w7 4

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 23 Nov 2010 21:23:49 +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/t1290547372h0pogn2g3jq1gxi.htm/, Retrieved Tue, 23 Nov 2010 22:23:03 +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/t1290547372h0pogn2g3jq1gxi.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 «

24 14 11 12 24 26 1 25 11 7 8 25 23 1 17 6 17 8 30 25 1 18 12 10 8 19 23 2 18 8 12 9 22 19 2 16 10 12 7 22 29 3 20 10 11 4 25 25 4 16 11 11 11 23 21 5 18 16 12 7 17 22 5 17 11 13 7 21 25 6 23 13 14 12 19 24 6 30 12 16 10 19 18 7 23 8 11 10 15 22 7 18 12 10 8 16 15 7 15 11 11 8 23 22 8 12 4 15 4 27 28 9 21 9 9 9 22 20 9 15 8 11 8 14 12 10 20 8 17 7 22 24 10 31 14 17 11 23 20 11 27 15 11 9 23 21 12 34 16 18 11 21 20 13 21 9 14 13 19 21 13 31 14 10 8 18 23 13 19 11 11 8 20 28 13 16 8 15 9 23 24 13 20 9 15 6 25 24 13 21 9 13 9 19 24 13 22 9 16 9 24 23 13 17 9 13 6 22 23 13 24 10 9 6 25 29 13 25 16 18 16 26 24 13 26 11 18 5 29 18 13 25 8 12 7 32 25 13 17 9 17 9 25 21 13 32 16 9 6 29 26 13 33 11 9 6 28 22 13 13 16 12 5 17 22 13 32 12 18 12 28 22 13 25 12 12 7 29 23 13 29 14 18 10 26 30 13 22 9 14 9 25 23 13 18 10 15 8 14 17 13 17 9 16 5 25 23 13 20 10 10 8 26 23 14 15 12 11 8 20 25 14 20 14 14 10 18 24 14 33 14 9 6 32 24 14 29 10 12 8 25 23 14 23 14 17 7 25 21 14 26 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation ConcernoverMistakes[t] = -3.51982791979792 + 0.80219509259368Doubtsaboutactions[t] + 0.237772147402074ParentalExpectations[t] + 0.199799804513788ParentalCriticism[t] + 0.570049530182517PersonalStandards[t] -0.101186974696885Organization[t] + 0.0862460960673614Date[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) -3.51982791979792 3.401621 -1.0348 0.302429 0.151215 Doubtsaboutactions 0.80219509259368 0.130535 6.1455 0 0 ParentalExpectations 0.237772147402074 0.133375 1.7827 0.076625 0.038313 ParentalCriticism 0.199799804513788 0.168579 1.1852 0.237789 0.118894 PersonalStandards 0.570049530182517 0.095871 5.946 0 0 Organization -0.101186974696885 0.103962 -0.9733 0.331949 0.165974 Date 0.0862460960673614 0.083643 1.0311 0.304123 0.152061 Multiple Linear Regression - Regression Statistics Multiple R 0.641321426439313 R-squared 0.411293172010155 Adjusted R-squared 0.388054744589504 F-TEST (value) 17.6988384181558 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 1.77635683940025e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.47678556971189 Sum Squared Residuals 3046.32457365145 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 23.8605681304310 0.139431869569033 2 25 20.5773054992593 4.42269450074065 3 17 21.5919252118305 -4.59192521183048 4 18 18.7587659490315 -0.758765949031489 5 18 18.3402261673098 -0.340226167309837 6 16 18.6193930925681 -2.61939309256809 7 20 19.9833641170271 0.0166358829728929 8 16 21.5350527757072 -5.53505277570717 9 18 21.4631170122305 -3.46311701223051 10 17 19.7527969893710 -2.75279698937095 11 23 21.5550462588612 1.44495374113882 12 30 21.5221637962928 8.47783620370725 13 23 14.4395766693901 8.56042333060994 14 18 18.2893436363958 -0.289343636395836 15 15 21.093204675671 -6.093204675671 16 12 17.3890507676845 -5.38905076768451 17 21 18.9316405154719 2.0683594845281 18 15 14.7405355653509 0.259464434649100 19 20 19.3135211903471 0.686478809652938 20 31 25.9869344890017 5.01306551099829 21 27 24.9479562095258 2.05204379047416 22 34 26.8614899533608 7.13851004663916 23 21 19.4533492895624 1.54665071043756 