paper multiple regression - Ouders

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 19 Dec 2010 08:10:19 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746135ma9w2a8xs2qrae1.htm/, Retrieved Sun, 19 Dec 2010 09:08:55 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292746135ma9w2a8xs2qrae1.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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46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 Ouders[t] = + 27.8552750214553 -0.136396824808244Carrièremogelijkheden[t] + 0.0675556474155193Geen_Motivatie[t] -0.060061041784308Leermogelijkheden[t] + 0.0319367375649696Persoonlijke_redenen[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) 27.8552750214553 4.28741 6.497 0 0 Carrièremogelijkheden -0.136396824808244 0.083313 -1.6372 0.103828 0.051914 Geen_Motivatie 0.0675556474155193 0.167978 0.4022 0.688168 0.344084 Leermogelijkheden -0.060061041784308 0.067798 -0.8859 0.377188 0.188594 Persoonlijke_redenen 0.0319367375649696 0.091112 0.3505 0.72647 0.363235 Multiple Linear Regression - Regression Statistics Multiple R 0.183615776054193 R-squared 0.0337147532159837 Adjusted R-squared 0.00630240578948671 F-TEST (value) 1.22991120356934 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 0.300915124782477 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.36992548276381 Sum Squared Residuals 4065.89005635150 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 23 20.0313142057519 2.96868579424807 2 15 20.8502977187529 -5.85029771875292 3 25 21.1738297505424 3.82617024945757 4 18 21.9296386378018 -3.92963863780180 5 21 21.0911705143671 -0.0911705143670777 6 19 20.1818655351742 -1.18186553517419 7 15 20.3516964084960 -5.35169640849596 8 22 20.1596036800147 1.84039631998530 9 19 19.9505532444681 -0.950553244468094 10 20 20.5137608550665 -0.513760855066537 11 26 20.6145546535869 5.38544534641312 12 26 19.6496953307809 6.3503046692191 13 21 19.8190539010112 1.18094609898878 14 18 19.4198034156748 -1.41980341567483 15 19 21.2920535592195 -2.29205355921955 16 19 20.5212452799350 -1.52124527993496 17 18 18.5192298309416 -0.519229830941602 18 19 20.3649176059269 -1.36491760592694 19 24 20.2778274886918 3.72217251130818 20 28 23.0782771537722 4.92172284622778 21 20 19.7867490570894 0.213250942910584 22 27 21.8429393037468 5.15706069625316 23 18 20.8311653311133 -2.83116533111334 24 19 23.2749355023362 -4.27493550233625 25 24 20.7647695371777 3.23523046282226 26 21 20.6310298767980 0.368970123202024 27 22 20.7772318450717 1.22276815492826 28 25 23.3380319956687 1.66196800433135 29 19 22.1858970240586 -3.18589702405856 30 15 21.2558660610407 -6.2558660610407 31 34 21.3997835558056 12.6002164441944 32 23 20.6802561826969 2.31974381730311 33 19 20.3008979861743 -1.30089798617430 34 26 18.8943032910271 7.10569670897289 35 15 20.5865005702790 -5.58650057027896 36 15 20.6851797283737 -5.68517972837369 37 17 20.4875507093013 -3.48755070930134 38 30 21.6032535441751 8.39674645582488 39 19 20.6108826620641 -1.61088266206409 40 28 21.5537406518307 6.44625934816933 41 23 22.0625911356212 0.937408864378753 42 23 21.6365414480659 1.36345855193406 43 21 21.4185149795022 -0.418514979502221 44 18 20.3679111095870 -2.36791110958696 45 19 21.4309070664848 -2.4309070664848 46 24 21.2605993055088 2.73940069449118 47 15 20.5941910733189 -5.59419107331887 48 20 19.8688975363872 0.131102463612813 49 24 22.8835873010111 1.11641269898889 50 9 20.0996728992904 -11.0996728992904 51 20 21.6737925144879 -1.67379251448786 52 20 20.9092034916201 -0.909203491620067 53 10 20.3841156299152 -10.3841156299152 54 44 22.0017395437745 21.9982604562255 55 20 21.8429393037468 -1.84293930374684 56 20 21.5511435275508 -1.55114352755082 57 11 21.7413481619034 -10.7413481619034 58 21 21.1001511319470 -0.100151131946966 59 21 21.9624486424012 -0.962448642401164 