Paper MR 2

*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 15:33:38 +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/t1292686359oy2ej64seyxrisl.htm/, Retrieved Sat, 18 Dec 2010 16:32:50 +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/t1292686359oy2ej64seyxrisl.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 «

69 26 9 15 25 25 53 20 9 15 25 24 43 21 9 14 19 21 60 31 14 10 18 23 49 21 8 10 18 17 62 18 8 12 22 19 45 26 11 18 29 18 50 22 10 12 26 27 75 22 9 14 25 23 82 29 15 18 23 23 60 15 14 9 23 29 59 16 11 11 23 21 21 24 14 11 24 26 62 17 6 17 30 25 54 19 20 8 19 25 47 22 9 16 24 23 59 31 10 21 32 26 37 28 8 24 30 20 43 38 11 21 29 29 48 26 14 14 17 24 79 25 11 7 25 23 62 25 16 18 26 24 16 29 14 18 26 30 38 28 11 13 25 22 58 15 11 11 23 22 60 18 12 13 21 13 67 21 9 13 19 24 55 25 7 18 35 17 47 23 13 14 19 24 59 23 10 12 20 21 49 19 9 9 21 23 47 18 9 12 21 24 57 18 13 8 24 24 39 26 16 5 23 24 49 18 12 10 19 23 26 18 6 11 17 26 53 28 14 11 24 24 75 17 14 12 15 21 65 29 10 12 25 23 49 12 4 15 27 28 48 25 12 12 29 23 45 28 12 16 27 22 31 20 14 14 18 24 61 17 9 17 25 21 49 17 9 13 22 23 69 20 10 10 26 23 54 31 14 17 23 20 80 21 10 12 16 23 57 19 9 13 27 21 34 23 14 13 25 27 69 15 8 11 14 12 44 24 9 13 19 15 70 28 8 12 20 22 51 16 9 12 16 21 66 19 9 12 18 21 18 21 9 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 Anxiety[t] = + 56.071093699893 -0.342980598360856Concern[t] + 0.0774281170230556Doubts[t] + 0.306566415060552Pexpectations[t] + 0.00433514557748334Standards[t] -0.0653485310147079Organization[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) 56.071093699893 9.180962 6.1073 0 0 Concern -0.342980598360856 0.247037 -1.3884 0.167138 0.083569 Doubts 0.0774281170230556 0.448984 0.1725 0.863321 0.431661 Pexpectations 0.306566415060552 0.355773 0.8617 0.390271 0.195135 Standards 0.00433514557748334 0.314703 0.0138 0.989028 0.494514 Organization -0.0653485310147079 0.317583 -0.2058 0.837258 0.418629 Multiple Linear Regression - Regression Statistics Multiple R 0.138481637077551 R-squared 0.0191771638076785 Adjusted R-squared -0.0144126593495926 F-TEST (value) 0.570921844925736 F-TEST (DF numerator) 5 F-TEST (DF denominator) 146 p-value 0.722183276880675 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.5237287475725 Sum Squared Residuals 26702.1209287361 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 69 50.923612785696 18.076387214304 2 53 53.0468449068757 -0.0468449068757286 3 43 52.5673326130335 -9.56733261303352 4 60 48.1633693466911 11.8366306533089 5 49 51.5206978142496 -2.52069781424960 6 62 53.0494159597338 8.95058404026621 7 45 52.4729485643365 -7.47294856433652 8 50 51.3269021345288 -1.32690213452875 9 75 52.1196658261082 22.8803341738918 10 82 51.4009657088077 30.5990342911923 11 60 52.9740770472035 7.02592295279655 12 59 53.5347331760122 5.4652668239878 13 21 50.7007652306985 -29.7007652306985 14 62 54.4129623778829 7.58703762211707 15 54 52.0042104825867 1.99578951741330 16 47 52.7284635106518 -5.72846351065177 17 59 51.0905338893056 7.90946611069438 18 37 53.267739590457 -16.267739590457 19 43 48.5580467880261 -5.55804678802611 20 48 51.0348543221454 -3.03485432214543 21 79 49.0996153596478 29.9003846403522 22 62 52.797973124992 9.20202687500804 23 16 50.8791033114142 -34.8791033114142 24 38 49.9754205859433 -11.9754205859433 25 