24 31 20.7418136607773 10.2581863392227 25 19 19.2071647172790 -0.207164717278953 26 16 20.0663643229551 -4.06636432295509 27 20 21.4092590623724 -1.40925906237244 28 21 18.1128170000146 2.88718299998545 29 22 21.7775680678302 0.222431932169756 30 17 19.3247531517176 -2.32475315171763 31 24 20.2788863970692 3.72111360293076 32 25 30.3059887280548 -5.30598872805481 33 26 26.4144858541636 -0.414485854163608 34 25 23.9827070686671 1.01729293133295 35 17 22.7877636948086 -5.7877636948086 36 32 27.6758159974520 4.32418400254796 37 33 23.4995389030887 9.50046109691133 38 13 21.7534861717418 -8.75348617174182 39 32 27.6404821493837 4.35951785061625 40 25 25.683712797888 -0.68371279788799 41 29 26.8956778676034 2.10432213239659 42 22 21.8720733032086 0.127926696791388 43 18 17.0488177548642 0.951182245135789 44 17 21.5484183799576 -4.54841837995761 45 20 22.1796756279301 -2.17967562793008 46 15 20.3991668300307 -5.39916683003065 47 20 22.0775609807837 -2.07756098078366 48 33 28.0701944482734 4.92980555172664 49 29 22.0851703925517 6.91482960744828 50 23 26.4853856448168 -3.48538564481679 51 26 23.1934506631822 2.8065493368178 52 18 18.9015902045311 -0.901590204531086 53 20 18.7289878681547 1.27101213184529 54 11 11.6583714407624 -0.658371440762435 55 28 28.8854522053579 -0.885452205357942 56 26 23.3145842919689 2.68541570803107 57 22 22.3367181200141 -0.336718120014052 58 17 20.1741084337227 -3.17410843372266 59 12 15.5606877917405 -3.56068779174054 60 14 20.8579280898601 -6.85792808986007 61 17 20.7965977209629 -3.7965977209629 62 21 21.3383525221142 -0.338352522114174 63 19 22.9755667604299 -3.97556676042985 64 18 23.1554764764262 -5.1554764764262 65 10 17.9139477465805 -7.91394774658054 66 29 24.3728840836821 4.62711591631793 67 31 18.5179185907852 12.4820814092148 68 19 22.9494665531863 -3.94946655318628 69 9 20.0801867320795 -11.0801867320795 70 20 22.5334654844169 -2.53346548441691 71 28 17.6181658227022 10.3818341772978 72 19 18.1759070285753 0.824092971424678 73 30 23.1188757790198 6.88112422098022 74 29 27.0683251891763 1.93167481082375 75 26 21.4805642798103 4.51943572018973 76 23 19.6097888831676 3.39021111683237 77 13 22.8220434233118 -9.82204342331182 78 21 22.7260613039473 -1.7260613039473 79 19 21.6392875665233 -2.63928756652331 80 28 22.9992171903515 5.0007828096485 81 23 25.6990668127033 -2.69906681270327 82 18 13.9471529013589 4.05284709864114 83 21 20.7807147108777 0.219285289122339 84 20 21.9027736234381 -1.90277362343807 85 23 20.1771559680962 2.82284403190378 86 21 20.9459579328325 0.0540420671674767 87 21 21.975599675296 -0.975599675295992 88 15 23.0921517003760 -8.09215170037595 89 28 27.3394723176941 0.660527682305897 90 19 17.7860865841737 1.21391341582635 91 26 21.3666501297757 4.63334987022435 92 10 13.4607608633575 -3.46076086335755 93 16 17.2729916761398 -1.27299167613976 94 22 21.2510378474756 0.748962152524445 95 19 19.018595030268 -0.0185950302680159 96 31 28.9698430126146 2.03015698738542 97 31 25.34985126551 5.65014873449002 98 29 24.8985018896661 4.10149811033386 99 19 17.6401871178299 1.35981288217015 100 22 19.0823281117332 2.91767188826684 101 23 22.6053457170837 0.394654282916301 102 15 16.4060466583112 -1.40604665831122 103 20 21.5500254970352 -1.55002549703520 104 18 19.7596694968344 -1.75966949683442 105 23 22.3433931097275 0.656606890272511 106 25 21.0508631423674 3.9491368576326 107 21 16.7671956292196 4.23280437078044 108 24 19.6545300571371 