60 19 22.8222226369890 -3.82222263698905 61 17 19.945775843414 -2.94577584341399 62 16 20.0253214956321 -4.02532149563206 63 14 20.3322921997364 -6.33229219973641 64 19 20.1309968919411 -1.13099689194112 65 21 21.0097809246063 -0.00978092460629047 66 16 19.1123456431535 -3.11234564315347 67 19 19.6742076836261 -0.674207683626076 68 19 21.0504377576310 -2.05043775763105 69 16 19.7325709324904 -3.73257093249036 70 24 22.6716838036273 1.32831619637267 71 29 18.7760092614386 10.2239907385614 72 21 23.2948221949500 -2.29482219495003 73 20 21.5251338637571 -1.52513386375709 74 23 21.2155014106615 1.78449858933850 75 18 21.1414105291232 -3.14141052912322 76 19 20.8523421382671 -1.85234213826710 77 23 21.7639622398571 1.23603776014293 78 19 20.4059051819832 -1.40590518198322 79 21 22.4155330016073 -1.41553300160730 80 26 19.6110284733217 6.38897152667828 81 13 21.0981225959954 -8.09812259599542 82 23 24.4073694979785 -1.40736949797845 83 17 20.5006959830210 -3.50069598302104 84 30 20.6402222753492 9.35977772465079 85 19 20.1239847701641 -1.12398477016415 86 22 21.0262720313800 0.973727968619965 87 14 20.3755960164269 -6.37559601642685 88 14 20.9736897116643 -6.9736897116643 89 21 21.0342932774515 -0.0342932774514690 90 21 19.8964192764550 1.10358072354497 91 33 20.7097362378049 12.2902637621951 92 23 22.459493719798 0.540506280201979 93 30 20.1423039309180 9.85769606908204 94 19 21.7420911678777 -2.74209116787772 95 21 22.1448981490024 -1.14489814900241 96 25 22.5226944098650 2.47730559013496 97 18 20.7690644543776 -2.76906445437761 98 25 19.0663144411231 5.93368555887687 99 21 20.6396797513463 0.360320248653658 100 16 19.9258189298888 -3.92581892988878 101 17 20.9237102190283 -3.92371021902825 102 23 22.1623723157452 0.83762768425482 103 26 21.8620829903863 4.13791700961372 104 18 19.4165335063321 -1.41653350633206 105 19 21.0152311107233 -2.01523111072331 106 28 21.2827909397569 6.7172090602431 107 20 20.6582809139829 -0.658280913982925 108 29 22.4070575447050 6.59294245529503 109 19 21.6373003376029 -2.63730033760292 110 18 22.0920292428411 -4.09202924284113 111 24 21.061961162142 2.93803883785799 112 12 21.5379382136825 -9.53793821368249 113 19 20.0680125674082 -1.06801256740822 114 25 19.9500707606139 5.04992923938614 115 12 20.3913984545750 -8.39139845457504 116 15 20.4242197581743 -5.42421975817426 117 25 21.603037178641 3.396962821359 118 14 20.8204756216136 -6.82047562161355 119 19 21.429735914005 -2.429735914005 120 23 22.1664826345362 0.833517365463779 121 19 20.1943278430682 -1.19432784306819 122 24 20.6329496314521 3.36705036854787 123 20 21.2002721454044 -1.20027214540439 124 16 21.155043989497 -5.15504398949702 125 13 19.4765945481164 -6.47659454811637 126 20 20.8586214348325 -0.858621434832529 127 30 21.2468254033122 8.75317459668782 128 18 20.3417009638218 -2.34170096382176 129 22 21.5042334622861 0.495766537713927 130 21 20.7751874255576 0.224812574442448 131 25 19.3788260203813 5.62117397961866 132 18 21.7956826118879 -3.79568261188792 133 25 20.5062220928493 4.49377790715066 134 44 20.3879982841722 23.6120017158278 135 12 20.5600740589796 -8.56007405897964 136 17 20.7523774501952 -3.75237745019517 137 26 20.2367583927242 5.76324160727575 138 18 19.9294410620257 -1.92944106202573 139 21 21.4341610922649 -0.434161092264918 140 24 20.7965760136827 3.20342398631734 141 20 22.1605159887418 -2.16051598874177 142 24 21.0336204923886 2.96637950761145 143 28 22.4545101339726 5.54548986602741 144 20 21.4884955422495 -1.48849554224946 145 33 21.5440011447138 11.4559988552862 146 19 21.4645676612952 -2.46456766129522 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.270104338901468 0.540208677802937 0.729895661098532 9 0.187122445293119 0.374244890586237 0.812877554706881 10 0.130235345309959 0.260470690619917 0.869764654690041 11 0.281763324158485 