Multiple Regression Gender

*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, 19 Dec 2010 15:50:49 +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/19/t1292774037r35z7cfj4n2rysb.htm/, Retrieved Sun, 19 Dec 2010 16:54:11 +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/19/t1292774037r35z7cfj4n2rysb.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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0 1 4 4 2 0 1 2 2 2 0 1 5 5 4 1 1 4 5 3 0 2 1 1 2 0 1 2 4 1 0 4 5 6 4 0 1 1 5 3 0 1 3 4 1 0 2 5 5 4 1 1 2 7 4 0 1 2 2 4 1 2 2 7 3 0 1 2 5 4 0 1 1 5 1 1 1 4 7 4 1 1 3 3 1 0 1 6 6 4 1 1 1 2 4 0 2 3 6 3 1 1 2 1 2 0 2 5 5 6 0 1 5 4 5 0 2 3 4 4 1 1 3 7 6 0 1 5 7 1 1 1 5 5 2 0 2 4 6 4 1 1 2 5 4 0 1 1 1 1 1 2 4 6 2 0 1 6 4 1 0 1 2 2 2 1 1 3 2 2 1 1 2 6 2 1 2 4 6 6 1 1 2 6 2 0 1 1 1 1 1 1 5 6 4 1 1 5 6 3 0 1 1 1 3 1 1 1 1 1 1 1 2 7 4 0 1 4 2 3 0 1 5 3 4 0 1 3 5 3 0 1 3 3 2 1 1 1 4 1 0 1 2 2 5 1 1 3 3 4 1 2 2 7 1 0 2 5 7 2 1 1 4 5 4 0 1 4 1 3 0 1 2 2 2 0 2 3 5 3 1 1 6 2 3 0 1 2 4 2 1 2 3 7 2 1 1 2 2 4 0 1 5 5 4 0 1 5 6 2 0 1 5 3 2 1 1 6 7 5 0 2 4 4 4 1 1 2 3 5 0 1 5 5 5 1 2 2 3 2 1 1 1 2 3 0 1 6 6 4 0 1 6 6 2 1 1 3 5 2 1 1 4 2 2 0 3 5 3 5 0 2 2 4 2 0 2 4 6 3 1 1 3 5 2 1 1 2 2 2 1 1 2 5 2 1 1 3 2 2 0 1 3 1 2 1 1 7 2 1 0 1 2 4 3 0 1 2 5 3 1 1 2 5 3 0 1 5 3 3 0 1 1 2 1 0 3 5 7 4 0 1 2 1 1 0 1 1 5 1 0 1 2 5 1 0 1 2 2 3 0 1 0 6 2 0 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Depressed[t] = + 0.752742283284845 -0.0807424499953264Gender[t] + 0.0476668071066975Cannotdo[t] + 0.0527632072156886Worrytoomuch[t] + 0.0627476309907595Limitactivity[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) 0.752742283284845 0.14768 5.0971 1e-06 1e-06 Gender -0.0807424499953264 0.093204 -0.8663 0.387739 0.19387 Cannotdo 0.0476668071066975 0.029538 1.6137 0.108731 0.054366 Worrytoomuch 0.0527632072156886 0.02741 1.925 0.056164 0.028082 Limitactivity 0.0627476309907595 0.036082 1.739 0.084127 0.042064 Multiple Linear Regression - Regression Statistics Multiple R 0.314529135026514 R-squared 0.0989285767805273 Adjusted R-squared 0.0744096264888409 F-TEST (value) 4.03478026602432 F-TEST (DF numerator) 4 F-TEST (DF denominator) 147 p-value 0.00391177248966845 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.555793587493188 Sum Squared Residuals 45.4092572490866 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 1.27995760255589 -0.279957602555887 2 1 1.07909757391114 -0.0790975739111352 3 1 1.50588287885981 -0.50588287885981 4 1 1.31472599076703 -0.314725990767028 5 2 0.978667559588746 1.02133244041125 6 1 1.12187635735175 -0.121876357351752 7 4 1.5586460860755 2.4413539139245 8 1 1.25246801944226 -0.252468019442262 9 1 1.16954316445845 -0.169543164458450 10 2 1.50588287885981 0.494117121140189 11 1 1.38766642197577 -0.38766642197577 12 1 1.20459283589265 -0.204592835892653 13 2 1.32491879098501 0.67508120901499 14 1 1.36288245753972 -0.362882457539719 15 1 1.12697275746074 -0.126972757460744 16 1 1.48300003618916 -0.483000036189165 17 1 1.03603750724743 -0.0360375072474348 18 1 1.60631289318220 -0.606312893182197 19 1 1.07618357879063 -0.0761835787906293 20 2 1.40056484087135 0.599435159128654 21 1 0.94559191670012 0.0544080832998805 22 2 1.63137814084133 0.36862185915867 23 1 1.51586730263488 -0.515867302634882 24 2 1.35778605743073 0.642213942569272 25 1 1.56082849106399 -0.560828491063986 26 1 1.42316640031891 -0.423166400318911 27 1 