p_Stress_MR3v2

*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 13:59:58 +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/t1291471120fbz96hld3ll3sqi.htm/, Retrieved Sat, 04 Dec 2010 14:58:51 +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/t1291471120fbz96hld3ll3sqi.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 0 0 53 7 12 2 4 21 6 0 0 86 4 11 4 3 21 13 0 0 66 6 14 7 5 21 12 1 0 67 5 12 3 3 24 8 0 0 76 4 21 7 6 22 6 0 0 78 3 12 2 5 21 10 0 0 53 5 22 7 6 22 10 0 0 80 6 11 2 6 21 9 0 0 74 5 10 1 5 20 9 0 0 76 6 13 2 5 22 7 1 0 79 7 10 6 3 21 5 0 0 54 6 8 1 5 21 14 1 0 67 7 15 1 7 23 6 0 0 87 6 10 1 5 22 10 1 0 58 4 14 2 5 23 10 1 0 75 6 14 2 3 22 7 0 0 88 4 11 2 5 24 10 1 0 64 5 10 1 6 23 8 0 0 57 3 13 7 5 21 6 1 0 66 3 7 1 2 23 10 0 0 54 4 12 2 5 23 12 0 0 56 5 14 4 4 21 7 1 0 86 3 11 2 6 20 15 0 0 80 7 9 1 3 32 8 1 0 76 7 11 1 5 22 10 0 0 69 4 15 5 4 21 13 1 0 67 4 13 2 5 21 8 0 0 80 5 9 1 2 21 11 1 0 54 6 15 3 2 22 7 0 0 71 5 10 1 5 21 9 0 0 84 4 11 2 2 21 10 1 0 74 6 13 5 2 21 8 1 0 71 5 8 2 2 22 15 1 0 63 5 20 6 5 21 9 1 0 71 6 12 4 5 21 7 0 0 76 2 10 1 1 21 11 1 0 69 6 10 3 5 21 9 1 0 74 7 9 6 2 23 8 0 0 75 5 14 7 6 21 8 1 0 54 5 8 4 1 23 12 0 0 69 5 11 5 3 23 13 0 0 68 6 13 3 2 21 9 0 0 75 4 11 2 5 21 11 1 0 75 6 11 2 3 20 8 0 0 72 5 10 2 4 21 10 1 0 67 5 14 2 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 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 7.6711792310796 -0.0928953860553277AGE[t] + 0.69427644065709Pstress_M[t] + 0.00132951929824787Pstress_OKT[t] -0.0313243922834601BelInSprt[t] + 0.191375049293081KunnenRekRel[t] + 0.407160247689161Depressie[t] -0.194751314134405Slaapgebrek[t] + 0.175770377072836`ToekZorgen `[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) 7.6711792310796 2.148488 3.5705 0.000497 0.000248 AGE -0.0928953860553277 0.066865 -1.3893 0.167063 0.083532 Pstress_M 0.69427644065709 0.33826 2.0525 0.042083 0.021041 Pstress_OKT 0.00132951929824787 0.33323 0.004 0.996823 0.498411 BelInSprt -0.0313243922834601 0.016238 -1.9291 0.055853 0.027926 KunnenRekRel 0.191375049293081 0.107753 1.7761 0.078011 0.039006 Depressie 0.407160247689161 0.061658 6.6036 0 0 Slaapgebrek -0.194751314134405 0.092416 -2.1073 0.036966 0.018483 `ToekZorgen ` 0.175770377072836 0.110789 1.5865 0.114993 0.057496 Multiple Linear Regression - Regression Statistics Multiple R 0.631622752228292 R-squared 0.398947301132442 Adjusted R-squared 0.362793755335897 F-TEST (value) 11.0348042589662 F-TEST (DF numerator) 8 F-TEST (DF denominator) 133 p-value 7.2215566859768e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.90495367626601 Sum Squared Residuals 482.636851659679 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.4135197581277 -0.413519758127713 2 6 8.01904718397413 -2.01904718397413 3 13 10.0170526830394 2.98294731696057 4 12 10.1017736891336 1.89822631086640 5 8 12.0682646143497 -4.06826461434965 6 6 9.13357551699705 -3.13357551699705 7 10 13.6659470920175 -3.66594709201745 8 10 9.41366200969305 0.586337990306955 9 9 9.11494938952846 -0.11494938952846 10 9 10.3633004692430 -1.36330046924303 11 7 8.61716125648139 -1.61716125648139 12 5 9.11849178911243 -4.11849178911242 13 14 12.7985886673474 1.20141133265259 14 6 8.7133165670259 -2.71331656702591 15 10 11.4600353479947 -1.46003534799474 16 10 10.8658346375611 -0.865834637561083 17 7 8.60454639576637 -1.60454639576637 18 10 10.019553971927 -0.0195539719270039 