*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: Fri, 10 Dec 2010 14:50:29 +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/10/t1291992621hswuu1auz2oe6nq.htm/, Retrieved Fri, 10 Dec 2010 15:50:21 +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/10/t1291992621hswuu1auz2oe6nq.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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23 13 14 22 11 23 8 1 6 15 20 12 7 20 22 24 4 2 5 23 26 26 22 25 23 24 7 2 20 26 19 16 12 23 21 21 4 2 12 19 17 18 15 20 19 21 4 2 11 19 17 12 9 22 12 19 5 2 12 16 21 18 20 18 24 12 15 1 11 23 18 20 10 22 21 21 5 1 9 22 16 18 12 23 21 25 7 2 13 19 26 24 23 28 26 27 4 2 9 24 20 17 10 19 18 21 4 1 14 19 14 19 11 26 21 27 7 1 12 25 22 12 20 27 22 20 8 1 18 23 23 25 11 23 26 16 4 2 9 31 25 23 22 27 20 26 8 1 15 29 24 22 19 23 20 24 4 2 12 18 24 23 20 23 26 25 5 2 12 17 16 16 16 19 27 25 16 1 12 22 16 16 12 21 27 27 7 1 15 21 20 15 14 25 16 23 4 2 11 24 20 24 14 22 26 22 6 1 13 22 15 18 9 13 20 10 4 1 10 16 22 23 19 12 25 25 5 2 17 22 20 18 17 20 16 18 4 1 13 21 20 19 14 24 20 21 4 1 17 25 24 17 19 23 20 20 6 1 15 22 27 22 20 25 24 18 4 1 13 24 25 22 20 28 24 25 4 1 17 25 13 8 9 24 22 28 4 1 21 29 15 12 10 18 18 27 8 1 12 19 19 22 6 19 21 20 5 2 12 29 20 16 15 24 17 20 4 1 15 25 11 12 9 22 15 20 10 2 8 19 28 28 24 28 28 27 4 2 15 27 21 15 11 24 23 23 4 1 16 25 25 17 4 28 19 23 4 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation PE[t] = + 8.53915846784639 + 0.0328034599961517`I/ToKnow`[t] -0.11938165883205`I/Accomp.`[t] + 0.0235331791205159`I/Exp.Stimulation`[t] -0.00277426290252649`E/Identified`[t] + 0.130427824598309`E/Introjected`[t] -1.11540755578888e-05`E/Ext.Regulation`[t] -0.0560877663415208Amotivation[t] -1.30866706505860gender[t] + 0.224081627325535PS[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) 8.53915846784639 2.977681 2.8677 0.004784 0.002392 `I/ToKnow` 0.0328034599961517 0.105726 0.3103 0.756824 0.378412 `I/Accomp.` -0.11938165883205 0.093553 -1.2761 0.204068 0.102034 `I/Exp.Stimulation` 0.0235331791205159 0.071281 0.3301 0.741789 0.370895 `E/Identified` -0.00277426290252649 0.102188 -0.0271 0.97838 0.48919 `E/Introjected` 0.130427824598309 0.078387 1.6639 0.098401 0.049201 `E/Ext.Regulation` -1.11540755578888e-05 0.083525 -1e-04 0.999894 0.499947 Amotivation -0.0560877663415208 0.109442 -0.5125 0.609129 0.304564 gender -1.30866706505860 0.582139 -2.248 0.02616 0.01308 PS 0.224081627325535 0.066786 3.3552 0.001024 0.000512 Multiple Linear Regression - Regression Statistics Multiple R 0.384075787173551 R-squared 0.147514210292983 Adjusted R-squared 0.0919173109642641 F-TEST (value) 2.65328124543061 F-TEST (DF numerator) 9 F-TEST (DF denominator) 138 p-value 0.00723210258212192 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.22509740153913 Sum Squared Residuals 1435.37294841919 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6 11.0484119477087 -5.0484119477087 2 5 13.0532314339314 -8.05323143393135 3 20 12.5522447501050 7.44775524989498 4 12 11.6255235738282 0.374476426171776 5 11 11.1392200130443 -0.139220013044329 6 12 10.0674572531533 1.93254274684670 7 11 13.6339159287121 -2.63391592871215 8 9 12.9957255638590 -3.99572556385905 9 13 11.1200419608489 1.87995803915113 10 9 12.9175685141238 -3.9175685141238 