WS7 days

*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: Thu, 02 Dec 2010 21:30:25 +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/02/t1291325377e93hmn68q3oimx3.htm/, Retrieved Thu, 02 Dec 2010 22:29:47 +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/02/t1291325377e93hmn68q3oimx3.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 «

20 10 11 4 25 25 1 16 11 11 11 23 21 2 18 16 12 7 17 22 2 17 11 13 7 21 25 3 23 13 14 12 19 24 3 30 12 16 10 19 18 4 23 8 11 10 15 22 4 18 12 10 8 16 15 4 15 11 11 8 23 22 6 12 4 15 4 27 28 7 21 9 9 9 22 20 7 15 8 11 8 14 12 8 20 8 17 7 22 24 8 31 14 17 11 23 20 11 27 15 11 9 23 21 12 34 16 18 11 21 20 13 21 9 14 13 19 21 13 31 14 10 8 18 23 13 19 11 11 8 20 28 13 16 8 15 9 23 24 13 20 9 15 6 25 24 13 21 9 13 9 19 24 13 22 9 16 9 24 23 13 17 9 13 6 22 23 13 24 10 9 6 25 29 13 25 16 18 16 26 24 13 26 11 18 5 29 18 13 25 8 12 7 32 25 13 17 9 17 9 25 21 13 32 16 9 6 29 26 13 33 11 9 6 28 22 13 13 16 12 5 17 22 13 32 12 18 12 28 22 13 25 12 12 7 29 23 13 29 14 18 10 26 30 13 22 9 14 9 25 23 13 18 10 15 8 14 17 13 17 9 16 5 25 23 13 20 10 10 8 26 23 14 15 12 11 8 20 25 14 20 14 14 10 18 24 14 33 14 9 6 32 24 14 29 10 12 8 25 23 14 23 14 17 7 25 21 14 26 16 5 4 23 24 14 18 9 12 8 21 24 14 20 10 12 8 20 28 14 11 6 6 4 15 16 14 28 8 24 20 30 20 14 26 13 12 8 24 29 14 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Org[t] = + 17.4786370540133 -0.0540349079271798concern[t] + 0.198638591131809doubts[t] -0.146651984124796Par_Crit[t] -0.257571601145397Par_Stan[t] + 0.410140826539548Pers_Stand[t] -0.0822244901852577Days[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) 17.4786370540133 2.275018 7.6829 0 0 concern -0.0540349079271798 0.063984 -0.8445 0.399771 0.199886 doubts 0.198638591131809 0.113947 1.7433 0.083394 0.041697 Par_Crit -0.146651984124796 0.10841 -1.3527 0.178227 0.089114 Par_Stan -0.257571601145397 0.133592 -1.928 0.055791 0.027895 Pers_Stand 0.410140826539548 0.077219 5.3114 0 0 Days -0.0822244901852577 0.069938 -1.1757 0.241639 0.120819 Multiple Linear Regression - Regression Statistics Multiple R 0.47825847489625 R-squared 0.228731168810087 Adjusted R-squared 0.197035189446118 F-TEST (value) 7.21640956991855 F-TEST (DF numerator) 6 F-TEST (DF denominator) 146 p-value 9.27772276893002e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.5095232926118 Sum Squared Residuals 1798.24604624218 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25 25.9121627501368 -0.912162750136833 2 21 23.6214336216953 -2.62143362169528 3 22 22.9293462227194 -0.929346222719448 4 25 23.4018750068357 1.59812499316427 5 24 21.2201510986054 2.77984890139461 6 18 20.7828828958393 -2.78288289583926 7 22 19.4592695012681 2.54073049873192 8 15 21.5959344183863 -6.59593441838635 9 22 24.1192853723176 -2.11928537231761 10 28 24.8929372422318 3.10706275776818 11 20 22.9411657928703 -2.9411657928703 12 12 19.6676531796957 -7.66765317969573 13 24 22.0562649487728 1.94373505122716 14 20 21.7868934597669 -1.7868934597669 15 21 23.5145022994617 -2.51450229946174 16 20 20.8906833006746 -0.89068330067457 17 21 19.4538500469345 1.54614995306546 18 23 21.3710190390084 1.62898096099159 19 28 22.0971518296934 5.90284817030656 20 24 22.0495837220536 1.95041627794639 21 24 23.625079137992 0.374920862008009 22 24 20.6307884356409 3.36921156435908 23 23 22.1875017080371 