0.56352664831697 0.718236675841515 12 0.274845831470726 0.549691662941451 0.725154168529274 13 0.186313975033297 0.372627950066593 0.813686024966703 14 0.162777059224020 0.325554118448039 0.83722294077598 15 0.114201758677636 0.228403517355271 0.885798241322364 16 0.0719264850636562 0.143852970127312 0.928073514936344 17 0.0569237236601443 0.113847447320289 0.943076276339856 18 0.0377918733320764 0.0755837466641528 0.962208126667924 19 0.0267797427462649 0.0535594854925298 0.973220257253735 20 0.043576277824293 0.087152555648586 0.956423722175707 21 0.027819655478102 0.055639310956204 0.972180344521898 22 0.0277715419062143 0.0555430838124286 0.972228458093786 23 0.0197813144985473 0.0395626289970946 0.980218685501453 24 0.0216365008532756 0.0432730017065512 0.978363499146724 25 0.0163656331279858 0.0327312662559716 0.983634366872014 26 0.0100363808903181 0.0200727617806362 0.989963619109682 27 0.00609492108318521 0.0121898421663704 0.993905078916815 28 0.00360948122816785 0.0072189624563357 0.996390518771832 29 0.00277242252969197 0.00554484505938393 0.997227577470308 30 0.00353750067577664 0.00707500135155328 0.996462499324223 31 0.0343169513469385 0.068633902693877 0.965683048653062 32 0.0270917707704237 0.0541835415408474 0.972908229229576 33 0.0186406600553065 0.037281320110613 0.981359339944693 34 0.0175094565930182 0.0350189131860364 0.982490543406982 35 0.0267170617454316 0.0534341234908633 0.973282938254568 36 0.0306640089755740 0.0613280179511481 0.969335991024426 37 0.0257805129639572 0.0515610259279145 0.974219487036043 38 0.0386872858578336 0.0773745717156672 0.961312714142166 39 0.0326402195418486 0.0652804390836972 0.967359780458151 40 0.0349961959882569 0.0699923919765138 0.965003804011743 41 0.0252353590642996 0.0504707181285992 0.9747646409357 42 0.0198464028330780 0.0396928056661559 0.980153597166922 43 0.0143264300676805 0.028652860135361 0.98567356993232 44 0.0110894093756702 0.0221788187513404 0.98891059062433 45 0.00825356574579487 0.0165071314915897 0.991746434254205 46 0.00613469586397439 0.0122693917279488 0.993865304136026 47 0.00602281360706031 0.0120456272141206 0.99397718639294 48 0.00405749213778953 0.00811498427557906 0.99594250786221 49 0.00283764698891132 0.00567529397782264 0.997162353011089 50 0.0161007592955575 0.0322015185911150 0.983899240704442 51 0.0119572501348120 0.0239145002696241 0.988042749865188 52 0.00846032823581096 0.0169206564716219 0.991539671764189 53 0.0215663006136068 0.0431326012272135 0.978433699386393 54 0.484968666625315 0.96993733325063 0.515031333374685 55 0.45613798505975 0.9122759701195 0.54386201494025 56 0.411940320162341 0.823880640324683 0.588059679837659 57 0.5872100383737 0.825579923252601 0.412789961626301 58 0.539640396533212 0.920719206933575 0.460359603466788 59 0.500676332733275 0.99864733453345 0.499323667266725 60 0.479746266239577 0.959492532479154 0.520253733760423 61 0.447978162180258 0.895956324360517 0.552021837819742 62 0.427292577391017 0.854585154782034 0.572707422608983 63 0.440907059054746 0.881814118109492 0.559092940945254 64 0.394939792337044 0.789879584674087 0.605060207662956 65 0.350221237395006 0.700442474790012 0.649778762604994 66 0.322212344170970 0.644424688341939 0.67778765582903 67 0.281756133099632 0.563512266199263 0.718243866900368 68 0.249260446083868 0.498520892167735 0.750739553916132 69 0.231727030830455 0.46345406166091 0.768272969169545 70 0.19927067160303 0.39854134320606 0.80072932839697 71 0.297994203536021 0.595988407072042 0.702005796463979 72 0.265076535790363 0.530153071580727 0.734923464209637 73 0.235494555629481 0.470989111258962 0.764505444370519 74 0.211609142850749 0.423218285701497 0.788390857149251 75 0.189362123543238 0.378724247086477 0.810637876456762 76 0.162145887178628 0.324291774357257 0.837854112821372 77 0.136445710767615 0.272891421535231 