11 14 12.4203596596251 1.57964034037493 12 12 13.5563319350571 -1.55633193505712 13 18 14.4897104581674 3.51028954183258 14 9 13.9999437264796 -4.99994372647963 15 15 14.4055561395476 0.594443860452394 16 12 10.8834402605098 1.11655973949023 17 12 11.2899781806455 0.710021819354517 18 12 13.7227240441565 -1.72272404415652 19 15 13.9037287634664 1.09627123653356 20 11 12.2874732305774 -1.28747323057742 21 13 13.2739787675797 -0.273978767579683 22 10 11.7188065611581 -1.71880656115805 23 17 12.2213352854898 4.77866471451018 24 13 12.6502770594152 0.349722940584771 25 17 13.8672031570802 3.13279684291977 26 15 13.5732112126500 1.42678878735005 27 13 14.1747703456721 -1.17477034567209 28 17 14.3248441857688 2.67515581423116 29 21 15.990215368627 5.00978463137301 30 12 12.6316069268877 -0.631606926887657 31 12 12.9638712587123 -0.963871258712325 32 15 13.8576089929775 1.14239100702247 33 8 10.6537148631648 -2.65371486316477 34 15 13.4622825090816 1.53771749091835 35 16 14.6981948806868 1.30180511931315 36 9 12.1362744794507 -3.13627447945073 37 13 12.0112247482425 0.988775251757525 38 11 14.3134232838367 -3.31342328383673 39 9 12.5466307129125 -3.54663071291247 40 15 12.8388144071634 2.16118559283659 41 9 13.6501263783843 -4.65012637838426 42 15 11.5309044779960 3.46909552200398 43 14 12.3166740875989 1.68332591240113 44 8 12.5896265559029 -4.58962655590291 45 11 12.1409810271029 -1.14098102710295 46 14 12.2738981512627 1.72610184873733 47 14 12.2165711476139 1.78342885238612 48 12 12.0613608846806 -0.0613608846805687 49 15 13.2978858821276 1.70211411787241 50 11 12.1771579222283 -1.17715792222827 51 11 13.3347178838296 -2.33471788382955 52 9 13.4125362056837 -4.4125362056837 53 8 11.6233399264475 -3.6233399264475 54 13 12.7338123391412 0.26618766085885 55 12 11.2077094899806 0.792290510019423 56 24 13.9493115620758 10.0506884379242 57 11 11.5962189454810 -0.596218945480953 58 11 13.5192504378576 -2.51925043785763 59 16 11.6276237915787 4.37237620842127 60 12 12.1941943299722 -0.194194329972162 61 18 12.3548550895478 5.64514491045225 62 12 10.1368158752402 1.86318412475978 63 14 10.2097148922715 3.79028510772851 64 16 12.3138337484557 3.68616625154431 65 24 12.7535121161777 11.2464878838223 66 13 14.0294753400870 -1.02947534008696 67 11 11.9964734149453 -0.996473414945308 68 14 14.1571264702155 -0.157126470215545 69 16 12.6950049829675 3.30499501703254 70 12 12.7588020136760 -0.758802013676048 71 21 15.7939680169425 5.20603198305751 72 11 10.6190809889127 0.380919011087297 73 6 12.3975164448178 -6.39751644481782 74 9 11.7725603310556 -2.77256033105557 75 14 13.5332465942612 0.466753405738816 76 16 14.1971125656983 1.80288743430174 77 18 15.5031301785523 2.49686982144766 78 9 12.6476070099501 -3.64760700995009 79 13 13.7266582277665 -0.726658227766493 80 17 11.8400007768086 5.15999922319145 81 11 13.0321385055612 -2.03213850556118 82 16 13.8677712832854 2.13222871671458 83 11 14.2279342055268 -3.22793420552682 84 11 13.4640027143945 -2.46400271439448 85 11 11.7253846302890 -0.725384630288966 86 20 14.0294699541098 5.97053004589023 87 10 12.2911931399818 -2.29119313998178 88 12 11.5488252728497 0.451174727150334 89 11 12.1627516813086 -1.16275168130863 90 14 13.8579738803745 0.142026119625547 91 12 14.0342034893621 -2.0342034893621 92 12 