19 8 9.13189604591151 -1.13189604591151 20 6 7.92827899558108 -1.92827899558108 21 10 9.98384059503784 0.0161594049621587 22 12 10.3616143498007 1.63838565019932 23 7 9.43876233482546 -2.43876233482546 24 15 8.64394751863436 6.35605248136564 25 8 9.51463814528535 -1.51463814528535 26 10 10.0683265204327 -0.0683265204326985 27 13 10.8638509558098 2.13614904419023 28 8 7.99253165692003 0.00746834307996646 29 11 11.7460762041063 -0.746076204106319 30 7 9.11602718032351 -2.11602718032351 31 9 8.29542821973703 0.704571780262972 32 10 9.91576523478999 0.0842347652100144 33 8 8.3668160663047 -0.366816066304697 34 15 13.1587446654679 1.84125533453214 35 9 10.3246406093041 -1.32464060930412 36 7 7.77509394879095 -0.775093948790954 37 11 9.76772021262712 1.23227978737288 38 9 8.28374797919202 0.716252020807981 39 8 9.5337377081574 -1.53373770815739 40 8 8.33405772978187 -0.334057729781871 41 12 8.36239481584097 3.63760518415903 42 13 9.61314700399181 3.38685299600819 43 9 9.10465888150667 -0.104658881506675 44 11 9.83014466660426 1.16985533339574 45 8 8.89997186894347 -0.899971868943468 46 10 11.1108454986463 -1.11084549864634 47 13 13.2040964053036 -0.204096405303569 48 12 10.4646909611310 1.53530903886903 49 12 10.3695793436997 1.63042065630031 50 9 8.57043350710771 0.429566492892294 51 8 9.66603333382005 -1.66603333382005 52 9 8.43232506663431 0.567674933365692 53 12 8.61710882573039 3.38289117426961 54 12 11.1797765462115 0.820223453788547 55 16 14.3160561512962 1.68394384870376 56 11 9.78526190094384 1.21473809905616 57 13 9.21171392594125 3.78828607405876 58 10 10.0302473713546 -0.0302473713545974 59 9 9.97576474771397 -0.975764747713973 60 14 9.98843110450774 4.01156889549226 61 13 11.1727010260941 1.82729897390595 62 12 10.7702845726036 1.22971542739642 63 9 10.5031813250981 -1.50318132509809 64 9 11.0183168889372 -2.01831688893717 65 10 10.9119944917688 -0.911994491768752 66 8 10.0738968343792 -2.07389683437918 67 9 9.66846848852655 -0.668468488526547 68 9 8.91613002639399 0.0838699736060096 69 11 9.07390917070987 1.92609082929013 70 7 9.23413994659314 -2.23413994659314 71 11 11.3373732173340 -0.337373217334017 72 9 9.81974531239653 -0.81974531239653 73 11 9.28219543485286 1.71780456514714 74 9 9.58207188627284 -0.582071886272845 75 8 10.2753911153100 -2.27539111531005 76 9 8.29138537898967 0.708614621010331 77 8 9.64765934410316 -1.64765934410316 78 9 9.1370606178854 -0.137060617885401 79 10 9.4032035763775 0.596796423622493 80 9 10.2440636456331 -1.24406364563309 81 17 13.8152875357967 3.18471246420325 82 7 9.07837232864177 -2.07837232864177 83 11 10.8339211538552 0.166078846144771 84 9 9.2734061692131 -0.273406169213109 85 10 9.60192115093135 0.398078849068647 86 11 8.86664651351111 2.13335348648889 87 8 8.50493029918662 -0.504930299186625 88 12 11.6968814193174 0.303118580682595 89 10 9.49662125061978 0.503378749380216 90 7 9.6279231599815 -2.6279231599815 91 9 9.0002190523059 -0.000219052305901545 92 7 8.27462486772217 -1.27462486772217 93 12 11.2215655360162 0.778434463983807 94 8 9.47415476597007 -1.47415476597007 95 13 10.5444331967755 2.4555668032245 96 9 11.0992586616482 -2.09925866164824 97 15 13.0441043807020 1.95589561929803 98 8 8.67840885057 -0.678408850570005 99 14 11.993822684116 2.00617731588401 100 14 13.7395564883574 0.260443511642557 101 9 10.0186792766742 -1.01867927667418 102 13 11.4263301875831 1.57366981241690 103 11 