*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: Tue, 21 Dec 2010 11:27:06 +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/21/t1292930786zjpyk17ne9h0fhg.htm/, Retrieved Tue, 21 Dec 2010 12:26:29 +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/21/t1292930786zjpyk17ne9h0fhg.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 «

14 11 23 8 1 6 7 22 24 4 2 5 22 23 24 7 2 20 12 21 21 4 2 12 15 19 21 4 2 11 9 12 19 5 2 12 20 24 12 15 1 11 10 21 21 5 1 9 12 21 25 7 2 13 23 26 27 4 2 9 10 18 21 4 1 14 11 21 27 7 1 12 20 22 20 8 1 18 11 26 16 4 2 9 22 20 26 8 1 15 19 20 24 4 2 12 20 26 25 5 2 12 16 27 25 16 1 12 12 27 27 7 1 15 14 16 23 4 2 11 14 26 22 6 1 13 9 20 10 4 1 10 19 25 25 5 2 17 17 16 18 4 1 13 14 20 21 4 1 17 19 20 20 6 1 15 20 24 18 4 1 13 20 24 25 4 1 17 9 22 28 4 1 21 10 18 27 8 1 12 6 21 20 5 2 12 15 17 20 4 1 15 9 15 20 10 2 8 24 28 27 4 2 15 11 23 23 4 1 16 4 19 23 4 2 9 12 15 22 5 2 13 22 26 26 5 1 11 16 20 21 4 1 9 14 11 17 6 1 15 13 17 27 4 2 9 13 16 16 4 2 15 10 21 26 4 1 14 12 18 17 4 1 8 13 17 24 4 2 11 16 21 23 4 2 14 18 18 20 6 1 14 10 16 10 4 1 12 12 13 21 5 1 15 9 28 25 4 1 11 7 25 28 4 1 11 16 24 25 5 2 9 12 15 20 10 2 8 15 21 20 10 1 13 15 11 27 4 1 12 8 27 26 4 1 24 14 23 19 4 2 11 13 21 26 8 1 11 18 16 20 4 2 16 11 20 22 14 1 12 12 21 19 4 2 18 12 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation E/Introjected[t] = + 11.8039719005893 + 0.195575908788545`I/Exp.Stimulation`[t] + 0.137257998942528`E/Ext.Regulation`[t] + 0.0608353917791318Amotivation[t] + 0.774431983252936gender[t] + 0.11210461946675PE[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) 11.8039719005893 3.170092 3.7235 0.000283 0.000141 `I/Exp.Stimulation` 0.195575908788545 0.075213 2.6003 0.010299 0.00515 `E/Ext.Regulation` 0.137257998942528 0.097968 1.401 0.163381 0.081691 Amotivation 0.0608353917791318 0.129275 0.4706 0.638656 0.319328 gender 0.774431983252936 0.738991 1.048 0.296438 0.148219 PE 0.11210461946675 0.106209 1.0555 0.292985 0.146492 Multiple Linear Regression - Regression Statistics Multiple R 0.271412626295825 R-squared 0.0736648137127972 Adjusted R-squared 0.0410473775759239 F-TEST (value) 2.2584489290843 F-TEST (DF numerator) 5 F-TEST (DF denominator) 142 p-value 0.0517919950824309 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.21458781831377 Sum Squared Residuals 2522.31056791559 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11 19.6327114335936 -8.63271143359357 2 22 18.819923867686 3.18007613231404 3 23 23.6176379668528 -0.617637966852778 4 21 20.1707617510683 0.829238248931653 5 19 20.6453848579672 -1.64538485796723 6 12 19.3703534185968 -7.37035341859679 7 24 20.2826997377447 3.71730026225528 8 21 18.7296994836172 2.2703005163828 9 21 21.0144045416426 -0.0144045416426038 10 26 22.8093308829973 3.19066911700274 11 18 19.2293871891718 -1.22938718917182 12 21 20.2068080280194 0.793191971980572 13 22 21.7396483230983 0.260351676901728 14 26 18.9525819891669 7.04741801083309 15 20 22.6180342759303 -2.61803427593028 16 20 21.9515671094157 -1.95156710941575 17 26 22.345236408926 3.65476359107405 18 27 21.4576901000893 5.54230989991071 19 27 20.7386977952082 6.26130220479178 20 16 20.7243249470637 -4.72432494706374 21 26 20.15851498736 5.84148501263996 22 20 17.0755548141485 