13.4478357959972 -1.44783579599725 93 12 15.7216194477623 -3.72161944776231 94 10 10.8067618846480 -0.806761884648027 95 12 11.7314797930945 0.268520206905495 96 10 11.9969492247895 -1.99694922478945 97 10 10.9231171730596 -0.923117173059569 98 13 11.9588215580983 1.04117844190168 99 12 13.2531303858511 -1.25313038585109 100 13 13.6559155607580 -0.655915560757962 101 9 13.2402851926328 -4.24028519263278 102 14 12.6334729382158 1.3665270617842 103 14 11.6874980154944 2.31250198450557 104 12 12.5497007875942 -0.549700787594184 105 18 14.2539994348300 3.74600056517004 106 17 13.6732634649158 3.32673653508419 107 12 10.9872228794642 1.01277712053576 108 15 13.5574461408138 1.44255385918618 109 8 13.9907315023649 -5.99073150236493 110 8 10.0003198674303 -2.00031986743034 111 12 14.8082032436240 -2.80820324362396 112 10 13.8550444291371 -3.85504442913713 113 18 15.1697814336016 2.83021856639844 114 15 14.3744177985499 0.625582201450097 115 16 12.6343169987064 3.36568300129361 116 11 12.7939835667638 -1.79398356676378 117 10 12.4098640068407 -2.40986400684074 118 7 11.7178592967391 -4.71785929673908 119 17 13.0501830627587 3.94981693724126 120 7 14.8367467384094 -7.83674673840944 121 14 11.3933126534709 2.60668734652914 122 12 13.2600220533565 -1.26002205335645 123 15 13.6014613341931 1.39853866580686 124 13 12.0523687170759 0.94763128292408 125 10 10.994055356021 -0.994055356020998 126 16 14.0739403517661 1.92605964823386 127 11 11.4209241542991 -0.420924154299101 128 7 11.6517221633458 -4.65172216334577 129 15 10.4950804804367 4.50491951956334 130 18 13.250991407762 4.749008592238 131 11 13.5846881044724 -2.58468810447241 132 13 11.2594113922295 1.74058860777048 133 11 10.9276764357638 0.0723235642361557 134 13 11.4987349150045 1.50126508499545 135 12 10.4859662414240 1.51403375857603 136 11 12.7161361153613 -1.71613611536130 137 11 14.7726911029711 -3.77269110297114 138 13 13.0473882440804 -0.0473882440803908 139 8 10.2894162447455 -2.28941624474551 140 12 12.5135941052091 -0.513594105209136 141 9 10.7237593763653 -1.72375937636527 142 14 13.0624988629235 0.93750113707646 143 18 14.2063146209185 3.79368537908148 144 15 13.6430798682807 1.35692013171928 145 9 11.8114280234618 -2.81142802346185 146 11 14.4573987634984 -3.45739876349843 147 17 14.5437443985820 2.45625560141803 148 12 11.3893012835920 0.610698716407979 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.991448457675362 0.0171030846492754 0.00855154232463768 14 0.991553165577044 0.0168936688459114 0.00844683442295568 15 0.982200293134872 0.0355994137302565 0.0177997068651282 16 0.966059826898382 0.0678803462032367 0.0339401731016184 17 0.943581700152069 0.112836599695862 0.0564182998479312 18 0.916123119118499 0.167753761763003 0.0838768808815014 19 0.911197052777354 0.177605894445293 0.0888029472226463 20 0.874853955922393 0.250292088155213 0.125146044077607 21 0.82446846703307 0.351063065933859 0.175531532966930 22 0.775616636316925 0.448766727366151 0.224383363683075 23 0.769618728068306 0.460762543863388 0.230381271931694 24 0.718426683615706 0.563146632768587 0.281573316384294 25 0.729800548032275 0.540398903935449 0.270199451967725 26 0.673807649784076 0.652384700431848 0.326192350215924 27 0.606988079589446 0.786023840821109 0.393011920410554 28 0.564519609596297 