58 53.8123652433583 4.18763475664166 26 60 54.0534508833973 5.94654911660266 27 67 52.0647206049289 14.9352793950712 28 55 52.5975760990848 2.40242390091524 29 47 51.9950382913599 -4.99503829135991 30 59 51.3500018487912 7.64999815120876 31 49 51.598434963578 -2.59843496357802 32 47 52.7957662761058 -5.79576627610583 33 57 51.8922185206883 5.10778147931171 34 39 48.4566236941115 -9.45662369411146 35 49 52.4715960369136 -3.47159603691364 36 26 52.1088778656368 -26.1088778656368 37 53 49.4595398992844 3.54046010071556 38 75 53.6959221791612 21.3040778208388 39 65 49.1830969244841 15.8169030755159 40 49 55.1508252757434 -6.15082527574342 41 48 50.7272161342836 -2.72721613428358 42 45 50.981218239303 -5.98121823930296 43 31 53.0970730578881 -22.0970730578881 44 61 54.8849651251235 6.11503487487649 45 49 53.5149969661194 -4.51499696611943 46 69 51.6611246251882 17.3388753748118 47 54 50.5270555730465 3.47294442695347 48 80 51.8879254011736 28.1120745988264 49 57 52.9814085593146 4.01859144068545 50 34 51.5958652737432 -17.5958652737432 51 69 54.1945498922389 14.8054501077611 52 44 51.6239155889786 -7.62391558897865 53 70 49.4148940919261 20.5851059080739 54 51 53.6560973379842 -2.65609733798425 55 66 52.6358258340566 13.3641741659434 56 18 51.1128545054779 -33.1128545054779 57 74 50.6985611078588 23.3014388921412 58 59 53.5695441832413 5.43045581675869 59 48 53.2367480803545 -5.23674808035455 60 55 50.2998120408944 4.70018795910561 61 44 48.8095960370112 -4.80959603701121 62 56 47.9093969526874 8.09060304731255 63 65 52.1215756330871 12.8784243669129 64 77 51.7538648347745 25.2461351652255 65 46 47.6879586676204 -1.68795866762042 66 70 53.0864037801844 16.9135962198156 67 39 55.505152477155 -16.505152477155 68 55 55.4312660158234 -0.431266015823426 69 44 51.8381977872098 -7.83819778720984 70 45 52.9089025784407 -7.90890257844075 71 45 52.1638244498393 -7.16382444983932 72 49 51.8326588326418 -2.83265883264185 73 65 51.3681362456361 13.6318637543639 74 45 47.0472573706292 -2.04725737062923 75 71 51.0440966796098 19.9559033203902 76 48 53.6807423306048 -5.68074233060483 77 41 51.9501736696403 -10.9501736696403 78 40 52.2827065307054 -12.2827065307054 79 64 51.72254832973 12.2774516702701 80 56 51.8404032730727 4.15959672692729 81 52 53.3556301612206 -1.35563016122058 82 41 49.4267996156825 -8.42679961568248 83 42 53.4511195749867 -11.4511195749867 84 54 54.7296377869182 -0.729637786918216 85 40 54.5240713456767 -14.5240713456767 86 40 51.9100869496649 -11.9100869496649 87 51 52.9813788482988 -1.98137884829885 88 48 53.2142221200736 -5.21422212007365 89 80 54.4477016480335 25.5522983519665 90 38 53.5348209925587 -15.5348209925586 91 57 54.3326688921411 2.66733110785889 92 28 47.9573591109086 -19.9573591109086 93 51 53.8572271390314 -2.85722713903145 94 46 51.1472282086333 -5.14722820863332 95 58 52.9665380677125 5.03346193228755 96 67 53.2759832049132 13.7240167950868 97 72 52.0353462270942 19.9646537729058 98 26 51.9021226564984 -25.9021226564984 99 54 54.2199873363711 -0.219987336371117 100 53 51.9215039267709 1.07849607322914 101 64 47.1661852419789 16.8338147580211 102 47 55.7186996859885 -8.71869968598851 103 43 52.6867005145034 -9.68670051450343 104 66 50.2267638215581 15.7732361784419 105 54 50.2139338101009 3.78606618989907 106 62 55.166022566791 6.83397743320901 107 52 53.7687344662939 -1.76873446629385 108 64 52.7101536885317 11.2898463114683 109 55 49.2844209717021 5.71557902829785 110 57 52.7882878035432 4.21171219645681 111 74 53.3582025771019 20.6417974228981 112 32 52.2166075742418 -20.2166075742418 113 38 51.046272454457 -13.046272454457 114 66 54.991592194706 11.0084078052941 115 37 52.0646327883824 -15.0646327883824 116 26 53.0613187574436 -27.0613187574436 117 64 53.7487363776026 10.2512636223974 118 28 50.0478967389701 -22.0478967389701 119 66 55.3671510048149 10.6328489951851 120 65 53.1384822696217 11.8615177303783 121 48 51.8007552201943 -3.80075522019429 122 44 54.8279925694031 -10.8279925694031 123 64 52.7648924103968 11.2351075896032 124 39 50.0370223249755 -11.0370223249755 125 50 53.6217249978521 -3.62172499785211 126 66 52.7089188055022 13.2910811944978 127 48 49.4950121523927 -1.49501215239269 128 70 52.4277020180274 17.5722979819726 129 66 53.8628419197477 12.1371580802523 130 61 52.7682005352823 8.23179946471774 131 31 50.5219700020202 -19.5219700020202 132 61 51.7060770634543 9.29392293654574 133 54 50.501792398984 3.49820760101603 134 34 50.8303596537053 -16.8303596537053 135 62 50.8015549407825 11.1984450592175 136 47 53.1436112297341 -6.1436112297341 137 52 49.9973609186757 2.00263908132434 138 37 52.812488793805 -15.8124887938050 139 46 47.520319575624 -1.52031957562402 140 38 52.2903631545898 -14.2903631545898 141 63 53.8123652433583 9.18763475664166 142 34 52.616576807791 -18.6165768077910 143 46 50.071659062135 -4.07165906213499 144 40 52.3260282754645 -12.3260282754645 145 30 54.5240713456767 -24.5240713456767 146 35 47.7973766427966 -12.7973766427966 147 51 51.8007552201943 -0.800755220194286 148 56 53.710865770865 2.28913422913496 149 68 52.5369432049051 15.4630567950949 150 39 54.152874712637 -15.152874712637 151 44 52.4715960369136 -8.47159603691363 152 58 54.152874712637 3.84712528736299 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.678830533198909 0.642338933602181 0.321169466801091 10 0.822885440412327 0.354229119175345 0.177114559587672 11 0.722037105883627 0.555925788232747 0.277962894116373 12 0.630174253743092 0.739651492513816 0.369825746256908 13 0.93213684899965 0.135726302000699 0.0678631510003493 14 0.893767904765459 0.212464190469083 0.106232095234541 15 0.845001412588649 0.309997174822702 0.154998587411351 16 0.847266310037505 0.30546737992499 0.152733689962495 17 0.793443478439552 0.413113043120896 0.206556521560448 18 0.862212745436376 0.275574509127248 0.137787254563624 19 0.84443337075188 0.311133258496242 0.155566629248121 20 0.811890444778311 0.376219110443377 0.188109555221689 21 0.866534380621751 0.266931238756498 0.133465619378249 22 0.843774617165963 0.312450765668074 0.156225382834037 23 0.952678664352583 0.0946426712948348 0.0473213356474174 24 0.960777311747941 0.0784453765041173 0.0392226882520587 25 0.944748625141498 0.110502749717005 0.0552513748585023 26 0.925375813862402 0.149248372275197 0.0746241861375983 27 0.925654703461842 0.148690593076317 0.0743452965381583 28 0.901794823156855 0.196410353686290 0.0982051768431449 29 0.875232752331216 0.249534495337567 0.124767247668784 30 0.843845657722669 0.312308684554662 0.156154342277331 31 0.819462810686936 0.361074378626127 0.180537189313064 32 0.788699199824348 0.422601600351304 0.211300800175652 33 0.745293851468375 0.50941229706325 