p_Stress_MR3

*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: Fri, 03 Dec 2010 20:44:44 +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/03/t12914090169zh33c58d15m363.htm/, Retrieved Fri, 03 Dec 2010 21:43:36 +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/03/t12914090169zh33c58d15m363.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 «

10 53 7 6 15 11 12 2 4 25 6 86 4 6 15 12 11 4 3 24 13 66 6 5 14 15 14 7 5 21 12 67 5 4 10 10 12 3 3 23 8 76 4 4 10 12 21 7 6 17 6 78 3 6 12 11 12 2 5 19 10 53 5 7 18 5 22 7 6 18 10 80 6 5 12 16 11 2 6 27 9 74 5 4 14 11 10 1 5 23 9 76 6 6 18 15 13 2 5 23 7 79 7 1 9 12 10 6 3 29 5 54 6 4 11 9 8 1 5 21 14 67 7 6 11 11 15 1 7 26 6 87 6 6 17 15 10 1 5 25 10 58 4 5 8 12 14 2 5 25 10 75 6 3 16 16 14 2 3 23 7 88 4 7 21 14 11 2 5 26 10 64 5 2 24 11 10 1 6 20 8 57 3 5 21 10 13 7 5 29 6 66 3 5 14 7 7 1 2 24 10 54 4 3 7 11 12 2 5 23 12 56 5 5 18 10 14 4 4 24 7 86 3 5 18 11 11 2 6 30 15 80 7 6 13 16 9 1 3 22 8 76 7 4 11 14 11 1 5 22 10 69 4 4 13 12 15 5 4 13 13 67 4 4 13 12 13 2 5 24 8 80 5 2 18 11 9 1 2 17 11 54 6 3 14 6 15 3 2 24 7 71 5 6 12 14 10 1 5 21 9 84 4 6 9 9 11 2 2 23 10 74 6 5 12 15 13 5 2 24 8 71 5 3 8 12 8 2 2 24 15 63 5 3 5 12 20 6 5 24 9 71 6 4 10 9 12 4 5 23 7 76 2 4 11 13 10 1 1 26 11 69 6 5 11 15 10 3 5 24 9 74 7 3 12 11 9 6 2 21 8 75 5 5 12 10 14 7 6 23 8 54 5 4 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 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 PStress[t] = + 5.77337275478613 -0.0342048600664453BelInSprt[t] + 0.162129769967189KunnenRekRel[t] -0.144754395838316ExtraCurAct[t] -0.0528074645624752Verwouders[t] + 0.0619866259734261Populariteit[t] + 0.405039685698546Depressie[t] -0.188350390479734Slaapgebrek[t] + 0.202001749841396ToekZorgen[t] + 0.0432050643236558`MateGeorgZijn `[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) 5.77337275478613 2.097336 2.7527 0.006743 0.003371 BelInSprt -0.0342048600664453 0.017296 -1.9776 0.050054 0.025027 KunnenRekRel 0.162129769967189 0.110528 1.4669 0.144792 0.072396 ExtraCurAct -0.144754395838316 0.123818 -1.1691 0.244475 0.122237 Verwouders -0.0528074645624752 0.048681 -1.0848 0.280004 0.140002 Populariteit 0.0619866259734261 0.057932 1.07 0.286576 0.143288 Depressie 0.405039685698546 0.063626 6.3659 0 0 Slaapgebrek -0.188350390479734 0.093402 -2.0165 0.045771 0.022886 ToekZorgen 0.202001749841396 0.116236 1.7379 0.084567 0.042284 `MateGeorgZijn ` 0.0432050643236558 0.04614 0.9364 0.350784 0.175392 Multiple Linear Regression - Regression Statistics Multiple R 0.623985664675957 R-squared 0.389358109721096 Adjusted R-squared 0.347723435383898 F-TEST (value) 9.35177507496987 F-TEST (DF numerator) 9 F-TEST (DF denominator) 132 p-value 6.5743854804623e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.92734869960743 Sum Squared Residuals 490.336837303956 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.4885471581556 -0.488547158155593 2 6 7.90843681121168 -1.90843681121168 3 13 10.2247714831643 2.7752285168357 4 12 9.70021679683811 2.29978320316189 5 8 12.59294701117 -4.59294701117001 6 6 9.08610033421258 -3.08610033421258 7 10 12.6994040268589 -2.69940402685891 8 10 10.1013700284188 -0.101370028418802 9 9 9.28216445334169 -0.282164453341689 10 9 10.1498610237591 -1.14986102375906 11 7 9.10916176310506 -2.10916176310506 12 5 9.26635106633395 -4.26635106633395 13 14 12.2735887368985 1.72641126310153 14 6 8.88605648962205 -2.88605648962205 15 10 11.4196079429094 -1.41960794290935 16 10 10.7869668124546 -0.786966812454587 17 7 8.36951556910205 -1.36951556910205 18 10 9.45803375692845 0.541966243071551 19 8 9.30724136094698 -1.30724136094698 20 6 7.06092365191068 -1.06092365191068 21 10 10.9402675133964 -0.940267513396408 22 12 10.3761919403133 1.62380805968670 23 7 8.91258883384755 -1.91258883384755 24 15 8.62217838592537 6.37782161407463 25 8 10.2442311661258 -2.24423116612577 26 10 10.0435975477385 -0.0435975477384701 27 13 10.5442365253151 2.45576347468492 