2.92444518585153 23 25 22.7101835974712 2.28981640252884 24 16 20.0745399343973 -4.0745399343973 25 20 20.3480046827263 -0.348004682726251 26 20 21.0860877723512 -1.08608777235121 27 24 20.6612676607629 3.33873233923706 28 24 22.0704921312276 1.92950786877237 29 22 20.7793496092482 1.22065039075178 30 18 20.07206751101 -2.07206751101001 31 21 18.9208836911737 2.07911630882632 32 17 20.1821133536388 -3.18211335363877 33 15 19.363369898568 -4.36336989856797 34 28 23.6775345085863 4.32246549141369 35 23 19.9236883347789 3.07631166522108 36 19 18.5443566202448 0.455643379755209 37 15 20.4809597612568 -5.48095976125676 38 26 21.9871096227259 4.01289037727411 39 20 19.8423195445693 0.157680455430658 40 11 19.6964342315809 -8.6964342315809 41 17 20.8535717951118 -3.85357179511181 42 16 20.0163615235445 -4.0163615235445 43 21 19.9156771838845 1.08432281611554 44 18 18.3988792941783 -0.3988792941783 45 17 20.6660070372177 -3.66600703721772 46 21 21.4517906230411 -0.451790623041083 47 18 20.7784072440959 -2.77840724409592 48 16 17.4953399618705 -1.49533996187051 49 13 19.7934790179948 -6.7934790179948 50 28 19.2465294177531 8.75347058224686 51 25 19.2671515970036 5.73284840299637 52 24 21.2266189153715 2.77338108462848 53 15 19.9500976249336 -4.95009762493361 54 21 20.3229164653801 0.67708353461994 55 11 20.8066054878362 -9.80660548783622 56 27 20.6455715609749 6.35442843902513 57 23 20.1752929512936 2.82470704870637 58 21 20.4094326189664 0.590567381033627 59 16 21.6553776827241 -5.65537768272409 60 20 19.9463657757607 0.0536342242392888 61 21 20.5688734699838 0.431126530016209 62 10 20.5061131407325 -10.5061131407325 63 18 23.7026878880621 -5.70268788806208 64 20 20.8042038862059 -0.804203886205856 65 21 20.9705338168047 0.0294661831953035 66 24 19.5255079386272 4.47449206137281 67 26 19.3073648095323 6.69263519046773 68 23 21.5657458431163 1.43425415688367 69 22 21.4169079617901 0.583092038209885 70 13 20.0619975832776 -7.06199758327756 71 27 23.1056383354603 3.89436166453973 72 24 20.9324882655179 3.06751173448214 73 19 21.0535394051324 -2.05353940513242 74 17 17.7791265364356 -0.779126536435641 75 16 21.0127100294523 -5.01271002945234 76 20 21.2137796072022 -1.21377960720223 77 8 19.5234741167513 -11.5234741167513 78 16 20.542846402592 -4.542846402592 79 17 20.9408186225142 -3.94081862251422 80 23 22.9821821134231 0.0178178865769025 81 18 20.3837754758496 -2.38377547584965 82 24 21.6255536040298 2.37444639597019 83 17 19.622094019946 -2.62209401994596 84 20 19.5587411462337 0.441258853766283 85 22 21.1838646022193 0.816135397780691 86 22 19.3761225713858 2.62387742861418 87 20 19.0449324576718 0.955067542328159 88 18 20.4400796674035 -2.4400796674035 89 21 20.2542330403901 0.745766959609858 90 23 20.7784072440959 2.22159275590408 91 28 20.9026191282778 7.09738087172224 92 19 20.1434551543072 -1.14345515430718 93 22 19.7925165492587 2.20748345074127 94 17 21.1989480539626 -4.19894805396263 95 25 21.2858992939536 3.7141007060464 96 22 20.3000085991164 1.69999140088359 97 21 19.9943181704628 1.00568182953717 98 15 20.7904908901205 -5.79049089012055 99 20 18.5763304020657 1.42366959793429 100 25 20.6735358643266 4.32646413567339 101 21 20.0378954533579 0.962104546642113 102 24 21.4491100234243 2.55088997657571 103 23 