p_Stress_MR3v3

*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 14:09:41 +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/t1291471684wk59fj0bhz9qx5w.htm/, Retrieved Sat, 04 Dec 2010 15:08:14 +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/t1291471684wk59fj0bhz9qx5w.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 12 2 4 21 6 86 4 6 11 4 3 21 13 66 6 5 14 7 5 21 12 67 5 4 12 3 3 24 8 76 4 4 21 7 6 22 6 78 3 6 12 2 5 21 10 53 5 7 22 7 6 22 10 80 6 5 11 2 6 21 9 74 5 4 10 1 5 20 9 76 6 6 13 2 5 22 7 79 7 1 10 6 3 21 5 54 6 4 8 1 5 21 14 67 7 6 15 1 7 23 6 87 6 6 10 1 5 22 10 58 4 5 14 2 5 23 10 75 6 3 14 2 3 22 7 88 4 7 11 2 5 24 10 64 5 2 10 1 6 23 8 57 3 5 13 7 5 21 6 66 3 5 7 1 2 23 10 54 4 3 12 2 5 23 12 56 5 5 14 4 4 21 7 86 3 5 11 2 6 20 15 80 7 6 9 1 3 32 8 76 7 4 11 1 5 22 10 69 4 4 15 5 4 21 13 67 4 4 13 2 5 21 8 80 5 2 9 1 2 21 11 54 6 3 15 3 2 22 7 71 5 6 10 1 5 21 9 84 4 6 11 2 2 21 10 74 6 5 13 5 2 21 8 71 5 3 8 2 2 22 15 63 5 3 20 6 5 21 9 71 6 4 12 4 5 21 7 76 2 4 10 1 1 21 11 69 6 5 10 3 5 21 9 74 7 3 9 6 2 23 8 75 5 5 14 7 6 21 8 54 5 4 8 4 1 23 12 69 5 3 11 5 3 23 13 68 6 3 13 3 2 21 9 75 4 4 11 2 5 21 11 75 6 6 11 2 3 20 8 72 5 5 10 2 4 21 10 67 5 3 14 2 3 21 13 63 3 4 18 1 6 22 12 62 4 2 14 2 4 21 12 63 4 3 11 1 5 21 9 76 2 5 12 2 2 22 8 7 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 8.87667015706705 -0.104269855315633AGE[t] -0.0321581842806803BelInSprt[t] + 0.206721936465705KunnenRekRel[t] -0.130417643958898ExtraCurAct[t] + 0.394290032329484Depressie[t] -0.21057047824519Slaapgebrek[t] + 0.199757036190737ToekZorgen[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) 8.87667015706705 2.124724 4.1778 5.3e-05 2.6e-05 AGE -0.104269855315633 0.066665 -1.5641 0.120156 0.060078 BelInSprt -0.0321581842806803 0.016627 -1.934 0.055215 0.027608 KunnenRekRel 0.206721936465705 0.108208 1.9104 0.058216 0.029108 ExtraCurAct -0.130417643958898 0.122977 -1.0605 0.290825 0.145412 Depressie 0.394290032329484 0.061975 6.362 0 0 Slaapgebrek -0.21057047824519 0.092495 -2.2766 0.024398 0.012199 ToekZorgen 0.199757036190737 0.112805 1.7708 0.078865 0.039432 Multiple Linear Regression - Regression Statistics Multiple R 0.62053624959846 R-squared 0.385065237065722 Adjusted R-squared 0.352941779300498 F-TEST (value) 11.9870419890660 F-TEST (DF numerator) 7 F-TEST (DF denominator) 134 p-value 8.17634848715443e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.91962362912285 Sum Squared Residuals 493.783953583226 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.5479949856644 -0.547994985664375 2 6 8.05996078062547 -2.05996078062547 3 13 10.1976587177637 2.80234128223626 4 12 9.74338401691657 2.25661598308343 5 8 12.2400283425347 -4.24002834253465 6 6 9.22117952429092 -3.22117952429092 7 10 13.5022351838557 -3.50223518385569 8 10 9.71291361294682 0.287086387053182 9 9 9.55035169116469 -0.550351691164693 10 9 10.5084914452101 -1.50849144521014 11 7 8.63762125564634 -1.63762125564634 12 5 9.61165724858504 -4.61165724858504 13 14 12.2990318001720 1.70096819982803 14 6 8.8696422334325 -2.86964223343249 15 10 10.9900628549881 -0.990062854988088 16 10 10.6138689553686 -0.613868955368622 17 7 8.58161194166143 -1.58161194166143 18 10 10.0197162921331 -0.0197162921331297 19 8 9.264086823932 -1.26408682393200 20 6 7.48161444295916 -1.48161444295916 21 10 10.486680960054 -0.486680960054003 22 12 