*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: Tue, 14 Dec 2010 18:47:29 +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/14/t1292352326767j83gx2o1kg40.htm/, Retrieved Tue, 14 Dec 2010 19:45:36 +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/14/t1292352326767j83gx2o1kg40.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 69 26 9 15 6 25 25 1 53 20 9 15 6 25 24 1 43 21 9 14 13 19 21 0 60 31 14 10 8 18 23 1 49 21 8 10 7 18 17 1 62 18 8 12 9 22 19 1 45 26 11 18 5 29 18 1 50 22 10 12 8 26 27 1 75 22 9 14 9 25 23 1 82 29 15 18 11 23 23 0 60 15 14 9 8 23 29 1 59 16 11 11 11 23 21 1 21 24 14 11 12 24 26 1 62 17 6 17 8 30 25 0 54 19 20 8 7 19 25 1 47 22 9 16 9 24 23 1 59 31 10 21 12 32 26 0 37 28 8 24 20 30 20 0 43 38 11 21 7 29 29 1 48 26 14 14 8 17 24 0 79 25 11 7 8 25 23 0 62 25 16 18 16 26 24 1 16 29 14 18 10 26 30 0 38 28 11 13 6 25 22 1 58 15 11 11 8 23 22 0 60 18 12 13 9 21 13 0 67 21 9 13 9 19 24 0 55 25 7 18 11 35 17 1 47 23 13 14 12 19 24 0 59 23 10 12 8 20 21 1 49 19 9 9 7 21 23 0 47 18 9 12 8 21 24 1 57 18 13 8 9 24 24 0 39 26 16 5 4 23 24 1 49 18 12 10 8 19 23 1 26 18 6 11 8 17 26 0 53 28 14 11 8 24 24 0 75 17 14 12 6 15 21 1 65 29 10 12 8 25 23 1 49 12 4 15 4 27 28 0 48 25 12 12 7 29 23 0 45 28 12 16 14 27 22 0 31 20 14 14 10 18 24 1 61 17 9 17 9 25 21 1 49 17 9 13 6 22 23 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 Anxiety[t] = + 62.6264784679251 -4.54909193536482Gender[t] -0.361672579691563Mistakes[t] + 0.169699251103092Doubts[t] + 0.788331768369544Expectations[t] -1.10287823451153Critism[t] + 0.108738082842006Pstandards[t] -0.232322040117943Organization[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) 62.6264784679251 9.241389 6.7767 0 0 Gender -4.54909193536482 2.197821 -2.0698 0.040256 0.020128 Mistakes -0.361672579691563 0.241604 -1.497 0.136591 0.068295 Doubts 0.169699251103092 0.441371 0.3845 0.701188 0.350594 Expectations 0.788331768369544 0.402988 1.9562 0.052375 0.026187 Critism -1.10287823451153 0.505322 -2.1825 0.030692 0.015346 Pstandards 0.108738082842006 0.309861 0.3509 0.726157 0.363079 Organization -0.232322040117943 0.316958 -0.733 0.464766 0.232383 Multiple Linear Regression - Regression Statistics Multiple R 0.275657312550536 R-squared 0.0759869539625837 Adjusted R-squared 0.0310696531135426 F-TEST (value) 1.69170792826493 F-TEST (DF numerator) 7 F-TEST (DF denominator) 144 p-value 0.115413556168906 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.2170763953282 Sum Squared Residuals 25155.5196153517 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 69 56.8683928424479 12.1316071575521 2 53 54.7216584253503 -1.72165842535032 3 43 45.8960440590103 -2.89604405901034 4 60 49.4645883889766 10.5354116110234 5 49 50.010837219128 -1.01083721912801 6 62 50.4370702770509 11.5629297229491 7 45 58.187779561103 -13.1877795611030 8 50 49.0090327054268 0.990967294573178 9 75 50.133668834181 24.8663311658190 10 82 49.3502507217298 32.6497492782702 11 60 53.6127760691744 6.38722393082557 12 59 48.3195189549568 10.6804810450432 13 21 43.7794857184743 -22.7794857184743 14 62 54.9798538529239 7.0201461470761 15 54 53.9931635522725 0.0068364477275441 