0.870960780807405 0.435480390403703 29 0.580092828114989 0.839814343770022 0.419907171885011 30 0.514155064162767 0.971689871674466 0.485844935837233 31 0.492587359781062 0.985174719562125 0.507412640218938 32 0.427861861533909 0.855723723067817 0.572138138466091 33 0.392936895849663 0.785873791699325 0.607063104150337 34 0.338140319271998 0.676280638543997 0.661859680728001 35 0.317256898593946 0.634513797187891 0.682743101406054 36 0.340856451693519 0.681712903387038 0.659143548306481 37 0.294537264087461 0.589074528174922 0.705462735912539 38 0.377030567581555 0.75406113516311 0.622969432418445 39 0.377972179950725 0.75594435990145 0.622027820049275 40 0.328454892178536 0.656909784357072 0.671545107821464 41 0.395206511042791 0.790413022085583 0.604793488957209 42 0.385962032456373 0.771924064912746 0.614037967543627 43 0.396708384397209 0.793416768794419 0.60329161560279 44 0.451302499864399 0.902604999728797 0.548697500135601 45 0.398456542223626 0.796913084447252 0.601543457776374 46 0.371127907036106 0.742255814072212 0.628872092963894 47 0.328135208398446 0.656270416796891 0.671864791601554 48 0.282053280775860 0.564106561551719 0.71794671922414 49 0.244360777673809 0.488721555347617 0.755639222326191 50 0.240580124436539 0.481160248873079 0.75941987556346 51 0.218709137953600 0.437418275907201 0.7812908620464 52 0.253688836365792 0.507377672731584 0.746311163634208 53 0.244142695992748 0.488285391985496 0.755857304007252 54 0.208114306651299 0.416228613302599 0.7918856933487 55 0.173058893088848 0.346117786177697 0.826941106911152 56 0.64598243463528 0.708035130729439 0.354017565364719 57 0.598561531565887 0.802876936868225 0.401438468434113 58 0.568613502995509 0.862772994008981 0.431386497004491 59 0.598646745096108 0.802706509807784 0.401353254903892 60 0.554795864598458 0.890408270803084 0.445204135401542 61 0.691497479910927 0.617005040178145 0.308502520089073 62 0.662049517896208 0.675900964207583 0.337950482103792 63 0.682024644922931 0.635950710154139 0.317975355077069 64 0.710318117122719 0.579363765754561 0.289681882877281 65 0.9797137966595 0.0405724066809989 0.0202862033404994 66 0.975013284652728 0.0499734306945445 0.0249867153472723 67 0.967489403878108 0.065021192243784 0.032510596121892 68 0.957421679658358 0.0851566406832837 0.0425783203416419 69 0.958890092665421 0.0822198146691579 0.0411099073345789 70 0.947867534263417 0.104264931473165 0.0521324657365826 71 0.968661230440877 0.0626775391182454 0.0313387695591227 72 0.959522273185971 0.080955453628058 0.040477726814029 73 0.980511263409835 0.0389774731803303 0.0194887365901652 74 0.982089846612982 0.0358203067740352 0.0179101533870176 75 0.976938502219816 0.0461229955603677 0.0230614977801838 76 0.972477713999487 0.0550445720010264 0.0275222860005132 77 0.96974545816514 0.0605090836697202 0.0302545418348601 78 0.97037607319862 0.0592478536027617 0.0296239268013809 79 0.961215873635689 0.0775682527286225 0.0387841263643113 80 0.986454765370782 0.027090469258435 0.0135452346292175 81 0.984242382405017 0.0315152351899654 0.0157576175949827 82 0.983609388234767 0.0327812235304652 0.0163906117652326 83 0.98349165480772 0.0330166903845593 0.0165083451922797 84 0.981613111230603 0.0367737775387936 0.0183868887693968 85 0.975664629085117 0.0486707418297668 