p_Stress_MR2v2

*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: Sat, 04 Dec 2010 14:03:11 +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/04/t1291471320iyqt3g67iw1h1yv.htm/, Retrieved Sat, 04 Dec 2010 15:02:00 +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/04/t1291471320iyqt3g67iw1h1yv.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 «

23 10 53 7 6 7 15 11 12 2 4 2 2 3,4 21 6 86 4 6 5 15 8 11 4 3 1 2 4 21 13 66 6 5 7 14 12 14 7 5 4 3,666666667 3,2 21 12 67 5 4 3 10 10 12 3 3 1 2,333333333 3,2 24 8 76 4 4 7 10 7 21 7 6 5 4 2,6 22 6 78 3 6 7 12 6 12 2 5 1 2,666666667 3,2 21 10 53 5 7 7 18 8 22 7 6 1 2,333333333 3,8 22 10 80 6 5 1 12 16 11 2 6 1 3,666666667 3,6 21 9 74 5 4 4 14 8 10 1 5 1 2,666666667 3,6 20 9 76 6 6 5 18 16 13 2 5 1 3 4 22 7 79 7 1 6 9 7 10 6 3 2 3 3,4 21 5 54 6 4 4 11 11 8 1 5 1 2 2,6 21 14 67 7 6 7 11 16 15 1 7 3 3 4,4 23 6 87 6 6 6 17 16 10 1 5 1 1,666666667 4 22 10 58 4 5 2 8 12 14 2 5 1 3 3,8 23 10 75 6 3 2 16 13 14 2 3 1 1,333333333 3,6 22 7 88 4 7 6 21 19 11 2 5 1 3 3,8 24 10 64 5 2 7 24 7 10 1 6 1 2 3,6 23 8 57 3 5 5 21 8 13 7 5 2 2,666666667 3,8 21 6 66 3 5 2 14 12 7 1 2 4 4 3,6 23 10 54 4 3 7 7 13 12 2 5 1 2,333333333 4 23 12 56 5 5 4 18 11 14 4 4 2 2,666666667 2,8 21 7 86 3 5 5 18 8 11 2 6 1 1 5 20 15 80 7 6 5 13 16 9 1 3 2 3 4,4 32 8 76 7 4 5 11 15 11 1 5 3 2,333333333 3,2 22 10 69 4 4 3 13 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 Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 8.27819374754516 -0.0998455093402849AGE[t] -0.0296434868412883BelInSprt[t] + 0.18758282353857KunnenRekRel[t] -0.147444084185755ExtraCurAct[t] -0.0155460018121020VerandVorigJr[t] -0.049878292885695VerwOuders[t] + 0.0277617502408969KenStudenten[t] + 0.40099080714446Depressie[t] -0.195498581354292Slaapgebrek[t] + 0.22382136308123Toekomstzorgen[t] + 0.0934682172168758Rookgedrag[t] -0.131850122994891MateAlcCon[t] + 0.236074957597958MateGEzGevarEten[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) 8.27819374754516 2.598816 3.1854 0.001816 0.000908 AGE -0.0998455093402849 0.071276 -1.4008 0.163682 0.081841 BelInSprt -0.0296434868412883 0.018261 -1.6234 0.106974 0.053487 KunnenRekRel 0.18758282353857 0.11226 1.671 0.09717 0.048585 ExtraCurAct -0.147444084185755 0.133048 -1.1082 0.269853 0.134926 VerandVorigJr -0.0155460018121020 0.102639 -0.1515 0.879849 0.439925 VerwOuders -0.049878292885695 0.05016 -0.9944 0.32191 0.160955 KenStudenten 0.0277617502408969 0.050472 0.55 0.583248 0.291624 Depressie 0.40099080714446 0.065214 6.1488 0 0 Slaapgebrek -0.195498581354292 0.097284 -2.0096 0.04658 0.02329 Toekomstzorgen 0.22382136308123 0.118947 1.8817 0.06215 0.031075 Rookgedrag 0.0934682172168758 0.186832 0.5003 0.617737 0.308868 MateAlcCon -0.131850122994891 0.251493 -0.5243 0.600998 0.300499 MateGEzGevarEten 0.236074957597958 0.338943 0.6965 0.487377 0.243688 Multiple Linear Regression - Regression Statistics Multiple R 0.628342873454682 R-squared 0.394814766621287 Adjusted R-squared 0.333350641356261 F-TEST (value) 6.42349931637188 F-TEST (DF numerator) 13 F-TEST (DF denominator) 128 p-value 2.96510604957945e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.94846738382742 Sum Squared Residuals 485.955218667425 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.3295093560924 -0.329509356092414 2 6 7.96839101570389 -1.96839101570389 3 13 10.1496176580874 2.85038234191258 4 12 9.71377460759896 2.28622539240104 5 8 12.3252593338968 -4.3252593338968 6 6 8.94400060000302 -2.94400060000302 7 10 13.2107401298339 -3.21074012983387 8 10 9.75121011038455 0.248789889615448 9 9 9.32282574240838 -0.322825742408377 10 9 10.3210675439694 -1.32106754396936 11 7 8.65996596086844 -1.65996596086844 12 5 9.38604194230615 -4.38604194230615 13 14 12.7201416255935 1.27985837440649 14 6 8.89811127563807 -2.89811127563807 15 10 11.2153874744951 -1.21538747449513 16 10 10.6352843988103 -0.635284398810316 