21.6161921010611 1.3838078989389 104 22 20.6487253096432 1.35127469035681 105 14 20.0895796638664 -6.0895796638664 106 11 19.5863232268226 -8.58632322682256 107 22 19.8905653478478 2.10943465215219 108 22 20.4164161389952 1.58358386100481 109 6 20.5432106672397 -14.5432106672397 110 15 17.9415379148539 -2.94153791485395 111 26 21.2002747873464 4.79972521265358 112 26 21.2651376157099 4.73486238429011 113 20 21.5556558118001 -1.55565581180011 114 26 20.6869011851892 5.3130988148108 115 15 19.5958893909141 -4.59588939091408 116 25 20.4910533076797 4.50894669232033 117 22 21.1808433566453 0.81915664335475 118 20 21.4699841011785 -1.46998410117851 119 18 20.5119238369607 -2.51192383696069 120 23 20.2149079597339 2.78509204026606 121 22 19.6709852646937 2.32901473530632 122 23 19.983057494181 3.01694250581895 123 17 21.7673428031976 -4.76734280319762 124 20 19.2570580505807 0.742941949419287 125 21 20.6976817165205 0.3023182834795 126 23 20.5687339709906 2.43126602900943 127 25 18.8439573203693 6.15604267963066 128 25 18.4574053802538 6.54259461974618 129 21 20.3386431797575 0.66135682024246 130 22 20.2916768724819 1.70832312751806 131 18 19.9498929638108 -1.94989296381081 132 18 20.9845533291512 -2.98455332915117 133 18 21.1531724952364 -3.15317249523636 134 21 21.1260053974684 -0.12600539746841 135 21 17.9575602166429 3.04243978335713 136 25 20.6076891273717 4.39231087262829 137 24 21.00671259685 2.99328740314996 138 24 20.2982904682357 3.70170953176431 139 28 21.8943004491258 6.10569955087416 140 24 23.0511174369167 0.948882563083262 141 22 20.9495854355534 1.05041456444665 142 22 19.7577970056914 2.24220299430856 143 20 20.1906282681742 -0.190628268174174 144 25 19.6768431983028 5.32315680169724 145 13 19.4511677269923 -6.45116772699225 146 21 20.3304925298699 0.669507470130138 147 23 20.4113575564385 2.5886424435615 148 18 21.7585086825603 -3.75850868256032 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.953070017715075 0.0938599645698492 0.0469299822849246 10 0.919448801464212 0.161102397071575 0.0805511985357877 11 0.868107041802275 0.263785916395451 0.131892958197725 12 0.814777378587382 0.370445242825237 0.185222621412619 13 0.730550944213977 0.538898111572046 0.269449055786023 14 0.79517564856402 0.409648702871959 0.20482435143598 15 0.726804365407287 0.546391269185427 0.273195634592713 16 0.670935885415761 0.658128229168477 0.329064114584239 17 0.632290461151926 0.735419077696148 0.367709538848074 18 0.66402885943425 0.6719422811315 0.33597114056575 19 0.754025537855299 0.491948924289402 0.245974462144701 20 0.763775499050853 0.472449001898294 0.236224500949147 21 0.797024634170058 0.405950731659883 0.202975365829942 22 0.755048782286197 0.489902435427606 0.244951217713803 23 0.708668389830058 0.582663220339883 0.291331610169942 24 0.699808149578707 0.600383700842587 0.300191850421293 25 0.636013181376997 0.727973637246006 0.363986818623003 26 0.57268715250755 0.8546256949849 0.42731284749245 27 0.555962878863575 0.88807424227285 0.444037121136425 28 0.503039052384283 0.993921895231434 0.496960947615717 29 0.438753225228858 0.877506450457716 0.561246774771142 30 0.404486609772612 0.808973219545223 0.595513390227388 31 0.350279398401411 0.700558796802823 0.649720601598589 32 0.328918000035766 0.657836000071533 