workshop 4

*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: Wed, 01 Dec 2010 15:08:04 +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/01/t1291215996kmgq96jtsum0gc3.htm/, Retrieved Wed, 01 Dec 2010 16:06:48 +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/01/t1291215996kmgq96jtsum0gc3.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 «

5 6 5 7 3 2 6 2 3 3 6 6 6 5 3 6 4 4 5 2 6 2 6 3 3 5 7 3 4 4 5 6 5 4 4 6 5 3 5 2 6 6 5 5 3 5 7 4 5 3 5 7 1 6 1 5 4 6 5 1 6 1 6 2 3 5 6 6 5 3 5 4 4 4 3 6 5 6 6 3 6 5 5 5 3 4 6 3 6 2 5 4 5 5 3 5 6 4 2 2 5 3 5 3 3 6 3 6 5 2 5 5 3 6 2 7 5 4 5 3 6 5 5 4 2 6 5 4 5 4 6 5 5 5 1 6 2 6 5 1 4 6 7 5 2 5 7 2 6 2 6 2 4 6 4 4 3 6 6 3 5 6 5 6 3 5 5 5 4 3 5 7 5 4 3 7 5 6 3 3 7 6 6 5 4 6 5 1 6 4 7 3 4 4 3 6 7 2 6 1 5 5 3 3 3 6 5 4 2 3 4 6 5 5 3 6 2 4 5 3 5 3 3 6 0 6 6 4 4 3 6 7 6 3 1 5 5 4 3 4 6 4 5 4 4 5 6 4 5 3 5 7 5 4 1 5 2 6 3 2 6 2 6 4 3 6 2 4 4 1 5 5 4 4 2 7 2 6 3 3 6 5 4 6 3 5 6 2 5 4 5 2 6 5 3 6 4 5 6 4 5 6 6 6 2 5 4 6 4 2 6 3 5 5 4 6 3 5 4 3 3 3 5 6 2 5 6 5 5 3 5 6 3 5 3 6 5 4 5 2 5 3 1 5 3 5 3 5 2 3 4 2 2 5 3 5 3 6 5 3 5 3 5 5 2 2 5 2 2 3 6 3 6 6 2 6 5 5 4 3 6 2 6 4 3 6 5 3 6 3 5 6 4 6 3 5 6 4 4 3 6 5 4 2 3 5 2 4 4 2 5 6 5 5 3 6 7 2 7 3 3 5 3 7 3 6 5 5 5 4 3 2 6 5 2 5 5 5 5 3 5 6 6 4 4 6 5 3 6 3 5 5 4 5 4 6 4 4 4 2 6 5 3 6 1 6 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation populariteit[t] = + 2.81665757771633 + 0.0945988532463486handgebruik[t] + 0.0180421171015053ontmoeting[t] -0.0443126088855232extravert[t] -0.0925306581000226blozen[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) 2.81665757771633 0.500111 5.6321 0 0 handgebruik 0.0945988532463486 0.064295 1.4713 0.143287 0.071644 ontmoeting 0.0180421171015053 0.04455 0.405 0.686062 0.343031 extravert -0.0443126088855232 0.051094 -0.8673 0.387171 0.193586 blozen -0.0925306581000226 0.057212 -1.6173 0.107897 0.053949 Multiple Linear Regression - Regression Statistics Multiple R 0.175438421515774 R-squared 0.0307786397439464 Adjusted R-squared 0.0051039017239185 F-TEST (value) 1.19879080051127 F-TEST (DF numerator) 4 F-TEST (DF denominator) 151 p-value 0.313738613320306 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.846709917689102 Sum Squared Residuals 108.254570391676 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 2.52862689542938 0.471373104570618 2 3 2.74789079474694 0.252109205253055 3 3 2.7639744559902 0.236025544009799 4 2 2.81651543955824 -0.816515439558236 5 3 2.87686730378422 0.123132696215776 6 4 2.91288620460195 1.08711379539805 7 4 2.8062188697294 1.19378113027060 8 2 2.87887016554527 -0.878870165545265 9 3 2.80828706487572 0.191712935124276 10 3 2.77604293761640 0.223957062383596 11 1 2.81645010617295 -1.81645010617295 12 1 2.63329136854084 -1.63329136854084 13 3 2.95135584478274 0.0486441552172578 14 3 2.66937560274385 0.330624397256148 15 3 2.81444724441191 0.185552755588089 16 3 2.65340168078867 0.346598319211327 17 3 2.79024494777422 0.209755052225781 18 2 2.61518391805405 -0.615183918054050 19 3 2.67760397742637 0.322396022573635 20 2 3.03559279481497 -1.03559279481497 21 3 2.84462317652490 0.155376823475095 22 2 2.70984810468569 -0.709848104685685 23 2 2.69174065419889 -0.691740654198894 24 3 2.92915640990609 0.0708435900939094 25 2 2.88277560587424 -0.882775605874241 26 4 2.83455755665974 1.16544244334026 27 1 2.79024494777422 -1.79024494777422 28 1 2.69180598758418 -1.69180598758418 29 2 2.53046414061198 -0.53046414061198 30 2 2.77213749728743 -0.772137497287428 31 4 2.68790054725520 1.31209945274480 32 3 2.42811974009297 0.571880259907035 33 3 2.62115755352935 0.378842446470647 34 3 2.78817675262789 0.211823247372107 35 3 2.82426098683090 0.175739013169097 36 3 3.02559250833509 -0.0255925083350893 37 4 2.85857330923655 1.14142669076345 38 4 2.87496472521629 