0.812498291962904 24 23 22.8500653504045 0.149934649595523 25 29 24.4874900021639 4.51250999783614 26 24 22.1398435989899 1.86015640101005 27 18 25.1563258276217 -7.15632582762173 28 25 26.2096361442301 -1.20963614423011 29 21 22.7211650900877 -1.72116509008775 30 26 26.8876055916955 -0.887605591695466 31 22 25.4302369015697 -3.43023690156969 32 22 22.8101945726083 -0.810194572608312 33 22 22.8176129366331 -0.817612936633136 34 23 25.7737680291387 -2.77376802913870 35 30 23.07185639189 6.92814360811001 36 23 22.8909465028262 0.109053497173763 37 17 18.9050952507523 -1.90509525075233 38 23 23.8981034787941 -0.89810347879413 39 23 24.3697507838113 -1.36975078381128 40 25 22.4297055623487 2.57029443765129 41 24 20.7814273972322 3.21857260276785 42 24 27.5844914909381 -3.58449149093805 43 23 23.1799918176775 -0.179991817677518 44 21 23.8230673102893 -2.82306731028925 45 24 25.7704967286260 -1.77049672862597 46 24 21.9351739075865 2.06482609241351 47 28 21.6156018563244 6.3843981436756 48 16 21.1668558397744 -5.1668558397744 49 20 20.0367706527965 -0.0367706527965078 50 29 23.5278714883149 5.47212851168506 51 27 23.8861525095221 3.11384749047793 52 22 23.1606074478565 -1.16060744785645 53 28 23.9535688344458 4.04643116555419 54 16 20.2432828259992 -4.24328282599918 55 25 22.8798352038017 2.12016479619833 56 24 23.4883969825361 0.511603017463852 57 28 23.5895203953839 4.41047960461607 58 24 24.2069625969009 -0.206962596900906 59 23 22.5829524064324 0.417047593567626 60 30 26.9699216336719 3.03007836632806 61 24 21.5086498338804 2.49135016611957 62 21 24.1131074845865 -3.11310748458655 63 25 23.1219834707344 1.87801652926562 64 25 23.9293720740935 1.07062792590652 65 22 20.9613925216035 1.03860747839652 66 23 22.4964420629938 0.503557937006159 67 26 22.9678479279926 3.03215207200742 68 23 21.6180519218872 1.38194807811276 69 25 23.1364207100584 1.86357928994160 70 21 21.2890481521723 -0.28904815217234 71 25 23.4025836406769 1.59741635932308 72 24 22.0638012339744 1.93619876602557 73 29 23.4261914140129 5.57380858598708 74 22 23.636707554532 -1.63670755453199 75 27 23.4725114629817 3.52748853701827 76 26 19.6808978317872 6.31910216821285 77 22 21.2376023396260 0.762397660374026 78 24 22.0028286875980 1.99717131240197 79 27 22.9505599399101 4.04944006008985 80 24 21.1844957336195 2.81550426638054 81 24 24.6951052246397 -0.695105224639718 82 29 24.1040357219248 4.89596427807521 83 22 22.0728745470201 -0.0728745470200756 84 21 20.4501124152226 0.549887584777446 85 24 20.3155468548645 3.68445314513546 86 24 21.6076948216318 2.39230517836819 87 23 21.7659548848011 1.23404511519892 88 20 22.1563274729098 -2.15632747290976 89 27 21.2659743109375 5.73402568906246 90 26 23.3460800553395 2.65391994466053 91 25 21.8256412025051 3.17435879749494 92 21 19.9784725050286 1.02152749497138 93 21 20.5793901604450 0.420609839554965 94 19 20.2117004425251 -1.21170044252511 95 21 21.3999224967553 -0.399922496755288 96 21 21.0659650375751 -0.0659650375750816 97 16 19.5463582132872 -3.54635821328719 98 22 20.4525382010485 1.54746179895153 99 29 21.5506623181631 7.44933768183694 100 15 21.5000280284170 -6.50002802841704 101 17 20.5659857217085 -3.56598572170846 102 15 19.7999896597877 -4.7999896597877 103 21 21.4186946177432 -0.41869461774322 104 21 20.7078020534852 0.292197946514808 105 19 19.0643977107181 -0.064397710718112 106 24 