0.254706148531625 34 0.75131051999509 0.497378960009821 0.248689480004910 35 0.71016162360638 0.579676752787241 0.289838376393620 36 0.80630184323401 0.387396313531979 0.193698156765989 37 0.765526512412031 0.468946975175938 0.234473487587969 38 0.809382109785259 0.381235780429483 0.190617890214741 39 0.811914740662897 0.376170518674206 0.188085259337103 40 0.776582435312889 0.446835129374222 0.223417564687111 41 0.740870137718641 0.518259724562717 0.259129862281359 42 0.708877343686495 0.582245312627010 0.291122656313505 43 0.768613584634518 0.462772830730964 0.231386415365482 44 0.733829163215186 0.532341673569628 0.266170836784814 45 0.693121713642939 0.613756572714123 0.306878286357061 46 0.704119081947445 0.591761836105111 0.295880918052555 47 0.658230227934137 0.683539544131726 0.341769772065863 48 0.790232459937975 0.419535080124051 0.209767540062025 49 0.753308929257636 0.493382141484728 0.246691070742364 50 0.768154669778468 0.463690660443063 0.231845330221532 51 0.757099825064172 0.485800349871656 0.242900174935828 52 0.759392485203962 0.481215029592075 0.240607514796038 53 0.790318956271592 0.419362087456816 0.209681043728408 54 0.756259464352528 0.487481071294945 0.243740535647472 55 0.747534272323968 0.504931455352065 0.252465727676033 56 0.917944403101005 0.164111193797990 0.0820555968989948 57 0.945687065815146 0.108625868369708 0.0543129341848538 58 0.933629639912938 0.132740720174125 0.0663703600870624 59 0.918725620139718 0.162548759720565 0.0812743798602823 60 0.900877209802445 0.198245580395110 0.0991227901975548 61 0.892396042711452 0.215207914577096 0.107603957288548 62 0.877624681976828 0.244750636046345 0.122375318023173 63 0.8726411817565 0.254717636486998 0.127358818243499 64 0.92359425244806 0.152811495103881 0.0764057475519407 65 0.90575206593843 0.188495868123142 0.094247934061571 66 0.91435412738902 0.171291745221961 0.0856458726109807 67 0.924353819838676 0.151292360322647 0.0756461801613236 68 0.905831730791853 0.188336538416295 0.0941682692081474 69 0.894761983863854 0.210476032272293 0.105238016136146 70 0.879956162882803 0.240087674234394 0.120043837117197 71 0.861973490947504 0.276053018104991 0.138026509052496 72 0.835153622469238 0.329692755061525 0.164846377530762 73 0.833999403290075 0.332001193419849 0.166000596709924 74 0.808247389232396 0.383505221535208 0.191752610767604 75 0.852208528684143 0.295582942631715 0.147791471315857 76 0.82889612013559 0.342207759728819 0.171103879864409 77 0.818362848282829 0.363274303434342 0.181637151717171 78 0.81336890792766 0.37326218414468 0.18663109207234 79 0.830673323882034 0.338653352235932 0.169326676117966 80 0.801894695335537 0.396210609328925 0.198105304664463 81 0.768893198980222 0.462213602039556 0.231106801019778 82 0.745135628997152 0.509728742005695 0.254864371002848 83 0.737299014906122 0.525401970187756 0.262700985093878 84 0.697404699273823 0.605190601452355 0.302595300726177 85 0.702638333900790 0.594723332198419 0.297361666099210 86 0.695073629828499 0.609852740343002 0.304926370171501 87 0.659849592463601 0.680300815072798 0.340150407536399 88 0.624629568314605 0.750740863370791 0.375370431685395 89 0.722882820822073 0.554234358355855 0.277117179177927 90 0.749606930807926 0.500786138384148 0.250393069192074 91 0.710016247864914 0.579967504270171 0.289983752135086 92 0.743181934402279 0.513636131195442 