1.12503527478371 39 3 2.98560283380310 0.0143971661968974 40 1 2.86673635053378 -1.86673635053378 41 3 2.96933262849896 0.0306673715010384 42 3 3.11214953095981 -0.112149530959809 43 3 2.61908935838303 0.380910641616973 44 3 2.78043120535523 0.219568794644774 45 0 2.65565641999588 -2.65565641999588 46 3 2.94513033186127 0.0548696681387303 47 1 2.96707788929175 -1.96707788929175 48 4 2.92502001961344 1.07497998038656 49 4 2.86473348877274 1.13526651122726 50 3 2.7580008205149 0.241999179485101 51 1 2.82426098683090 -1.82426098683090 52 2 2.78226845053788 -0.782268450537876 53 3 2.7843366456842 0.215663354315798 54 1 2.87296186345525 -1.87296186345525 55 2 2.83248936151342 -0.832489361513416 56 3 2.97146615703057 0.0285338429694265 57 3 2.74202689855972 0.257973101440281 58 4 2.84662603828595 1.15337396171405 59 3 2.59720713433783 0.402792865662169 60 4 2.67967217257269 1.32032782742731 61 2 2.57684494464383 -0.57684494464383 62 2 2.72582202664086 -0.725822026640864 63 4 2.75416071357121 1.24583928642879 64 3 2.84669137167123 0.153308628328769 65 2 2.37783349573214 -0.37783349573214 66 3 2.71368821162938 0.286311788370625 67 3 2.80231342940042 0.197686570599578 68 2 2.83455755665974 -0.834557556659742 69 3 2.83681229586695 0.163187704133048 70 3 2.93715383462493 0.0628461653750727 71 3 2.67985871663358 0.320141283366425 72 3 2.61524925143934 0.384750748560663 73 2 2.65956186032486 -0.65956186032486 74 3 2.82237933574546 0.177620664254539 75 2 2.61731744658566 -0.617317446585663 76 3 2.88277560587424 0.117224394125759 77 3 2.7843366456842 0.215663354315798 78 3 2.78633950744524 0.213660492554757 79 3 2.66547016241488 0.334529837585124 80 3 2.85053147861492 0.149468521385079 81 3 3.11214953095981 -0.112149530959809 82 2 2.7783630102089 -0.7783630102089 83 3 2.71368821162938 0.286311788370625 84 3 2.77420569243375 0.225794307566246 85 3 2.41001228960617 0.589987710393826 86 4 2.79024494777422 1.20975505222578 87 2 2.40800942784513 -0.408009427845134 88 3 2.69564609452787 0.30435390547213 89 4 2.76190626084388 1.23809373915613 90 3 2.78633950744524 0.213660492554757 91 4 2.73995870341339 1.26004129658661 92 2 2.90904609765826 -0.909046097658259 93 1 2.78633950744524 -1.78633950744524 94 3 2.90904609765826 0.0909539023417408 95 2 2.79640512731041 -0.796405127310405 96 3 2.63938621469174 0.360613785308257 97 3 2.78020025539155 0.21979974460845 98 4 2.9193426674871 1.08065733251290 99 3 2.77220283067271 0.227797169327286 100 3 2.75416071357121 0.245839286428792 101 3 2.60311543642785 0.396884563572152 102 1 2.60104724128152 -1.60104724128152 103 4 2.87296186345525 1.12703813654475 104 3 2.92318277443079 0.0768172255692117 105 3 2.73611859646970 0.263881403530297 106 2 2.80231342940042 -0.802313429400422 107 3 2.69180598758418 0.30819401241582 108 3 2.7662945285827 0.233705471417303 109 4 2.97726471983251 1.02273528016749 110 3 2.64749337869866 0.352506621301343 111 4 2.84462317652491 1.15537682347509 112 3 2.65133348564235 0.348666514357653 113 3 2.44409366204814 0.555906337951856 114 4 2.44616185719447 1.55383814280553 115 2 2.89691228264677 -0.89691228264677 116 2 2.68973779243785 -0.689737792437854 117 3 2.64517330610616 0.354826693893839 118 2 2.78020025539155 -0.78020025539155 119 3 3.06783692207429 -0.0678369220742863 120 2 2.65749366517853 -0.657493665178533 121 0 2.37783349573214 -2.37783349573214 122 3 2.79024494777422 0.209755052225781 123 4 2.83248936151342 1.16751063848658 124 3 2.91111429280459 0.0888857071954147 125 3 2.70984810468569 0.290151895314315 126 4 2.69180598758418 1.30819401241582 127 3 2.85053147861492 0.149468521385079 128 2 2.83048649975238 -0.830486499752376 129 3 2.42598621156135 0.574013788438646 130 2 2.7580008205149 -0.758000820514898 131 2 2.67779052148725 -0.677790521487249 