4.34546994286288 109 25 25.4438763617897 -0.44387636178969 110 17 19.7412141857369 -2.74121418573691 111 13 14.7601678538999 -1.76016785389988 112 28 18.4845187133228 9.51548128667724 113 21 20.4453589568771 0.554641043122917 114 25 28.3572189470004 -3.35721894700041 115 9 21.1498833511096 -12.1498833511096 116 16 18.0827522300676 -2.08275223006756 117 19 21.3209179157288 -2.32091791572878 118 17 19.673934066056 -2.67393406605600 119 25 24.7407158286715 0.259284171328542 120 20 15.6533374465301 4.34666255346995 121 29 21.9155264192287 7.08447358077128 122 14 19.1804626463191 -5.18046264631912 123 22 27.1186267543289 -5.11862675432893 124 15 15.9469104978874 -0.946910497887429 125 19 25.678546178147 -6.678546178147 126 20 22.2272585817625 -2.22725858176251 127 15 17.8259316095974 -2.82593160959743 128 20 22.2072133638070 -2.20721336380696 129 18 20.6079859273857 -2.60798592738574 130 33 25.8550846766499 7.14491532335009 131 22 24.0916528697276 -2.09165286972755 132 16 16.7861339580460 -0.78613395804597 133 17 19.4333742348957 -2.43337423489572 134 16 15.4091483161622 0.590851683837825 135 21 17.3859551012459 3.61404489875412 136 26 27.8959994437931 -1.89599944379313 137 18 21.4066502846375 -3.40665028463755 138 18 23.2900346362305 -5.29003463623053 139 17 18.730425396788 -1.730425396788 140 22 25.0213323802660 -3.02133238026604 141 30 24.9822483174777 5.01775168252226 142 30 27.6150922047366 2.38490779526337 143 24 30.0112772165830 -6.01127721658304 144 21 22.3577310898782 -1.35773108987824 145 21 25.6723342155531 -4.67233421555315 146 29 27.7311117576891 1.26888824231090 147 31 23.5076142206065 7.49238577939346 148 20 19.2999966317396 0.700003368260428 149 16 14.3751323790397 1.62486762096026 150 22 19.2404678647110 2.75953213528904 151 20 20.7430797570986 -0.743079757098599 152 28 27.6094235131795 0.390576486820503 153 38 26.9318217601358 11.0681782398642 154 22 19.5691561027094 2.43084389729058 155 20 26.0395296572432 -6.03952965724324 156 17 18.4207011830354 -1.42070118303538 157 28 24.9024186870491 3.09758131295087 158 22 24.5994327781874 -2.59943277818742 159 31 26.5942778672080 4.40572213279196 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.104875860961637 0.209751721923273 0.895124139038363 11 0.34940967169086 0.69881934338172 0.65059032830914 12 0.749410771391836 0.501178457216328 0.250589228608164 13 0.743698316492561 0.512603367014878 0.256301683507439 14 0.722351872625248 0.555296254749504 0.277648127374752 15 0.708624255547002 0.582751488905997 0.291375744452998 16 0.629104121531489 0.741791756937022 0.370895878468511 17 0.563064841082566 0.873870317834868 0.436935158917434 18 0.529167620883208 0.941664758233584 0.470832379116792 19 0.470604239195674 0.941208478391349 0.529395760804326 20 0.498257092794899 0.996514185589797 0.501742907205101 21 0.430306539644020 0.860613079288041 0.56969346035598 22 0.414454498549501 0.828908997099001 0.585545501450499 23 0.373570729239994 0.747141458479988 0.626429270760006 24 0.534818444362889 0.930363111274222 0.465181555637111 25 0.485980238399543 0.971960476799085 0.514019761600457 26 0.494375830478694 0.988751660957388 0.505624169521306 27 0.426653481718109 0.853306963436218 0.573346518281891 28 0.369848668388682 0.739697336777363 0.630151331611319 29 0.307621284790943 0.615242569581887 0.692378715209057 30 0.263798869150315 0.52759773830063 0.736201130849685 31 0.263562075758299 0.527124151516599 0.7364379242417 32 0.364737702050325 