16 47 51.6015942880781 -4.60159428807813 17 59 49.3222030026525 9.6777969973475 18 37 49.3353396789258 -12.3353396789258 19 43 56.0004969349573 -13.0004969349573 20 48 49.5361263025878 -1.53612630258779 21 79 49.5216973886021 29.4783026113979 22 62 50.0952332628143 11.9047667371857 23 16 48.9833896728386 -32.9833896728386 24 38 55.6047487688857 -17.6047487688857 25 58 51.757504198065 6.242495801935 26 60 57.7384851430633 2.26151485693674 27 67 53.3713510436979 13.6286489563021 28 55 56.6872282018478 -1.68722820184783 29 47 46.2574080181973 0.742591981802725 30 59 53.9379558047557 5.06204419524429 31 49 49.047831869063 -0.0478318690630597 32 47 54.9883914145986 -7.9883914145986 33 57 47.1881054241825 9.81189457581754 34 39 52.3935722599311 -13.3935722599311 35 49 49.3865795702379 -0.386579570237898 36 26 48.242273545951 -22.2422735459511 37 53 51.7580443533549 1.24195564664509 38 75 58.4488543421305 16.5511456578695 39 65 47.2978747252156 17.7021252747844 40 49 58.2604872816027 -9.26048728160273 41 48 55.1708860474325 -7.17088604743246 42 45 49.5338936146891 -4.53389361468915 43 31 54.1582353299209 -23.1582353299209 44 61 54.7716711179834 6.22832888201662 45 49 54.1361204192779 -5.1361204192779 46 69 49.0850024885426 19.9149975114574 47 54 52.9149325985926 1.08506740140735 48 80 53.7617045525349 26.2382954474651 49 57 56.7644452206825 0.235554779317547 50 34 52.3490862820169 -18.3490862820169 51 69 57.1420760357219 11.8579239642781 52 44 48.7252614958083 -4.72526149580835 53 70 52.6607505984853 17.3392494015147 54 51 55.8650122801255 -4.86501228012553 55 66 51.5512570058816 14.4487429941184 56 18 46.9244344438527 -28.9244344438527 57 74 49.7394531084645 24.2605468915355 58 59 54.6995302279488 4.30046977205118 59 48 55.9314419418487 -7.93144194184865 60 55 49.8045672759454 5.1954327240546 61 44 50.5696397410586 -6.56963974105858 62 56 51.986479526336 4.01352047366403 63 65 55.3351280673687 9.66487193263131 64 77 52.6150190165715 24.3849809834285 65 46 45.3857436450983 0.61425635490167 66 70 54.3410779681867 15.6589220318133 67 39 54.5524444273522 -15.5524444273522 68 55 59.3390187494025 -4.33901874940246 69 44 53.0384156384161 -9.03841563841607 70 45 56.4683324091105 -11.4683324091105 71 45 52.8442834771808 -7.84428347718081 72 49 58.2605569672452 -9.26055696724524 73 65 48.1551418995639 16.8448581004361 74 45 50.9692730454757 -5.9692730454757 75 71 49.3155468247754 21.6844531752246 76 48 50.9040230802883 -2.90402308028825 77 41 46.3865725417958 -5.38657254179578 78 40 54.4234088893673 -14.4234088893673 79 64 52.9991764651861 11.0008235348139 80 56 53.6776831573423 2.32231684265774 81 52 55.1672318876934 -3.16723188769337 82 41 47.1669139630747 -6.16691396307471 83 42 52.7597642203348 -10.7597642203348 84 54 58.1303769171605 -4.13037691716054 85 40 47.8255198689898 -7.8255198689898 86 40 49.4696513449374 -9.46965134493737 87 51 55.4680335780203 -4.46803357802025 88 48 55.6558883643725 -7.65588836437252 89 80 62.4793001427889 17.5206998572111 90 38 57.3483504592595 -19.3483504592595 91 57 57.0216305591598 -0.0216305591597555 92 28 44.8882630066128 -16.8882630066128 93 51 53.8701651397952 -2.87016513979517 