WS7 - Minitutorail blog 3

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 23 Nov 2010 19:17:19 +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/Nov/23/t1290539805d25mauq54av2gdm.htm/, Retrieved Tue, 23 Nov 2010 20:16:55 +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/Nov/23/t1290539805d25mauq54av2gdm.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 «

12 6 15 4 7 2 2 2 2 9 11 6 15 3 5 4 1 2 2 9 14 13 14 5 7 7 4 3 4 9 12 8 10 3 3 3 1 2 3 9 21 7 10 6 7 7 5 4 4 9 12 9 12 5 7 2 1 2 3 9 22 5 18 6 7 7 1 2 3 9 11 8 12 6 1 2 1 3 4 9 10 9 14 5 4 1 1 2 3 9 13 11 18 5 5 2 1 2 4 9 10 8 9 3 6 6 2 3 3 9 8 11 11 5 4 1 1 2 2 9 15 12 11 7 7 1 3 3 3 9 10 8 17 5 6 1 1 1 3 9 14 7 8 5 2 2 1 3 3 9 14 9 16 3 2 2 1 1 2 9 11 12 21 5 6 2 1 3 3 9 10 20 24 6 7 1 1 2 2 9 13 7 21 5 5 7 2 3 4 9 7 8 14 2 2 1 4 4 5 9 12 8 7 5 7 2 1 3 3 9 14 16 18 4 4 4 2 3 3 9 11 10 18 6 5 2 1 1 1 9 9 6 13 3 5 1 2 2 4 9 11 8 11 5 5 1 3 1 3 9 15 9 13 4 3 5 1 3 4 9 13 9 13 5 5 2 1 3 3 9 9 11 18 2 1 1 1 2 3 9 15 12 14 2 1 3 1 2 1 9 10 8 12 5 3 1 1 3 4 9 11 7 9 2 2 2 2 2 4 9 13 8 12 2 3 5 1 2 2 9 8 9 8 2 2 2 1 2 2 9 20 4 5 5 5 6 1 1 1 9 12 8 10 5 2 4 1 2 3 9 10 8 11 1 3 1 1 3 4 9 10 8 11 5 4 3 1 1 1 9 9 6 12 2 6 6 1 2 3 9 14 8 12 6 2 7 2 3 3 9 8 4 15 1 7 4 1 2 2 9 14 7 12 4 6 1 2 1 4 9 11 14 16 3 5 5 1 1 3 9 13 10 14 2 3 3 1 3 3 9 11 9 17 5 3 2 2 3 2 9 11 8 10 3 4 2 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Depression[t] = + 3.57528362188804 + 0.0487723304199297CriticParents[t] -0.0653193315455429ExpecParents[t] + 0.583907859497645FutureWorrying[t] + 0.209150813801292SleepDepri[t] + 0.344831674824937ChangesLastYear[t] -0.0957309283109171FreqSmoking[t] + 0.278761636850479FreqHighAlc[t] + 0.219683555262319FreqBeerOrWine[t] + 0.41893516356332`Month `[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) 3.57528362188804 5.104194 0.7005 0.484846 0.242423 CriticParents 0.0487723304199297 0.116324 0.4193 0.675678 0.337839 ExpecParents -0.0653193315455429 0.08672 -0.7532 0.45263 0.226315 FutureWorrying 0.583907859497645 0.168517 3.465 0.000711 0.000356 SleepDepri 0.209150813801292 0.141664 1.4764 0.14217 0.071085 ChangesLastYear 0.344831674824937 0.135865 2.538 0.012283 0.006142 FreqSmoking -0.0957309283109171 0.275714 -0.3472 0.728973 0.364487 FreqHighAlc 0.278761636850479 0.31551 0.8835 0.378523 0.189261 FreqBeerOrWine 0.219683555262319 0.293406 0.7487 0.455319 0.22766 `Month ` 0.41893516356332 0.523545 0.8002 0.425007 0.212504 Multiple Linear Regression - Regression Statistics Multiple R 0.457575654121686 R-squared 0.209375479244889 Adjusted R-squared 0.156667177861214 F-TEST (value) 3.97234351607735 F-TEST (DF numerator) 9 F-TEST (DF denominator) 135 p-value 0.000162058967582701 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.90350467507246 Sum Squared Residuals 1138.09581875263 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 11.9533231151477 0.0466768848522711 2 11 11.7365079060082 -0.736507906008179 3 14 15.1947818840083 -1.19478188400829 4 12 11.6171994774105 0.382800522589452 5 21 15.9303637957081 5.06963620429189 6 12 13.1949204441149 -1.19492044411492 7 22 14.9159813667843 7.08401863321572 8 11 12.9735962824977 -1.97359628249768 9 10 12.0919976647950 -2.09199766479502 10 13 12.7019310433413 0.298068956658741 11 10 13.5274969833744 -3.52749698337435 12 8 12.1658167650092 -4.16581676500919 13 15 14.3168405913193 0.683159408680745 14 10 11.9868073304906 -1.98680733049057 15 14 12.5916606773013 