17 7 8.31195271637543 -1.31195271637543 18 10 9.35315101175953 0.646848988240468 19 8 8.91043435879894 -0.910434358798939 20 6 7.4096708874879 -1.40967088748789 21 10 10.8620475742288 -0.862047574228807 22 12 10.0912998544374 1.90870014556261 23 7 9.20900373023911 -2.20900373023911 24 15 8.9712587114413 6.0287412885587 25 8 9.40627224121629 -1.40627224121629 26 10 10.2402977039064 -0.240297703906412 27 13 10.3572204348816 2.64277956511844 28 8 8.56121615871997 -0.56121615871997 29 11 11.3994650494006 -0.399465049400585 30 7 9.60994731360776 -2.60994731360776 31 9 8.48754123602544 0.512458763974561 32 10 9.48225165960313 0.517748340396871 33 8 8.23901338905743 -0.239013389057431 34 15 13.2158849021413 1.78411509785868 35 9 10.3341923065893 -1.33419230658930 36 7 8.04282624466055 -1.04282624466055 37 11 9.69221059760005 1.30778940239995 38 9 7.93629277106504 1.06370722893496 39 8 9.65070677964992 -1.65070677964992 40 8 7.74267196220176 0.257328037798243 41 12 8.60669753309623 3.39330246690377 42 13 9.74486473227348 3.25513526772653 43 9 9.30831986535724 -0.308319865357237 44 11 9.36859853439705 1.63140146560295 45 8 8.69165359250469 -0.691653592504686 46 10 10.9096033768969 -0.909603376896922 47 13 12.7203000177992 0.279699982200848 48 12 10.8977455023625 1.1022544976375 49 12 10.1035396048765 1.89646039512346 50 9 8.9055701896644 0.0944298103356004 51 8 10.0563203608934 -2.05632036089338 52 9 8.55015974227356 0.449840257726438 53 12 8.46633762629441 3.53366237370559 54 12 11.4417067043951 0.558293295604887 55 16 14.3390890214992 1.66091097850075 56 11 8.74041727038935 2.25958272961065 57 13 9.6548114065461 3.34518859345391 58 10 10.7282845843357 -0.728284584335746 59 9 10.9177623409140 -1.91776234091404 60 14 10.1798523601497 3.82014763985035 61 13 11.7940372293593 1.20596277064073 62 12 10.2482718133661 1.75172818663389 63 9 10.9401867149500 -1.94018671494997 64 9 10.4289121158514 -1.42891211585138 65 10 11.1293997846369 -1.12939978463687 66 8 10.3355384310652 -2.33553843106519 67 9 10.3539654331024 -1.35396543310244 68 9 9.1596020957055 -0.159602095705508 69 11 8.60268310441434 2.39731689558566 70 7 9.1983165082478 -2.1983165082478 71 11 11.4247175610532 -0.424717561053238 72 9 9.4664149141439 -0.466414914143892 73 11 8.83905663447442 2.16094336552558 74 9 9.1680379298611 -0.168037929861102 75 8 10.1334861404498 -2.13348614044980 76 9 8.10037000751395 0.899629992486047 77 8 9.53261001659409 -1.53261001659409 78 9 9.46000198095027 -0.460001980950268 79 10 9.79787029046769 0.202129709532314 80 9 10.0102282385258 -1.01022823852581 81 17 13.8818497956681 3.11815020433193 82 7 9.60246315239215 -2.60246315239215 83 11 11.1791700963881 -0.179170096388071 84 9 10.0473574021618 -1.04735740216184 85 10 9.92043596997106 0.0795640300289396 86 11 8.42467310218565 2.57532689781435 87 8 8.49119790548052 -0.491197905480517 88 12 12.0506247904276 -0.0506247904276325 89 10 10.0625426656147 -0.0625426656147502 90 7 9.04301720231648 -2.04301720231648 91 9 8.67492075883795 0.325079241162049 92 7 8.25373171181212 -1.25373171181212 93 12 10.4883743440261 1.51162565597386 94 8 9.18993403148611 -1.18993403148611 95 13 10.3383127327978 2.66168726720224 96 9 11.1635887738345 -2.16358877383446 97 15 12.7790229904009 2.22097700959914 98 8 9.10329991550478 -1.10329991550478 99 14 11.9342452525959 2.06575474740409 100 14 13.7674891901414 0.232510809858569 101 9 10.3858399157176 -1.38583991571757 102 13 11.4427228673220 1.55727713267797 103 11 9.29096071526047 1.70903928473953 104 10 11.7568778947816 -1.75687789478165 105 6 9.91527672190481 -3.91527672190481 106 8 8.35810579320368 -0.358105793203678 107 10 11.4763057351964 -1.47630573519640 108 10 8.05715144978085 1.94284855021915 109 10 9.31400633157394 0.685993668426065 110 12 12.1091247890297 -0.109124789029656 111 10 9.472197814322 0.527802185677994 112 9 9.19204743917179 -0.192047439171790 