WS 10 - MR: Parental Criticism

*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, 14 Dec 2010 15:58:01 +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/14/t1292342192zhf8z80kzw7c7z2.htm/, Retrieved Tue, 14 Dec 2010 16:56:35 +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/14/t1292342192zhf8z80kzw7c7z2.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 «

0 25 11 7 8 25 23 0 17 6 17 8 30 25 0 18 8 12 9 22 19 0 16 10 12 7 22 29 0 20 10 11 4 25 25 0 16 11 11 11 23 21 0 18 16 12 7 17 22 0 17 11 13 7 21 25 0 30 12 16 10 19 18 0 23 8 11 10 15 22 0 18 12 10 8 16 15 0 21 9 9 9 22 20 0 31 14 17 11 23 20 0 27 15 11 9 23 21 0 21 9 14 13 19 21 0 16 8 15 9 23 24 0 20 9 15 6 25 24 0 17 9 13 6 22 23 0 25 16 18 16 26 24 0 26 11 18 5 29 18 0 25 8 12 7 32 25 0 17 9 17 9 25 21 0 32 12 18 12 28 22 0 22 9 14 9 25 23 0 17 9 16 5 25 23 0 20 14 14 10 18 24 0 29 10 12 8 25 23 0 23 14 17 7 25 21 0 20 10 12 8 20 28 0 11 6 6 4 15 16 0 26 13 12 8 24 29 0 22 10 12 8 26 27 0 14 15 13 8 14 16 0 19 12 14 7 24 28 0 20 11 11 8 25 25 0 28 8 12 7 20 22 0 19 9 9 7 21 23 0 30 9 15 9 27 26 0 29 15 18 11 23 23 0 26 9 15 6 25 25 0 23 10 12 8 20 21 0 21 12 14 9 22 24 0 28 11 13 6 25 22 0 23 14 13 10 25 27 0 18 6 11 8 17 26 0 20 8 16 10 25 24 0 21 10 11 5 26 24 0 28 12 16 14 27 22 0 10 5 8 6 19 24 0 22 10 15 6 22 20 0 31 10 21 12 32 26 0 29 13 18 12 21 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation PC[t] = + 2.61515355647915 -0.186727876789125Gender[t] + 0.0419567686003162CM[t] + 0.115537660006242D[t] + 0.416737645843195PE[t] + 0.00686726142762619PS[t] -0.0890820950933585O[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) 2.61515355647915 1.451109 1.8022 0.073499 0.03675 Gender -0.186727876789125 0.379455 -0.4921 0.623362 0.311681 CM 0.0419567686003162 0.038703 1.0841 0.280046 0.140023 D 0.115537660006242 0.070319 1.643 0.10244 0.05122 PE 0.416737645843195 0.055211 7.5481 0 0 PS 0.00686726142762619 0.051158 0.1342 0.893394 0.446697 O -0.0890820950933585 0.050809 -1.7533 0.08157 0.040785 Multiple Linear Regression - Regression Statistics Multiple R 0.627923982678522 R-squared 0.394288528022856 Adjusted R-squared 0.370378864655337 F-TEST (value) 16.4907603240661 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 1.37667655053519e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.14801385679095 Sum Squared Residuals 701.322456402819 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 5.97494390100148 2.02505609899852 2 8 9.0851500275511 -1.0851500275511 3 9 7.75404836608707 1.24595163391293 4 7 7.01038919796534 -0.0103891979653377 5 4 7.13840879117972 -3.13840879117972 6 11 7.42871323430288 3.57128676569712 7 7 8.3767670537188 -1.3767670537188 8 7 7.9340823913609 -0.934082391360898 9 10 10.4551111234991 -0.455111123499092 10 10 7.231777447972 2.768222552028 11 8 7.69784852623333 0.302151473766673 12 9 6.65616129927132 2.34383870072868 13 11 10.9941857134789 0.00581428652112604 14 9 8.35238832893132 0.647611671068675 15 13 8.63016564911106 4.36983435088894 16 9 8.48180455237686 0.518195447623143 17 6 8.77890380963962 -2.77890380963962 18 6 7.88803852296276 -1.88803852296276 19 16 11.0545314716421 4.9454685283579 20 5 11.0738942950542 -6.07389429505424 21 7 7.5819257900054 -0.581925790005396 22 9 9.75375508080513 -0.753755080805132 23 12 11.0779769248613 0.922023075138685 24 9 8.53516179609041 0.464838203909588 25 5 9.15885324477522 -4.15885324477522 26 10 8.89178363383425 1.10821636616575 27 8 8.11092154461248 -0.110921544612478 28 7 10.5831839924382 -3.58318399243824 29 8 7.25356384460471 0.746436155395291 30 4 4.9480252460999 -0.9480252460999 31 8 7.79030438684248 0.209695613157522 32 8 7.46776304546446 0.532236954535543 