1.29964516688297 -0.299645166882966 28 2 1.51097927896880 0.489020721031197 29 1 1.28214000754439 -0.282140007544393 30 1 0.91591992859799 0.0840800714020108 31 2 1.30474156699196 0.695258433008043 32 1 1.31254358577854 -0.312543585778542 33 1 1.07909757391113 -0.0790975739111346 34 1 1.04602193102251 -0.0460219310225055 35 1 1.20940795277856 -0.209407952778562 36 2 1.55573209095499 0.444267909045005 37 1 1.20940795277856 -0.209407952778562 38 1 0.91591992859799 0.0840800714020108 39 1 1.47790363608017 -0.477903636080173 40 1 1.41515600508941 -0.415156005089414 41 1 1.04141519057951 -0.0414151905795079 42 1 0.835177478602663 0.164822521397337 43 1 1.38766642197577 -0.38766642197577 44 1 1.23717881911529 -0.237178819115289 45 1 1.40035646442843 -0.400356464428434 46 1 1.34780163365566 -0.347801633655657 47 1 1.17952758823352 -0.179527588233521 48 1 0.993467100249728 0.00653289975027157 49 1 1.26734046688341 -0.267340466883413 50 1 1.22428040021971 -0.224280400219713 51 2 1.19942352900349 0.800576470996508 52 2 1.48591403130967 0.51408596869033 53 1 1.37747362175779 -0.377473621757787 54 1 1.1844156118996 -0.184415611899600 55 1 1.07909757391113 -0.0790975739111346 56 2 1.34780163365566 0.652198366344343 57 1 1.25176998333336 -0.251769983333357 58 1 1.18462398834251 -0.184623988342512 59 2 1.30983796710095 0.690162032899052 60 1 1.12385038589733 -0.123850385897327 61 1 1.50588287885981 -0.505882878859811 62 1 1.43315082409398 -0.433150824093981 63 1 1.27486120244692 -0.274861202446916 64 1 1.64108128139332 -0.641081281393319 65 2 1.40545286453743 0.594547135462575 66 1 1.23936122410377 -0.239361224103775 67 1 1.56863050985057 -0.56863050985057 68 2 1.05111833113150 0.948881668868503 69 1 1.01343594779987 -0.0134359477998699 70 1 1.60631289318220 -0.606312893182197 71 1 1.48081763120068 -0.480817631200679 72 1 1.20431155266957 -0.204311552669571 73 1 1.09368873812920 -0.093688738129203 74 3 1.46310409541919 1.53689590458081 75 2 1.18462398834251 0.815376011657488 76 2 1.44823164797804 0.551768352021957 77 1 1.20431155266957 -0.204311552669571 78 1 0.998355123915808 0.00164487608419195 79 1 1.15664474556287 -0.156644745562874 80 1 1.04602193102251 -0.0460219310225055 81 1 1.07400117380214 -0.0740011738021434 82 1 1.17394152845854 -0.173941528458536 83 1 1.24737161933327 -0.247371619333271 84 1 1.30013482654896 -0.30013482654896 85 1 1.21939237655363 -0.219392376553633 86 1 1.33760883343767 -0.337608833437675 87 1 0.968683135813678 0.0313168641863222 88 3 1.61140929329119 1.38859070670881 89 1 0.963586735704687 0.0364132642953134 90 1 1.12697275746074 -0.126972757460744 91 1 1.17463956456744 -0.174639564567441 92 1 1.14184520490189 -0.141845204901894 93 1 1.19481678856049 -0.194816788560494 94 1 1.28484562622199 -0.284845626221986 95 1 1.47329689563718 -0.473296895637176 96 1 1.19460841211758 -0.194608412117582 97 1 1.14645194534489 -0.146451945344892 98 1 1.15664474556287 -0.156644745562874 99 1 1.30013482654896 -0.30013482654896 100 2 1.37747362175779 0.622526378242213 101 1 1.36797885764871 -0.36797885764871 102 1 1.36239279787373 -0.362392797873726 103 1 1.33272080977160 -0.332720809771595 104 2 1.46282281219611 0.537177187803888 105 1 1.14184520490189 -0.141845204901894 106 2 1.42514042886448 0.574859571135515 107 2 1.10897793845618 0.891022061543824 108 3 1.45291129520121 1.54708870479879 109 2 1.38038761687829 0.619612383121708 110 2 1.33781720988059 