10.5359333120184 1.46406668798160 23 7 9.00406855318125 -2.00406855318125 24 15 8.8204769210989 6.1795230789011 25 8 9.14680081939226 -1.14680081939227 26 10 10.3295620332627 -0.329562033262679 27 13 10.5410366634070 2.45896333659299 28 8 8.62467673249671 -0.624676732496711 29 11 11.4816930537877 -0.481693053787729 30 7 9.2817211007733 -2.28172110077331 31 9 8.34566106948647 0.654338930513528 32 10 9.36797305910698 0.63202694089302 33 8 8.17882223648926 -0.178822236489261 34 15 12.8202874389643 2.17971256103567 35 9 10.0104168103958 -1.01041681039584 36 7 8.06684136844327 -1.06684136844327 37 11 9.36630594858452 1.63369405141548 38 9 8.04779967592735 0.952200324072646 39 8 9.69273044833138 -1.69273044833138 40 8 7.97419573262081 0.0258042673791896 41 12 8.78551459286297 3.21448540713703 42 13 10.0343586985680 2.96564130143203 43 9 9.4951911245026 -0.495191124502602 44 11 9.24828563713474 1.75171436286526 45 8 9.17819275664686 -1.17819275664686 46 10 10.8729522037796 -0.872952203779624 47 13 12.8447251401474 0.155274859852624 48 12 11.0529260135513 0.947073986448677 49 12 10.2220774580749 1.77792254192515 50 9 8.71419034708888 0.285809652911115 51 8 9.87451993770201 -1.87451993770201 52 9 8.71946118364165 0.280538816358349 53 12 8.2364440347675 3.76355596523249 54 12 11.4118985488808 0.588101451119181 55 16 14.0783397912971 1.92166020870292 56 11 9.15988129531041 1.84011870468959 57 13 9.6654853412732 3.33451465872681 58 10 10.5489186948252 -0.548918694825226 59 9 10.4413195102797 -1.44131951027968 60 14 9.7262171919217 4.27378280807829 61 13 11.5556047729678 1.44439522703222 62 12 10.4805069215294 1.51949307847062 63 9 10.8833562372704 -1.88335623727041 64 9 10.2730354588327 -1.27303545883274 65 10 10.8227666521497 -0.822766652149711 66 8 10.5638390315842 -2.56383903158416 67 9 9.87573070069794 -0.875730700697939 68 9 8.69140291026038 0.308597089739623 69 11 8.44114825036158 2.55885174963842 70 7 9.25953412174369 -2.25953412174369 71 11 11.6239567932327 -0.623956793232679 72 9 9.13723232052656 -0.137232320526557 73 11 8.71280089499812 2.28719910500188 74 9 9.26228853206425 -0.262288532064248 75 8 10.2095835641493 -2.20958356414931 76 9 8.3993947154622 0.60060528453779 77 8 9.38634052140954 -1.38634052140954 78 9 9.33600656330263 -0.336006563302625 79 10 9.83730586242147 0.162694137578529 80 9 9.87344367472564 -0.873443674725642 81 17 13.8257935268737 3.17420647312629 82 7 9.59162713907362 -2.59162713907362 83 11 10.7432192827160 0.256780717284018 84 9 9.84857867378243 -0.848578673782434 85 10 9.96109808453157 0.0389019154684335 86 11 9.00622724279712 1.99377275720288 87 8 8.8306162754021 -0.830616275402092 88 12 12.0660693644905 -0.0660693644905469 89 10 10.0488787114804 -0.0488787114804262 90 7 9.17878927513459 -2.17878927513459 91 9 8.66044616107043 0.339553838929566 92 7 8.51333576566367 -1.51333576566367 93 12 10.6645587207013 1.33544127929872 94 8 9.4788554994894 -1.4788554994894 95 13 10.2896972950558 2.71030270494425 96 9 11.1698974910867 -2.16989749108668 97 15 12.7728389547080 2.22716104529196 98 8 8.77335961225478 -0.773359612254775 99 14 11.7738209123581 2.22617908764191 100 14 14.1238006273393 -0.123800627339295 101 9 10.3525326179901 -1.35253261799014 102 13 11.9544286141118 1.04557138588818 103 11 9.18519330541975 1.81480669458025 104 10 11.5293391406500 -1.52933914064999 105 6 10.2707668454828 -4.27076684548282 