28 8 7.88493890520508 0.115061094794922 29 11 11.0489101529417 -0.0489101529417004 30 7 9.30043492026231 -2.30043492026231 31 9 8.23922540759356 0.760774592406436 32 10 9.55201857046539 0.447981429534612 33 8 8.34713489565026 -0.347134895650259 34 15 13.4922760858571 1.50772391414294 35 9 9.87919360976915 -0.879193609769153 36 7 8.3313692625469 -1.33136926254689 37 11 9.5434373087481 1.45656269125189 38 9 7.81758630197062 1.18241369802938 39 8 9.83889165034563 -1.83889165034563 40 8 8.07031522147137 -0.0703152214713721 41 12 9.00669091002757 2.99330908997243 42 13 10.1938691224284 2.80613087757164 43 9 9.2436464741124 -0.243646474112408 44 11 9.45442935521885 1.54557064478115 45 8 8.40345679870989 -0.403456798709888 46 10 10.6138127734815 -0.613812773481454 47 13 12.5569150675629 0.443084932437137 48 12 11.3374227461521 0.662577253847944 49 12 9.66726228579605 2.33273771420395 50 9 8.61543483870245 0.384565161297553 51 8 10.0263023009330 -2.02630230093305 52 9 8.39973740079584 0.600262599204165 53 12 8.36412893020432 3.63587106979568 54 12 11.6555759394845 0.344424060515546 55 16 14.3401293956828 1.65987060431725 56 11 8.93466858177642 2.06533141822358 57 13 10.2389228412485 2.76107715875153 58 10 10.9074268945231 -0.907426894523076 59 9 10.5593292803811 -1.55932928038106 60 14 9.73959672006318 4.26040327993682 61 13 11.6106132975683 1.38938670243169 62 12 10.5332068411698 1.46679315883024 63 9 10.9521337491224 -1.95213374912245 64 9 10.4424726882582 -1.44247268825816 65 10 11.0397300873270 -1.03973008732702 66 8 10.3875465176335 -2.38754651763355 67 9 10.3037781735247 -1.30377817352473 68 9 8.95405028690126 0.0459497130987453 69 11 8.7071087453227 2.29289125467730 70 7 9.86048859103846 -2.86048859103846 71 11 11.6516072530218 -0.651607253021812 72 9 9.1024947350616 -0.102494735061600 73 11 8.97762664879536 2.02237335120464 74 9 9.53450545254268 -0.534505452542676 75 8 10.2034978250591 -2.20349782505915 76 9 8.15308223745137 0.846917762548632 77 8 9.06360266535608 -1.06360266535608 78 9 10.0392733091713 -1.03927330917134 79 10 10.2405298607058 -0.240529860705809 80 9 9.80865647825014 -0.808656478250142 81 17 13.7775190200029 3.22248097999715 82 7 9.29252641671997 -2.29252641671997 83 11 11.0592868587740 -0.059286858773962 84 9 10.0754655383367 -1.07546553833666 85 10 9.79908879635722 0.200911203642778 86 11 8.64880989710351 2.35119010289649 87 8 8.31194325951977 -0.311943259519767 88 12 12.2222556983569 -0.22225569835692 89 10 10.0956129682348 -0.095612968234843 90 7 8.92833116250647 -1.92833116250647 91 9 8.74246424937154 0.257535750628456 92 7 8.19120228467682 -1.19120228467682 93 12 10.5742183520702 1.42578164792983 94 8 9.2329686710533 -1.23296867105330 95 13 10.3608779917470 2.63912200825305 96 9 10.8694697382500 -1.86946973825004 97 15 12.3919528721844 2.60804712781557 98 8 9.1812368254096 -1.18123682540959 99 14 11.7210569498502 2.27894305014981 100 14 13.5496466438415 0.450353356158483 101 9 10.2459753700833 -1.24597537008334 102 13 11.8717470047749 1.12825299522511 103 11 8.9162903102105 2.0837096897895 104 10 11.8054836546751 -1.80548365467515 105 6 10.0875608331275 -4.08756083312749 106 8 8.3071254679926 -0.3071254679926 107 10 11.3708417970852 -1.37084179708517 108 10 7.75540665882537 2.24459334117463 109 10 8.8916316824268 1.10836831757320 110 12 12.0646908202124 -0.0646908202123775 111 10 9.58635003076956 0.413649969230443 112 9 8.93901690395283 0.0609830960471657 113 9 7.55180574094984 1.44819425905016 114 11 9.37796060830912 1.62203939169088 115 7 8.08456321677665 -1.08456321677665 116 7 8.82160573601362 -1.82160573601362 117 5 8.42600672452766 -3.42600672452766 118 9 8.94034948748323 0.0596505125167735 119 11 9.84573976488388 1.15426023511611 120 15 12.2661109861046 2.73388901389538 121 9 8.07842530288505 0.921574697114953 122 9 9.32014194927413 -0.320141949274125 123 8 9.53513582770015 -1.53513582770015 124 13 15.2727635081820 -2.27276350818202 125 10 10.1807928233401 -0.180792823340132 126 13 11.5275253036514 1.47247469634861 127 9 7.41021949303544 1.58978050696456 128 11 9.58506563963663 1.41493436036337 129 8 10.5899908351982 -2.58999083519818 130 10 9.11296908588648 0.887030914113524 131 9 8.87711240060514 0.122887599394861 132 8 7.9346581640629 0.0653418359371003 133 8 7.93538711232431 0.0646128876756898 134 13 10.7492878445701 2.25071215542986 135 11 10.8278636250908 0.172136374909205 136 8 10.2442311661258 -2.24423116612577 137 12 10.1544663322319 1.84553366776814 138 15 12.1102724622765 2.88972753772349 139 11 11.0592868587740 -0.059286858773962 140 10 10.5101020130621 -0.510102013062077 141 5 8.42600672452766 -3.42600672452766 142 11 7.35104395061007 3.64895604938993 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.987801563184236 0.0243968736315286 0.0121984368157643 14 0.975157057249854 0.0496858855002911 0.0248429427501455 15 0.980365607542201 0.0392687849155978 0.0196343924577989 16 0.963167798986413 0.0736644020271731 0.0368322010135866 17 0.941257409166478 0.117485181667045 0.0587425908335223 18 0.94729223711537 0.105415525769261 0.0527077628846306 19 0.93256895471233 0.134862090575340 0.0674310452876698 20 0.908068664142656 0.183862671714689 0.0919313358573445 21 0.867661921888747 0.264676156222506 0.132338078111253 22 0.8518144943615 0.296371011277001 0.148185505638501 23 0.810463111757351 0.379073776485298 0.189536888242649 24 0.981091992071239 0.0378160158575223 0.0189080079287611 25 0.980837515211083 0.0383249695778346 0.0191624847889173 26 0.973755373069017 0.0524892538619667 0.0262446269309834 27 0.985472411037038 0.029055177925924 0.014527588962962 28 0.978327007237391 0.0433459855252172 0.0216729927626086 29 0.96795807113826 0.0640838577234786 0.0320419288617393 30 0.970591892063405 0.0588162158731896 0.0294081079365948 31 0.967185117039918 0.065629765920164 0.032814882960082 32 0.957381482789997 0.0852370344200053 0.0426185172100027 33 0.94194172011288 0.116116559774241 0.0580582798871203 34 0.943607634621057 0.112784730757885 0.0563923653789426 35 0.930008491149178 0.139983017701644 0.0699915088508219 36 0.922721173657916 0.154557652684167 0.0772788263420836 37 0.909814726970869 0.180370546058262 0.090185273029131 38 0.889836977567245 0.22032604486551 0.110163022432755 39 0.872834280729473 0.254331438541055 0.127165719270527 40 0.858376592719229 0.283246814561542 0.141623407280771 41 0.87911933195454 0.241761336090921 0.120880668045461 42 0.889266406847766 0.221467186304468 0.110733593152234 43 0.861758543319866 0.276482913360268 0.138241456680134 44 0.840627882445169 0.318744235109663 0.159372117554831 45 0.809220560703224 0.381558878593551 0.190779439296776 46 0.770469787931018 0.459060424137965 0.229530212068983 47 0.768936924794103 0.462126150411795 0.231063075205897 48 0.729353093087311 0.541293813825377 0.270646906912689 49 0.766974019562422 0.466051960875156 0.233025980437578 50 0.727877702130775 0.54424459573845 0.272122297869225 51 0.719505015497481 0.560989969005038 0.280494984502519 52 0.686148791619196 0.627702416761608 0.313851208380804 53 0.79035391674149 0.41929216651702 0.20964608325851 54 0.752888466248317 0.494223067503366 0.247111533751683 55 0.779737275439672 0.440525449120657 0.220262724560328 56 0.82109726039763 0.357805479204742 0.178902739602371 57 0.854829931629606 0.290340136740789 0.145170068370394 58 0.832777007785071 0.334445984429858 0.167222992214929 59 0.824847766929916 0.350304466140167 0.175152233070084 60 0.949354752232041 0.101290495535918 0.0506452477679588 61 0.943243645800666 0.113512708398669 0.0567563541993345 62 0.936627352677479 0.126745294645042 0.0633726473225212 63 0.93978259619928 0.12043480760144 0.06021740380072 64 0.933350772158143 0.133298455683714 0.066649227841857 65 0.931644055815797 0.136711888368407 0.0683559441842033 66 0.936341800159163 0.127316399681675 0.0636581998408373 67 0.92644585661745 0.147108286765099 0.0735541433825493 68 0.907227651076119 