33 8 9.02403075143176 -1.02403075143176 34 7 8.30362673341377 -1.30362673341377 35 8 7.25394645118596 0.746053548814037 36 7 7.8926352439549 -0.892635243954906 37 7 6.29813421536299 0.701865784637014 38 9 9.03404182831131 -0.0340418283113137 39 11 11.1753011968476 -0.175301196847603 40 6 8.94156232614815 -2.94156232614815 41 8 8.00300881605917 -0.0030088160591673 42 9 8.73013412813259 0.269865871867416 43 6 8.69032217695496 -2.69032217695496 44 10 8.38174083850531 1.61825916149469 45 8 6.44832442743975 1.55167557256025 46 10 9.08010379547657 0.919896204523431 47 5 7.27631491630102 -2.27631491630102 48 14 10.069807297346 3.93019270265396 49 6 4.93881839414337 1.06118160585663 50 6 9.31408160293704 -3.31408160293704 51 12 11.7262984391152 0.27370156088483 52 12 11.1086555441666 0.891344455833416 53 8 8.5422193606335 -0.54221936063351 54 10 9.27382353787163 0.72617646212837 55 10 11.1241748466211 -1.12417484662113 56 10 8.73014775690895 1.26985224309105 57 5 8.64622613371884 -3.64622613371884 58 7 7.19713852467784 -0.197138524677844 59 10 8.87379087962896 1.12620912037104 60 11 9.8130204536696 1.1869795463304 61 7 7.48932643166448 -0.489326431664481 62 12 9.36768711428572 2.63231288571428 63 11 10.7000725500881 0.299927449911931 64 11 5.17015399491892 5.82984600508108 65 5 7.63459473783522 -2.63459473783522 66 8 10.1146303036405 -2.11463030364055 67 4 6.23872033192373 -2.23872033192373 68 7 8.16574424984648 -1.16574424984648 69 11 9.46917847649505 1.53082152350495 70 6 4.57952509181155 1.42047490818845 71 4 6.34929229313294 -2.34929229313294 72 8 7.15799709466498 0.842002905335022 73 9 8.18815705070481 0.81184294929519 74 8 8.15286468443642 -0.152864684436425 75 11 8.78804268429887 2.21195731570113 76 8 7.56630473014021 0.433695269859793 77 4 6.03612446527292 -2.03612446527292 78 6 8.22205278674204 -2.22205278674204 79 9 9.16056196108776 -0.160561961087758 80 13 8.85589212089298 4.14410787910702 81 9 8.15282034869315 0.847179651306848 82 10 9.75071771155872 0.249282288441275 83 20 14.4947382822574 5.50526171774264 84 11 10.2809161772828 0.719083822717247 85 6 8.31206228656863 -2.31206228656863 86 9 10.5193140313963 -1.51931403139628 87 7 7.44081256092977 -0.440812560929767 88 9 8.23775792232132 0.762242077678682 89 10 8.65253411422612 1.34746588577388 90 9 7.0659093578687 1.9340906421313 91 8 10.3331919629014 -2.33319196290141 92 7 11.8476854097607 -4.84768540976067 93 6 9.4738612735516 -3.4738612735516 94 13 11.4431284778139 1.55687152218612 95 6 8.0456009288666 -2.0456009288666 96 10 9.48253039276905 0.517469607230946 97 16 12.017532756612 3.98246724338801 98 12 7.48570927229584 4.51429072770416 99 8 6.81906567298021 1.18093432701979 100 12 8.72225566426745 3.27774433573255 101 8 7.11094649382008 0.88905350617992 102 4 7.33623962649857 -3.33623962649857 103 8 7.5933491118863 0.406650888113696 104 7 9.28951178024776 -2.28951178024776 105 11 11.5674065854911 -0.567406585491123 106 8 7.58871172336918 0.41128827663082 107 8 6.72367921337831 1.27632078662168 108 9 7.75945384119866 1.24054615880134 109 9 9.17504194956005 -0.175041949560051 110 6 5.92970431673204 0.0702956832679627 111 6 7.2532997565626 -1.2532997565626 112 6 7.06702934407751 -1.06702934407751 113 5 7.9802553339281 -2.98025533392809 114 7 8.01491095914508 -1.01491095914508 115 10 10.2700627786816 -0.270062778681604 116 8 9.17183484560572 -1.17183484560572 117 8 6.71997472016174 1.28002527983826 118 8 6.93863608426337 1.06136391573663 119 6 7.26294717962003 -1.26294717962003 120 4 5.47156918320888 -1.47156918320888 121 8 7.23058041240977 0.76941958759023 122 20 12.9535959217471 7.0464040782529 123 6 8.1053272499592 -2.1053272499592 124 4 4.31461513263361 -0.314615132633609 