94 46 51.490009762044 -5.49000976204398 95 58 53.3339480184928 4.66605198150716 96 67 50.9287780045707 16.0712219954293 97 72 48.5906673900651 23.4093326099349 98 26 48.6181524816782 -22.6181524816782 99 54 53.8390817363771 0.160918263622943 100 53 53.3967192630048 -0.396719263004798 101 64 46.8995871145523 17.1004128854477 102 47 48.5957112245472 -1.59571122454718 103 43 54.7710864290161 -11.7710864290161 104 66 47.4292694491622 18.5707305508378 105 54 50.1728990825374 3.82710091746262 106 62 56.7738835878157 5.22611641218431 107 52 50.8934831444521 1.10651685554786 108 64 53.0741070795385 10.9258929204615 109 55 49.7186490580538 5.28135094194622 110 57 54.9531190994082 2.04688090059182 111 74 54.035664947457 19.9643350525430 112 32 49.0143670547102 -17.0143670547102 113 38 52.9323366417668 -14.9323366417668 114 66 52.1031182054533 13.8968817945467 115 37 55.5088808818292 -18.5088808818292 116 26 49.5283945733107 -23.5283945733107 117 64 51.3482231288045 12.6517768711955 118 28 47.3893198940499 -19.3893198940499 119 66 60.5866201128332 5.41337988716681 120 65 54.2982054513544 10.7017945486456 121 48 48.4936803092093 -0.493680309209308 122 44 54.7518905242933 -10.7518905242933 123 64 56.7921441833638 7.2078558166362 124 39 48.72029818031 -9.72029818031 125 50 53.3495799354858 -3.34957993548582 126 66 51.6122181741426 14.3877818258574 127 48 52.0982219457131 -4.09822194571310 128 70 55.4479711117546 14.5520288882454 129 66 58.7784256393873 7.22157436061267 130 61 49.7056967811452 11.2943032188548 131 31 48.732352955723 -17.7323529557230 132 61 54.6760347657122 6.32396523428777 133 54 45.0684743181211 8.93152568187891 134 34 45.2149963154671 -11.2149963154671 135 62 49.8284566108289 12.1715433891711 136 47 53.841147467513 -6.84114746751297 137 52 48.1021138553018 3.89788614469821 138 37 59.8945094173258 -22.8945094173258 139 46 46.8097998676881 -0.809799867688128 140 38 53.0809431521072 -15.0809431521072 141 63 51.757504198065 11.242495801935 142 34 54.9949948139028 -20.9949948139028 143 46 47.225674051785 -1.22567405178502 144 40 46.3566514362257 -6.35665143622572 145 30 47.8255198689898 -17.8255198689898 146 35 48.2784881954771 -13.2784881954771 147 51 48.4936803092093 2.50631969079069 148 56 55.089911859418 0.910088140582046 149 68 52.9455708120432 15.0544291879568 150 39 51.4191899341407 -12.4191899341407 151 44 53.9356715056027 -9.93567150560273 152 58 51.4191899341407 6.58081006585932 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.906584485556144 0.186831028887712 0.0934155144438562 12 0.833002869091047 0.333994261817906 0.166997130908953 13 0.954564785331215 0.0908704293375694 0.0454352146687847 14 0.920552515093133 0.158894969813733 0.0794474849068666 15 0.876230730015829 0.247538539968343 0.123769269984171 16 0.871639559504647 0.256720880990706 0.128360440495353 17 0.817158875581931 0.365682248836138 0.182841124418069 18 0.836383348190564 0.327233303618872 0.163616651809436 19 0.872476752495675 0.255046495008650 0.127523247504325 20 0.861312475865695 0.277375048268610 0.138687524134305 21 0.919959732552526 0.160080534894948 0.0800402674474739 22 0.908510319228728 0.182979361542543 