113 9 7.42083099737023 1.57916900262977 114 11 9.22557062350672 1.77442937649328 115 7 8.17452156666495 -1.17452156666495 116 7 8.92838174917736 -1.92838174917736 117 5 7.84981479877876 -2.84981479877876 118 9 8.9847478498587 0.0152521501412979 119 11 11.7714294450356 -0.771429445035647 120 15 12.0444546650933 2.95554533490672 121 9 7.90491245758554 1.09508754241446 122 9 9.4405031891988 -0.440503189198792 123 8 9.21943442324335 -1.21943442324335 124 13 15.4242750118656 -2.42427501186561 125 10 10.3885123000406 -0.388512300040590 126 13 11.3430688202836 1.65693117971635 127 9 7.86614802699764 1.13385197300236 128 11 9.3777675419687 1.62223245803131 129 8 10.3226206044035 -2.32262060440353 130 10 9.25239454961805 0.747605450381946 131 9 8.7435978160912 0.256402183908801 132 8 8.61205217478287 -0.612052174782866 133 8 7.90585777296854 0.0941422270314624 134 13 10.646896334473 2.35310366552699 135 11 10.9458367884160 0.0541632115840376 136 8 9.40627224121629 -1.40627224121629 137 12 9.8354212979658 2.16457870203421 138 15 11.6711137192106 3.32888628078939 139 11 11.1791700963881 -0.179170096388071 140 10 10.5120409386982 -0.512040938698177 141 5 7.84981479877876 -2.84981479877876 142 11 7.06216846657392 3.93783153342608 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.933834052421835 0.132331895156329 0.0661659475781645 18 0.920517753277835 0.15896449344433 0.079482246722165 19 0.892283395349944 0.215433209300111 0.107716604650056 20 0.963647445411644 0.0727051091767124 0.0363525545883562 21 0.937459965684402 0.125080068631197 0.0625400343155983 22 0.948922816842576 0.102154366314847 0.0510771831574237 23 0.929687337434221 0.140625325131557 0.0703126625657785 24 0.984239826511808 0.0315203469763838 0.0157601734881919 25 0.974890438713772 0.0502191225724551 0.0251095612862276 26 0.96238197988482 0.0752360402303585 0.0376180201151792 27 0.982293611683097 0.0354127766338054 0.0177063883169027 28 0.981753910825299 0.0364921783494022 0.0182460891747011 29 0.972515832563125 0.0549683348737501 0.0274841674368751 30 0.975122563735608 0.0497548725287843 0.0248774362643922 31 0.975952616039736 0.0480947679205281 0.0240473839602641 32 0.964639254584574 0.0707214908308523 0.0353607454154261 33 0.949057587456819 0.101884825086363 0.0509424125431813 34 0.968799432307 0.0624011353860016 0.0312005676930008 35 0.959630822925478 0.080738354149045 0.0403691770745225 36 0.94856354520713 0.102872909585739 0.0514364547928693 37 0.940581696771045 0.118836606457910 0.0594183032289549 38 0.922323058022739 0.155353883954522 0.0776769419772612 39 0.908883365286681 0.182233269426637 0.0911166347133187 40 0.883645835345603 0.232708329308794 0.116354164654397 41 0.938183437360645 0.123633125278711 0.0618165626393554 42 0.956189571084993 0.0876208578300139 0.0438104289150069 43 0.942335193332611 0.115329613334777 0.0576648066673887 44 0.932917587550318 0.134164824899365 0.0670824124496825 45 0.914208113557884 0.171583772884232 0.0857918864421161 46 0.896467318456359 0.207065363087282 0.103532681543641 47 0.875378621946992 0.249242756106016 0.124621378053008 48 0.850933290322179 0.298133419355642 0.149066709677821 49 0.850577805280351 0.298844389439298 0.149422194719649 50 0.815680925158068 0.368638149683863 0.184319074841932 51 0.80711511267264 0.385769774654719 0.192884887327359 52 0.771337405362938 0.457325189274124 0.228662594637062 53 0.849763807935124 0.300472384129753 0.150236192064876 54 0.818929936730582 0.362140126538836 0.181070063269418 55 0.821689272902129 0.356621454195743 0.178310727097871 56 0.870213915580808 0.259572168838383 0.129786084419192 57 0.905470123386772 0.189059753226455 0.0945298766132277 58 0.882407498965858 0.235185002068284 0.117592501034142 59 0.874427073576578 0.251145852846844 0.125572926423422 60 0.942749470894081 0.114501058211837 0.0572505291059186 61 0.931987130107108 0.136025739785784 0.0680128698928922 62 