Multiple Regression Leercompetentie

*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: Mon, 13 Dec 2010 16:07:23 +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/13/t1292256538zxrlpkkxgiexot1.htm/, Retrieved Mon, 13 Dec 2010 17:11:12 +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/13/t1292256538zxrlpkkxgiexot1.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 «

0 13 26 9 6 25 25 0 16 20 9 6 25 24 0 19 21 9 13 19 21 1 15 31 14 8 18 23 0 14 21 8 7 18 17 0 13 18 8 9 22 19 0 19 26 11 5 29 18 0 15 22 10 8 26 27 0 14 22 9 9 25 23 0 15 29 15 11 23 23 1 16 15 14 8 23 29 0 16 16 11 11 23 21 1 16 24 14 12 24 26 0 17 17 6 8 30 25 1 15 19 20 7 19 25 1 15 22 9 9 24 23 0 20 31 10 12 32 26 1 18 28 8 20 30 20 0 16 38 11 7 29 29 1 16 26 14 8 17 24 0 19 25 11 8 25 23 0 16 25 16 16 26 24 1 17 29 14 10 26 30 0 17 28 11 6 25 22 1 16 15 11 8 23 22 0 15 18 12 9 21 13 1 14 21 9 9 19 24 0 15 25 7 11 35 17 1 12 23 13 12 19 24 0 14 23 10 8 20 21 0 16 19 9 7 21 23 1 14 18 9 8 21 24 1 7 18 13 9 24 24 1 10 26 16 4 23 24 1 14 18 12 8 19 23 0 16 18 6 8 17 26 1 16 28 14 8 24 24 1 16 17 14 6 15 21 0 14 29 10 8 25 23 1 20 12 4 4 27 28 1 14 25 12 7 29 23 0 14 28 12 14 27 22 0 11 20 14 10 18 24 0 15 17 9 9 25 21 0 16 17 9 6 22 23 1 14 20 10 8 26 23 0 16 31 14 11 23 20 1 14 21 10 8 16 23 1 12 19 9 8 27 21 0 16 23 14 10 25 27 1 9 15 8 8 14 12 0 14 24 9 1 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 12.8466322963478 -0.519508200233652Gender[t] + 0.0150227529352552Concern[t] -0.274396434721787Doubts[t] + 0.0644169575826978Criticism[t] + 0.0264468826309861Standards[t] + 0.171915898709033Organization[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) 12.8466322963478 1.421693 9.0362 0 0 Gender -0.519508200233652 0.379011 -1.3707 0.172618 0.086309 Concern 0.0150227529352552 0.037152 0.4044 0.68655 0.343275 Doubts -0.274396434721787 0.070116 -3.9135 0.00014 7e-05 Criticism 0.0644169575826978 0.068865 0.9354 0.351153 0.175576 Standards 0.0264468826309861 0.049381 0.5356 0.593086 0.296543 Organization 0.171915898709033 0.051742 3.3225 0.001133 0.000566 Multiple Linear Regression - Regression Statistics Multiple R 0.450207146949892 R-squared 0.202686475164762 Adjusted R-squared 0.169232760836011 F-TEST (value) 6.05871363559073 F-TEST (DF numerator) 6 F-TEST (DF denominator) 143 p-value 1.12911825665973e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.07168736145602 Sum Squared Residuals 613.740058877174 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 16.1132272391654 -3.11322723916539 2 16 15.8511748228445 0.148825177155521 3 19 15.6426872869456 3.35731271305438 4 15 13.8967245693292 1.10327543067083 5 14 14.8164714987041 -0.816471498704096 6 13 15.3498564830057 -2.34985648300574 7 19 14.4023936516995 4.59760634830050 8 15 16.2778523879167 -1.27785238791669 9 14 15.9025553027541 -1.90255530275406 10 15 14.4372761148735 0.562723885126455 11 16 14.8200903277742 1.17990967222578 12 16 14.9957342681843 1.00426573181569 13 16 14.7236621210262 1.27633787897381 14 17 17.0622800952334 -0.0622800952334465 15 15 12.3759346482417 2.62406535175826 16 15 15.3566002198894 -0.356600219889425 17 20 16.6574903917417 3.34250960825834 18 18 16.0726533052912 1.92734669470876 19 16 16.6025754878873 -0.602575487887315 20 16 13.9670798207309 2.03292017926906 21 19 15.3344137345336 3.66558626546645 22 16 14.6761300029262 1.32386999707378 23 17 15.4104993306352 1.58950066936483 24 17 15.0787321794649 1.92126782053511 25 16 14.4398683409763 1.56013165902365 26 15 13.1943284692334 