106 8 8.12892006159349 -0.128920061593489 107 10 11.2141827729859 -1.21418277298591 108 10 7.89017219186783 2.10982780813217 109 10 9.2884626573801 0.71153734261991 110 12 12.0663643431552 -0.0663643431552397 111 10 9.60480926477124 0.395190735228758 112 9 9.18495391336668 -0.18495391336668 113 9 7.20319343097818 1.79680656902182 114 11 9.1521393673552 1.84786063264479 115 7 8.12378793433091 -1.12378793433091 116 7 8.71254454153847 -1.71254454153847 117 5 8.11263330493375 -3.11263330493375 118 9 9.21475404914818 -0.214754049148183 119 11 11.7333379889673 -0.733337988967322 120 15 11.8885556939456 3.11144430605437 121 9 7.70902940854731 1.29097059145269 122 9 9.19195058858166 -0.191950588581656 123 8 9.26922452703493 -1.26922452703493 124 13 15.7760295793906 -2.77602957939064 125 10 10.4304302239581 -0.430430223958086 126 13 11.5777733393750 1.42222666062498 127 9 8.2451463814899 0.754853618510096 128 11 9.6071759545737 1.39282404542630 129 8 10.2466742936676 -2.24667429366758 130 10 9.15400539969255 0.845994600307451 131 9 8.867127215165 0.132872784834995 132 8 8.49789270747833 -0.497892707478333 133 8 8.06017966548954 -0.0601796654895347 134 13 10.3996804052793 2.60031959472070 135 11 11.0299940371696 -0.0299940371696264 136 8 9.14680081939226 -1.14680081939227 137 12 9.95414160508582 2.04585839491418 138 15 11.7675218441812 3.23247815581881 139 11 10.7432192827160 0.256780717284018 140 10 10.2291116719602 -0.229111671960160 141 5 8.11263330493375 -3.11263330493375 142 11 7.13870299923505 3.86129700076495 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.90032525115696 0.199349497686079 0.0996747488430396 12 0.988425757105336 0.023148485789329 0.0115742428946645 13 0.984583407781313 0.0308331844373745 0.0154165922186873 14 0.982916272616521 0.0341674547669572 0.0170837273834786 15 0.978043513126984 0.0439129737460314 0.0219564868730157 16 0.961995595292821 0.0760088094143574 0.0380044047071787 17 0.94323158813339 0.113536823733219 0.0567684118666096 18 0.945390637740578 0.109218724518845 0.0546093622594225 19 0.930464653186187 0.139070693627627 0.0695353468138134 20 0.900933069854361 0.198133860291278 0.0990669301456388 21 0.86878193504315 0.262436129913702 0.131218064956851 22 0.882828966318572 0.234342067362855 0.117171033681428 23 0.851732846206896 0.296534307586209 0.148267153793104 24 0.983331011068676 0.0333379778626472 0.0166689889313236 25 0.976767470785615 0.0464650584287704 0.0232325292143852 26 0.968832527833269 0.062334944333463 0.0311674721667315 27 0.983163833747656 0.0336723325046873 0.0168361662523437 28 0.977304431945668 0.0453911361086632 0.0226955680543316 29 0.970729420450168 0.058541159099664 0.029270579549832 30 0.969915569262712 0.0601688614745762 0.0300844307372881 31 0.96064425806529 0.0787114838694216 0.0393557419347108 32 0.94614398022681 0.107712039546382 0.0538560197731908 33 0.927949394844341 0.144101210311318 0.0720506051556591 34 0.9442580039673 0.111483992065402 0.0557419960327008 35 0.930318884620045 0.139362230759910 0.0696811153799548 36 0.910625183043882 0.178749633912236 0.0893748169561178 37 0.903566903454318 0.192866193091363 0.0964330965456815 38 0.879341528284935 0.241316943430131 0.120658471715065 39 0.860319811268134 0.279360377463732 0.139680188731866 40 0.826913146430022 0.346173707139956 0.173086853569978 41 0.896637101376407 0.206725797247187 0.103362898623593 