Multiple Regression NVCA en SPCCS

*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, 04 Dec 2010 19:35:54 +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/04/t12914913364s3e3i06m6awk81.htm/, Retrieved Sat, 04 Dec 2010 20:35:36 +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/04/t12914913364s3e3i06m6awk81.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 «

10 5 4 20 2 2 40 6 5 67 6 5 38 5 2 61 5 2 29 6 4 0 5 7 30 6 6 39 5 4 70 6 1 65 5 4 5 5 1 30 4 5 50 7 5 90 5 5 45 4 4 75 6 3 76 6 5 15 5 5 10 5 5 0 5 4 60 6 4 67 5 2 60 6 1 70 6 2 70 5 3 87 6 3 27 6 2 65 5 2 56 5 6 82 6 5 30 5 3 38 6 5 56 6 5 70 6 2 80 6 4 71 6 3 50 5 1 31 5 2 40 6 5 71 6 2 71 5 2 10 5 5 20 5 5 40 6 2 55 2 2 80 7 3 80 5 1 72 7 2 60 6 2 29 6 4 70 5 2 60 4 5 63 6 2 70 7 2 38 5 2 40 6 5 80 6 2 24 5 5 40 5 4 47 6 1 70 5 1 70 5 2 75 2 5 60 5 5 65 5 3 91 5 2 68 5 5 80 6 2 90 4 5 20 5 2 61 6 3 13 3 6 80 6 3 40 5 4 70 5 2 39 6 3 93 6 5 10 6 5 25 6 3 61 5 2 18 3 5 60 6 2 74 6 3 35 5 1 0 5 5 71 5 2 100 6 1 64 6 5 50 6 2 40 5 2 35 4 4 60 5 4 70 7 2 55 3 4 65 6 2 30 6 2 25 2 1 80 7 4 26 5 6 78 6 4 10 5 7 70 4 1 0 3 2 65 6 1 80 6 2 60 5 1 67 6 5 49 6 3 70 5 2 66 6 3 65 4 3 65 6 5 40 6 1 40 5 2 20 7 2 90 6 5 48 6 2 25 6 1 35 5 2 40 6 5 77 5 2 70 3 5 82 5 1 80 5 2 52 3 5 71 5 4 70 5 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Talk[t] = + 34.200144640016 + 5.72914103472968Hands[t] -3.20162175385841`Anxiety `[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) 34.200144640016 10.558605 3.2391 0.001489 0.000745 Hands 5.72914103472968 1.783814 3.2117 0.001628 0.000814 `Anxiety ` -3.20162175385841 1.206926 -2.6527 0.008881 0.00444 Multiple Linear Regression - Regression Statistics Multiple R 0.336414700021497 R-squared 0.113174850390554 Adjusted R-squared 0.100857834423756 F-TEST (value) 9.18849587397072 F-TEST (DF numerator) 2 F-TEST (DF denominator) 144 p-value 0.000175523819369916 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 22.6677401037284 Sum Squared Residuals 73991.0075630657 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 50.0393627982308 -40.0393627982308 2 20 39.2551832017585 -19.2551832017585 3 40 52.566882079102 -12.5668820791020 4 67 52.566882079102 14.4331179208980 5 38 56.4426063059476 -18.4426063059476 6 61 56.4426063059476 4.55739369405244 7 29 55.7685038329604 -26.7685038329604 8 0 40.4344975366555 -40.4344975366555 9 30 49.3652603252436 -19.3652603252436 10 39 50.0393627982307 -11.0393627982307 11 70 65.3733690945356 4.62663090546436 12 65 50.0393627982307 14.9606372017693 13 5 59.644228059806 -54.644228059806 14 30 41.1086000096427 -11.1086000096427 15 50 58.2960231138317 -8.2960231138317 16 90 46.8377410443723 43.1622589556277 17 45 44.3102217635011 0.689778236498927 18 75 58.9701255868188 16.0298744131812 19 76 52.566882079102 23.433117920898 20 15 46.8377410443724 -31.8377410443723 21 10 46.8377410443723 -36.8377410443723 22 0 50.0393627982307 -50.0393627982307 23 60 55.7685038329604 4.23149616703957 24 67 56.4426063059476 10.5573936940524 25 60 65.3733690945356 -5.37336909453564 26 70 62.1717473406772 7.82825265932276 27 70 53.2409845520892 16.7590154479108 28 87 58.9701255868188 28.0298744131812 29 27 62.1717473406772 -35.1717473406772 30 65 56.4426063059476 8.55739369405244 31 56 43.6361192905139 12.3638807094861 32 82 52.566882079102 29.433117920898 33 30 53.2409845520892 -23.2409845520892 34 38 52.566882079102 -14.5668820791020 35 56 52.566882079102 3.43311792089798 36 70 62.1717473406772 7.82825265932276 37 80 55.7685038329604 24.2314961670396 38 71 58.9701255868188 12.0298744131812 39 50 59.644228059806 -9.64422805980597 40 31 56.4426063059476 -25.4426063059476 41 40 52.566882079102 -12.5668820791020 42 71 62.1717473406772 8.82825265932276 43 71 56.4426063059476 14.5573936940524 44 10 46.8377410443723 -36.8377410443723 45 20 46.8377410443723 -26.8377410443723 46 40 62.1717473406772 -22.1717473406772 47 55 39.2551832017585 15.7448167982415 48 80 64.6992666215485 15.3007333784515 49 80 59.644228059806 20.3557719401940 50 72 67.9008883754069 4.09911162459309 51 60 62.1717473406772 -2.17174734067724 52 29 55.7685038329604 -26.7685038329604 53 70 56.4426063059476 13.5573936940524 54 60 41.1086000096427 18.8913999903573 55 63 62.1717473406772 0.828252659322764 56 70 67.9008883754069 2.09911162459309 57 38 56.4426063059476 -18.4426063059476 58 40 52.566882079102 -12.5668820791020 59 80 62.1717473406772 17.8282526593228 60 24 46.8377410443723 -22.8377410443723 61 40 50.0393627982307 -10.0393627982307 62 47 65.3733690945356 -18.3733690945356 63 70 59.644228059806 10.3557719401940 64 70 56.4426063059476 13.5573936940524 65 75 29.6503179401833 45.3496820598167 66 60 46.8377410443723 13.1622589556277 67 65 53.2409845520892 11.7590154479108 68 91 56.4426063059476 34.5573936940524 69 68 46.8377410443723 21.1622589556277 70 80 62.1717473406772 17.8282526593228 71 90 41.1086000096427 48.8913999903573 72 20 56.4426063059476 -36.4426063059476 73 61 58.9701255868188 2.02987441318117 74 13 32.1778372210546 -19.1778372210546 75 80 58.9701255868188 21.0298744131812 76 40 50.0393627982307 -10.0393627982307 77 70 56.4426063059476 13.5573936940524 78 39 58.9701255868188 -19.9701255868188 79 93 52.566882079102 40.433117920898 80 10 52.566882079102 -42.566882079102 81 25 58.9701255868188 -33.9701255868188 82 61 56.4426063059476 4.55739369405244 83 18 35.379458974913 -17.379458974913 84 60 62.1717473406772 -2.17174734067724 85 74 58.9701255868188 15.0298744131812 86 35 59.644228059806 -24.6442280598060 87 0 46.8377410443723 -46.8377410443723 88 71 56.4426063059476 14.5573936940524 89 100 65.3733690945356 34.6266309054644 90 64 52.566882079102 11.4331179208980 91 50 62.1717473406772 -12.1717473406772 92 40 56.4426063059476 -16.4426063059476 93 35 44.3102217635011 -9.31022176350107 94 60 50.0393627982307 9.96063720176925 95 70 67.9008883754069 2.09911162459309 96 55 38.5810807287714 16.4189192712286 97 65 62.1717473406772 2.82825265932276 98 30 62.1717473406772 -32.1717473406772 99 25 42.4568049556169 -17.4568049556169 100 80 61.4976448676901 18.5023551323099 101 26 43.6361192905139 -17.6361192905139 102 78 55.7685038329604 22.2314961670396 103 10 40.4344975366555 -30.4344975366555 104 70 53.9150870250763 16.0849129749237 105 0 44.9843242364882 -44.9843242364882 106 65 65.3733690945356 -0.373369094535645 107 80 62.1717473406772 17.8282526593228 108 60 59.644228059806 0.355771940194033 