1.40833932269875 16 14 10.2216281378182 3.7783718621818 17 11 12.822974274514 -1.82297427451401 18 10 12.9669767295980 -2.96697672959802 19 13 14.2180728096892 -1.21807280968916 20 7 10.5828977275724 -3.58289772757242 21 12 13.7515064082732 -1.75150640827319 22 14 12.8057445250691 1.19425547493091 23 11 12.2992542697815 -1.29925426978154 24 9 11.1762877268382 -2.17628772683818 25 11 11.9781106493407 -0.9781106493407 26 15 13.2420302144542 1.75796978554582 27 13 12.9900611218173 0.00993887818272487 28 9 9.5490889795559 -0.549088979555896 29 15 10.1094348752832 4.89056512471677 30 10 12.4631583757777 -2.46315837577768 31 11 10.6198087573637 0.380191242636305 32 13 11.3726327492094 1.62736725079062 33 8 10.4390365675354 -2.43903656753538 34 20 13.6511904371561 6.34880956284388 35 12 12.9206960574295 -0.92069605742949 36 10 10.1928462693326 -0.192846269332646 37 10 12.2107179312865 -2.21071793128648 38 9 12.4670557598607 -3.46705575986066 39 14 14.5914909868504 -0.591490986850424 40 8 10.8894491537756 -2.88944915377562 41 14 11.8046764252521 2.19532357474789 42 11 12.3471208110589 -1.34712081105886 43 13 11.1483205894211 1.85167941057892 44 11 11.9950676844593 -0.995067684459282 45 11 11.7602802532374 -0.76028025323739 46 10 12.022737041715 -2.022737041715 47 14 10.4932112151686 3.5067887848314 48 18 13.5986666036726 4.40133339632736 49 14 12.3853147696904 1.61468523030963 50 11 12.8792701052554 -1.87927010525538 51 12 11.2399242855605 0.760075714439515 52 13 12.9578357925230 0.042164207477042 53 9 13.6333342612761 -4.63333426127614 54 10 12.1190206854671 -2.11902068546714 55 15 13.3752910749508 1.62470892504915 56 20 13.9297282690710 6.07027173092905 57 12 11.4933487866714 0.506651213328632 58 12 11.8440973968949 0.155902603105073 59 14 11.137961364127 2.86203863587299 60 13 14.7716651111975 -1.77166511119747 61 11 14.5449237881039 -3.54492378810391 62 17 13.4503920813122 3.54960791868781 63 12 12.4077588600124 -0.407758860012392 64 13 13.0464947493062 -0.0464947493062124 65 14 14.0165698714801 -0.0165698714801257 66 13 10.9297068813012 2.07029311869878 67 15 14.0584518933057 0.94154810669432 68 13 12.1115362928085 0.888463707191539 69 10 13.7865150405731 -3.78651504057308 70 11 11.4674111337216 -0.467411133721637 71 13 12.9315489921367 0.0684510078632942 72 17 14.3213587128994 2.67864128710062 73 13 13.0819489180606 -0.0819489180606048 74 9 12.1870200286794 -3.18702002867938 75 11 12.5150377167482 -1.51503771674821 76 10 10.4214916885705 -0.421491688570493 77 9 10.4375160260926 -1.43751602609263 78 12 11.4858523436620 0.514147656337974 79 12 12.5846485397974 -0.584648539797394 80 13 12.4810074574869 0.518992542513072 81 13 12.5491790888401 0.45082091115986 82 22 14.6752667778843 7.32473322211569 83 13 12.0861672840315 0.913832715968464 84 15 13.8610948220537 1.13890517794630 85 13 14.2571287471371 -1.25712874713711 86 15 11.7415211597877 3.25847884021226 87 10 11.7572104598007 -1.75721045980065 88 11 10.9439191718146 0.0560808281854015 89 16 14.2545830300528 1.74541696994719 90 11 12.0969226922594 -1.09692269225940 91 11 12.4886531309515 -1.48865313095152 92 10 12.7440907661687 -2.7440907661687 93 10 14.6275953538625 -4.62759535386246 94 16 14.2441791332578 1.75582086674217 95 12 13.4379495945200 -1.43794959451998 96 11 13.9211569602615 -2.92115696026154 97 16 14.4668407048852 1.53315929511485 98 19 15.0135944077371 3.9864055922629 99 11 14.9549427180191 -3.95494271801909 100 