8.6696615695344 2.3303384304656 104 10 11.9229874256271 -1.92298742562706 105 6 9.86457435479679 -3.86457435479679 106 8 8.64427002491804 -0.644270024918035 107 10 10.9752609867872 -0.975260986787247 108 10 7.91444965327187 2.08555034672813 109 10 9.45318194074275 0.54681805925725 110 12 11.3653411971812 0.634658802818793 111 10 9.34706179464707 0.652938205352933 112 9 9.0368003475435 -0.0368003475435022 113 9 6.98128262286103 2.01871737713897 114 11 8.78256325269535 2.21743674730465 115 7 8.51064924979036 -1.51064924979036 116 7 8.49269612671155 -1.49269612671155 117 5 8.08072701379303 -3.08072701379303 118 9 9.07511111164341 -0.0751111116434137 119 11 11.6816142036233 -0.681614203623329 120 15 12.4280818060805 2.57191819391945 121 9 7.43847143855444 1.56152856144556 122 9 9.71143835021613 -0.711438350216127 123 8 9.28322017144213 -1.28322017144213 124 13 15.9025205273436 -2.9025205273436 125 10 10.8189686267102 -0.818968626710225 126 13 11.3967025890554 1.60329741094457 127 9 8.23934372656328 0.760656273436715 128 11 10.1972438638762 0.802756136123795 129 8 10.7296972594544 -2.72969725945438 130 10 8.85162518851217 1.14837481148783 131 9 9.38812824994953 -0.388128249949526 132 8 8.43336675499014 -0.433366754990136 133 8 8.08378073489939 -0.0837807348993903 134 13 10.8483617679934 2.15163823200663 135 11 11.0140004999754 -0.0140004999754446 136 8 9.51463814528535 -1.51463814528535 137 12 9.7022252237409 2.29777477625911 138 15 11.6869471161515 3.31305288384848 139 11 10.8339211538552 0.166078846144771 140 10 10.0642268276895 -0.0642268276895192 141 5 8.08072701379303 -3.08072701379303 142 11 7.0930430148743 3.9069569851257 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.99510508951911 0.0097898209617801 0.00489491048089005 13 0.988436109799826 0.0231277804003481 0.0115638902001741 14 0.982274063713581 0.0354518725728376 0.0177259362864188 15 0.967201279853347 0.0655974402933051 0.0327987201466525 16 0.943618456795666 0.112763086408668 0.056381543204334 17 0.915986581813286 0.168026836373428 0.0840134181867142 18 0.884164416926782 0.231671166146436 0.115835583073218 19 0.851829388223435 0.296341223553131 0.148170611776565 20 0.813344747218104 0.373310505563793 0.186655252781896 21 0.824324318451225 0.35135136309755 0.175675681548775 22 0.877961534184838 0.244076931630323 0.122038465815162 23 0.857609953760566 0.284780092478869 0.142390046239434 24 0.990445221882706 0.0191095562345873 0.00955477811729367 25 0.986702958019461 0.0265940839610774 0.0132970419805387 26 0.98257028972696 0.0348594205460813 0.0174297102730406 27 0.98851090403909 0.0229781919218203 0.0114890959609101 28 0.982566872252443 0.0348662554951140 0.0174331277475570 29 0.979271865109838 0.0414562697803231 0.0207281348901615 30 0.978390687601419 0.0432186247971625 0.0216093123985812 31 0.972203057296567 0.0555938854068669 0.0277969427034334 32 0.961552311320964 0.0768953773580723 0.0384476886790361 33 0.949237404872301 0.101525190255398 0.0507625951276989 34 0.955209947736795 0.0895801045264093 0.0447900522632046 35 0.950570528127885 0.0988589437442293 0.0494294718721146 36 0.93681672933426 0.126366541331481 0.0631832706657405 37 0.92203518728886 0.155929625422279 0.0779648127111395 38 0.899223172183142 0.201553655633717 0.100776827816858 39 0.890219137513639 0.219561724972723 0.109780862486361 40 0.865597837913787 0.268804324172426 0.134402162086213 41 0.935197982323837 