125 9 7.12014333299897 1.87985666700103 126 6 7.90932780834303 -1.90932780834303 127 9 6.04638225334484 2.95361774665516 128 5 6.6690738500084 -1.66907385000839 129 5 6.20714729321226 -1.20714729321226 130 8 7.25061035979207 0.749389640207929 131 8 7.99772468069911 0.00227531930088638 132 6 6.37882822539689 -0.378828225396891 133 6 7.92614042018923 -1.92614042018923 134 8 6.35399891673088 1.64600108326912 135 8 8.2842310114041 -0.284231011404103 136 5 6.07012116393445 -1.07012116393445 137 7 9.24760936016835 -2.24760936016835 138 8 6.0005095164989 1.99949048350109 139 7 7.6278520805859 -0.627852080585899 140 8 8.9499291072194 -0.949929107219393 141 5 6.18914230707489 -1.18914230707489 142 10 6.58855324988682 3.41144675011318 143 9 9.3291752208506 -0.329175220850604 144 7 7.51918168200728 -0.519181682007283 145 6 7.12714654902116 -1.12714654902116 146 10 7.14903717270495 2.85096282729505 147 6 7.99235466055498 -1.99235466055498 148 11 6.3676302853312 4.63236971466879 149 6 6.60802924033152 -0.60802924033152 150 9 9.3590462916004 -0.359046291600403 151 4 6.80358364085604 -2.80358364085604 152 7 6.77368423975736 0.226315760242644 153 8 9.71833959393222 -1.71833959393222 154 5 6.6074563306348 -1.60745633063479 155 8 7.52673416617219 0.473265833827812 156 10 11.5747868653077 -1.57478686530767 157 9 7.07308598380321 1.92691401619679 158 5 5.9659884490322 -0.965988449032196 159 8 7.83170053688383 0.168299463116175 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.731509331271365 0.53698133745727 0.268490668728635 11 0.722251082160055 0.55549783567989 0.277748917839945 12 0.601198849456443 0.797602301087114 0.398801150543557 13 0.542372293855122 0.915255412289756 0.457627706144878 14 0.430478492708622 0.860956985417245 0.569521507291378 15 0.605172809164436 0.789654381671128 0.394827190835564 16 0.505878426329618 0.988243147340764 0.494121573670382 17 0.540567719326042 0.918864561347915 0.459432280673958 18 0.538050088444841 0.923899823110318 0.461949911555159 19 0.910380559802638 0.179238880394725 0.0896194401973624 20 0.98269753576335 0.0346049284733008 0.0173024642366504 21 0.973292946861726 0.0534141062765472 0.0267070531382736 22 0.960911947016484 0.0781761059670321 0.0390880529835161 23 0.9494333496686 0.101133300662801 0.0505666503314006 24 0.929408876745724 0.141182246508551 0.0705911232542757 25 0.95112358306377 0.0977528338724603 0.0488764169362301 26 0.932876089680552 0.134247820638896 0.0671239103194481 27 0.913422171856898 0.173155656286204 0.086577828143102 28 0.926676893207095 0.14664621358581 0.0733231067929051 29 0.905187965917578 0.189624068164845 0.0948120340824223 30 0.888941482593987 0.222117034812027 0.111058517406013 31 0.86580173884925 0.2683965223015 0.13419826115075 32 0.83071228435448 0.338575431291041 0.16928771564552 33 0.795024490999777 0.409951018000445 0.204975509000223 34 0.765547361480863 0.468905277038274 0.234452638519137 35 0.723137052476948 0.553725895046105 0.276862947523052 36 0.716088045186278 0.567823909627443 0.283911954813722 37 0.666784942684516 0.666430114630968 0.333215057315484 38 0.613499475716707 0.773001048566585 0.386500524283293 39 0.558124653653004 0.883750692693992 0.441875346346996 40 0.598056272363199 0.803887455273602 0.401943727636801 41 0.544442340017017 0.911115319965966 0.455557659982983 42 0.490019915197005 0.98003983039401 0.509980084802995 43 0.522259147655188 0.955481704689625 0.477740852344812 44 0.487969750711953 0.975939501423906 0.512030249288047 45 0.448800896913462 0.897601793826925 0.551199103086538 46 0.43005554613151 0.86011109226302 0.56994445386849 47 0.431319721472337 0.862639442944675 0.568680278527663 48 0.593738405215228 0.812523189569544 