0.671081999964234 33 0.361624805201614 0.723249610403228 0.638375194798386 34 0.353673537429573 0.707347074859145 0.646326462570427 35 0.322125506561075 0.64425101312215 0.677874493438925 36 0.271248922373771 0.542497844747542 0.728751077626229 37 0.312459128172283 0.624918256344566 0.687540871827717 38 0.291401899384268 0.582803798768536 0.708598100615732 39 0.244262708097977 0.488525416195954 0.755737291902023 40 0.414923403192702 0.829846806385405 0.585076596807298 41 0.410028548474026 0.820057096948053 0.589971451525974 42 0.388705410972872 0.777410821945744 0.611294589027128 43 0.338749051828913 0.677498103657826 0.661250948171087 44 0.290283537840553 0.580567075681105 0.709716462159447 45 0.274782938789058 0.549565877578117 0.725217061210942 46 0.231393042825493 0.462786085650985 0.768606957174508 47 0.212254802984295 0.424509605968591 0.787745197015705 48 0.179643662840357 0.359287325680714 0.820356337159643 49 0.238739103764975 0.47747820752995 0.761260896235025 50 0.380209119960288 0.760418239920577 0.619790880039712 51 0.400871495585198 0.801742991170397 0.599128504414802 52 0.369954861988236 0.739909723976472 0.630045138011764 53 0.380228171359906 0.760456342719811 0.619771828640094 54 0.333163359624482 0.666326719248963 0.666836640375518 55 0.590352936743003 0.819294126513994 0.409647063256997 56 0.642936920461593 0.714126159076814 0.357063079538407 57 0.624084588041106 0.751830823917788 0.375915411958894 58 0.577177631928717 0.845644736142566 0.422822368071283 59 0.606766761369538 0.786466477260925 0.393233238630462 60 0.558695721042387 0.882608557915226 0.441304278957613 61 0.512762471949882 0.974475056100236 0.487237528050118 62 0.740190730908078 0.519618538183844 0.259809269091922 63 0.767553301274614 0.464893397450771 0.232446698725386 64 0.73136695354241 0.53726609291518 0.26863304645759 65 0.68948023212669 0.621039535746619 0.31051976787331 66 0.693999520832072 0.612000958335857 0.306000479167928 67 0.753273937275461 0.493452125449079 0.246726062724539 68 0.716789018344378 0.566421963311244 0.283210981655622 69 0.67505389502827 0.649892209943459 0.324946104971729 70 0.753449434464443 0.493101131071114 0.246550565535557 71 0.74448547145158 0.511029057096839 0.255514528548419 72 0.727149050460766 0.545701899078467 0.272850949539234 73 0.694730200709686 0.610539598580627 0.305269799290314 74 0.661524831230938 0.676950337538124 0.338475168769062 75 0.679344040507057 0.641311918985886 0.320655959492943 76 0.638911718173941 0.722176563652118 0.361088281826059 77 0.876610054178836 0.246779891642327 0.123389945821164 78 0.885743535358303 0.228512929283394 0.114256464641697 79 0.88313663723065 0.233726725538698 0.116863362769349 80 0.858845961200969 0.282308077598062 0.141154038799031 81 0.839913905279209 0.320172189441581 0.160086094720791 82 0.817942640998998 0.364114718002004 0.182057359001002 83 0.797595454130231 0.404809091739538 0.202404545869769 84 0.761402434205042 0.477195131589917 0.238597565794958 85 0.723142239911204 0.553715520177592 0.276857760088796 86 0.693597269787546 0.612805460424908 0.306402730212454 87 0.650737298830672 0.698525402338656 0.349262701169328 88 0.620065713429883 0.759868573140234 0.379934286570117 89 0.576925516876332 0.846148966247335 0.423074483123668 90 0.539202107533398 