0.863554289232385 78 0.115283184171311 0.230566368342621 0.88471681582869 79 0.0948502806591268 0.189700561318254 0.905149719340873 80 0.111082944859067 0.222165889718134 0.888917055140933 81 0.144828972884646 0.289657945769292 0.855171027115354 82 0.119425017458974 0.238850034917948 0.880574982541026 83 0.105645089498412 0.211290178996824 0.894354910501588 84 0.157282706598797 0.314565413197594 0.842717293401203 85 0.131955392717563 0.263910785435125 0.868044607282437 86 0.108463239655613 0.216926479311225 0.891536760344387 87 0.116962538420601 0.233925076841201 0.8830374615794 88 0.133234430590894 0.266468861181787 0.866765569409106 89 0.108997978613639 0.217995957227278 0.89100202138636 90 0.088179579375356 0.176359158750712 0.911820420624644 91 0.212927090127833 0.425854180255665 0.787072909872167 92 0.178745994732712 0.357491989465424 0.821254005267288 93 0.27339508054801 0.54679016109602 0.72660491945199 94 0.239009482060125 0.478018964120249 0.760990517939875 95 0.203332343825051 0.406664687650101 0.79666765617495 96 0.174598411835197 0.349196823670394 0.825401588164803 97 0.153728460694983 0.307456921389966 0.846271539305017 98 0.149113787082299 0.298227574164597 0.850886212917701 99 0.121592577229098 0.243185154458197 0.878407422770902 100 0.105008829166532 0.210017658333063 0.894991170833468 101 0.0908134934105922 0.181626986821184 0.909186506589408 102 0.0718012745061188 0.143602549012238 0.928198725493881 103 0.06443064531159 0.12886129062318 0.93556935468841 104 0.0500233597319862 0.100046719463972 0.949976640268014 105 0.0433712064147285 0.086742412829457 0.956628793585272 106 0.055419861006554 0.110839722013108 0.944580138993446 107 0.0421199585818998 0.0842399171637996 0.9578800414181 108 0.0589913331174004 0.117982666234801 0.9410086668826 109 0.0473426932530464 0.0946853865060929 0.952657306746954 110 0.0526405956336863 0.105281191267373 0.947359404366314 111 0.0454761235106368 0.0909522470212737 0.954523876489363 112 0.072247083882384 0.144494167764768 0.927752916117616 113 0.0616643221691943 0.123328644338389 0.938335677830806 114 0.0556150582794884 0.111230116558977 0.944384941720512 115 0.0651767631012029 0.130353526202406 0.934823236898797 116 0.0737540990301218 0.147508198060244 0.926245900969878 117 0.0601401661078772 0.120280332215754 0.939859833892123 118 0.0657202992045995 0.131440598409199 0.9342797007954 119 0.0517199993188667 0.103439998637733 0.948280000681133 120 0.0385131162509967 0.0770262325019933 0.961486883749003 121 0.0270541691651289 0.0541083383302578 0.972945830834871 122 0.0236067988419885 0.0472135976839771 0.976393201158011 123 0.0174479280080973 0.0348958560161945 0.982552071991903 124 0.0162974270020861 0.0325948540041721 0.983702572997914 125 0.0193522649228784 0.0387045298457568 0.980647735077122 126 0.0124161643544246 0.0248323287088492 0.987583835645575 127 0.0110226423787622 0.0220452847575244 0.988977357621238 128 0.0089646938146154 0.0179293876292308 0.991035306185385 129 0.00575283325874285 0.0115056665174857 0.994247166741257 130 0.00604866322402902 0.0120973264480580 0.993951336775971 131 0.00424542840605981 0.00849085681211962 0.99575457159394 132 0.00301190229503720 0.00602380459007441 0.996988097704963 133 0.00162944457687243 0.00325888915374485 0.998370555423128 134 0.358899549686884 0.717799099373767 0.641100450313116 135 0.881741815307287 0.236516369385426 0.118258184692713 136 0.817793814350214 0.364412371299572 0.182206185649786 137 0.700978212757405 0.59804357448519 0.299021787242595 138 0.53789470895261 0.92421058209478 0.46210529104739 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.0610687022900763 NOK 5% type I error level 34 0.259541984732824 NOK 10% type I error level 54 0.412213740458015 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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