0.72947540410065 0.635262297949675 33 0.313265314006366 0.626530628012732 0.686734685993634 34 0.286225679188757 0.572451358377513 0.713774320811243 35 0.312971664791115 0.625943329582229 0.687028335208886 36 0.283409702397035 0.566819404794071 0.716590297602965 37 0.407163716244752 0.814327432489503 0.592836283755248 38 0.678860573505525 0.642278852988951 0.321139426494475 39 0.670052271820665 0.65989545635867 0.329947728179335 40 0.628306208479315 0.74338758304137 0.371693791520685 41 0.597946309653346 0.804107380693309 0.402053690346654 42 0.545187415130207 0.909625169739585 0.454812584869793 43 0.49234703989226 0.98469407978452 0.50765296010774 44 0.475856924868257 0.951713849736513 0.524143075131743 45 0.465883270631937 0.931766541263873 0.534116729368063 46 0.519311810600384 0.96137637879923 0.480688189399615 47 0.485117083878377 0.970234167756754 0.514882916121623 48 0.469639010678557 0.939278021357114 0.530360989321443 49 0.519076882455218 0.961846235089563 0.480923117544782 50 0.494131212831006 0.988262425662012 0.505868787168994 51 0.461936166922320 0.923872333844641 0.53806383307768 52 0.41593350368167 0.83186700736334 0.58406649631833 53 0.371579913529704 0.743159827059408 0.628420086470296 54 0.335361470464077 0.670722940928154 0.664638529535923 55 0.299520345130029 0.599040690260058 0.700479654869971 56 0.271327781346132 0.542655562692264 0.728672218653868 57 0.23334897880275 0.4666979576055 0.76665102119725 58 0.212963418589005 0.425926837178011 0.787036581410995 59 0.202408814364788 0.404817628729576 0.797591185635212 60 0.267466772410046 0.534933544820093 0.732533227589954 61 0.263323059969605 0.52664611993921 0.736676940030395 62 0.225778013754529 0.451556027509057 0.774221986245471 63 0.215418492658594 0.430836985317189 0.784581507341406 64 0.259421545579777 0.518843091159555 0.740578454420223 65 0.34338743687107 0.68677487374214 0.65661256312893 66 0.341357755106034 0.682715510212068 0.658642244893966 67 0.655826530152335 0.68834693969533 0.344173469847665 68 0.65257593744701 0.69484812510598 0.34742406255299 69 0.843941766636264 0.312116466727472 0.156058233363736 70 0.82756140350472 0.344877192990561 0.172438596495281 71 0.925023739641174 0.149952520717651 0.0749762603588256 72 0.907326519981929 0.185346960036142 0.0926734800180708 73 0.9300845682394 0.139830863521200 0.0699154317606001 74 0.916700053336646 0.166599893326708 0.0832999466633541 75 0.918680587702693 0.162638824594613 0.0813194122973067 76 0.911663556907444 0.176672886185113 0.0883364430925563 77 0.965065523527598 0.0698689529448046 0.0349344764724023 78 0.956809688200234 0.0863806235995317 0.0431903117997659 79 0.949095686592199 0.101808626815602 0.0509043134078010 80 0.95118863831422 0.0976227233715599 0.0488113616857799 81 0.942927770792397 0.114144458415206 0.0570722292076029 82 0.941349407235624 0.117301185528753 0.0586505927643763 83 0.926267798024247 0.147464403951506 0.073732201975753 84 0.912227157981199 0.175545684037603 0.0877728420188013 85 0.901100563687248 0.197798872625504 0.0988994363127519 86 0.88235945831541 0.235281083369180 0.117640541684590 87 0.859112522168217 0.281774955663567 0.140887477831783 88 0.912439621615438 0.175120756769125 0.0875603783845623 89 0.894906191923514 0.210187616152971 0.105093808076486 90 0.872530684289875 0.254938631420250 0.127469315710125 91 0.871841376030771 0.256317247938458 0.128158623969229 92 0.862483317626455 0.27503336474709 0.137516682373545 93 0.83949109650125 