1.80567153076660 27 14 15.3812589525083 -1.38125895250827 28 15 15.8582237802245 -0.85822378022446 29 12 14.5069695922397 -2.50696959223973 30 14 15.1026984528118 -1.10269845281183 31 16 15.6228655982590 0.377134401741045 32 14 15.3246675013818 -1.32466750138178 33 7 14.3708393679703 -7.37083936797029 34 10 13.3193004167425 -3.31930041674249 35 14 14.2766685332454 -0.276668533245414 36 16 16.9054092726749 -0.905409272674915 37 16 14.1822535050184 1.81774649498165 38 16 13.1343996677592 2.86560033224082 39 14 15.6689011809964 -1.66890118099636 40 20 17.1951902176704 2.80480978232955 41 14 14.5818796725194 -0.581879672519363 42 14 15.3725651706667 -1.37256517066666 43 11 14.5517323011494 -3.55173230114944 44 15 15.4836097406597 -0.483609740659719 45 16 15.5548500174367 0.445149982563266 46 14 15.0406350869764 -1.04063508697640 47 16 14.2259703593387 1.77402964066126 48 14 14.7911890136018 -0.791189013601796 49 12 14.9826238539759 -2.98262385397585 50 16 15.2976764344992 0.702323565500791 51 9 13.3058767143725 -4.3058767143725 52 14 14.4630092807491 -0.463009280749088 53 16 15.8341040280580 0.16589597194198 54 16 15.1661480864629 0.833851913537109 55 15 14.6801849527143 0.319815047285719 56 16 15.2924442057987 0.707555794201252 57 12 13.5301062791087 -1.53010627910873 58 16 15.5911393670224 0.408860632977633 59 16 16.3613431796166 -0.361343179616587 60 14 16.6221460569512 -2.62214605695119 61 16 13.0506182989839 2.94938170101611 62 17 16.0438382600420 0.956161739958021 63 18 14.6909501371965 3.30904986280346 64 18 15.4289742089588 2.57102579104119 65 12 15.0016851338993 -3.00168513389928 66 16 15.6253182526312 0.374681747368784 67 10 14.082065204359 -4.082065204359 68 14 12.5772507134545 1.42274928654548 69 18 16.0901389784441 1.90986102155590 70 18 16.2397665003502 1.76023349964985 71 16 15.0458460581933 0.954153941806678 72 16 15.6198603716663 0.38013962833366 73 16 14.6537603792198 1.34623962078022 74 13 14.7136783918005 -1.71367839180047 75 16 15.2290601207143 0.770939879285746 76 16 14.6018789544887 1.3981210455113 77 20 15.9842718656095 4.01572813439047 78 16 15.3108730158702 0.689126984129812 79 15 12.4456023606307 2.55439763936933 80 15 15.7486007833534 -0.748600783353391 81 16 15.9780280218944 0.0219719781055923 82 14 13.8355211027793 0.164478897220684 83 15 13.4093643730382 1.59063562696179 84 12 14.9343337421162 -2.93433374211618 85 17 16.7650702644504 0.234929735549553 86 16 15.6031317672753 0.396868232724656 87 15 13.3415088959138 1.65849110408616 88 13 14.0729301694969 -1.07293016949692 89 16 15.569773707747 0.430226292253006 90 16 15.3878590203629 0.612140979637064 91 16 16.2058373533114 -0.205837353311379 92 16 15.8208066068929 0.179193393107128 93 14 15.1309167354651 -1.13091673546512 94 16 14.1529688358084 1.84703116419157 95 16 14.2645003168789 1.73549968312107 96 20 16.3832390878971 3.61676091210293 97 15 15.4521140970786 -0.452114097078607 98 16 14.2813028578053 1.71869714219474 99 13 13.6617315972799 -0.66173159727986 100 17 16.2610414849693 0.7389585150307 101 16 14.3401084155771 1.65989158442288 102 12 12.9285109546839 -0.928510954683935 103 16 14.8398196512641 1.16018034873588 104 16 15.3302691498733 0.669730850126732 105 17 15.3500964843352 1.64990351566477 106 13 12.6859084929629 0.314091507037111 107 12 15.9035742540845 -3.90357425408455 108 18 15.5131194383785 2.48688056162153 109 14 14.1184983214712 -0.118498321471246 110 14 14.6181299748476 -0.618129974847617 111 13 13.8771986874648 -0.877198687464781 112 16 15.5933865857765 