109 67 52.566882079102 14.4331179208980 110 49 58.9701255868188 -9.97012558681883 111 70 56.4426063059476 13.5573936940524 112 66 58.9701255868188 7.02987441318117 113 65 47.5118435173595 17.4881564826405 114 65 52.566882079102 12.4331179208980 115 40 65.3733690945356 -25.3733690945356 116 40 56.4426063059476 -16.4426063059476 117 20 67.9008883754069 -47.9008883754069 118 90 52.566882079102 37.433117920898 119 48 62.1717473406772 -14.1717473406772 120 25 65.3733690945356 -40.3733690945356 121 35 56.4426063059476 -21.4426063059476 122 40 52.566882079102 -12.5668820791020 123 77 56.4426063059476 20.5573936940524 124 70 35.379458974913 34.620541025087 125 82 59.644228059806 22.3557719401940 126 80 56.4426063059476 23.5573936940524 127 52 35.379458974913 16.620541025087 128 71 50.0393627982308 20.9606372017692 129 70 56.4426063059476 13.5573936940524 130 50 52.566882079102 -2.56688207910202 131 72 52.566882079102 19.433117920898 132 80 58.9701255868188 21.0298744131812 133 91 65.3733690945356 25.6266309054644 134 18 39.2551832017585 -21.2551832017585 135 70 47.5118435173595 22.4881564826405 136 76 53.9150870250763 22.0849129749237 137 65 62.1717473406772 2.82825265932276 138 35 62.1717473406772 -27.1717473406772 139 62 62.1717473406772 -0.171747340677236 140 76 62.1717473406772 13.8282526593228 141 50 52.566882079102 -2.56688207910202 142 68 55.7685038329604 12.2314961670396 143 80 56.4426063059476 23.5573936940524 144 90 61.4976448676901 28.5023551323099 145 79 46.8377410443723 32.1622589556277 146 30 41.1086000096427 -11.1086000096427 147 60 46.8377410443723 13.1622589556277 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.62626081955159 0.747478360896819 0.373739180448409 7 0.558201534815135 0.88359693036973 0.441798465184865 8 0.464512249249046 0.929024498498092 0.535487750750954 9 0.339925016025662 0.679850032051324 0.660074983974338 10 0.242884093358746 0.485768186717493 0.757115906641254 11 0.159533952950602 0.319067905901205 0.840466047049398 12 0.256954712948185 0.51390942589637 0.743045287051815 13 0.617275243618171 0.765449512763657 0.382724756381829 14 0.545409358832404 0.909181282335192 0.454590641167596 15 0.457670295962503 0.915340591925006 0.542329704037497 16 0.81001057652429 0.379978846951421 0.189989423475710 17 0.768246089928463 0.463507820143073 0.231753910071537 18 0.767704621695244 0.464590756609512 0.232295378304756 19 0.786530802858973 0.426938394282055 0.213469197141027 20 0.793259663348507 0.413480673302986 0.206740336651493 21 0.817179955606889 0.365640088786223 0.182820044393111 22 0.893448614021584 0.213102771956833 0.106551385978416 23 0.866566607793897 0.266866784412205 0.133433392206103 24 0.853631591253781 0.292736817492438 0.146368408746219 25 0.81401860069273 0.371962798614538 0.185981399307269 26 0.778045918775494 0.443908162449012 0.221954081224506 27 0.781276168992587 0.437447662014825 0.218723831007413 28 0.807412093291475 0.38517581341705 0.192587906708525 29 0.852432243024425 0.295135513951151 0.147567756975575 30 0.829469431027383 0.341061137945234 0.170530568972617 31 0.821795911223965 0.356408177552070 0.178204088776035 32 0.850343987566976 0.299312024866048 0.149656012433024 33 0.837439383579813 