p_Stress_MR1

*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 20:24:39 +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/t1291407869ze7kt3waqxz7w9u.htm/, Retrieved Fri, 03 Dec 2010 21:24:29 +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/t1291407869ze7kt3waqxz7w9u.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:

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10 53 7 6 7 6 15 11 11 12 2 4 25 25 2 2 3.4 6 86 4 6 5 6 15 12 8 11 4 3 25 24 1 2 4 13 66 6 5 7 13 14 15 12 14 7 5 19 21 4 3.666666667 3.2 12 67 5 4 3 8 10 10 10 12 3 3 18 23 1 2.333333333 3.2 8 76 4 4 7 7 10 12 7 21 7 6 18 17 5 4 2.6 6 78 3 6 7 9 12 11 6 12 2 5 22 19 1 2.666666667 3.2 10 53 5 7 7 5 18 5 8 22 7 6 29 18 1 2.333333333 3.8 10 80 6 5 1 8 12 16 16 11 2 6 26 27 1 3.666666667 3.6 9 74 5 4 4 9 14 11 8 10 1 5 25 23 1 2.666666667 3.6 9 76 6 6 5 11 18 15 16 13 2 5 23 23 1 3 4 7 79 7 1 6 8 9 12 7 10 6 3 23 29 2 3 3.4 5 54 6 4 4 11 11 9 11 8 1 5 23 21 1 2 2.6 14 67 7 6 7 12 11 11 16 15 1 7 24 26 3 3 4.4 6 87 6 6 6 8 17 15 16 10 1 5 30 25 1 1.666666667 4 10 58 4 5 2 7 8 12 12 14 2 5 19 25 1 3 3.8 10 75 6 3 2 9 16 16 13 14 2 3 24 23 1 1.333333333 3.6 7 88 4 7 6 12 21 14 19 11 2 5 32 26 1 3 3.8 10 64 5 2 7 20 24 11 7 10 1 6 30 20 1 2 3.6 8 57 3 5 5 7 21 10 8 13 7 5 29 29 2 2.666666667 3.8 6 66 3 5 2 8 14 7 12 7 1 2 17 24 4 4 3.6 10 54 4 3 7 8 7 11 13 12 2 5 25 23 1 2.333333333 4 12 56 5 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 9 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 5.22652616884922 -0.0342689806008217BelInSprt[t] + 0.156751517105329KunnenRekRel[t] -0.137087238624758ExtraCurAct[t] -0.0131171061742578Verandvorigjaar[t] + 0.0306992360511323Kritouders[t] -0.060701263441551Verwouders[t] + 0.0651926129660706Populariteit[t] + 0.000472082504175796KenMedeStud[t] + 0.402890559410430Depressie[t] -0.177299512348825Slaapgebrek[t] + 0.215782954579822ToekZorgen[t] -0.0326727049326701PersStand[t] + 0.0520518310861078MateGeorgZijn[t] + 0.0264275257714054Rookgedrag[t] -0.0883024675116791MateAlcoholCon[t] + 0.295536842036837MateGezondGevarEetg[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) 5.22652616884922 2.53035 2.0655 0.040939 0.020469 BelInSprt -0.0342689806008217 0.019649 -1.744 0.083609 0.041804 KunnenRekRel 0.156751517105329 0.115107 1.3618 0.175713 0.087856 ExtraCurAct -0.137087238624758 0.138554 -0.9894 0.324372 0.162186 Verandvorigjaar -0.0131171061742578 0.108761 -0.1206 0.904197 0.452099 Kritouders 0.0306992360511323 0.083353 0.3683 0.713269 0.356635 Verwouders -0.060701263441551 0.06191 -0.9805 0.328746 0.164373 Populariteit 0.0651926129660706 0.073274 0.8897 0.37533 0.187665 KenMedeStud 0.000472082504175796 0.061376 0.0077 0.993875 0.496938 Depressie 0.402890559410430 0.066614 6.0482 0 0 Slaapgebrek -0.177299512348825 0.099054 -1.7899 0.075887 0.037943 ToekZorgen 0.215782954579822 0.123795 1.7431 0.083782 0.041891 PersStand -0.0326727049326701 0.051558 -0.6337 0.527426 0.263713 MateGeorgZijn 0.0520518310861078 0.051126 1.0181 0.310593 0.155296 Rookgedrag 0.0264275257714054 0.189098 0.1398 0.889078 0.444539 MateAlcoholCon -0.0883024675116791 0.270555 -0.3264 0.744686 0.372343 MateGezondGevarEetg 0.295536842036837 0.346167 0.8537 0.394882 0.197441 Multiple Linear Regression - Regression Statistics Multiple R 0.628535092629106 R-squared 0.395056362666279 Adjusted R-squared 0.317623577087563 F-TEST (value) 5.10192626693862 F-TEST (DF numerator) 16 F-TEST (DF denominator) 125 p-value 4.64995227877907e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.97131675881084 Sum Squared Residuals 485.761220446058 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.2979487964583 -0.297948796458302 2 6 7.91239867279394 -1.91239867279394 3 13 10.339098541163 2.660901458837 4 12 9.74711678506372 2.25288321493628 5 8 12.3608178413918 -4.36081784139178 6 6 8.94314617595213 -2.94314617595213 7 10 12.3832569313601 -2.38325693136006 