15 12.7467497913397 2.2532502086603 101 24 16.5654766923619 7.4345233076381 102 14 11.6718440641491 2.32815593585087 103 15 13.6178655357985 1.38213446420149 104 11 14.9018027041945 -3.90180270419452 105 15 13.4960563784255 1.50394362157449 106 12 12.9193610665679 -0.91936106656788 107 10 10.8141552461896 -0.814155246189576 108 14 13.8970822521993 0.102917747800676 109 9 12.8565796395828 -3.85657963958282 110 15 10.6670023978373 4.33299760216274 111 15 9.76840117718311 5.23159882281689 112 14 13.0733803334639 0.926619666536133 113 11 14.1722117496141 -3.17221174961414 114 8 14.3678314589529 -6.36783145895287 115 11 14.2391592631075 -3.23915926310749 116 8 11.7883536510655 -3.7883536510655 117 10 11.2820620724470 -1.28206207244703 118 11 13.6154650677525 -2.61546506775249 119 13 14.0867271285035 -1.08672712850354 120 11 13.7609378831198 -2.76093788311982 121 20 12.7206524039161 7.27934759608392 122 10 12.1998906866310 -2.19989068663104 123 12 10.9300857065190 1.06991429348097 124 14 12.5379370806619 1.46206291933814 125 23 13.9441318144583 9.05586818554169 126 14 12.9274004552264 1.07259954477358 127 16 15.2096972308605 0.790302769139498 128 11 13.4278088667962 -2.42780886679623 129 12 14.1771717857407 -2.17717178574068 130 10 13.6412497455121 -3.64124974551207 131 14 13.7968317896223 0.203168210377721 132 12 9.82080097266093 2.17919902733907 133 12 13.6239633838582 -1.62396338385822 134 11 10.1695516112381 0.830448388761868 135 12 12.3549832889945 -0.354983288994520 136 13 15.7227265786984 -2.72272657869835 137 17 16.2977833528086 0.702216647191386 138 11 11.9781106493407 -0.9781106493407 139 12 13.6294305470718 -1.62943054707183 140 19 14.6536400014960 4.34635999850403 141 15 13.8610948220537 1.13890517794630 142 14 13.4120016939615 0.587998306038517 143 11 13.6154650677525 -2.61546506775249 144 9 10.5268165962368 -1.52681659623684 145 18 11.7548319454804 6.2451680545196 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.463720170630141 0.927440341260281 0.53627982936986 14 0.595244491345927 0.809511017308145 0.404755508654073 15 0.526502848626357 0.946994302747286 0.473497151373643 16 0.660271780959514 0.679456438080972 0.339728219040486 17 0.572663919387063 0.854672161225874 0.427336080612937 18 0.47944735384196 0.95889470768392 0.52055264615804 19 0.578406806815173 0.843186386369654 0.421593193184827 20 0.501828616792978 0.996342766414043 0.498171383207022 21 0.427941971951781 0.855883943903562 0.572058028048219 22 0.470760245443116 0.941520490886231 0.529239754556884 23 0.512864764058792 0.974270471882415 0.487135235941208 24 0.435760484905663 0.871520969811325 0.564239515094337 25 0.373551320595144 0.747102641190288 0.626448679404856 26 0.327701944707683 0.655403889415366 0.672298055292317 27 0.271362906144581 0.542725812289162 0.728637093855419 28 0.230300343626697 0.460600687253394 0.769699656373303 29 0.292648707853310 0.585297415706619 0.70735129214669 30 0.246676194296136 0.493352388592272 0.753323805703864 31 0.204583908374283 0.409167816748567 0.795416091625717 32 0.162541543461697 0.325083086923394 0.837458456538303 33 0.155648368273335 0.311296736546669 0.844351631726665 34 0.175082523361786 0.350165046723573 0.824917476638214 35 0.176379133808796 0.352758267617592 0.823620866191204 36 0.180373827697340 0.360747655394680 0.81962617230266 37 0.222498539458227 0.444997078916455 0.777501460541773 38 0.27636677028634 0.55273354057268 0.72363322971366 39 0.299865162589139 