ws 7

*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: Wed, 01 Dec 2010 13:24:40 +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/01/t1291209857zhoh4m814790ltr.htm/, Retrieved Wed, 01 Dec 2010 14:24:38 +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/01/t1291209857zhoh4m814790ltr.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 «

2 5 2 3 3 4 4 2 4 2 4 3 4 4 4 4 2 4 2 5 4 2 4 2 2 2 2 4 3 2 2 2 3 2 4 4 5 1 3 2 4 5 3 5 1 2 1 4 4 3 4 3 3 3 4 3 3 3 2 3 2 4 4 2 4 1 3 2 2 4 4 4 4 3 3 3 4 4 2 2 4 2 4 4 3 3 3 2 2 3 4 3 3 2 2 2 4 2 4 4 1 1 3 4 3 4 5 1 1 1 4 4 3 4 2 3 3 4 3 3 2 2 2 2 2 2 3 4 2 2 3 4 4 4 4 2 3 4 4 3 2 4 1 4 2 4 3 5 4 2 4 3 3 4 4 4 4 3 5 2 3 2 4 2 2 2 4 3 3 5 2 3 2 2 4 4 4 2 4 3 3 4 4 4 2 3 2 4 4 3 4 2 2 2 3 4 4 4 3 1 2 4 4 4 4 2 3 2 4 4 1 4 1 2 3 4 5 4 4 4 4 4 4 4 5 2 1 4 1 4 4 2 4 2 5 3 4 4 4 4 2 2 3 4 3 3 5 2 4 2 5 4 2 5 2 4 1 4 3 4 4 2 2 1 2 4 5 3 2 4 2 4 4 4 4 2 4 2 4 3 4 5 2 2 2 5 5 4 4 2 3 1 4 4 3 4 2 2 2 2 3 4 5 2 4 1 4 3 2 4 2 3 2 4 3 2 5 1 1 2 4 4 4 4 2 2 4 2 4 2 4 1 5 2 5 4 4 4 2 2 2 4 4 4 3 1 4 2 4 4 1 4 1 4 1 4 4 4 4 2 2 2 4 4 2 4 2 2 2 4 5 1 2 1 2 1 3 3 4 3 5 4 5 5 3 3 5 2 3 2 4 5 2 4 2 4 2 4 5 4 4 1 2 2 4 4 3 5 1 3 1 4 4 2 3 2 2 3 2 3 2 5 2 2 1 4 4 3 4 1 3 1 4 4 2 5 1 2 2 4 5 1 4 2 3 3 4 4 3 4 1 2 2 3 4 2 5 1 4 2 4 5 3 4 2 2 2 2 4 3 4 1 5 4 4 3 3 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation standards[t] = + 2.48963932953509 + 0.0538450994962259organization[t] + 0.387877857721709punished[t] + 0.0566181025382133secondrate[t] -0.0508237544025616mistakes[t] -0.079317766161663competent[t] -0.0330405295816517neat[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) 2.48963932953509 0.554111 4.493 1.4e-05 7e-06 organization 0.0538450994962259 0.093358 0.5768 0.564956 0.282478 punished 0.387877857721709 0.085727 4.5246 1.2e-05 6e-06 secondrate 0.0566181025382133 0.077433 0.7312 0.46579 0.232895 mistakes -0.0508237544025616 0.087434 -0.5813 0.56191 0.280955 competent -0.079317766161663 0.096687 -0.8204 0.413298 0.206649 neat -0.0330405295816517 0.100069 -0.3302 0.74172 0.37086 Multiple Linear Regression - Regression Statistics Multiple R 0.376060259714053 R-squared 0.141421318936201 Adjusted R-squared 0.107530055209998 F-TEST (value) 4.1727956820584 F-TEST (DF numerator) 6 F-TEST (DF denominator) 152 p-value 0.000651696167687899 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.912727461888245 Sum Squared Residuals 126.626855792114 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2 3.10257040389327 -1.10257040389327 2 2 3.10534340693532 -1.10534340693532 3 4 3.07684939517622 0.923150604823783 4 2 3.20156648858478 -1.20156648858478 5 3 3.04305253518977 -0.0430525351897662 6 4 2.73247577099253 1.26752422900747 7 3 2.75972195243853 0.240278047561468 8 3 3.46964369170047 -0.469643691700466 9 3 3.04570395930344 -0.0457039593034414 10 2 2.87030673340128 -0.870306733401284 11 4 3.90379878600219 0.0962012139978136 12 4 3.04847696234543 0.951523037654572 13 3 3.4562814806486 -0.4562814806486 14 3 3.05516691592853 -0.0551669159285313 15 4 2.58065177118062 1.41934822881938 16 4 2.70310384990032 1.29689615009968 17 3 3.08176583397876 -0.0817658339787574 18 3 3.15995734875563 -0.159957348755631 19 3 2.99210720185889 0.0078927981411074 20 4 3.03094207957620 0.969057920423804 21 2 2.80132983319782 -0.801329833197823 22 5 3.18466117309698 1.81533882690302 23 4 3.91450957294038 0.0854904270596224 24 2 3.07597148584311 -1.07597148584311 25 3 3.31202969061922 -0.312029690619219 26 4 3.18466117309698 0.815338826903018 27 4 3.09954905879967 0.900450941200333 28 3 3.12224872242312 -0.122248722423117 29 4 3.37419071144495 0.62580928855505 30 4 3.09954905879967 0.900450941200333 31 1 2.57118881455553 -1.57118881455553 32 4 3.83027536797618 0.169724632023825 33 5 2.71142285902628 2.28857714097372 34 2 3.16196150947353 -1.16196150947353 35 4 3.02514773144054 0.974852268559456 36 3 3.13069449467244 -0.130694494672444 37 2 3.29387654481832 -1.29387654481832 38 4 3.25239024298734 0.747609757012658 39 5 3.10232206184165 1.89767793815835 40 4 3.18920769091953 0.810792309080468 41 4 2.98441776001437 1.01558223998563 42 4 3.15037281320223 0.849627186797771 43 3 3.23460701816643 -0.234607018166432 44 4 3.29387654481832 0.70612345518168 45 2 3.13258958838132 -1.13258958838132 46 2 2.65228009549776 -0.652280095497758 47 4 3.09991897977966 0.900081020220343 48 2 2.74558963999272 -0.745589639992722 49 4 3.04293095626145 0.957069043738545 50 4 2.71444420411995 1.28555579588005 51 1 2.81911305801873 -1.81911305801873 52 4 3.04293095626145 0.957069043738545 53 2 3.0098904266798 -1.00989042667980 54 1 2.71054494969317 -1.71054494969317 55 4 4.06720713521908 -0.0672071352190847 56 3 3.12035362871424 -0.120353628714242 57 2 3.12312663175623 -1.12312663175623 58 4 2.65505309853974 1.34494690146025 59 3 2.81634005497675 0.183659945023254 60 2 3.12993816426764 -1.12993816426764 61 2 3.14759981016024 -1.14759981016024 62 3 2.76249495548052 0.23750504451948 63 2 2.67585766845432 -0.67585766845432 64 1 3.04872530439711 -2.04872530439711 65 3 2.73437086470141 0.265629135298592 66 2 2.78909387353075 -0.789093873530746 67 3 3.20156648858478 -0.20156648858478 68 3 2.75630042693091 0.243699573069087 69 3 2.70310384990032 0.29689615009968 70 2 3.09954905879967 -1.09954905879967 71 3 2.60120799904352 0.398792000956481 72 2 2.65505309853975 -0.655053098539745 73 4 3.5412720160176 0.458727983982398 74 4 3.67720788471748 0.322792115282522 75 4 3.26083601523667 0.739163984763332 76 2 3.07597148584311 -1.07597148584311 77 3 2.71167120107796 0.288328798922041 78 4 3.63093064813747 0.369069351862533 79 3 3.06373552617603 -0.0637355261760286 80 4 3.09375471066402 0.906245289335984 81 2 3.07532422244733 -1.07532422244733 82 3 3.1146808595069 -0.114680859506902 83 3 2.79678331537527 0.203216684624727 84 4 3.73936890554321 0.260631094456791 85 2 2.84181272164218 -0.841812721642183 86 4 2.84760706977783 1.15239293022217 87 2 2.71167120107796 -0.711671201077958 88 2 2.67006332031867 -0.670063320318668 89 4 3.87530477424309 0.124695225756915 90 3 3.12679524024567 -0.126795240245667 91 4 3.04293095626145 0.957069043738545 92 2 2.81546214564363 -0.815462145643634 93 2 2.65782610158173 -0.657826101581733 94 3 2.60120799904352 0.398792000956481 95 3 3.49044826161504 -0.490448261615041 96 5 3.86142080384895 1.13857919615105 97 2 3.92612852864565 -1.92612852864565 98 3 3.52626179423868 -0.526261794238679 99 4 3.28543077256899 0.714569227431007 100 3 3.20611300640733 -0.206113006407330 101 4 3.59978521226469 0.400214787735309 102 3 2.81823514868562 0.181764851314379 103 3 3.44606611874390 -0.446066118743905 104 2 3.07597148584311 -1.07597148584311 105 3 3.27898916103757 -0.278989161037568 106 2 3.30131764461117 -1.30131764461117 107 3 3.18832978158642 -0.188329781586420 108 2 3.80065510483228 -1.80065510483228 109 4 3.57129120050559 0.428708799494410 