132 2 2.83455755665974 -0.834557556659742 133 4 2.7580008205149 1.24199917948510 134 3 2.78427131229892 0.215728687701083 135 3 2.79249968698143 0.207500313018571 136 2 2.35540311089181 -0.355403110891812 137 3 2.97323806882794 0.0267619311720623 138 3 2.81444724441191 0.185552755588089 139 3 2.56087102268865 0.439128977311349 140 4 2.93922202977125 1.06077797022875 141 3 3.09636215306551 -0.0963621530655147 142 3 2.69564609452787 0.30435390547213 143 3 2.88070741072792 0.119292589272085 144 3 2.73611859646970 0.263881403530297 145 3 2.66163005547119 0.338369944528814 146 3 2.92117991266975 0.078820087330252 147 3 2.85259967376125 0.147400326238753 148 3 2.71966184710468 0.280338152895322 149 4 3.0157787659161 0.984221234083903 150 2 2.89490942088573 -0.89490942088573 151 3 2.86082804844376 0.13917195155624 152 1 2.7157564067757 -1.7157564067757 153 3 2.78640484083053 0.213595159169472 154 4 2.43805469318756 1.56194530681244 155 3 2.87686730378422 0.123132696215775 156 2 2.68583235210888 -0.685832352108878 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.324407871335714 0.648815742671427 0.675592128664286 9 0.199539967270351 0.399079934540702 0.80046003272965 10 0.126015019085425 0.25203003817085 0.873984980914575 11 0.123176959765087 0.246353919530175 0.876823040234913 12 0.491390387923919 0.982780775847838 0.508609612076081 13 0.388659441957547 0.777318883915094 0.611340558042453 14 0.304016707679978 0.608033415359957 0.695983292320022 15 0.269713217860414 0.539426435720828 0.730286782139586 16 0.240924743261068 0.481849486522135 0.759075256738932 17 0.181355368770887 0.362710737541774 0.818644631229113 18 0.132179488709811 0.264358977419621 0.867820511290189 19 0.127350914043685 0.25470182808737 0.872649085956315 20 0.357081381260329 0.714162762520658 0.642918618739671 21 0.290654477282689 0.581308954565377 0.709345522717311 22 0.254510921190263 0.509021842380527 0.745489078809737 23 0.202316766112094 0.404633532224188 0.797683233887906 24 0.177184604561375 0.35436920912275 0.822815395438625 25 0.194745890878237 0.389491781756474 0.805254109121763 26 0.322571548403582 0.645143096807165 0.677428451596418 27 0.528965387705384 0.942069224589231 0.471034612294616 28 0.600417755856167 0.799164488287666 0.399582244143833 29 0.607888069395432 0.784223861209136 0.392111930604568 30 0.565779249067781 0.868441501864438 0.434220750932219 31 0.786642162459443 0.426715675081114 0.213357837540557 32 0.767051271036759 0.465897457926483 0.232948728963241 33 0.731735608380597 0.536528783238806 0.268264391619403 34 0.685620771833087 0.628758456333825 0.314379228166913 35 0.633951424087636 0.732097151824729 0.366048575912364 36 0.578660710728517 0.842678578542967 0.421339289271483 37 0.616249149306855 0.76750170138629 0.383750850693145 38 0.697168425360371 0.605663149279258 0.302831574639629 39 0.647628355712838 0.704743288574325 0.352371644287163 40 0.787428596244815 0.42514280751037 0.212571403755185 41 0.747620232085114 0.504759535829772 0.252379767914886 42 0.703315091308614 0.593369817382773 0.296684908691386 43 0.665206911355607 0.669586177288787 0.334793088644393 44 0.623486223457192 0.753027553085617 0.376513776542808 45 0.887497282523856 0.225005434952288 0.112502717476144 46 0.861115962741497 0.277768074517005 0.138884037258503 47 0.95288728368829 0.0942254326234202 0.0471127163117101 48 0.960217444373385 0.0795651112532298 0.0397825556266149 49 0.96768826685405 0.064623466291901 0.0323117331459505 50 0.959023480303565 0.0819530393928709 0.0409765196964354 51 0.98438569717915 0.0312286056416981 0.0156143028208491 52 0.98333004788355 0.0333399042328989 0.0166699521164494 53 0.978028263354787 0.0439434732904259 0.0219717366452130 54 0.99205980053012 0.0158803989397594 0.00794019946987969 55 0.991708642999263 