0.921595784933204 0.460797892466602 91 0.628288693141772 0.743422613716455 0.371711306858228 92 0.582816828798586 0.834366342402827 0.417183171201414 93 0.54632470821173 0.90735058357654 0.45367529178827 94 0.564668922124259 0.870662155751483 0.435331077875741 95 0.544319656938551 0.911360686122898 0.455680343061449 96 0.499797990415753 0.999595980831506 0.500202009584247 97 0.452176596834093 0.904353193668186 0.547823403165907 98 0.494050134715081 0.988100269430161 0.505949865284919 99 0.447445273757192 0.894890547514384 0.552554726242808 100 0.441639378241678 0.883278756483356 0.558360621758322 101 0.39180827104664 0.78361654209328 0.60819172895336 102 0.361733447235142 0.723466894470283 0.638266552764858 103 0.315897631194164 0.631795262388329 0.684102368805836 104 0.273707285037067 0.547414570074135 0.726292714962933 105 0.320251577078833 0.640503154157667 0.679748422921167 106 0.515874562006191 0.968250875987618 0.484125437993809 107 0.492144268702187 0.984288537404374 0.507855731297813 108 0.439593364282243 0.879186728564486 0.560406635717757 109 0.93453579568882 0.13092840862236 0.0654642043111802 110 0.9554133579332 0.0891732841336012 0.0445866420668006 111 0.957368092679316 0.0852638146413677 0.0426319073206839 112 0.966451686368085 0.0670966272638292 0.0335483136319146 113 0.95581032460062 0.0883793507987598 0.0441896753993799 114 0.971826112105296 0.0563477757894077 0.0281738878947039 115 0.98768199878246 0.0246360024350817 0.0123180012175409 116 0.983178526756068 0.0336429464878634 0.0168214732439317 117 0.974848338459022 0.050303323081956 0.025151661540978 118 0.970805764841875 0.0583884703162493 0.0291942351581247 119 0.970009517309614 0.0599809653807716 0.0299904826903858 120 0.960649511482784 0.0787009770344322 0.0393504885172161 121 0.94747134751973 0.105057304960541 0.0525286524802706 122 0.941744529198987 0.116510941602027 0.0582554708010135 123 0.939355346475372 0.121289307049256 0.0606446535246282 124 0.920455625312313 0.159088749375374 0.0795443746876868 125 0.88724384869152 0.22551230261696 0.11275615130848 126 0.852455047703284 0.295089904593432 0.147544952296716 127 0.880973019652924 0.238053960694153 0.119026980347076 128 0.850590036754416 0.298819926491167 0.149409963245584 129 0.799557582857205 0.40088483428559 0.200442417142795 130 0.743596993898824 0.512806012202351 0.256403006101176 131 0.69780360905122 0.604392781897561 0.302196390948781 132 0.612785124207186 0.774429751585628 0.387214875792814 133 0.562508628503842 0.874982742992315 0.437491371496158 134 0.481200446161771 0.962400892323543 0.518799553838229 135 0.491781583114757 0.983563166229514 0.508218416885243 136 0.385892601073705 0.77178520214741 0.614107398926295 137 0.281468276192009 0.562936552384019 0.718531723807991 138 0.196858139767826 0.393716279535653 0.803141860232174 139 0.291908217752631 0.583816435505261 0.708091782247369 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 2 0.0152671755725191 OK 10% type I error level 12 0.0916030534351145 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

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

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

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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