8 10 9.98916578898839 0.0108342110116108 9 9 9.18671490635274 -0.186714906352737 10 9 10.2562755658211 -1.25627556582115 11 7 9.04890707973803 -2.04890707973803 12 5 9.06217141802112 -4.06217141802112 13 14 12.5992504412756 1.40074955872441 14 6 8.79656241340654 -2.79656241340653 15 10 11.6013799337748 -1.60137993377483 16 10 10.8325459887723 -0.832545988772341 17 7 8.16337216151061 -1.16337216151061 18 10 9.4495669990925 0.550433000907498 19 8 9.16610993350092 -1.16610993350092 20 6 7.16588821534516 -1.16588821534516 21 10 10.9857794411093 -0.985779441109293 22 12 10.2480404759917 1.75195952400826 23 7 9.43366041479678 -2.43366041479678 24 15 8.6525702976235 6.3474297023765 25 8 10.1491084527023 -2.14910845270229 26 10 9.9528383000774 0.0471616999226065 27 13 10.5163226876453 2.48367731235471 28 8 7.8666564301064 0.133343569893595 29 11 11.3207867552581 -0.320786755258090 30 7 9.7319542572747 -2.73195425727469 31 9 8.37401382563088 0.625986174369125 32 10 9.66753825634607 0.332461743653928 33 8 8.10270249854559 -0.102702498545585 34 15 13.4045067064971 1.59549329350287 35 9 10.1456662951063 -1.14566629510635 36 7 8.38185235282574 -1.38185235282574 37 11 9.61233675864213 1.38766324135787 38 9 7.81087994604013 1.18912005395987 39 8 9.76712144951388 -1.76712144951388 40 8 7.74562669178148 0.254373308218522 41 12 9.07311848742598 2.92688151257402 42 13 10.2887463008469 2.71125369915311 43 9 8.9949997246505 0.00500027534950619 44 11 9.36789415375542 1.63210584624458 45 8 8.19415957056963 -0.19415957056963 46 10 10.8119350981055 -0.811935098105464 47 13 12.2862118957468 0.713788104253187 48 12 11.2142484254755 0.78575157452449 49 12 9.56137505751008 2.43862494248992 50 9 8.83962774433189 0.160372255668115 51 8 10.2068011119551 -2.20680111195511 52 9 8.49554011973329 0.504459880266712 53 12 8.46662566622833 3.53337433377167 54 12 11.7579883798354 0.242011620164587 55 16 14.2251908227223 1.77480917727766 56 11 8.89114012244742 2.10885987755258 57 13 10.2139947685109 2.78600523148912 58 10 10.6085494175370 -0.608549417536961 59 9 10.6229671645408 -1.62296716454078 60 14 9.52861714354525 4.47138285645474 61 13 11.9729843095194 1.02701569048063 62 12 10.3282302492077 1.67176975079226 63 9 10.8763043033581 -1.87630430335811 64 9 10.5644844003651 -1.56448440036514 65 10 11.2699183270100 -1.26991832701005 66 8 10.3411115868558 -2.34111158685581 67 9 10.4275714591011 -1.42757145910115 68 9 8.98446394539893 0.0155360546010725 69 11 8.76431422182515 2.23568577817485 70 7 9.50950617187987 -2.50950617187987 71 11 11.5959093237277 -0.595909323727745 72 9 9.04322706372827 -0.0432270637282655 73 11 9.15058431512889 1.84941568487111 74 9 9.58582093389701 -0.585820933897015 75 8 10.3495296341019 -2.34952963410189 76 9 8.13012030756874 0.86987969243126 77 8 9.06927861793022 -1.06927861793022 78 9 9.94476031937515 -0.94476031937515 79 10 10.2566345301288 -0.256634530128837 80 9 9.89037004592268 -0.890370045922676 81 17 13.8711372194071 3.12886278059293 82 7 9.46628918867527 -2.46628918867527 83 11 11.2710971912694 -0.271097191269415 84 9 9.99419258295186 -0.994192582951863 85 10 9.78264770319177 0.217352296808229 86 11 8.36485016705854 2.63514983294146 87 8 8.07350215496662 -0.0735021549666187 88 12 12.2362440267885 -0.236244026788495 89 10 10.1366359196199 -0.136635919619902 90 7 8.90866042512573 -1.90866042512573 91 9 8.83185268258191 0.168147317418089 92 7 8.20186818590794 -1.20186818590794 93 12 10.6016031024213 1.39839689757873 94 8 9.3770524761292 -1.37705247612920 95 13 10.5879448570348 2.41205514296516 96 9 10.8991606935624 -1.89916069356244 97 15 12.7139128309047 2.28608716909534 98 8 9.60084209563988 -1.60084209563988 99 14 11.5246911205427 2.47530887945727 100 14 13.8911655616869 0.108834438313056 101 9 10.2593802311929 -1.25938023119287 102 13 11.3876024718801 1.61239752811987 103 11 9.06522093273864 1.93477906726136 104 10 11.9457960705909 -1.94579607059089 105 6 10.0273934103186 -4.02739341031865 106 8 8.39056873028816 -0.390568730288156 107 10 11.4319914342948 -1.43199143429479 108 10 7.85785787476011 2.14214212523989 109 10 8.93355445152864 1.06644554847136 110 12 12.0502741750573 -0.0502741750573411 111 10 9.68071384708567 0.319286152914328 112 9 9.01387427623435 -0.0138742762343448 113 9 7.67328747190242 1.32671252809758 114 11 9.43116941816502 1.56883058183498 115 7 8.0289016846006 -1.02890168460060 116 7 8.8372557113458 -1.83725571134581 117 5 7.95064640131373 -2.95064640131373 118 9 8.84291655797827 0.157083442021736 119 11 9.82802974708223 1.17197025291777 120 15 12.2669949575172 2.73300504248278 121 9 8.03154310589767 0.968456894102328 122 9 9.46720704583193 -0.467207045831931 123 8 9.51378642904974 -1.51378642904975 124 13 15.1340650336149 -2.13406503361495 125 10 10.3946259832878 -0.394625983287812 126 13 11.3389486924832 1.66105130751682 127 9 7.51817428392064 1.48182571607936 128 11 9.58893439708289 1.41106560291711 129 8 10.7738589128412 -2.77385891284124 130 10 9.3707542161555 0.629245783844505 131 9 8.84792204418642 0.152077955813578 132 8 7.94231069744937 0.0576893025506331 133 8 7.7686511920712 0.231348807928797 134 13 10.4146147589015 2.58538524109854 135 11 10.9005342743888 0.0994657256111754 136 8 10.1491084527023 -2.14910845270229 137 12 10.2502498999142 1.74975010008577 138 15 11.7898391071569 3.21016089284307 139 11 11.2710971912694 -0.271097191269415 140 10 10.2216210600961 -0.221621060096082 141 5 7.95064640131373 -2.95064640131373 142 11 7.24621465424016 3.75378534575984 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.825848480991068 0.348303038017863 0.174151519008932 21 0.879272198707035 0.24145560258593 0.120727801292965 22 0.815256898689907 0.369486202620186 0.184743101310093 23 0.739327385005278 0.521345229989445 0.260672614994722 24 0.859077324222484 0.281845351555032 0.140922675777516 25 0.803467573495001 0.393064853009998 0.196532426504999 26 0.730333626226064 0.539332747547872 0.269666373773936 27 0.950784041445515 0.098431917108971 0.0492159585544855 28 0.930244358653806 0.139511282692388 0.069755641346194 29 0.904695346963915 0.190609306072170 0.0953046530360849 30 0.956279873891018 0.0874402522179646 0.0437201261089823 31 0.94562790805665 0.108744183886701 0.0543720919433504 32 0.924838460130035 0.150323079739931 0.0751615398699654 33 0.905950889262755 0.188098221474491 0.0940491107372453 34 0.935138402883507 0.129723194232986 0.064861597116493 35 0.925531367630562 0.148937264738875 0.0744686323694376 36 0.914732519678418 0.170534960643163 0.0852674803215817 37 0.921941338782957 0.156117322434086 0.0780586612170432 38 0.90913692797174 0.181726144056521 0.0908630720282606 39 0.890889249961782 0.218221500076437 0.109110750038218 40 0.862407943763437 0.275184112473127 0.137592056236563 41 0.853185097947188 0.293629804105624 0.146814902052812 42 0.851980131165417 0.296039737669166 0.148019868834583 43 0.856044672999587 0.287910654000826 0.143955327000413 44 0.832657901016099 0.334684197967802 0.167342098983901 45 0.800284153556475 0.39943169288705 0.199715846443525 46 0.757565701534098 0.484868596931804 0.242434298465902 47 0.772596252441146 0.454807495117709 0.227403747558854 48 0.731542216562298 0.536915566875403 0.268457783437702 49 0.785867357283639 0.428265285432722 0.214132642716361 50 0.75209882096036 0.495802358079279 0.247901179039640 51 0.749284914291976 0.501430171416049 0.250715085708024 52 0.706888773726127 0.586222452547746 0.293111226273873 53 0.800956986258401 0.398086027483197 0.199043013741599 54 0.774338954187602 0.451322091624796 0.225661045812398 55 0.819403023293158 0.361193953413683 0.180596976706842 56 0.845150338317517 0.309699323364965 0.154849661682483 57 0.873504791069003 0.252990417861994 0.126495208930997 58 0.843997435231989 0.312005129536023 0.156002564768011 59 0.82996824971239 0.340063500575220 