0.599730325178277 0.700134837410861 40 0.292063534348118 0.584127068696235 0.707936465651882 41 0.302935039537297 0.605870079074593 0.697064960462703 42 0.270313473678946 0.540626947357892 0.729686526321054 43 0.26822901004801 0.53645802009602 0.73177098995199 44 0.230089334479770 0.460178668959541 0.76991066552023 45 0.193131218912177 0.386262437824354 0.806868781087823 46 0.171518784121948 0.343037568243896 0.828481215878052 47 0.176046933409106 0.352093866818213 0.823953066590894 48 0.279061580149718 0.558123160299436 0.720938419850282 49 0.261565770475928 0.523131540951856 0.738434229524072 50 0.233270653692691 0.466541307385382 0.766729346307309 51 0.198045349341860 0.396090698683719 0.80195465065814 52 0.162870339696552 0.325740679393104 0.837129660303448 53 0.240803835693615 0.481607671387231 0.759196164306385 54 0.239566050756154 0.479132101512308 0.760433949243846 55 0.220051694281134 0.440103388562268 0.779948305718866 56 0.299422245994341 0.598844491988682 0.700577754005659 57 0.261079547251513 0.522159094503025 0.738920452748487 58 0.233430179356408 0.466860358712815 0.766569820643592 59 0.208640400482261 0.417280800964521 0.79135959951774 60 0.225035891630721 0.450071783261442 0.774964108369279 61 0.190370363429096 0.380740726858192 0.809629636570904 62 0.313765748543129 0.627531497086259 0.686234251456871 63 0.269372641102558 0.538745282205117 0.730627358897442 64 0.230563214323947 0.461126428647894 0.769436785676053 65 0.195260604154597 0.390521208309195 0.804739395845403 66 0.180468658689432 0.360937317378863 0.819531341310568 67 0.15265943383562 0.30531886767124 0.84734056616438 68 0.127599442360225 0.255198884720449 0.872400557639775 69 0.140213172726677 0.280426345453354 0.859786827273323 70 0.113993867525851 0.227987735051702 0.886006132474149 71 0.0914660400172556 0.182932080034511 0.908533959982744 72 0.0952469983178697 0.190493996635739 0.90475300168213 73 0.0757159917290826 0.151431983458165 0.924284008270917 74 0.076856470739402 0.153712941478804 0.923143529260598 75 0.063403676525013 0.126807353050026 0.936596323474987 76 0.0505960446579487 0.101192089315897 0.949403955342051 77 0.0439903870261913 0.0879807740523826 0.956009612973809 78 0.0338735042272436 0.0677470084544872 0.966126495772756 79 0.0256777645474870 0.0513555290949741 0.974322235452513 80 0.0194334983233036 0.0388669966466071 0.980566501676696 81 0.0149266667374899 0.0298533334749798 0.98507333326251 82 0.0744444847094754 0.148888969418951 0.925555515290525 83 0.0587009727597632 0.117401945519526 0.941299027240237 84 0.0462159134693274 0.0924318269386549 0.953784086530673 85 0.0373253017283394 0.0746506034566787 0.96267469827166 86 0.0369379141445012 0.0738758282890024 0.96306208585550 87 0.031624012608112 0.063248025216224 0.968375987391888 88 0.0234805572533352 0.0469611145066705 0.976519442746665 89 0.0200946369969564 0.0401892739939128 0.979905363003044 90 0.0158451879948606 0.0316903759897213 0.98415481200514 91 0.013041039214474 0.026082078428948 0.986958960785526 92 0.0128584972400251 0.0257169944800501 0.987141502759975 93 0.0191508695850257 0.0383017391700514 0.980849130414974 94 0.0150794607464794 0.0301589214929588 0.98492053925352 95 0.0117979736717177 0.0235959473434354 0.988202026328282 96 0.0121891469592923 0.0243782939185847 0.987810853040708 97 0.0107091873776448 0.0214183747552897 0.989290812622355 98 0.0166773467199409 0.0333546934398818 0.98332265328006 99 0.0190369700066788 