0.129604035352325 0.0648020176761626 42 0.952884682636637 0.0942306347267256 0.0471153173633628 43 0.94164886168976 0.116702276620480 0.0583511383102399 44 0.926050275235094 0.147899449529813 0.0739497247649064 45 0.913974634345278 0.172050731309444 0.086025365654722 46 0.902926290381068 0.194147419237864 0.0970737096189318 47 0.893532537896425 0.212934924207149 0.106467462103575 48 0.886774347135489 0.226451305729023 0.113225652864511 49 0.887412593644184 0.225174812711633 0.112587406355816 50 0.866178139385204 0.267643721229591 0.133821860614796 51 0.870157592825949 0.259684814348103 0.129842407174051 52 0.84334483160181 0.313310336796378 0.156655168398189 53 0.891086807734394 0.217826384531212 0.108913192265606 54 0.867642795535532 0.264714408928935 0.132357204464468 55 0.877586938665849 0.244826122668302 0.122413061334151 56 0.866233439473616 0.267533121052768 0.133766560526384 57 0.914362434989603 0.171275130020795 0.0856375650103973 58 0.892443390181634 0.215113219636733 0.107556609818366 59 0.875219602638867 0.249560794722267 0.124780397361133 60 0.897684185314426 0.204631629371149 0.102315814685574 61 0.898043269724021 0.203913460551958 0.101956730275979 62 0.907104416694997 0.185791166610007 0.0928955833050033 63 0.928434649357427 0.143130701285145 0.0715653506425727 64 0.936974384009276 0.126051231981448 0.0630256159907241 65 0.936990793281826 0.126018413436349 0.0630092067181743 66 0.939441554746647 0.121116890506706 0.060558445253353 67 0.924866570436865 0.15026685912627 0.075133429563135 68 0.906039358731307 0.187921282537386 0.093960641268693 69 0.912857123960432 0.174285752079137 0.0871428760395683 70 0.927524626373076 0.144950747253849 0.0724753736269244 71 0.912850841529227 0.174298316941546 0.0871491584707728 72 0.903255530032795 0.193488939934409 0.0967444699672046 73 0.906233306075593 0.187533387848814 0.0937666939244069 74 0.884059893867501 0.231880212264998 0.115940106132499 75 0.890231903757515 0.219536192484970 0.109768096242485 76 0.873428714839384 0.253142570321233 0.126571285160616 77 0.864154354827998 0.271691290344005 0.135845645172002 78 0.844032293671265 0.311935412657469 0.155967706328735 79 0.816301681558434 0.367396636883133 0.183698318441566 80 0.793100592173427 0.413798815653147 0.206899407826573 81 0.848936269684308 0.302127460631383 0.151063730315692 82 0.850102762246174 0.299794475507652 0.149897237753826 83 0.826647220084563 0.346705559830873 0.173352779915437 84 0.795154492786663 0.409691014426673 0.204845507213337 85 0.757784669154073 0.484430661691854 0.242215330845927 86 0.791685125171176 0.416629749657649 0.208314874828824 87 0.755094908555147 0.489810182889706 0.244905091444853 88 0.711714853578809 0.576570292842381 0.288285146421191 89 0.671210652985185 0.65757869402963 0.328789347014815 90 0.706938132061836 0.586123735876328 0.293061867938164 91 0.660308520234755 0.67938295953049 0.339691479765245 92 0.64295663753468 0.71408672493064 0.35704336246532 93 0.600948431719327 0.798103136561346 0.399051568280673 94 0.587079336907729 0.825841326184542 0.412920663092271 95 0.6522472454762 0.6955055090476 0.3477527545238 96 0.654005152648582 0.691989694702837 0.345994847351418 97 0.66025850697357 0.67948298605286 0.33974149302643 98 0.616641784306771 0.766716431386458 0.383358215693229 99 0.644533468313375 0.71093306337325 0.355466531686625 100 0.627966721899754 0.744066556200492 0.372033278100246 101 0.614275466080988 