110 2 2.84395615740274 -0.843956157402736 111 4 2.62390766266697 1.37609233733303 112 4 2.99790154999454 1.00209845000546 113 1 2.69666097929904 -1.69666097929904 114 5 3.58742641459944 1.41257358540056 115 2 3.0594360913353 -1.05943609133530 116 3 3.22969057936389 -0.229690579363892 117 4 3.05793991897052 0.942060081029482 118 1 2.62365932061529 -1.62365932061529 119 5 3.56674468268304 1.43325531731696 120 3 2.87674834493271 0.12325165506729 121 3 2.79553548506217 0.204464514937828 122 3 3.10144415250854 -0.101444152508543 123 3 3.52324044914501 -0.523240449145015 124 2 3.01745828959602 -1.01745828959602 125 2 2.60422934413718 -0.604229344137184 126 4 3.18163982800332 0.818360171996682 127 4 2.93147135376205 1.06852864623795 128 3 3.17886682496133 -0.178866824961330 129 3 2.79098896723962 0.209011032760379 130 3 3.04872530439711 -0.0487253043971058 131 4 3.55628097872667 0.443719021273333 132 3 3.12224872242312 -0.122248722423117 133 4 2.72402999874321 1.27597000125679 134 4 2.84181272164218 1.15818727835782 135 2 3.20156648858478 -1.20156648858478 136 4 3.09954905879967 0.900450941200333 137 2 2.63424852862517 -0.634248528625171 138 4 3.09122879060385 0.908771209396152 139 3 3.53825067092394 -0.538250670923938 140 3 3.69321324789151 -0.69321324789151 141 2 2.97986998312196 -0.979869983121957 142 2 3.88564564020129 -1.88564564020129 143 5 4.12484242213306 0.875157577866938 144 2 2.70587685294231 -0.705876852942307 145 4 3.4005412874435 0.599458712556501 146 3 3.15616716133788 -0.156167161337881 147 3 3.05604482526164 -0.0560448252616432 148 3 3.12779472850709 -0.127794728507091 149 3 2.63159710451150 0.368402895488504 150 4 3.9319228767813 0.0680771232187017 151 4 3.23246358240588 0.767536417594121 152 4 3.09954905879967 0.900450941200333 153 4 3.3126561701042 0.687343829895803 154 4 2.69276172487226 1.30723827512774 155 5 3.93406631254185 1.06593368745815 156 3 3.15528925200477 -0.155289252004769 157 3 3.02023129263800 -0.0202312926380044 158 4 3.87530477424309 0.124695225756915 159 4 3.91148822784671 0.0885117721532867 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.55725716979339 0.885485660413221 0.442742830206611 11 0.428539507443499 0.857079014886998 0.571460492556501 12 0.293454951887948 0.586909903775897 0.706545048112052 13 0.297469316757875 0.59493863351575 0.702530683242125 14 0.268049342126501 0.536098684253002 0.731950657873499 15 0.325549490697584 0.651098981395168 0.674450509302416 16 0.250968154353744 0.501936308707489 0.749031845646256 17 0.182587624125541 0.365175248251083 0.817412375874458 18 0.170531255406562 0.341062510813125 0.829468744593438 19 0.137383633037538 0.274767266075077 0.862616366962462 20 0.182486811742659 0.364973623485318 0.817513188257341 21 0.149582062582616 0.299164125165231 0.850417937417384 22 0.576663886434535 0.84667222713093 0.423336113565465 23 0.546478597381214 0.907042805237573 0.453521402618786 24 0.57469253766948 0.85061492466104 0.42530746233052 25 0.516904341347138 0.966191317305724 0.483095658652862 26 0.49559543030846 0.99119086061692 0.50440456969154 27 0.472821554178043 0.945643108356086 0.527178445821957 28 0.406377908735262 0.812755817470525 0.593622091264738 29 0.357932058654779 0.715864117309557 0.642067941345221 30 0.333440272439605 0.66688054487921 0.666559727560395 31 0.620532596645171 0.758934806709658 0.379467403354829 32 0.56040069493545 0.8791986101291 0.43959930506455 33 0.710641582727918 