0.0380739400133575 0.98096302999332 100 0.0156005130819242 0.0312010261638484 0.984399486918076 101 0.0760643702125682 0.152128740425136 0.923935629787432 102 0.0681050584495527 0.136210116899105 0.931894941550447 103 0.0555274685363109 0.111054937072622 0.944472531463689 104 0.0574774401070363 0.114954880214073 0.942522559892964 105 0.0464580373234465 0.092916074646893 0.953541962676554 106 0.0354768242551100 0.0709536485102201 0.96452317574489 107 0.030039437722173 0.060078875444346 0.969960562277827 108 0.0217056107382437 0.0434112214764874 0.978294389261756 109 0.0198900178712116 0.0397800357424232 0.980109982128788 110 0.0286583683914527 0.0573167367829054 0.971341631608547 111 0.0368310941687365 0.073662188337473 0.963168905831264 112 0.0271148334988265 0.054229666997653 0.972885166501174 113 0.0260231344095799 0.0520462688191597 0.97397686559042 114 0.0480050441505055 0.096010088301011 0.951994955849495 115 0.0484218533181728 0.0968437066363456 0.951578146681827 116 0.0764824247386656 0.152964849477331 0.923517575261334 117 0.0580699834566578 0.116139966913316 0.941930016543342 118 0.0477062594770188 0.0954125189540377 0.95229374052298 119 0.0339263480430335 0.067852696086067 0.966073651956966 120 0.0402146914142725 0.080429382828545 0.959785308585728 121 0.174552049455104 0.349104098910208 0.825447950544896 122 0.196099843750635 0.392199687501271 0.803900156249365 123 0.147395101421045 0.294790202842091 0.852604898578955 124 0.12502982965789 0.25005965931578 0.87497017034211 125 0.538487830094849 0.923024339810302 0.461512169905151 126 0.892142779320441 0.215714441359118 0.107857220679559 127 0.895619632230892 0.208760735538215 0.104380367769108 128 0.846594078681532 0.306811842636936 0.153405921318468 129 0.838951245997927 0.322097508004146 0.161048754002073 130 0.79359862394103 0.41280275211794 0.20640137605897 131 0.741400233805957 0.517199532388085 0.258599766194043 132 0.594843135016627 0.810313729966745 0.405156864983373 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 0.141666666666667 NOK 10% type I error level 36 0.3 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/10u2yd1290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/10u2yd1290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/1njj21290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/1njj21290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/2ys041290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/2ys041290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/3ys041290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/3ys041290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/4ys041290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/4ys041290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/5rjz81290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/5rjz81290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/6rjz81290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/6rjz81290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/71aga1290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/71aga1290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/81aga1290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/81aga1290539827.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/9u2yd1290539827.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290539805d25mauq54av2gdm/9u2yd1290539827.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by