18.0199714557424 5.98002854425756 107 20 22.1431962604890 -2.14319626048896 108 17 24.9200992724603 -7.9200992724603 109 23 24.5916940901740 -1.59169409017404 110 24 22.1417699908794 1.85823000912059 111 14 21.8636286510571 -7.86362865105707 112 19 22.6025463651619 -3.60254636516190 113 24 21.933837820034 2.06616217996599 114 13 20.2312510420191 -7.2312510420191 115 22 25.2234195998134 -3.22341959981341 116 16 20.836701055805 -4.8367010558050 117 19 22.9578738052682 -3.9578738052682 118 25 22.5546563599952 2.44534364000478 119 25 23.7611634259337 1.23883657406633 120 23 21.0799989024455 1.92000109755452 121 24 23.2212487886807 0.778751211319337 122 26 23.1903332868129 2.80966671318710 123 26 21.1718144783970 4.82818552160297 124 25 23.9040769102341 1.09592308976591 125 18 22.0162446992728 -4.01624469927276 126 21 19.5814171398168 1.41858286018318 127 26 23.3177348052275 2.68226519477253 128 23 21.6733025587201 1.32669744127991 129 23 19.5098811913127 3.49011880868727 130 22 22.2486716151786 -0.248671615178583 131 20 22.0134673703991 -2.01346737039914 132 13 21.7157436447854 -8.71574364478544 133 24 21.1218761723102 2.87812382768982 134 15 21.2247490102913 -6.22474901029129 135 14 22.8714915215012 -8.87149152150118 136 22 23.7975757925807 -1.79757579258068 137 10 17.3423519415139 -7.34235194151386 138 24 24.0597530037297 -0.0597530037296909 139 22 21.4708080264248 0.529191973575171 140 24 25.4427777530368 -1.44277775303677 141 19 21.4713294379957 -2.47132943799568 142 20 21.8202052260760 -1.82020522607604 143 13 16.8630005794341 -3.86300057943414 144 20 19.8906852367397 0.109314763260257 145 22 22.9584889742194 -0.958488974219369 146 24 23.0179870584305 0.982012941569469 147 29 23.0594608367188 5.94053916328116 148 12 20.6272449157283 -8.62724491572833 149 20 20.5003829337775 -0.5003829337775 150 21 21.044071439489 -0.0440714394890285 151 24 23.2717453066056 0.728254693394364 152 22 21.3930588809826 0.606941119017426 153 20 17.2376943167788 2.76230568322118 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.685160708846054 0.629678582307892 0.314839291153946 11 0.5948479568571 0.8103040862858 0.4051520431429 12 0.695991168718186 0.608017662563627 0.304008831281814 13 0.590676311058693 0.818647377882614 0.409323688941307 14 0.482027443346449 0.964054886692897 0.517972556653551 15 0.482480076823677 0.964960153647353 0.517519923176323 16 0.383229343141282 0.766458686282564 0.616770656858718 17 0.361135061879226 0.722270123758451 0.638864938120774 18 0.510936499860675 0.97812700027865 0.489063500139325 19 0.723720943180791 0.552558113638419 0.276279056819209 20 0.650516434465266 0.698967131069468 0.349483565534734 21 0.5843100029923 0.8313799940154 0.4156899970077 22 0.549159124759206 0.901681750481587 0.450840875240794 23 0.478128576905257 0.956257153810513 0.521871423094743 24 0.407130656821965 0.81426131364393 0.592869343178035 25 0.401146485615755 0.80229297123151 0.598853514384245 26 0.338099599305596 0.676199198611192 0.661900400694404 27 0.626758543136807 0.746482913726386 0.373241456863193 28 0.592059148333509 0.815881703332982 0.407940851666491 29 0.55799061452178 0.88401877095644 0.44200938547822 30 0.495662717835621 0.991325435671241 0.504337282164379 31 0.488618843156897 0.977237686313795 0.511381156843103 32 0.43042977961718 0.86085955923436 0.569570220382820 33 0.376358768812761 