0.406261594784772 49 0.5541471460591 0.8917057078818 0.4458528539409 50 0.599534142595242 0.800931714809516 0.400465857404758 51 0.567776683879909 0.864446632240182 0.432223316120091 52 0.524570891619125 0.95085821676175 0.475429108380875 53 0.47760521144842 0.95521042289684 0.52239478855158 54 0.438786122748006 0.877572245496012 0.561213877251994 55 0.397828723190977 0.795657446381954 0.602171276809023 56 0.374723677404446 0.749447354808892 0.625276322595554 57 0.464618586104059 0.929237172208117 0.535381413895941 58 0.415647421367282 0.831294842734564 0.584352578632718 59 0.392311769290039 0.784623538580078 0.607688230709961 60 0.361073356136333 0.722146712272665 0.638926643863667 61 0.31845560147254 0.63691120294508 0.68154439852746 62 0.322079180420962 0.644158360841924 0.677920819579038 63 0.326326541399648 0.652653082799296 0.673673458600352 64 0.638681506473539 0.722636987052921 0.361318493526461 65 0.657795479199947 0.684409041600106 0.342204520800053 66 0.646506590246336 0.706986819507327 0.353493409753664 67 0.663339483947296 0.673321032105408 0.336660516052704 68 0.62928955072441 0.741420898551181 0.370710449275591 69 0.612073290008839 0.775853419982323 0.387926709991161 70 0.587436479556048 0.825127040887904 0.412563520443952 71 0.598289849609013 0.803420300781974 0.401710150390987 72 0.558498256651697 0.883003486696605 0.441501743348303 73 0.519695630060456 0.960608739879087 0.480304369939544 74 0.474808882387347 0.949617764774695 0.525191117612653 75 0.482609774673043 0.965219549346087 0.517390225326957 76 0.44072374158562 0.88144748317124 0.55927625841438 77 0.432698657431803 0.865397314863606 0.567301342568197 78 0.431099240225043 0.862198480450087 0.568900759774957 79 0.391039554937728 0.782079109875456 0.608960445062272 80 0.511009637794639 0.977980724410722 0.488990362205361 81 0.470104126989592 0.940208253979185 0.529895873010408 82 0.424078912157844 0.848157824315688 0.575921087842156 83 0.680837347828273 0.638325304343454 0.319162652171727 84 0.646756676619909 0.706486646760183 0.353243323380091 85 0.643299761589071 0.713400476821858 0.356700238410929 86 0.616241308895893 0.767517382208214 0.383758691104107 87 0.571327674172938 0.857344651654123 0.428672325827062 88 0.535928538583822 0.928142922832357 0.464071461416178 89 0.514738852592685 0.97052229481463 0.485261147407315 90 0.526433651800167 0.947132696399667 0.473566348199833 91 0.516350009571011 0.967299980857978 0.483649990428989 92 0.723987355998504 0.552025288002992 0.276012644001496 93 0.785242066775927 0.429515866448146 0.214757933224073 94 0.75800188551815 0.483996228963701 0.241998114481851 95 0.791646455969422 0.416707088061156 0.208353544030578 96 0.787861985747534 0.424276028504932 0.212138014252466 97 0.79429301229891 0.41141397540218 0.20570698770109 98 0.858570473674881 0.282859052650237 0.141429526325119 99 0.852953822137315 0.29409235572537 0.147046177862685 100 0.880176413418914 0.239647173162172 0.119823586581086 101 0.866914296963688 0.266171406072625 0.133085703036312 102 0.94352008883983 0.112959822320341 0.0564799111601703 103 0.936033488034972 0.127933023930056 0.0639665119650282 104 0.95232844446761 0.0953431110647797 0.0476715555323899 105 0.940190566515771 0.119618866968457 0.0598094334842287 106 0.928695235453273 0.142609529093453 0.0713047645467266 107 0.913013579563642 0.173972840872716 0.086986420436358 108 0.895802392801554 0.208395214396891 0.104197607198445 109 0.873473568630238 0.253052862739524 0.126526431369762 110 0.847652337261566 0.304695325476867 0.152347662738434 111 0.828030043836583 0.343939912326835 0.171969956163418 112 0.808145944542512 0.383708110914977 0.191854055457488 113 0.816596513656416 0.366806972687168 