0.406613414223484 113 13 12.7310713068999 0.268928693100056 114 16 15.0391901790127 0.960809820987319 115 13 15.1221406550671 -2.12214065506711 116 16 16.1679663409847 -0.167966340984661 117 15 14.6939710621260 0.306028937873954 118 16 15.8056921276484 0.194307872351554 119 15 14.5120285064589 0.487971493541146 120 17 15.8422829298316 1.15771707016835 121 15 16.2758336502491 -1.27583365024908 122 12 13.6897601426519 -1.68976014265193 123 16 14.2748748240866 1.72512517591337 124 10 14.2974021505662 -4.29740215056615 125 16 14.7705294194450 1.22947058055496 126 14 14.3793416353615 -0.379341635361508 127 15 16.5913787876252 -1.59137878762517 128 13 14.1498208933421 -1.14982089334215 129 15 14.6340042390997 0.365995760900317 130 11 13.9717434494744 -2.97174344947439 131 12 14.2457041903224 -2.24570419032235 132 8 14.1487084385333 -6.14870843853331 133 16 16.4471268481276 -0.447126848127603 134 15 14.9897656185287 0.010234381471306 135 17 15.8200067229749 1.17999327702509 136 16 14.7300790505303 1.26992094946971 137 10 15.1978366114683 -5.19783661146835 138 18 13.6595482484207 4.34045175157931 139 13 14.0407839427234 -1.0407839427234 140 15 14.5965699722997 0.40343002770027 141 16 14.4398683409763 1.56013165902365 142 16 15.1569184964695 0.843081503530482 143 14 13.9345648964196 0.0654351035804267 144 10 13.6079695566325 -3.60796955663248 145 17 16.7650702644504 0.234929735549553 146 13 14.9913678998657 -1.99136789986574 147 15 16.2758336502491 -1.27583365024908 148 16 15.8952757577186 0.104724242281385 149 12 15.5796022535339 -3.57960225353393 150 13 13.6965565585874 -0.696556558587408 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.926722612064215 0.146554775871571 0.0732773879357853 11 0.86290060490997 0.27419879018006 0.13709939509003 12 0.786665019958027 0.426669960083945 0.213334980041973 13 0.732174179097525 0.535651641804949 0.267825820902475 14 0.644180079347572 0.711639841304855 0.355819920652428 15 0.551146469529026 0.897707060941949 0.448853530470974 16 0.481833210934455 0.96366642186891 0.518166789065545 17 0.462899434672878 0.925798869345756 0.537100565327122 18 0.467152336178339 0.934304672356679 0.532847663821661 19 0.379718261611043 0.759436523222086 0.620281738388957 20 0.370575842591019 0.741151685182037 0.629424157408981 21 0.465754051461915 0.931508102923831 0.534245948538085 22 0.458652190258959 0.917304380517918 0.541347809741041 23 0.398431380352901 0.796862760705802 0.601568619647099 24 0.344082689936246 0.688165379872492 0.655917310063754 25 0.284687622115640 0.569375244231281 0.71531237788436 26 0.246342491821716 0.492684983643431 0.753657508178284 27 0.200899134618214 0.401798269236427 0.799100865381786 28 0.262934986187427 0.525869972374854 0.737065013812573 29 0.359240732796154 0.718481465592308 0.640759267203846 30 0.312664552427836 0.625329104855672 0.687335447572164 31 0.270973330854787 0.541946661709574 0.729026669145213 32 0.227422580167568 0.454845160335135 0.772577419832432 33 0.90355491626924 0.19289016746152 0.09644508373076 34 0.94348261881205 0.113034762375899 0.0565173811879493 35 0.925614393705486 0.148771212589028 0.0743856062945139 36 0.90559457493546 0.188810850129082 0.0944054250645408 37 0.895091371174956 0.209817257650089 0.104908628825045 38 0.916645286557642 0.166709426884717 0.0833547134423583 39 0.913585195768623 0.172829608462755 0.0864148042313775 40 0.958747291865244 0.0825054162695115 0.0412527081347558 41 0.947617417231267 0.104765165537466 0.0523825827687332 42 0.943544534028032 