0.578716834544165 0.289358417272082 34 0.770829163835612 0.458341672328777 0.229170836164388 35 0.765475609683249 0.469048780633502 0.234524390316751 36 0.726568796742454 0.546862406515092 0.273431203257546 37 0.747224550555308 0.505550898889385 0.252775449444692 38 0.741891210907907 0.516217578184186 0.258108789092093 39 0.81794586345486 0.364108273090279 0.182054136545140 40 0.805930760551131 0.388138478897738 0.194069239448869 41 0.79229041604673 0.415419167906541 0.207709583953270 42 0.769916883125992 0.460166233748016 0.230083116874008 43 0.729547258622842 0.540905482754316 0.270452741377158 44 0.7121587240489 0.575682551902199 0.287841275951100 45 0.749600705035705 0.500798589928590 0.250399294964295 46 0.73082575589584 0.538348488208321 0.269174244104160 47 0.745937181676097 0.508125636647806 0.254062818323903 48 0.752988269247949 0.494023461504103 0.247011730752051 49 0.740641151209613 0.518717697580775 0.259358848790387 50 0.755649526023372 0.488700947953257 0.244350473976629 51 0.868990283599095 0.26201943280181 0.131009716400905 52 0.862293198435398 0.275413603129203 0.137706801564602 53 0.889215722958122 0.221568554083755 0.110784277041878 54 0.945083355067076 0.109833289865849 0.0549166449329244 55 0.934656733045419 0.130686533909162 0.065343266954581 56 0.918823766647645 0.16235246670471 0.081176233352355 57 0.930483160990702 0.139033678018596 0.0695168390092978 58 0.94406409914646 0.111871801707080 0.0559359008535402 59 0.929856534396761 0.140286931206478 0.0701434656032388 60 0.936887718551488 0.126224562897024 0.0631122814485118 61 0.943951744310953 0.112096511378094 0.056048255689047 62 0.930210762604652 0.139578474790696 0.0697892373953481 63 0.923185077068578 0.153629845862844 0.076814922931422 64 0.971022165609714 0.057955668780572 0.028977834390286 65 0.963054499965237 0.0738910000695269 0.0369455000347634 66 0.959296830760054 0.0814063384798917 0.0407031692399458 67 0.948749973907655 0.102500052184690 0.0512500260923448 68 0.936561558543793 0.126876882912415 0.0634384414562074 69 0.922342866542098 0.155314266915804 0.0776571334579022 70 0.929403186879061 0.141193626241877 0.0705968131209386 71 0.916361899810571 0.167276200378858 0.083638100189429 72 0.907412411794552 0.185175176410896 0.0925875882054482 73 0.892325907918899 0.215348184162203 0.107674092081101 74 0.874002904654893 0.251994190690214 0.125997095345107 75 0.865241086079496 0.269517827841008 0.134758913920504 76 0.874026696101304 0.251946607797393 0.125973303898696 77 0.851174456766812 0.297651086466377 0.148825543233188 78 0.826995716307276 0.346008567385447 0.173004283692724 79 0.795266892582418 0.409466214835165 0.204733107417582 80 0.793587774193195 0.412824451613609 0.206412225806805 81 0.804819515895252 0.390360968209496 0.195180484104748 82 0.771593284011894 0.456813431976213 0.228406715988106 83 0.738039189900595 0.523921620198809 0.261960810099405 84 0.705299369493388 0.589401261013224 0.294700630506612 85 0.699720144410008 0.600559711179985 0.300279855589992 86 0.719243067203892 0.561513865592215 0.280756932796108 87 0.702043558083836 0.595912883832329 0.297956441916164 88 0.680174798698625 0.63965040260275 0.319825201301375 89 0.637657904773231 0.724684190453538 0.362342095226769 90 0.593230815962599 0.813538368074802 0.406769184037401 91 0.597471342617749 0.805057314764501 0.402528657382251 92 0.591038003389677 0.817923993220645 0.408961996610323 93 0.568294022594385 