0.752717537625522 0.623641231187239 34 0.350977165265543 0.701954330531086 0.649022834734457 35 0.526505894524673 0.946988210950654 0.473494105475327 36 0.469155301774269 0.938310603548538 0.530844698225731 37 0.448158317620889 0.896316635241778 0.551841682379111 38 0.399050800865459 0.798101601730917 0.600949199134541 39 0.358430121020074 0.716860242040149 0.641569878979926 40 0.32538261539509 0.65076523079018 0.67461738460491 41 0.29727877270495 0.5945575454099 0.70272122729505 42 0.286295581242768 0.572591162485536 0.713704418757232 43 0.244309905501591 0.488619811003182 0.755690094498409 44 0.240527313000121 0.481054626000241 0.75947268699988 45 0.216325887953687 0.432651775907373 0.783674112046313 46 0.182642749155040 0.365285498310081 0.81735725084496 47 0.244372453802771 0.488744907605543 0.755627546197229 48 0.337089145779105 0.674178291558211 0.662910854220895 49 0.329244355796532 0.658488711593064 0.670755644203468 50 0.386000474458133 0.772000948916265 0.613999525541868 51 0.36259307798612 0.72518615597224 0.63740692201388 52 0.328658702828558 0.657317405657116 0.671341297171442 53 0.329176082913474 0.658352165826948 0.670823917086526 54 0.402971270320294 0.805942540640588 0.597028729679706 55 0.357932048276886 0.715864096553771 0.642067951723114 56 0.313816765645807 0.627633531291613 0.686183234354193 57 0.320049503819737 0.640099007639473 0.679950496180263 58 0.283212362511101 0.566424725022202 0.716787637488899 59 0.244198662870556 0.488397325741113 0.755801337129444 60 0.229983415291975 0.45996683058395 0.770016584708025 61 0.200730028319071 0.401460056638142 0.799269971680929 62 0.218840430458807 0.437680860917615 0.781159569541193 63 0.188559552151459 0.377119104302918 0.811440447848541 64 0.157679478392270 0.315358956784539 0.84232052160773 65 0.130595741489630 0.261191482979259 0.86940425851037 66 0.10785598848703 0.21571197697406 0.89214401151297 67 0.0929425547360386 0.185885109472077 0.907057445263961 68 0.0754617495730467 0.150923499146093 0.924538250426953 69 0.0609925580314137 0.121985116062827 0.939007441968586 70 0.0500341404338396 0.100068280867679 0.94996585956616 71 0.0395548282870849 0.0791096565741699 0.960445171712915 72 0.0306077603826847 0.0612155207653695 0.969392239617315 73 0.0369619627536271 0.0739239255072542 0.963038037246373 74 0.0328161762197153 0.0656323524394306 0.967183823780285 75 0.0280881814883856 0.0561763629767713 0.971911818511614 76 0.0374210507625804 0.0748421015251609 0.96257894923742 77 0.0286864489585422 0.0573728979170844 0.971313551041458 78 0.0221225794683745 0.044245158936749 0.977877420531626 79 0.0215994488979033 0.0431988977958066 0.978400551102097 80 0.0175935738247355 0.0351871476494711 0.982406426175265 81 0.0136665150236770 0.0273330300473539 0.986333484976323 82 0.0156271781391358 0.0312543562782716 0.984372821860864 83 0.0125666200226473 0.0251332400452946 0.987433379977353 84 0.00936858482495938 0.0187371696499188 0.99063141517504 85 0.00843143763815089 0.0168628752763018 0.99156856236185 86 0.00698808768641923 0.0139761753728385 0.99301191231358 87 0.00518215577180655 0.0103643115436131 0.994817844228193 88 0.00458248347314176 0.00916496694628353 0.995417516526858 89 0.00756214568072326 0.0151242913614465 0.992437854319277 90 0.00668517134357354 0.0133703426871471 0.993314828656426 91 0.00638116876881507 0.0127623375376301 