0.662182790119414 111 1 1.51097927896880 -0.510979278968803 112 1 1.33469483831717 -0.334694838317170 113 1 1.00636551914530 -0.00636551914530451 114 1 0.94070389303404 0.0592961069659602 115 1 1.67904494794803 -0.679044947948027 116 1 1.26997317878084 -0.269973178780836 117 1 1.07909757391113 -0.0790975739111346 118 2 1.30744718566955 0.692552814330449 119 1 1.35268965732174 -0.352689657321737 120 1 1.38548401698728 -0.385484016987284 121 1 1.45821607175311 -0.458216071753114 122 1 1.01634994292038 -0.0163499429203752 123 1 1.45311967164412 -0.453119671644123 124 1 1.10876956201326 -0.108769562013265 125 1 1.01634994292038 -0.0163499429203752 126 1 1.28505400266490 -0.285054002664898 127 1 1.52096370274387 -0.520963702743873 128 2 1.30962959065804 0.690370409341963 129 2 1.34731197398966 0.652688026010336 130 1 1.02633436669545 -0.026334366695446 131 1 1.28214000754439 -0.282140007544393 132 1 1.28214000754439 -0.282140007544393 133 1 1.36288245753972 -0.362882457539719 134 4 1.33490321476008 2.66509678523992 135 1 1.42514042886448 -0.425140428864485 136 2 1.36288245753972 0.637117542460281 137 1 1.33781720988059 -0.337817209880586 138 1 1.47280723597118 -0.472807235971182 139 1 1.33272080977160 -0.332720809771595 140 1 1.40517158131434 -0.405171581314343 141 1 0.91591992859799 0.0840800714020108 142 1 1.11386596212226 -0.113865962122256 143 1 1.2373871955582 -0.237387195558200 144 1 1.17463956456744 -0.174639564567441 145 1 1.46282281219611 -0.462822812196112 146 1 1.16662916933794 -0.166629169337945 147 1 1.07909757391113 -0.0790975739111346 148 2 1.32520007420809 0.674799925791908 149 1 1.29964516688297 -0.299645166882966 150 1 1.47329689563718 -0.473296895637176 151 3 1.55354968596651 1.44645031403349 152 1 1.12697275746074 -0.126972757460744 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.99983087004226 0.000338259915480276 0.000169129957740138 9 0.999492035612066 0.0010159287758685 0.00050796438793425 10 0.998892477523671 0.00221504495265781 0.00110752247632890 11 0.997558018720619 0.00488396255876223 0.00244198127938111 12 0.997425885259168 0.00514822948166457 0.00257411474083229 13 0.998238129912283 0.00352374017543347 0.00176187008771673 14 0.997968989705386 0.00406202058922865 0.00203101029461433 15 0.996176377152965 0.00764724569406972 0.00382362284703486 16 0.99531040651027 0.00937918697946135 0.00468959348973067 17 0.99222955870741 0.0155408825851818 0.00777044129259088 18 0.994855943474432 0.0102881130511358 0.00514405652556792 19 0.991406568932097 0.0171868621358057 0.00859343106790285 20 0.989712526679766 0.0205749466404681 0.0102874733202340 21 0.98429471499923 0.0314105700015412 0.0157052850007706 22 0.977130255409904 0.0457394891801929 0.0228697445900964 23 0.977878990776694 0.0442420184466123 0.0221210092233062 24 0.975855206730186 0.0482895865396272 0.0241447932698136 25 0.972137895520613 0.055724208958775 0.0278621044793875 26 0.96748341606743 0.0650331678651389 0.0325165839325695 27 0.955406510203668 0.0891869795926643 0.0445934897963322 28 0.947164781037885 0.105670437924231 0.0528352189621154 29 0.930699440011294 0.138601119977412 0.069300559988706 30 0.908464744916176 0.183070510167648 0.091535255083824 31 0.92829375099185 0.1434124980163 0.07170624900815 32 0.91443401420294 0.171131971594119 0.0855659857970593 33 0.891046833352348 0.217906333295304 0.108953166647652 34 0.8620460162251 0.275907967549799 