0.321017806997499 0.160508903498749 94 0.809692761067682 0.380614477864636 0.190307238932318 95 0.775218955235317 0.449562089529365 0.224781044764683 96 0.742908564069756 0.514182871860489 0.257091435930244 97 0.762319264809298 0.475361470381405 0.237680735190702 98 0.763318814952038 0.473362370095925 0.236681185047962 99 0.726418510296056 0.547162979407888 0.273581489703944 100 0.701652233489789 0.596695533020422 0.298347766510211 101 0.660585645829766 0.678828708340468 0.339414354170234 102 0.620911834206296 0.758176331587407 0.379088165793704 103 0.580272096393637 0.839455807212725 0.419727903606363 104 0.537553618132743 0.924892763734513 0.462446381867257 105 0.494339969878799 0.988679939757598 0.505660030121201 106 0.475200765203759 0.950401530407518 0.524799234796241 107 0.473270375913097 0.946540751826194 0.526729624086903 108 0.495396829601417 0.990793659202834 0.504603170398583 109 0.450533881889866 0.901067763779732 0.549466118110134 110 0.416674277259048 0.833348554518095 0.583325722740952 111 0.373341416135789 0.746682832271578 0.626658583864211 112 0.674000732538492 0.651998534923016 0.325999267461508 113 0.691898795945881 0.616202408108237 0.308101204054119 114 0.666249568411289 0.667500863177422 0.333750431588711 115 0.838279721488251 0.323440557023497 0.161720278511749 116 0.809087812022828 0.381824375954344 0.190912187977172 117 0.78714791797743 0.425704164045141 0.212852082022570 118 0.755293697377946 0.489412605244108 0.244706302622054 119 0.720368927327897 0.559262145344207 0.279631072672103 120 0.753370661005152 0.493258677989696 0.246629338994848 121 0.812879244176 0.374241511648001 0.187120755824000 122 0.79518862017221 0.409622759655579 0.204811379827789 123 0.778472181953462 0.443055636093077 0.221527818046538 124 0.73250509500467 0.53498980999066 0.26749490499533 125 0.736054515127971 0.527890969744058 0.263945484872029 126 0.701974856857941 0.596050286284118 0.298025143142059 127 0.69545539882499 0.60908920235002 0.30454460117501 128 0.65874521595997 0.682509568080061 0.341254784040031 129 0.625226489942138 0.749547020115723 0.374773510057861 130 0.701301493824572 0.597397012350855 0.298698506175428 131 0.645443579606505 0.70911284078699 0.354556420393495 132 0.58019535200714 0.83960929598572 0.41980464799286 133 0.577698633943808 0.844602732112385 0.422301366056192 134 0.514928430229753 0.970143139540494 0.485071569770247 135 0.489482972573236 0.978965945146472 0.510517027426764 136 0.450268063486699 0.900536126973399 0.549731936513301 137 0.439752964226314 0.879505928452627 0.560247035773686 138 0.480192110179667 0.960384220359334 0.519807889820333 139 0.407558378835399 0.815116757670798 0.592441621164601 140 0.333408898503180 0.666817797006359 0.66659110149682 141 0.434247790121972 0.868495580243944 0.565752209878028 142 0.378465786854592 0.756931573709185 0.621534213145408 143 0.304279193041291 0.608558386082582 0.695720806958709 144 0.228441287501421 0.456882575002841 0.771558712498579 145 0.280438595658607 0.560877191317214 0.719561404341393 146 0.194202304924582 0.388404609849165 0.805797695075418 147 0.240947244665844 0.481894489331687 0.759052755334156 148 0.149146966389850 0.298293932779699 0.85085303361015 149 0.0797275456702617 0.159455091340523 0.920272454329738 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 3 0.0214285714285714 OK

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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