0.771449067838025 0.385724533919012 102 0.610482284930631 0.779035430138739 0.389517715069369 103 0.66227656285294 0.67544687429412 0.33772343714706 104 0.657847129797136 0.684305740405728 0.342152870202864 105 0.79975002770558 0.400499944588839 0.200249972294419 106 0.773383327947248 0.453233344105505 0.226616672052752 107 0.797753461114053 0.404493077771893 0.202246538885947 108 0.781569203506601 0.436861592986798 0.218430796493399 109 0.737325764825587 0.525348470348827 0.262674235174413 110 0.730411964047687 0.539176071904626 0.269588035952313 111 0.714015544473803 0.571968911052395 0.285984455526197 112 0.652974858418993 0.694050283162014 0.347025141581007 113 0.606414836928784 0.787170326142433 0.393585163071216 114 0.596837812394998 0.806324375210005 0.403162187605002 115 0.536916359175594 0.926167281648812 0.463083640824406 116 0.473784587217197 0.947569174434394 0.526215412782803 117 0.600175483604872 0.799649032790256 0.399824516395128 118 0.526804754003094 0.946390491993813 0.473195245996906 119 0.488649540283932 0.977299080567865 0.511350459716068 120 0.788966303847298 0.422067392305403 0.211033696152702 121 0.730506388927056 0.538987222145889 0.269493611072944 122 0.676539847346737 0.646920305306527 0.323460152653263 123 0.589228707054996 0.821542585890008 0.410771292945004 124 0.513762280143544 0.972475439712912 0.486237719856456 125 0.42974842989228 0.85949685978456 0.57025157010772 126 0.336631721608633 0.673263443217266 0.663368278391367 127 0.427561584456892 0.855123168913783 0.572438415543108 128 0.328553204511407 0.657106409022814 0.671446795488593 129 0.365462795103714 0.730925590207428 0.634537204896286 130 0.314003382110287 0.628006764220574 0.685996617889713 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.00840336134453781 OK 5% type I error level 10 0.0840336134453782 NOK 10% type I error level 16 0.134453781512605 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/1024uz1291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/1024uz1291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/1dlf51291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/1dlf51291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/2dlf51291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/2dlf51291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/3ovw81291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/3ovw81291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/4ovw81291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/4ovw81291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/5ovw81291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/5ovw81291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/6y4vb1291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/6y4vb1291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/7rdcw1291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/7rdcw1291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/8rdcw1291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/8rdcw1291471186.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/9rdcw1291471186.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/04/t1291471120fbz96hld3ll3sqi/9rdcw1291471186.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') }

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Software written by Ed van Stee & Patrick Wessa

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