0.325121232840375 0.162560616420187 34 0.812465405326418 0.375069189347163 0.187534594673582 35 0.774385522346359 0.451228955307282 0.225614477653641 36 0.737023788552955 0.52595242289409 0.262976211447045 37 0.744899410165122 0.510201179669756 0.255100589834878 38 0.71180169565614 0.576396608687719 0.288198304343859 39 0.667263022197964 0.665473955604073 0.332736977802036 40 0.66010507879753 0.67978984240494 0.33989492120247 41 0.624204519945568 0.751590960108863 0.375795480054432 42 0.580644186627951 0.838711626744098 0.419355813372049 43 0.56590503471797 0.86818993056406 0.43409496528203 44 0.61729176853751 0.76541646292498 0.38270823146249 45 0.61575836540858 0.76848326918284 0.38424163459142 46 0.616580754663692 0.766838490672615 0.383419245336308 47 0.658652148915474 0.682695702169053 0.341347851084526 48 0.628940161830054 0.742119676339892 0.371059838169946 49 0.625003731441061 0.749992537117878 0.374996268558939 50 0.576624592878214 0.846750814243572 0.423375407121786 51 0.527229773526389 0.945540452947222 0.472770226473611 52 0.542124598067477 0.915750803865046 0.457875401932523 53 0.515504947255514 0.968990105488973 0.484495052744486 54 0.52931317279249 0.94137365441502 0.47068682720751 55 0.479739989741036 0.959479979482072 0.520260010258964 56 0.430693964955552 0.861387929911103 0.569306035044449 57 0.410462589770664 0.820925179541329 0.589537410229336 58 0.376381228060864 0.752762456121728 0.623618771939136 59 0.358767561080039 0.717535122160077 0.641232438919961 60 0.353071583866205 0.70614316773241 0.646928416133795 61 0.316505885935383 0.633011771870766 0.683494114064617 62 0.304917223436945 0.60983444687389 0.695082776563055 63 0.273941520520899 0.547883041041798 0.726058479479101 64 0.251852852570749 0.503705705141497 0.748147147429251 65 0.417199649753728 0.834399299507456 0.582800350246272 66 0.390365005854292 0.780730011708584 0.609634994145708 67 0.358164014284545 0.716328028569091 0.641835985715455 68 0.419098978858889 0.838197957717778 0.580901021141111 69 0.415295407545502 0.830590815091004 0.584704592454498 70 0.397298074484193 0.794596148968386 0.602701925515807 71 0.564543413064678 0.870913173870644 0.435456586935322 72 0.636441695927622 0.727116608144756 0.363558304072378 73 0.591461394858395 0.81707721028321 0.408538605141605 74 0.580196864580578 0.839606270838844 0.419803135419422 75 0.572621755859684 0.854756488280632 0.427378244140316 76 0.535542225253131 0.928915549493737 0.464457774746869 77 0.504141798908733 0.991716402182533 0.495858201091267 78 0.494047255482559 0.988094510965119 0.505952744517441 79 0.590839625429806 0.818320749140389 0.409160374570194 80 0.709264232595666 0.581471534808669 0.290735767404335 81 0.76303239215609 0.47393521568782 0.23696760784391 82 0.725524947217339 0.548950105565322 0.274475052782661 83 0.713548703702237 0.572902592595526 0.286451296297763 84 0.671625629808135 0.65674874038373 0.328374370191865 85 0.644429839736224 0.711140320527551 0.355570160263776 86 0.652078241124593 0.695843517750813 0.347921758875407 87 0.807683566593329 0.384632866813343 0.192316433406671 88 0.786382208035178 0.427235583929643 0.213617791964822 89 0.836360704473474 0.327278591053053 