0.137953983774900 35 0.830331996005342 0.339336007989316 0.169668003994658 36 0.815342573432119 0.369314853135762 0.184657426567881 37 0.778198489253058 0.443603021493884 0.221801510746942 38 0.734681284766291 0.530637430467418 0.265318715233709 39 0.713502463958428 0.572995072083143 0.286497536041572 40 0.680984324734862 0.638031350530275 0.319015675265138 41 0.63503868818921 0.729922623621581 0.364961311810790 42 0.593730647791425 0.81253870441715 0.406269352208575 43 0.561187719140629 0.877624561718742 0.438812280859371 44 0.522389925348266 0.955220149303469 0.477610074651734 45 0.499626321215849 0.999252642431698 0.500373678784151 46 0.472087119403831 0.944174238807661 0.527912880596169 47 0.426049689638083 0.852099379276166 0.573950310361917 48 0.376820412426164 0.753640824852328 0.623179587573836 49 0.344558366861668 0.689116733723335 0.655441633138332 50 0.302205109459384 0.604410218918768 0.697794890540616 51 0.35213596676224 0.70427193352448 0.64786403323776 52 0.338606879198174 0.677213758396347 0.661393120801826 53 0.307150619087972 0.614301238175943 0.692849380912028 54 0.266454407518607 0.532908815037213 0.733545592481393 55 0.227769983711981 0.455539967423962 0.77223001628802 56 0.234215286976087 0.468430573952175 0.765784713023912 57 0.202975407529654 0.405950815059308 0.797024592470346 58 0.176561821262862 0.353123642525724 0.823438178737138 59 0.193031081716593 0.386062163433186 0.806968918283407 60 0.163251080190596 0.326502160381193 0.836748919809404 61 0.157574973356002 0.315149946712005 0.842425026643998 62 0.148080203296894 0.296160406593788 0.851919796703106 63 0.125912635958819 0.251825271917637 0.874087364041181 64 0.126840003561236 0.253680007122471 0.873159996438764 65 0.132872024427051 0.265744048854101 0.86712797557295 66 0.112716090396591 0.225432180793182 0.88728390960341 67 0.112645574848330 0.225291149696659 0.88735442515167 68 0.168474286861653 0.336948573723306 0.831525713138347 69 0.139999912875313 0.279999825750627 0.860000087124687 70 0.142414189603986 0.284828379207972 0.857585810396014 71 0.133860781359342 0.267721562718684 0.866139218640658 72 0.112418329012196 0.224836658024392 0.887581670987804 73 0.0922369922671573 0.184473984534315 0.907763007732843 74 0.314710271978309 0.629420543956618 0.685289728021691 75 0.356249974434478 0.712499948868957 0.643750025565522 76 0.351090680697581 0.702181361395162 0.648909319302419 77 0.313433721124382 0.626867442248764 0.686566278875618 78 0.272623845958979 0.545247691917957 0.727376154041022 79 0.237932540320298 0.475865080640596 0.762067459679702 80 0.203501588761053 0.407003177522106 0.796498411238947 81 0.171908760097515 0.343817520195031 0.828091239902485 82 0.150848639326035 0.301697278652070 0.849151360673965 83 0.131465449526955 0.262930899053910 0.868534550473045 84 0.116186560636054 0.232373121272107 0.883813439363946 85 0.0981891310578988 0.196378262115798 0.901810868942101 86 0.0854973308717686 0.170994661743537 0.914502669128231 87 0.0688845990249056 0.137769198049811 0.931115400975094 88 0.195229357166915 0.39045871433383 0.804770642833085 89 0.163698529475592 0.327397058951184 0.836301470524408 90 0.139571310314952 0.279142620629905 0.860428689685048 91 0.117672326412533 0.235344652825066 0.882327673587467 92 0.0971413767857336 0.194282753571467 0.902858623214266 93 0.0819136134846528 0.163827226969306 0.918086386515347 94 0.0698860835599303 