0.185544697847763 0.0927723489238813 69 0.91996165487211 0.160076690255779 0.0800383451278895 70 0.93995220310525 0.1200955937895 0.06004779689475 71 0.926172430761427 0.147655138477145 0.0738275692385726 72 0.908511111391024 0.182977777217952 0.0914888886089761 73 0.911184099726409 0.177631800547183 0.0888159002735913 74 0.890420018522719 0.219159962954563 0.109579981477281 75 0.893287860784196 0.213424278431608 0.106712139215804 76 0.88219924132358 0.235601517352839 0.117800758676420 77 0.868049567792504 0.263900864414993 0.131950432207496 78 0.851185411747269 0.297629176505462 0.148814588252731 79 0.819242469522926 0.361515060954147 0.180757530477074 80 0.790841092804882 0.418317814390235 0.209158907195118 81 0.855989943289641 0.288020113420717 0.144010056710359 82 0.867930886724433 0.264138226551134 0.132069113275567 83 0.839946191480067 0.320107617039865 0.160053808519933 84 0.832744966827067 0.334510066345866 0.167255033172933 85 0.799420589346397 0.401158821307206 0.200579410653603 86 0.878892093794063 0.242215812411874 0.121107906205937 87 0.859678657994885 0.28064268401023 0.140321342005115 88 0.827944507684684 0.344110984630633 0.172055492315316 89 0.793270938430668 0.413458123138665 0.206729061569332 90 0.814467313564391 0.371065372871217 0.185532686435609 91 0.779928398323273 0.440143203353453 0.220071601676727 92 0.782725131054616 0.434549737890767 0.217274868945384 93 0.78489763184184 0.430204736316321 0.215102368158161 94 0.75084757884866 0.498304842302681 0.249152421151341 95 0.779590847563779 0.440818304872443 0.220409152436221 96 0.757259048174845 0.485481903650311 0.242740951825155 97 0.768663237833643 0.462673524332713 0.231336762166357 98 0.731525340573243 0.536949318853513 0.268474659426757 99 0.71335107797891 0.573297844042179 0.286648922021089 100 0.714073350066445 0.57185329986711 0.285926649933555 101 0.719166811946541 0.561666376106918 0.280833188053459 102 0.732012299032871 0.535975401934257 0.267987700967129 103 0.754772740065203 0.490454519869594 0.245227259934797 104 0.734396492339723 0.531207015320553 0.265603507660277 105 0.847147171822692 0.305705656354617 0.152852828177308 106 0.809426648278778 0.381146703442445 0.190573351721222 107 0.83347701408962 0.333045971820759 0.166522985910379 108 0.820524728840807 0.358950542318386 0.179475271159193 109 0.791134984916375 0.417730030167251 0.208865015083625 110 0.814036107072263 0.371927785855473 0.185963892927737 111 0.793691174573195 0.412617650853609 0.206308825426805 112 0.739269320154653 0.521461359690694 0.260730679845347 113 0.687922620110909 0.624154759778182 0.312077379889091 114 0.638313960445231 0.723372079109537 0.361686039554769 115 0.573175224976648 0.853649550046705 0.426824775023352 116 0.512864896031708 0.974270207936584 0.487135103968292 117 0.610124236181294 0.779751527637412 0.389875763818706 118 0.530818450568873 0.938363098862254 0.469181549431127 119 0.451576699414531 0.903153398829062 0.548423300585469 120 0.443569835478506 0.887139670957013 0.556430164521494 121 0.369393955425812 0.738787910851623 0.630606044574188 122 0.292869924857937 0.585739849715873 0.707130075142063 123 0.240305941827111 0.480611883654223 0.759694058172889 124 0.318679532593643 0.637359065187286 0.681320467406357 125 0.455418612841984 0.910837225683967 0.544581387158016 126 0.393708550931195 0.787417101862391 0.606291449068804 127 0.289272838341346 0.578545676682693 0.710727161658654 128 0.223576789601950 0.447153579203901 0.77642321039805 129 0.162047403621754 0.324094807243508 0.837952596378246 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 7 0.0598290598290598 NOK 10% type I error level 13 0.111111111111111 NOK

Charts produced by software:

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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') }

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Software written by Ed van Stee & Patrick Wessa

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