778971 -0.204202377897087 31 9 8.60867437586244 0.391325624137565 32 18 18.0717900238226 -0.0717900238226058 33 18 15.5126561412416 2.48734385875843 34 12 11.7654403623872 0.23455963761279 35 17 15.9533674942458 1.04663250575424 36 9 9.73994282533566 -0.739942825335656 37 9 10.0945972746802 -1.09459727468024 38 12 10.8097141209882 1.19028587901178 39 18 17.1978393260192 0.80216067398083 40 12 11.8841241476318 0.115875852368246 41 18 18.3087161840174 -0.308716184017430 42 14 13.9034194459864 0.096580554013632 43 15 15.3583326939882 -0.358332693988225 44 16 16.5756783300809 -0.575678330080885 45 10 11.5026866827912 -1.50268668279115 46 11 11.3236366995712 -0.323636699571206 47 14 14.1334254325684 -0.133425432568411 48 9 10.2815454848804 -1.28154548488042 49 12 12.1776652544908 -0.177665254490788 50 17 14.9538617416909 2.04613825830906 51 5 4.42474845419696 0.575251545803043 52 12 11.9495364984274 0.0504635015726222 53 12 11.9276229341784 0.0723770658216422 54 6 5.68944562251121 0.310554377488794 55 24 24.1940683193530 -0.194068319352962 56 12 11.4978990524380 0.502100947561969 57 12 12.1240506408088 -0.124050640808757 58 14 14.2785506948979 -0.278550694897936 59 7 6.73107422924293 0.268925770757075 60 13 13.3499528417808 -0.349952841780756 61 12 12.6540727377824 -0.654072737782425 62 13 13.1259791121347 -0.125979112134686 63 14 12.6291360771868 1.37086392281317 64 8 7.52722769374786 0.472772306252141 65 11 10.5856454039719 0.414354596028115 66 9 9.39190224187301 -0.391902241873014 67 11 11.1473204132457 -0.147320413245734 68 13 13.5383971172275 -0.538397117227497 69 10 10.1566179309609 -0.156617930960873 70 11 10.8176387281669 0.182361271833136 71 12 11.7815826001991 0.218417399800924 72 9 8.6676834721573 0.332316527842702 73 15 14.9073206989695 0.0926793010304781 74 18 18.3020217735624 -0.302021773562364 75 15 15.2018369603855 -0.201836960385467 76 12 12.0420103843867 -0.0420103843866668 77 13 13.4133002319974 -0.413300231997438 78 14 13.4485043859649 0.551495614035051 79 10 9.7588715135792 0.241128486420798 80 13 12.9947321995908 0.00526780040917663 81 13 12.9087941909155 0.0912058090844854 82 11 10.8809159401145 0.119084059885525 83 13 13.0811661890698 -0.081166189069839 84 16 15.3462798185433 0.653720181456747 85 8 7.72347819099464 0.276521809005361 86 16 16.4371206292828 -0.43712062928278 87 11 10.9600579414247 0.0399420585752663 88 9 10.0995542833964 -1.09955428339636 89 16 16.8190670212398 -0.819067021239801 90 12 12.0471427886204 -0.0471427886204004 91 14 14.1367790178915 -0.136779017891527 92 8 7.73471143162069 0.265288568379308 93 9 9.0215986035023 -0.0215986035023019 94 15 15.3425422885411 -0.342542288541094 95 11 10.6843934288566 0.315606571143370 96 21 21.3379608954175 -0.337960895417492 97 14 13.9838942887557 0.0161057112443153 98 18 16.6102929326927 1.38970706730728 99 12 11.6298800504185 0.370119949581526 100 13 12.6167508103159 0.383249189684101 101 15 15.0557809459990 -0.0557809459990443 102 12 11.5699827388406 0.430017261159416 103 19 19.5617535527112 -0.56175355271117 104 15 15.1277722604393 -0.127772260439273 105 11 11.2854732045681 -0.285473204568122 106 11 10.5880410150887 0.411958984911257 107 10 9.74021393157792 0.259786068422081 108 13 12.8302534307888 0.169746569211176 109 15 14.8355066900732 0.164493309926800 110 12 12.2632081527268 -0.263208152726758 111 12 12.1535711603963 -0.153571160396323 112 16 15.9042746931148 0.0957253068851773 113 9 8.3309523580861 0.669047641913901 114 18 18.1353349731023 -0.135334973102288 115 8 7.39905888237169 0.600941117628312 116 13 11.6335364398144 1.36646356018559 117 17 17.3954286994524 -0.395428699452431 118 9 8.83802430351337 0.161975696486628 119 15 15.1718137246818 -0.171813724681842 120 8 7.84853142418156 0.151468575818437 121 7 6.44241413297891 0.557585867021086 122 12 12.1823892781054 -0.182389278105418 123 14 14.9313746103498 -0.931374610349844 124 6 7.92237291987548 -1.92237291987548 125 8 8.91839571134878 -0.918395711348782 126 17 17.4994012580397 -0.499401258039671 127 10 10.0239761692677 -0.0239761692676851 128 11 11.5046331936122 -0.504633193612202 129 14 14.1226004162867 -0.122600416286716 130 11 10.5988268669180 0.401173133082016 131 13 12.8199477437570 0.180052256243035 132 12 11.8631654689618 0.136834531038244 133 11 10.4871663702046 0.512833629795364 134 9 8.83580638751198 0.16419361248802 135 12 11.9413824449371 0.0586175550628829 136 20 17.5822687459947 2.41773125400534 137 12 11.4629975439773 0.537002456022705 138 13 14.0332818951934 -1.03328189519336 139 12 11.8833655126117 0.116634487388332 140 12 11.6496291027098 0.350370897290232 141 9 8.42472503219791 0.575274967802092 142 15 15.0195032480584 -0.0195032480583685 143 24 24.0354877283835 -0.0354877283835392 144 7 8.00755235831579 -1.00755235831579 145 17 16.1123694222906 0.887630577709371 146 11 10.8581667782402 0.141833221759800 147 17 17.1301679489920 -0.130167948992033 148 11 11.4786431655151 -0.478643165515068 149 12 12.3740446850354 -0.374044685035397 150 14 14.0234733525441 -0.0234733525441383 151 11 10.6578755974255 0.342124402574511 152 16 16.3143234741159 -0.314323474115905 153 21 21.5420338421294 -0.542033842129365 154 14 13.3292130787119 0.67078692128807 155 20 20.4923907824139 -0.492390782413934 156 13 13.2272551833787 -0.22725518337873 157 11 10.7228230536504 0.277176946349599 158 15 15.1535624733354 -0.153562473335444 159 19 18.0955513679399 0.90444863206013 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.789962845310063 0.420074309379875 0.210037154689937 17 0.708777579109477 0.582444841781045 0.291222420890523 18 0.618136952300701 0.763726095398598 0.381863047699299 19 0.924717243657155 0.150565512685690 0.0752827563428449 20 0.879257595951904 0.241484808096193 0.120742404048096 21 0.89021357733421 0.219572845331580 0.109786422665790 22 0.904142675255625 0.19171464948875 0.095857324744375 23 0.859629679795954 0.280740640408093 0.140370320204046 24 0.889469266322935 0.22106146735413 0.110530733677065 25 0.88829773968836 0.223404520623281 0.111702260311641 26 0.86666919076968 0.266661618460640 0.133330809230320 27 0.820880630450203 0.358238739099595 0.179119369549797 28 0.766081803167315 0.467836393665369 0.233918196832685 29 0.871647578584388 0.256704842831224 0.128352421415612 30 0.833324008744715 0.33335198251057 0.166675991255285 31 0.888597751573913 0.222804496852174 0.111402248426087 32 0.870672043820313 0.258655912359374 0.129327956179687 33 0.993626779758823 0.0127464404823548 0.0063732202411774 34 0.996297372792085 0.0074052544158308 0.0037026272079154 35 0.998037010257105 0.00392597948578951 0.00196298974289476 36 0.998977384669256 0.00204523066148897 0.00102261533074448 37 0.99922536557649 0.00154926884702089 0.000774634423510447 38 0.999264495857807 0.00147100828438559 0.000735504142192795 39 0.999675915657168 0.000648168685663758 0.000324084342831879 40 0.999534867455117 0.000930265089766878 0.000465132544883439 41 0.99946258277984 0.00107483444031903 0.000537417220159516 42 0.999146869031277 0.00170626193744568 0.00085313096872284 43 0.998885384877645 0.00222923024470930 0.00111461512235465 44 0.998689927257759 0.00262014548448309 0.00131007274224155 45 0.999705081499605 0.000589837000790424 0.000294918500395212 46 0.999570191063337 0.000859617873325662 0.000429808936662831 47 0.999324614041903 0.00135077191619331 0.000675385958096657 48 0.999802326731875 0.000395346536250929 0.000197673268125465 49 0.999751802279312 0.000496395441375993 0.000248197720687996 50 0.999987589366197 2.4821267605259e-05 1.24106338026295e-05 51 0.99999080126717 1.83974656618052e-05 9.19873283090262e-06 52 0.999984074470075 3.18510598492757e-05 1.59255299246378e-05 53 0.99997234523558 5.53095288409733e-05 2.76547644204867e-05 54 0.999964683723343 7.0632553314277e-05 3.53162766571385e-05 55 0.999940566930482 0.000118866139037081 5.94330695185407e-05 56 0.999922049454815 0.000155901090370484 7.79505451852422e-05 57 0.999874198980085 0.000251602039830235 0.000125801019915117 58 0.999806935431901 0.000386129136197752 0.000193064568098876 59 0.999722155082953 0.000555689834094068 0.000277844917047034 60 0.9996255715509 0.000748856898197979 0.000374428449098989 61 0.99958748164739 0.000825036705220529 0.000412518352610264 62 0.999364254045453 0.00127149190909315 