0.183403486343584 114 0.798601752612993 0.402796494774014 0.201398247387007 115 0.770968406936499 0.458063186127001 0.229031593063501 116 0.731210309979254 0.537579380041492 0.268789690020746 117 0.693070679392612 0.613858641214775 0.306929320607388 118 0.658742484162671 0.682515031674659 0.341257515837329 119 0.666393746388045 0.667212507223909 0.333606253611954 120 0.640213783279163 0.719572433441674 0.359786216720837 121 0.590121496257418 0.819757007485164 0.409878503742582 122 0.959855250949253 0.080289498101494 0.040144749050747 123 0.957367096322364 0.0852658073552716 0.0426329036776358 124 0.957391895669351 0.0852162086612973 0.0426081043306487 125 0.950779095305504 0.0984418093889913 0.0492209046944956 126 0.95144625690338 0.097107486193241 0.0485537430966205 127 0.96291526267457 0.0741694746508589 0.0370847373254294 128 0.956546483284021 0.0869070334319581 0.0434535167159791 129 0.991294857339188 0.0174102853216237 0.00870514266081184 130 0.989754540615198 0.020490918769605 0.0102454593848025 131 0.984469712540978 0.031060574918043 0.0155302874590215 132 0.976914071132233 0.0461718577355347 0.0230859288677673 133 0.966748978304442 0.0665020433911156 0.0332510216955578 134 0.950992330628986 0.0980153387420277 0.0490076693710138 135 0.930695410803573 0.138609178392855 0.0693045891964274 136 0.951597197700142 0.0968056045997169 0.0484028022998584 137 0.942042619711218 0.115914760577565 0.0579573802887823 138 0.918154978988672 0.163690042022656 0.0818450210113282 139 0.879474096972402 0.241051806055196 0.120525903027598 140 0.829214460008977 0.341571079982046 0.170785539991023 141 0.789049630349895 0.42190073930021 0.210950369650105 142 0.766830017125939 0.466339965748122 0.233169982874061 143 0.692816623063783 0.614366753872434 0.307183376936217 144 0.594040154148304 0.811919691703392 0.405959845851696 145 0.499366093146376 0.998732186292751 0.500633906853624 146 0.470057760237561 0.940115520475123 0.529942239762439 147 0.353861728587268 0.707723457174536 0.646138271412732 148 0.697677375728875 0.60464524854225 0.302322624271125 149 0.587806678161713 0.824386643676574 0.412193321838287 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 5 0.0357142857142857 OK 10% type I error level 19 0.135714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/1094wx1292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/1094wx1292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/12lz41292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/12lz41292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/22lz41292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/22lz41292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/3duy71292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/3duy71292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/4duy71292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/4duy71292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/5duy71292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/5duy71292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/654xs1292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/654xs1292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/7gvec1292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/7gvec1292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/8gvec1292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/8gvec1292342268.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/9gvec1292342268.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342192zhf8z80kzw7c7z2/9gvec1292342268.ps (open in new window)

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') }

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