0.0243353709148834 86 0.98807547298074 0.0238490540385185 0.0119245270192593 87 0.986156228960402 0.0276875420791958 0.0138437710395979 88 0.980950098262606 0.0380998034747878 0.0190499017373939 89 0.97471866951231 0.0505626609753811 0.0252813304876905 90 0.965885442211527 0.0682291155769465 0.0341145577884733 91 0.958003009477509 0.083993981044982 0.041996990522491 92 0.946727039243296 0.106545921513408 0.0532729607567041 93 0.958477005293253 0.0830459894134945 0.0415229947067472 94 0.946373153850201 0.107253692299597 0.0536268461497987 95 0.934301184355811 0.131397631288377 0.0656988156441886 96 0.918374108903757 0.163251782192487 0.0816258910962434 97 0.899044328949851 0.201911342100297 0.100955671050149 98 0.874724985741676 0.250550028516648 0.125275014258324 99 0.863362987886334 0.273274024227332 0.136637012113666 100 0.836183537208451 0.327632925583097 0.163816462791549 101 0.867052378144257 0.265895243711486 0.132947621855743 102 0.843349228448544 0.313301543102912 0.156650771551456 103 0.846233268332007 0.307533463335987 0.153766731667994 104 0.80982090335588 0.380358193288241 0.190179096644121 105 0.819836339129247 0.360327321741507 0.180163660870753 106 0.864916938596945 0.27016612280611 0.135083061403055 107 0.833543764114143 0.332912471771714 0.166456235885857 108 0.829196415586991 0.341607168826018 0.170803584413009 109 0.87165205932316 0.256695881353679 0.128347940676839 110 0.87057423871474 0.25885152257052 0.12942576128526 111 0.843104165544453 0.313791668911094 0.156895834455547 112 0.846977087292126 0.306045825415747 0.153022912707874 113 0.857936259079214 0.284127481841572 0.142063740920786 114 0.819158088106783 0.361683823786434 0.180841911893217 115 0.82079059403669 0.358418811926620 0.179209405963310 116 0.78321912506435 0.4335617498713 0.21678087493565 117 0.750986886221757 0.498026227556487 0.249013113778243 118 0.735407639375083 0.529184721249835 0.264592360624917 119 0.758541739286055 0.48291652142789 0.241458260713945 120 0.94709587231789 0.105808255364222 0.0529041276821108 121 0.92609071481799 0.147818570364019 0.0739092851820096 122 0.911579324485585 0.176841351028829 0.0884206755144146 123 0.884887164139685 0.23022567172063 0.115112835860315 124 0.851117149138217 0.297765701723566 0.148882850861783 125 0.81121943510853 0.377561129782940 0.188780564891470 126 0.808062316920147 0.383875366159706 0.191937683079853 127 0.764224756254961 0.471550487490077 0.235775243745039 128 0.86255095468309 0.274898090633820 0.137449045316910 129 0.854904267831365 0.29019146433727 0.145095732168635 130 0.87377696449118 0.25244607101764 0.12622303550882 131 0.811227660052142 0.377544679895716 0.188772339947858 132 0.71772367207299 0.56455265585402 0.28227632792701 133 0.608720995548257 0.782558008903486 0.391279004451743 134 0.644600482941789 0.710799034116423 0.355399517058211 135 0.513189460883668 0.973621078232664 0.486810539116332 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 17 0.138211382113821 NOK 10% type I error level 31 0.252032520325203 NOK

Charts produced by software:

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Parameters (Session):

par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 9 ; 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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