0.000635745954546577 63 0.99981505420449 0.000369891591018548 0.000184945795509274 64 0.999771376859854 0.000457246280291166 0.000228623140145583 65 0.999762419117352 0.00047516176529694 0.00023758088264847 66 0.999728533663533 0.000542932672934504 0.000271466336467252 67 0.99987023505129 0.000259529897418045 0.000129764948709022 68 0.999841733357276 0.000316533285447256 0.000158266642723628 69 0.999884957073382 0.000230085853236631 0.000115042926618315 70 0.999820589988733 0.000358820022533593 0.000179410011266797 71 0.999728963012594 0.000542073974811823 0.000271036987405911 72 0.999616346106814 0.000767307786371721 0.000383653893185860 73 0.999406234915286 0.00118753016942784 0.000593765084713918 74 0.999172864046638 0.00165427190672336 0.00082713595336168 75 0.998785006594313 0.00242998681137425 0.00121499340568713 76 0.998527094233988 0.00294581153202359 0.00147290576601179 77 0.998074448806611 0.00385110238677773 0.00192555119338886 78 0.997901438030819 0.00419712393836244 0.00209856196918122 79 0.996995005284042 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0.92815402799823 0.143691944003539 0.0718459720017693 97 0.908651681169288 0.182696637661424 0.091348318830712 98 0.93638027295489 0.127239454090220 0.0636197270451098 99 0.921614569843008 0.156770860313985 0.0783854301569925 100 0.903438383243567 0.193123233512865 0.0965616167564326 101 0.878799512041083 0.242400975917835 0.121200487958917 102 0.865460482001806 0.269079035996388 0.134539517998194 103 0.846212812745894 0.307574374508211 0.153787187254106 104 0.812707185343339 0.374585629313323 0.187292814656661 105 0.786063329913647 0.427873340172707 0.213936670086353 106 0.746590752814814 0.506818494370373 0.253409247185186 107 0.707457638432218 0.585084723135564 0.292542361567782 108 0.662263539746954 0.675472920506093 0.337736460253046 109 0.613420392903074 0.773159214193852 0.386579607096926 110 0.564448683028446 0.871102633943108 0.435551316971554 111 0.510794576382737 0.978410847234525 0.489205423617263 112 0.456845260843123 0.913690521686245 0.543154739156877 113 0.44074311424353 0.88148622848706 0.55925688575647 114 0.391585926070925 0.78317185214185 0.608414073929075 115 0.367931632066202 0.735863264132404 0.632068367933798 116 0.560410580121211 0.879178839757578 0.439589419878789 117 0.53336225140375 0.9332754971925 0.46663774859625 118 0.474768110153709 0.949536220307419 0.525231889846291 119 0.415318530140355 0.830637060280711 0.584681469859645 120 0.365962197618586 0.731924395237172 0.634037802381414 121 0.325878828492406 0.651757656984812 0.674121171507594 122 0.298112725072929 0.596225450145859 0.701887274927071 123 0.305038135323441 0.610076270646882 0.69496186467656 124 0.610025834040073 0.779948331919853 0.389974165959927 125 0.584193129975003 0.831613740049993 0.415806870024997 126 0.538789455799655 0.92242108840069 0.461210544200345 127 0.469829146966126 0.939658293932253 0.530170853033874 128 0.493546010885045 0.98709202177009 0.506453989114955 129 0.421728171157786 0.843456342315571 0.578271828842214 130 0.383672577968608 0.767345155937215 0.616327422031392 131 0.314007840496355 0.62801568099271 0.685992159503645 132 0.249058391349176 0.498116782698352 0.750941608650824 133 0.197878661814529 0.395757323629058 0.802121338185471 134 0.150027317532234 0.300054635064468 0.849972682467766 135 0.112549283686449 0.225098567372898 0.88745071631355 136 0.635272733611546 0.729454532776909 0.364727266388454 137 0.748453402108847 0.503093195782306 0.251546597891153 138 0.723506579094221 0.552986841811558 0.276493420905779 139 0.628947722450498 0.742104555099003 0.371052277549501 140 0.514741162355779 0.970517675288443 0.485258837644221 141 0.3938825269471 0.7877650538942 0.6061174730529 142 0.340185516592125 0.68037103318425 0.659814483407875 143 0.995088059313845 0.0098238813723107 0.00491194068615535 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 48 0.375 NOK 5% type I error level 59 0.4609375 NOK 10% type I error level 63 0.4921875 NOK

Charts produced by software:

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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