0.170031750287610 60 0.964348155472516 0.071303689054969 0.0356518445274845 61 0.955435001740347 0.0891299965193056 0.0445649982596528 62 0.955202908968965 0.089594182062071 0.0447970910310355 63 0.95626955584784 0.0874608883043194 0.0437304441521597 64 0.950013013464541 0.0999739730709173 0.0499869865354587 65 0.946731501558266 0.106536996883468 0.0532684984417338 66 0.945906739408377 0.108186521183245 0.0540932605916226 67 0.93672013447762 0.126559731044759 0.0632798655223796 68 0.919041434564337 0.161917130871326 0.080958565435663 69 0.92275112226229 0.154497755475421 0.0772488777377105 70 0.941110154366456 0.117779691267088 0.0588898456335438 71 0.925056265927682 0.149887468144636 0.074943734072318 72 0.904457089934938 0.191085820130124 0.0955429100650622 73 0.913961981455476 0.172076037089047 0.0860380185445236 74 0.890625595576612 0.218748808846777 0.109374404423388 75 0.88530093364568 0.229398132708641 0.114699066354320 76 0.878580234779531 0.242839530440937 0.121419765220469 77 0.866483821633786 0.267032356732429 0.133516178366214 78 0.84229764304711 0.315404713905779 0.157702356952890 79 0.806555587912617 0.386888824174767 0.193444412087383 80 0.773844321080162 0.452311357839675 0.226155678919838 81 0.813763355290407 0.372473289419185 0.186236644709593 82 0.821345474424699 0.357309051150603 0.178654525575302 83 0.782424907684836 0.435150184630328 0.217575092315164 84 0.763100700381541 0.473798599236918 0.236899299618459 85 0.718152938552715 0.56369412289457 0.281847061447285 86 0.826265747808887 0.347468504382225 0.173734252191113 87 0.797229386306872 0.405541227386257 0.202770613693128 88 0.754563816587095 0.49087236682581 0.245436183412905 89 0.707144225504111 0.585711548991778 0.292855774495889 90 0.71320880237775 0.573582395244501 0.286791197622250 91 0.664437397649156 0.671125204701689 0.335562602350844 92 0.654138323231745 0.691723353536509 0.345861676768255 93 0.645114348681699 0.709771302636602 0.354885651318301 94 0.600719218987665 0.79856156202467 0.399280781012335 95 0.646958173980171 0.706083652039658 0.353041826019829 96 0.619172590596992 0.761654818806017 0.380827409403008 97 0.608803367075364 0.782393265849271 0.391196632924636 98 0.559147560078767 0.881704879842465 0.440852439921233 99 0.539955271802407 0.920089456395187 0.460044728197593 100 0.528818020919096 0.942363958161808 0.471181979080904 101 0.531004101176197 0.937991797647606 0.468995898823803 102 0.614483839687349 0.771032320625303 0.385516160312651 103 0.583642249185971 0.832715501628059 0.416357750814029 104 0.607674992436822 0.784650015126356 0.392325007563178 105 0.705827753719763 0.588344492560473 0.294172246280237 106 0.676910298544017 0.646179402911966 0.323089701455983 107 0.687054877607624 0.625890244784753 0.312945122392376 108 0.660952589335192 0.678094821329616 0.339047410664808 109 0.598701127944201 0.802597744111598 0.401298872055799 110 0.631033048103315 0.737933903793371 0.368966951896685 111 0.565368138445283 0.869263723109434 0.434631861554717 112 0.491610459130177 0.983220918260354 0.508389540869823 113 0.419426917889632 0.838853835779265 0.580573082110368 114 0.397258152131075 0.794516304262151 0.602741847868925 115 0.347269754222934 0.694539508445868 0.652730245777066 116 0.297867583156859 0.595735166313718 0.702132416843141 117 0.276831262049745 0.55366252409949 0.723168737950255 118 0.199758269383625 0.39951653876725 0.800241730616375 119 0.13376782292325 0.2675356458465 0.86623217707675 120 0.100798881989172 0.201597763978344 0.899201118010828 121 0.0564209291559408 0.112841858311882 0.94357907084406 122 0.0282589185920823 0.0565178371841647 0.971741081407918 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 0 0 OK 10% type I error level 8 0.0776699029126214 OK

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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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