0.112910931943936 0.056455465971968 43 0.964785948015577 0.0704281039688454 0.0352140519844227 44 0.953760167330882 0.0924796653382354 0.0462398326691177 45 0.940604746833143 0.118790506333714 0.0593952531668571 46 0.928526433670357 0.142947132659286 0.071473566329643 47 0.918939762889635 0.16212047422073 0.081060237110365 48 0.899590065566925 0.200819868866150 0.100409934433075 49 0.918904402811708 0.162191194376583 0.0810955971882915 50 0.899801986091366 0.200396027817268 0.100198013908634 51 0.944438073681694 0.111123852636612 0.0555619263183061 52 0.929360721388514 0.141278557222972 0.070639278611486 53 0.911240281275085 0.177519437449829 0.0887597187249147 54 0.896025059469128 0.207949881061745 0.103974940530872 55 0.876057578197265 0.247884843605469 0.123942421802735 56 0.851982208567923 0.296035582864153 0.148017791432077 57 0.837472051166126 0.325055897667747 0.162527948833874 58 0.809724920207143 0.380550159585715 0.190275079792857 59 0.774788955308651 0.450422089382698 0.225211044691349 60 0.797541177298233 0.404917645403535 0.202458822701767 61 0.819799023526583 0.360401952946833 0.180200976473417 62 0.79295616155855 0.414087676882899 0.207043838441450 63 0.840224208490346 0.319551583019308 0.159775791509654 64 0.858579430601645 0.282841138796711 0.141420569398355 65 0.89122935408129 0.217541291837421 0.108770645918711 66 0.868158139161899 0.263683721676202 0.131841860838101 67 0.925958123283653 0.148083753432695 0.0740418767163473 68 0.915982351180543 0.168035297638914 0.0840176488194568 69 0.914361791800735 0.171276416398531 0.0856382081992654 70 0.911104446025693 0.177791107948615 0.0888955539743075 71 0.894466983533374 0.211066032933252 0.105533016466626 72 0.871312811902193 0.257374376195613 0.128687188097807 73 0.859022115337794 0.281955769324411 0.140977884662206 74 0.850579024403875 0.298841951192249 0.149420975596125 75 0.824285635212585 0.35142872957483 0.175714364787415 76 0.805269797147896 0.389460405704208 0.194730202852104 77 0.884723845033498 0.230552309933003 0.115276154966502 78 0.863192172594901 0.273615654810198 0.136807827405099 79 0.873242169321752 0.253515661356496 0.126757830678248 80 0.849462929624987 0.301074140750026 0.150537070375013 81 0.81961398624815 0.360772027503702 0.180386013751851 82 0.789418917031828 0.421162165936343 0.210581082968172 83 0.78113989491289 0.43772021017422 0.21886010508711 84 0.815291968496896 0.369416063006209 0.184708031503104 85 0.781075788807611 0.437848422384778 0.218924211192389 86 0.745032201117412 0.509935597765177 0.254967798882588 87 0.732793207817668 0.534413584364663 0.267206792182332 88 0.700924588241739 0.598150823516523 0.299075411758262 89 0.657524709897595 0.684950580204809 0.342475290102405 90 0.61683839564979 0.766323208700421 0.383161604350211 91 0.568548641520535 0.86290271695893 0.431451358479465 92 0.519305710555407 0.961388578889186 0.480694289444593 93 0.490190788147677 0.980381576295353 0.509809211852323 94 0.51073715149231 0.97852569701538 0.48926284850769 95 0.499983059086244 0.999966118172487 0.500016940913756 96 0.610598776792069 0.778802446415861 0.389401223207931 97 0.561466215366656 0.877067569266687 0.438533784633344 98 0.585287143656545 0.82942571268691 0.414712856343455 99 0.542797555747814 0.914404888504373 0.457202444252186 100 0.520547873616498 0.958904252767004 0.479452126383502 101 0.510181642161424 0.979636715677151 0.489818357838576 102 0.472565336451383 0.945130672902767 0.527434663548617 103 0.439617033571312 0.879234067142624 0.560382966428688 104 