0.86341195481123 0.431705977405615 94 0.532066010491016 0.935867979017967 0.467933989508984 95 0.501342673402376 0.997314653195249 0.498657326597624 96 0.524267954178641 0.951464091642717 0.475732045821359 97 0.688841086984798 0.622317826030404 0.311158913015202 98 0.658883613880541 0.682232772238918 0.341116386119459 99 0.635539140809813 0.728921718380374 0.364460859190187 100 0.595010107020176 0.809979785959649 0.404989892979824 101 0.552626128315932 0.894747743368135 0.447373871684068 102 0.504700389090737 0.990599221818527 0.495299610909263 103 0.469799447169828 0.939598894339655 0.530200552830172 104 0.495186846469915 0.99037369293983 0.504813153530085 105 0.461499237105885 0.92299847421177 0.538500762894115 106 0.515631226126805 0.96873754774639 0.484368773873194 107 0.478798124241861 0.957596248483723 0.521201875758139 108 0.619880818885527 0.760238362228947 0.380119181114473 109 0.576360562235783 0.847278875528433 0.423639437764216 110 0.628381759191462 0.743236481617076 0.371618240808538 111 0.690958558707385 0.61808288258523 0.309041441292615 112 0.74261585142438 0.51476829715124 0.25738414857562 113 0.81359050068464 0.372818998630719 0.186409499315359 114 0.850342964264472 0.299314071471056 0.149657035735528 115 0.868826716450453 0.262346567099095 0.131173283549547 116 0.84815700186124 0.303685996277519 0.151842998138760 117 0.863794162942835 0.27241167411433 0.136205837057165 118 0.918686130789387 0.162627738421227 0.0813138692106135 119 0.938687827860126 0.122624344279748 0.0613121721398739 120 0.933988261823118 0.132023476353764 0.066011738176882 121 0.912789820128158 0.174420359743684 0.0872101798718422 122 0.89581824730444 0.208363505391119 0.104181752695560 123 0.878847080290484 0.242305839419032 0.121152919709516 124 0.875499179420225 0.249001641159549 0.124500820579775 125 0.855381012038872 0.289237975922257 0.144618987961128 126 0.844534982485511 0.310930035028978 0.155465017514489 127 0.836108163394507 0.327783673210987 0.163891836605493 128 0.8005780758732 0.3988438482536 0.1994219241268 129 0.751632574910053 0.496734850179895 0.248367425089947 130 0.69537209097326 0.609255818053479 0.304627909026739 131 0.653620531136643 0.692758937726714 0.346379468863357 132 0.60924488760075 0.7815102247985 0.39075511239925 133 0.582829806719693 0.834340386560614 0.417170193280307 134 0.596543847745902 0.806912304508195 0.403456152254098 135 0.78716354454136 0.425672910917281 0.212836455458641 136 0.783955757432298 0.432088485135404 0.216044242567702 137 0.777858625542718 0.444282748914564 0.222141374457282 138 0.757413243663845 0.48517351267231 0.242586756336155 139 0.728954267476286 0.542091465047429 0.271045732523714 140 0.747000417709115 0.505999164581769 0.252999582290885 141 0.715194201797544 0.569611596404912 0.284805798202456 142 0.94528008581827 0.109439828363459 0.0547199141817295 143 0.912933135250857 0.174133729498285 0.0870668647491426 144 0.953827591507468 0.092344816985064 0.046172408492532 145 0.938589611278607 0.122820777442786 0.061410388721393 146 0.889152536164159 0.221694927671682 0.110847463835841 147 0.816843481501102 0.366313036997796 0.183156518498898 148 0.706793627938016 0.586412744123967 0.293206372061984 149 0.555601391166321 0.888797217667359 0.444398608833679 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 4 0.0285714285714286 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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