42 0.916044369036896 0.167911261926207 0.0839556309631035 43 0.895474348788269 0.209051302423462 0.104525651211731 44 0.884358587302525 0.231282825394949 0.115641412697474 45 0.866679151293204 0.266641697413592 0.133320848706796 46 0.846251447992308 0.307497104015383 0.153748552007691 47 0.827442951595048 0.345114096809903 0.172557048404952 48 0.804064068748376 0.391871862503248 0.195935931251624 49 0.808728328236417 0.382543343527167 0.191271671763583 50 0.779907817604002 0.440184364791996 0.220092182395998 51 0.766243752170506 0.467512495658987 0.233756247829494 52 0.725427013866439 0.549145972267122 0.274572986133561 53 0.84640583194435 0.3071883361113 0.15359416805565 54 0.81569112658548 0.368617746829041 0.184308873414521 55 0.831501921153516 0.336996157692969 0.168498078846484 56 0.851112214069568 0.297775571860863 0.148887785930432 57 0.895061939450075 0.20987612109985 0.104938060549925 58 0.870951870912431 0.258096258175138 0.129048129087569 59 0.853818926641896 0.292362146716208 0.146181073358104 60 0.942650315194118 0.114699369611764 0.0573496848058819 61 0.935837393330538 0.128325213338924 0.0641626066694622 62 0.929847000223023 0.140305999553955 0.0701529997769773 63 0.935243333676102 0.129513332647795 0.0647566663238976 64 0.928680668312241 0.142638663375518 0.0713193316877591 65 0.929231848354114 0.141536303291773 0.0707681516458864 66 0.937949346116222 0.124101307767555 0.0620506538837777 67 0.925857695763829 0.148284608472343 0.0741423042361714 68 0.908232826867507 0.183534346264986 0.091767173132493 69 0.927178827400308 0.145642345199384 0.0728211725996921 70 0.940615189971605 0.118769620056789 0.0593848100283946 71 0.927893156322542 0.144213687354916 0.0721068436774582 72 0.912315303179378 0.175369393641245 0.0876846968206224 73 0.92779644687721 0.144407106245580 0.0722035531227898 74 0.909402571484375 0.181194857031250 0.0905974285156249 75 0.912056569766817 0.175886860466366 0.0879434302331828 76 0.898241964050213 0.203516071899573 0.101758035949787 77 0.888821482039577 0.222357035920845 0.111178517960423 78 0.870731582278945 0.25853683544211 0.129268417721055 79 0.842108195148951 0.315783609702098 0.157891804851049 80 0.8180300765395 0.363939846920999 0.181969923460499 81 0.870552031858074 0.258895936283852 0.129447968141926 82 0.891395663353048 0.217208673293904 0.108604336646952 83 0.869497364879132 0.261005270241735 0.130502635120868 84 0.854594149859478 0.290811700281044 0.145405850140522 85 0.822948822043323 0.354102355913354 0.177051177956677 86 0.859582712432533 0.280834575134933 0.140417287567467 87 0.838117854230981 0.323764291538038 0.161882145769019 88 0.803903851297862 0.392192297404277 0.196096148702138 89 0.765927775625452 0.468144448749096 0.234072224374548 90 0.778681425749425 0.44263714850115 0.221318574250575 91 0.740509496308124 0.518981007383751 0.259490503691876 92 0.73993334089423 0.52013331821154 0.26006665910577 93 0.715260675060489 0.569478649879022 0.284739324939511 94 0.690306218917889 0.619387562164222 0.309693781082111 95 0.751441235414534 0.497117529170933 0.248558764585466 96 0.746055555721782 0.507888888556437 0.253944444278218 97 0.746189010545597 0.507621978908806 0.253810989454403 98 0.70548978698935 0.5890204260213 0.29451021301065 99 0.717714533619461 0.564570932761078 0.282285466380539 100 0.738039164916137 0.523921670167727 0.261960835083863 101 0.75020230841867 