0.256818065597721 93 0.703552289890415 0.59289542021917 0.296447710109585 94 0.669969442604019 0.660061114791962 0.330030557395981 95 0.626529455503049 0.746941088993903 0.373470544496952 96 0.620344091716249 0.759311816567502 0.379655908283751 97 0.662844647287015 0.67431070542597 0.337155352712985 98 0.78221701100183 0.435565977996339 0.217782988998170 99 0.743569658303418 0.512860683393163 0.256430341696582 100 0.701004350863531 0.597991298272938 0.298995649136469 101 0.765702360188893 0.468595279622214 0.234297639811107 102 0.753601498916245 0.492797002167509 0.246398501083755 103 0.733491440558442 0.533017118883117 0.266508559441558 104 0.757752290941465 0.484495418117069 0.242247709058535 105 0.738381039724045 0.52323792055191 0.261618960275955 106 0.699722733482706 0.600554533034587 0.300277266517294 107 0.652582150336161 0.694835699327678 0.347417849663839 108 0.638585758684082 0.722828482631837 0.361414241315918 109 0.61529634305272 0.76940731389456 0.38470365694728 110 0.564928602528296 0.870142794943407 0.435071397471704 111 0.639716292551582 0.720567414896836 0.360283707448418 112 0.687263737174216 0.625472525651569 0.312736262825784 113 0.658123051072501 0.683753897854998 0.341876948927499 114 0.627016150262427 0.745967699475147 0.372983849737573 115 0.615754233388123 0.768491533223753 0.384245766611877 116 0.78735959774339 0.425280804513218 0.212640402256609 117 0.766192204879547 0.467615590240906 0.233807795120453 118 0.851403954764125 0.297192090471751 0.148596045235875 119 0.828139427457229 0.343721145085543 0.171860572542771 120 0.821353016837211 0.357293966325578 0.178646983162789 121 0.777735376372793 0.444529247254415 0.222264623627207 122 0.74653237792864 0.50693524414272 0.25346762207136 123 0.733959494156703 0.532081011686594 0.266040505843297 124 0.718534542987407 0.562930914025186 0.281465457012593 125 0.661799074946922 0.676401850106156 0.338200925053078 126 0.66456620535149 0.67086758929702 0.33543379464851 127 0.597712075230486 0.804575849539028 0.402287924769514 128 0.662147447181924 0.675705105636151 0.337852552818076 129 0.655811175761992 0.688377648476017 0.344188824238008 130 0.719611913986716 0.560776172026568 0.280388086013284 131 0.761947946250951 0.476104107498099 0.238052053749049 132 0.776763509068425 0.446472981863151 0.223236490931575 133 0.71461831668823 0.57076336662354 0.28538168331177 134 0.802521173291219 0.394957653417562 0.197478826708781 135 0.738827826079626 0.522344347840748 0.261172173920374 136 0.688926392082367 0.622147215835266 0.311073607917633 137 0.658411331673227 0.683177336653545 0.341588668326773 138 0.56596296674926 0.868074066501479 0.434037033250740 139 0.467306602409845 0.93461320481969 0.532693397590155 140 0.359718971255751 0.719437942511503 0.640281028744249 141 0.491152228740997 0.982304457481993 0.508847771259003 142 0.596079394809157 0.807841210381687 0.403920605190843 143 0.434855269659406 0.869710539318811 0.565144730340594 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0148148148148148 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

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

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

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Software written by Ed van Stee & Patrick Wessa

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