0.993618831231185 92 0.00518898729341944 0.0103779745868389 0.99481101270658 93 0.00395273640491287 0.00790547280982573 0.996047263595087 94 0.00325224341479013 0.00650448682958025 0.99674775658521 95 0.00255371479550356 0.00510742959100711 0.997446285204497 96 0.00187864648908171 0.00375729297816343 0.998121353510918 97 0.00214819348505547 0.00429638697011094 0.997851806514945 98 0.00175138260290924 0.00350276520581848 0.99824861739709 99 0.00846736959058756 0.0169347391811751 0.991532630409412 100 0.0189192919709565 0.037838583941913 0.981080708029043 101 0.0197535995645308 0.0395071991290616 0.980246400435469 102 0.0255774967529259 0.0511549935058518 0.974422503247074 103 0.0206781957510034 0.0413563915020067 0.979321804248997 104 0.0154428892301454 0.0308857784602908 0.984557110769855 105 0.0114983939143913 0.0229967878287826 0.988501606085609 106 0.0410134310952607 0.0820268621905214 0.95898656890474 107 0.0422330537645198 0.0844661075290396 0.95776694623548 108 0.0981661907655589 0.196332381531118 0.901833809234441 109 0.086085684950896 0.172171369901792 0.913914315049104 110 0.0751312795413084 0.150262559082617 0.924868720458692 111 0.162397840741528 0.324795681483057 0.837602159258472 112 0.152344960234352 0.304689920468703 0.847655039765648 113 0.154594801964200 0.309189603928401 0.8454051980358 114 0.220161799622457 0.440323599244914 0.779838200377543 115 0.202215138835516 0.404430277671031 0.797784861164484 116 0.222102987146934 0.444205974293868 0.777897012853066 117 0.209160896999597 0.418321793999194 0.790839103000403 118 0.216147536157873 0.432295072315745 0.783852463842127 119 0.216928313398242 0.433856626796485 0.783071686601758 120 0.182190197747022 0.364380395494044 0.817809802252978 121 0.145645735644910 0.291291471289820 0.85435426435509 122 0.149042434053455 0.29808486810691 0.850957565946545 123 0.216359043826398 0.432718087652796 0.783640956173602 124 0.194712392142003 0.389424784284005 0.805287607857997 125 0.171885514589612 0.343771029179224 0.828114485410388 126 0.158384804616360 0.316769609232719 0.84161519538364 127 0.143299192225236 0.286598384450472 0.856700807774764 128 0.119441853058068 0.238883706116136 0.880558146941932 129 0.193522539615623 0.387045079231246 0.806477460384377 130 0.148229882564912 0.296459765129824 0.851770117435088 131 0.112369928354094 0.224739856708188 0.887630071645906 132 0.227804729178251 0.455609458356502 0.772195270821749 133 0.320741234095704 0.641482468191407 0.679258765904296 134 0.286468416300824 0.572936832601649 0.713531583699176 135 0.489913653170988 0.979827306341977 0.510086346829012 136 0.432068722948002 0.864137445896004 0.567931277051998 137 0.677611580864494 0.644776838271012 0.322388419135506 138 0.613763114818354 0.772473770363293 0.386236885181647 139 0.502755535638005 0.99448892872399 0.497244464361995 140 0.440113157139238 0.880226314278475 0.559886842860762 141 0.461725802190097 0.923451604380194 0.538274197809903 142 0.326361514734642 0.652723029469285 0.673638485265358 143 0.213958000036500 0.427916000073001 0.7860419999635 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.0522388059701493 NOK 5% type I error level 27 0.201492537313433 NOK 10% type I error level 37 0.276119402985075 NOK

Charts produced by software:

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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