0.499595383162659 0.249797691581330 102 0.726853266732361 0.546293466535278 0.273146733267639 103 0.721926690430775 0.556146619138451 0.278073309569225 104 0.706806395753245 0.586387208493509 0.293193604246755 105 0.862887202193328 0.274225595613345 0.137112797806672 106 0.835745226745595 0.328509546508810 0.164254773254405 107 0.846413021047443 0.307173957905115 0.153586978952558 108 0.829549682115813 0.340900635768374 0.170450317884187 109 0.802325930741743 0.395348138516514 0.197674069258257 110 0.77748145793663 0.445037084126739 0.222518542063370 111 0.778816713496631 0.442366573006738 0.221183286503369 112 0.730307733553833 0.539384532892334 0.269692266446167 113 0.684395684363773 0.631208631272454 0.315604315636227 114 0.65786691613047 0.68426616773906 0.34213308386953 115 0.595234015265325 0.809531969469349 0.404765984734674 116 0.535275311691948 0.929449376616103 0.464724688308052 117 0.644901136347819 0.710197727304363 0.355098863652181 118 0.574279056423375 0.851441887153251 0.425720943576625 119 0.499433918166013 0.998867836332025 0.500566081833987 120 0.551503350144452 0.896993299711096 0.448496649855548 121 0.474641677415818 0.949283354831636 0.525358322584182 122 0.390479092133626 0.780958184267252 0.609520907866374 123 0.343264609779481 0.686529219558961 0.65673539022052 124 0.42873593268777 0.85747186537554 0.57126406731223 125 0.41953807389383 0.83907614778766 0.58046192610617 126 0.335110346766005 0.670220693532009 0.664889653233995 127 0.544785676831109 0.910428646337782 0.455214323168891 128 0.475464434411271 0.950928868822543 0.524535565588729 129 0.693533221655916 0.612933556688168 0.306466778344084 130 0.55810039459868 0.883799210802639 0.441899605401319 131 0.819840560526373 0.360318878947253 0.180159439473627 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 8 0.0661157024793388 NOK 10% type I error level 13 0.107438016528926 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/10fv6j1291471768.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/10fv6j1291471768.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/1fj6c1291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/1fj6c1291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/2qtnx1291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/2qtnx1291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/3qtnx1291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/3qtnx1291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/4qtnx1291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/4qtnx1291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/5jk501291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/5jk501291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/6jk501291471767.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/6jk501291471767.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/74l7y1291471768.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/74l7y1291471768.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/84l7y1291471768.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/84l7y1291471768.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/9fv6j1291471768.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471684wk59fj0bhz9qx5w/9fv6j1291471768.ps (open in new window)

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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by