0.0165827140014737 0.00829135700073685 56 0.988604195746084 0.0227916085078309 0.0113958042539154 57 0.9852737329108 0.0294525341784000 0.0147262670892000 58 0.989288993829136 0.0214220123417271 0.0107110061708636 59 0.986704485180075 0.0265910296398510 0.0132955148199255 60 0.991415062676095 0.0171698746478095 0.00858493732390475 61 0.989916561939236 0.0201668761215283 0.0100834380607641 62 0.988915252196536 0.0221694956069281 0.0110847478034641 63 0.992351570073273 0.0152968598534543 0.00764842992672716 64 0.989600518542823 0.0207989629143548 0.0103994814571774 65 0.986357667455588 0.0272846650888238 0.0136423325444119 66 0.98227475690931 0.0354504861813794 0.0177252430906897 67 0.977021949011053 0.0459561019778939 0.0229780509889470 68 0.977266964414958 0.0454660711700849 0.0227330355850424 69 0.970853496855268 0.0582930062894642 0.0291465031447321 70 0.962377778231458 0.0752444435370836 0.0376222217685418 71 0.95409393986187 0.0918121202762595 0.0459060601381297 72 0.94448199517752 0.111036009644960 0.0555180048224802 73 0.93866677746899 0.122666445062019 0.0613332225310095 74 0.92456310144939 0.150873797101220 0.0754368985506098 75 0.916190818321103 0.167618363357794 0.0838091816788969 76 0.897300622824879 0.205398754350243 0.102699377175121 77 0.876262149199565 0.247475701600870 0.123737850800435 78 0.852507955629446 0.294984088741109 0.147492044370554 79 0.828192834036453 0.343614331927094 0.171807165963547 80 0.798013772442293 0.403972455115415 0.201986227557707 81 0.766176888677602 0.467646222644795 0.233823111322397 82 0.759045393947994 0.481909212104012 0.240954606052006 83 0.724665394706218 0.550669210587564 0.275334605293782 84 0.686461201773395 0.62707759645321 0.313538798226605 85 0.664829083889586 0.670341832220828 0.335170916110414 86 0.700953105706951 0.598093788586098 0.299046894293049 87 0.666213735901709 0.667572528196582 0.333786264098291 88 0.626845891152939 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0.311950465738688 106 0.69851184501202 0.60297630997596 0.30148815498798 107 0.659235755828565 0.68152848834287 0.340764244171435 108 0.616779040301421 0.766441919397158 0.383220959698579 109 0.623266067801345 0.75346786439731 0.376733932198655 110 0.582362645171551 0.835274709656898 0.417637354828449 111 0.629558345167568 0.740883309664863 0.370441654832432 112 0.584967531305234 0.830064937389532 0.415032468694766 113 0.563178909394169 0.873642181211662 0.436821090605831 114 0.713867605653743 0.572264788692514 0.286132394346257 115 0.77314776850724 0.453704462985520 0.226852231492760 116 0.742354641698531 0.515290716602938 0.257645358301469 117 0.700480691205424 0.599038617589152 0.299519308794576 118 0.666841025265425 0.66631794946915 0.333158974734575 119 0.620327080220914 0.759345839558172 0.379672919779086 120 0.582999153926704 0.834001692146592 0.417000846073296 121 0.878987973869605 0.242024052260789 0.121012026130395 122 0.846326971148417 0.307346057703165 0.153673028851583 123 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0.615869169062721 0.768261661874557 0.384130830937279 141 0.577766494205491 0.844467011589017 0.422233505794509 142 0.488841360694362 0.977682721388723 0.511158639305638 143 0.389450529327228 0.778901058654455 0.610549470672773 144 0.300024867783749 0.600049735567498 0.699975132216251 145 0.262820920141927 0.525641840283854 0.737179079858073 146 0.178073419683199 0.356146839366397 0.821926580316801 147 0.106299176113967 0.212598352227935 0.893700823886033 148 0.166195024665894 0.332390049331789 0.833804975334106 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 18 0.127659574468085 NOK 10% type I error level 25 0.177304964539007 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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