0.930199087386483 0.139601825227034 0.0698009126135168 63 0.938309751005065 0.123380497989871 0.0616902489949353 64 0.931194230666293 0.137611538667414 0.0688057693337072 65 0.932697001109348 0.134605997781305 0.0673029988906523 66 0.934409337405983 0.131181325188033 0.0655906625940166 67 0.927449140076873 0.145101719846255 0.0725508599231275 68 0.909799680141245 0.180400639717510 0.0902003198587548 69 0.921187576544523 0.157624846910954 0.0788124234554771 70 0.932684759057839 0.134630481884322 0.067315240942161 71 0.915197161229975 0.169605677540051 0.0848028387700255 72 0.894924776149672 0.210150447700657 0.105075223850328 73 0.908803839361163 0.182392321277673 0.0911961606388365 74 0.885304160559605 0.229391678880791 0.114695839440395 75 0.884306604825159 0.231386790349683 0.115693395174841 76 0.868468877005239 0.263062245989523 0.131531122994761 77 0.858636818785114 0.282726362429772 0.141363181214886 78 0.839900580365214 0.320198839269572 0.160099419634786 79 0.805706368541787 0.388587262916426 0.194293631458213 80 0.777037824579505 0.44592435084099 0.222962175420495 81 0.829755416267613 0.340489167464774 0.170244583732387 82 0.847234890587456 0.305530218825088 0.152765109412544 83 0.81745259264606 0.365094814707882 0.182547407353941 84 0.801985642285204 0.396028715429591 0.198014357714796 85 0.763174320684758 0.473651358630484 0.236825679315242 86 0.85860344072597 0.282793118548059 0.141396559274030 87 0.828045155648031 0.343909688703938 0.171954844351969 88 0.790512756247176 0.418974487505649 0.209487243752824 89 0.74791946449341 0.50416107101318 0.25208053550659 90 0.760090821802653 0.479818356394694 0.239909178197347 91 0.71735977228691 0.565280455426179 0.282640227713090 92 0.697780805862018 0.604438388275964 0.302219194137982 93 0.689957979280061 0.620084041439878 0.310042020719939 94 0.656475831466134 0.687048337067733 0.343524168533866 95 0.695837410075005 0.60832517984999 0.304162589924995 96 0.679676778283778 0.640646443432443 0.320323221716222 97 0.66629836574207 0.667403268515861 0.333701634257931 98 0.624410194966975 0.75117961006605 0.375589805033025 99 0.607413033410397 0.785173933179206 0.392586966589603 100 0.61100698067364 0.77798603865272 0.38899301932636 101 0.631964318291254 0.736071363417491 0.368035681708746 102 0.699559262518544 0.600881474962911 0.300440737481456 103 0.672235216019586 0.655529567960828 0.327764783980414 104 0.662136989288882 0.675726021422236 0.337863010711118 105 0.793808571698775 0.41238285660245 0.206191428301225 106 0.798823041635601 0.402353916728797 0.201176958364399 107 0.805297907779392 0.389404184441215 0.194702092220608 108 0.777178753332452 0.445642493335096 0.222821246667548 109 0.729090643581825 0.54181871283635 0.270909356418175 110 0.687467738603257 0.625064522793486 0.312532261396743 111 0.654776937726287 0.690446124547427 0.345223062273714 112 0.58151679608508 0.836966407829839 0.418483203914920 113 0.532784391913662 0.934431216172677 0.467215608086338 114 0.488667052833257 0.977334105666515 0.511332947166743 115 0.412475535357217 0.824951070714434 0.587524464642783 116 0.342539535881926 0.685079071763852 0.657460464118074 117 0.368551843589464 0.737103687178927 0.631448156410536 118 0.286216216436022 0.572432432872045 0.713783783563978 119 0.216908829047611 0.433817658095221 0.78309117095239 120 0.191875690032194 0.383751380064388 0.808124309967806 121 0.131337277632745 0.262674555265490 0.868662722367255 122 0.0837263028893059 0.167452605778612 0.916273697110694 123 0.0608924609342449 0.121784921868490 0.939107539065755 124 0.127149181447296 0.254298362894592 0.872850818552704 125 0.196291479606036 0.392582959212072 0.803708520393964 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 0.0458715596330275 OK 10% type I error level 13 0.119266055045872 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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