0.43134962078847 0.86269924157694 0.56865037921153 105 0.447382906757489 0.894765813514977 0.552617093242511 106 0.395254537956243 0.790509075912486 0.604745462043757 107 0.5194731570465 0.961053685907 0.4805268429535 108 0.547171229898434 0.905657540203131 0.452828770101566 109 0.533162289543140 0.933675420913719 0.466837710456859 110 0.483450995712807 0.966901991425615 0.516549004287193 111 0.43110622901917 0.86221245803834 0.56889377098083 112 0.380993519326153 0.761987038652305 0.619006480673847 113 0.34658775979444 0.69317551958888 0.65341224020556 114 0.331839043234735 0.66367808646947 0.668160956765265 115 0.321423569080533 0.642847138161066 0.678576430919467 116 0.269647223732303 0.539294447464606 0.730352776267697 117 0.253439157084959 0.506878314169917 0.746560842915041 118 0.265905982858297 0.531811965716594 0.734094017141703 119 0.218468479224313 0.436936958448626 0.781531520775687 120 0.194093656422748 0.388187312845496 0.805906343577252 121 0.163743904536326 0.327487809072652 0.836256095463674 122 0.141365397687321 0.282730795374642 0.85863460231268 123 0.128961719173221 0.257923438346443 0.871038280826779 124 0.180250098025394 0.360500196050788 0.819749901974606 125 0.142908650765724 0.285817301531448 0.857091349234276 126 0.112975668937071 0.225951337874142 0.887024331062929 127 0.0859688923087947 0.171937784617589 0.914031107691205 128 0.0667789885957007 0.133557977191401 0.9332210114043 129 0.0460130125818058 0.0920260251636115 0.953986987418194 130 0.0371307831473585 0.074261566294717 0.962869216852641 131 0.0274085518919564 0.0548171037839129 0.972591448108044 132 0.212367842227788 0.424735684455577 0.787632157772212 133 0.157020889264583 0.314041778529166 0.842979110735417 134 0.108701816843205 0.217403633686409 0.891298183156795 135 0.146095768943771 0.292191537887542 0.853904231056229 136 0.133478151438437 0.266956302876874 0.866521848561563 137 0.156107263766905 0.312214527533809 0.843892736233095 138 0.474053558702569 0.948107117405139 0.525946441297431 139 0.421386145776767 0.842772291553534 0.578613854223233 140 0.352668640130936 0.705337280261872 0.647331359869064 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 6 0.0458015267175573 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/106gno1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/106gno1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/1a6pf1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/1a6pf1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/2a6pf1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/2a6pf1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/3a6pf1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/3a6pf1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/4kxoi1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/4kxoi1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/5kxoi1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/5kxoi1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/6kxoi1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/6kxoi1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/7d76l1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/7d76l1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/86gno1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/86gno1292256432.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/96gno1292256432.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/13/t1292256538zxrlpkkxgiexot1/96gno1292256432.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