0.139772167119861 0.93011391644007 95 0.0645415877936177 0.129083175587235 0.935458412206382 96 0.0521802816535358 0.104360563307072 0.947819718346464 97 0.0425885978954946 0.0851771957909892 0.957411402104505 98 0.0335369188083822 0.0670738376167644 0.966463081191618 99 0.0272500055715905 0.054500011143181 0.97274999442841 100 0.0279349375585377 0.0558698751170754 0.972065062441462 101 0.0231589453245212 0.0463178906490423 0.976841054675479 102 0.0201909688656638 0.0403819377313275 0.979809031134336 103 0.0163070990662332 0.0326141981324663 0.983692900933767 104 0.0151677463142856 0.0303354926285712 0.984832253685714 105 0.0113074504753167 0.0226149009506334 0.988692549524683 106 0.0107580979704900 0.0215161959409800 0.98924190202951 107 0.0166831211314913 0.0333662422629827 0.983316878868509 108 0.0833229818220863 0.166645963644173 0.916677018177914 109 0.0894801673226626 0.178960334645325 0.910519832677337 110 0.0987740270864258 0.197548054172852 0.901225972913574 111 0.0892772759483687 0.178554551896737 0.910722724051631 112 0.0768955439792973 0.153791087958595 0.923104456020703 113 0.0593051554026388 0.118610310805278 0.940694844597361 114 0.0452493865592044 0.0904987731184089 0.954750613440795 115 0.0467411042558804 0.0934822085117607 0.95325889574412 116 0.0360165737306955 0.072033147461391 0.963983426269305 117 0.0263988602662201 0.0527977205324403 0.97360113973378 118 0.0391527303699782 0.0783054607399563 0.960847269630022 119 0.030843943457708 0.061687886915416 0.969156056542292 120 0.0238488899597507 0.0476977799195015 0.97615111004025 121 0.0202173299326503 0.0404346598653005 0.97978267006735 122 0.0142449500898288 0.0284899001796575 0.985755049910171 123 0.0117209250464874 0.0234418500929748 0.988279074953513 124 0.00815083176369887 0.0163016635273977 0.9918491682363 125 0.00539893337971928 0.0107978667594386 0.99460106662028 126 0.00364377621257544 0.00728755242515088 0.996356223787425 127 0.00406481356377987 0.00812962712755974 0.99593518643622 128 0.0044597974196451 0.0089195948392902 0.995540202580355 129 0.00712909174902621 0.0142581834980524 0.992870908250974 130 0.00453470947208894 0.00906941894417788 0.995465290527911 131 0.00381884133543111 0.00763768267086222 0.996181158664569 132 0.00382127653537072 0.00764255307074144 0.99617872346463 133 0.00486739235588199 0.00973478471176398 0.995132607644118 134 0.789326256854501 0.421347486290997 0.210673743145499 135 0.744193927841775 0.511612144316451 0.255806072158225 136 0.735108006881059 0.529783986237882 0.264891993118941 137 0.663669865749353 0.672660268501293 0.336330134250647 138 0.652956388592391 0.694087222815217 0.347043611407609 139 0.625894820932975 0.74821035813405 0.374105179067025 140 0.520209648092532 0.959580703814936 0.479790351907468 141 0.405746302638071 0.811492605276142 0.594253697361929 142 0.300638000091947 0.601276000183894 0.699361999908053 143 0.201466346382593 0.402932692765186 0.798533653617407 144 0.112829884648104 0.225659769296207 0.887170115351896 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.116788321167883 NOK 5% type I error level 38 0.277372262773723 NOK 10% type I error level 51 0.372262773722628 NOK

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Parameters (Session):

par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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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