0.163639295526526 90 0.81017643107319 0.379647137853621 0.189823568926810 91 0.786058891386536 0.427882217226929 0.213941108613464 92 0.770146673756549 0.459706652486903 0.229853326243451 93 0.744377741357708 0.511244517284584 0.255622258642292 94 0.707772195522755 0.58445560895449 0.292227804477245 95 0.663206726970579 0.673586546058842 0.336793273029421 96 0.633418196101197 0.733163607797606 0.366581803898803 97 0.585068913176361 0.829862173647278 0.414931086823639 98 0.642128081303267 0.715743837393466 0.357871918696733 99 0.623078428400202 0.753843143199595 0.376921571599798 100 0.598105444768831 0.803789110462338 0.401894555231169 101 0.610585188649569 0.778829622700861 0.389414811350431 102 0.595347256735213 0.809305486529574 0.404652743264787 103 0.731303177515652 0.537393644968696 0.268696822484348 104 0.714863006284395 0.570273987431211 0.285136993715605 105 0.872266474089325 0.25546705182135 0.127733525910675 106 0.841950938050455 0.316098123899089 0.158049061949545 107 0.832595873676285 0.334808252647429 0.167404126323715 108 0.795413970963888 0.409172058072223 0.204586029036112 109 0.75978186488856 0.480436270222881 0.240218135111440 110 0.729513823666943 0.540972352666113 0.270486176333057 111 0.694938891963887 0.610122216072225 0.305061108036113 112 0.645176365591123 0.709647268817755 0.354823634408877 113 0.604984272822686 0.790031454354629 0.395015727177314 114 0.553030755113392 0.893938489773216 0.446969244886608 115 0.551515964016705 0.89696807196659 0.448484035983295 116 0.536153534436892 0.927692931126216 0.463846465563108 117 0.756703844549345 0.48659231090131 0.243296155450655 118 0.788068002867614 0.423863994264772 0.211931997132386 119 0.776136092580635 0.447727814838731 0.223863907419365 120 0.919801621629331 0.160396756741338 0.080198378370669 121 0.951364395770867 0.0972712084582663 0.0486356042291331 122 0.959932450164564 0.0801350996708724 0.0400675498354362 123 0.947502326400089 0.104995347199822 0.0524976735999109 124 0.962023339414332 0.0759533211713354 0.0379766605856677 125 0.952005603769173 0.0959887924616532 0.0479943962308266 126 0.942869292067369 0.114261415865262 0.0571307079326311 127 0.928411448104016 0.143177103791967 0.0715885518959837 128 0.911260193238157 0.177479613523686 0.088739806761843 129 0.876740741498404 0.246518517003192 0.123259258501596 130 0.856053294104574 0.287893411790852 0.143946705895426 131 0.808519048841897 0.382961902316207 0.191480951158103 132 0.75749459867745 0.4850108026451 0.24250540132255 133 0.730464381988872 0.539071236022255 0.269535618011128 134 0.771286241909231 0.457427516181538 0.228713758090769 135 0.712920232941342 0.574159534117316 0.287079767058658 136 0.701278626609414 0.597442746781172 0.298721373390586 137 0.595555018200887 0.808889963598226 0.404444981799113 138 0.810784136356572 0.378431727286856 0.189215863643428 139 0.796119130194666 0.407761739610668 0.203880869805334 140 0.709037650853336 0.581924698293329 0.290962349146664 141 0.688006732033195 0.62398653593361 0.311993267966805 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 4 0.0294117647058824 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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