0.0914896807712715 23 0.964971575311987 0.0700568493760255 0.0350284246880127 24 0.9817162190004 0.0365675619991991 0.0182837809995995 25 0.972966234121493 0.0540675317570141 0.0270337658785071 26 0.963585597822903 0.0728288043541937 0.0364144021770969 27 0.95586706852882 0.08826586294236 0.04413293147118 28 0.940742266020964 0.118515467958071 0.0592577339790355 29 0.919193971075365 0.161612057849271 0.0808060289246353 30 0.893771031724157 0.212457936551686 0.106228968275843 31 0.86872591453739 0.262548170925221 0.131274085462610 32 0.861393315372932 0.277213369254136 0.138606684627068 33 0.833079594073844 0.333840811852313 0.166920405926156 34 0.846211097833152 0.307577804333697 0.153788902166848 35 0.811287820625366 0.377424358749268 0.188712179374634 36 0.87594144853743 0.248117102925139 0.124058551462569 37 0.843594243949253 0.312811512101495 0.156405756050747 38 0.844940654072231 0.310118691855539 0.155059345927769 39 0.866330347766281 0.267339304467438 0.133669652233719 40 0.844673120788621 0.310653758422757 0.155326879211379 41 0.822835101175625 0.354329797648750 0.177164898824375 42 0.793173158849349 0.413653682301303 0.206826841150651 43 0.854539401894449 0.290921196211102 0.145460598105551 44 0.826565701582842 0.346868596834316 0.173434298417158 45 0.796041793246357 0.407916413507286 0.203958206753643 46 0.819301134237778 0.361397731524445 0.180698865762222 47 0.782088522130919 0.435822955738163 0.217911477869082 48 0.871100441400848 0.257799117198305 0.128899558599153 49 0.84227745049085 0.315445099018301 0.157722549509150 50 0.857104875460693 0.285790249078613 0.142895124539307 51 0.843497596819683 0.313004806360633 0.156502403180317 52 0.834757830732526 0.330484338534948 0.165242169267474 53 0.84556454715253 0.308870905694940 0.154435452847470 54 0.821556501419026 0.356886997161948 0.178443498580974 55 0.818493012806894 0.363013974386213 0.181506987193106 56 0.929814941449645 0.14037011710071 0.070185058550355 57 0.9582857949304 0.083428410139199 0.0417142050695995 58 0.94705619827365 0.105887603452700 0.0529438017263501 59 0.936788713938335 0.126422572123329 0.0632112860616647 60 0.921930907635948 0.156138184728104 0.078069092364052 61 0.915252301658444 0.169495396683112 0.0847476983415558 62 0.897124425485178 0.205751149029643 0.102875574514822 63 0.885162536384724 0.229674927230551 0.114837463615276 64 0.933152355886663 0.133695288226673 0.0668476441133366 65 0.916158732830082 0.167682534339837 0.0838412671699184 66 0.922610086779135 0.154779826441731 0.0773899132208654 67 0.931037394180722 0.137925211638556 0.068962605819278 68 0.915235794094206 0.169528411811588 0.0847642059057941 69 0.90825192263048 0.183496154739042 0.0917480773695209 70 0.902653156518795 0.194693686962410 0.0973468434812049 71 0.889998154381407 0.220003691237187 0.110001845618593 72 0.877055964754627 0.245888070490746 0.122944035245373 73 0.889452689004102 0.221094621991795 0.110547310995898 74 0.870351284316133 0.259297431367734 0.129648715683867 75 0.925390698773926 0.149218602452148 0.074609301226074 76 0.907911386593024 0.184177226813952 0.092088613406976 77 0.890234683739222 0.219530632521555 0.109765316260778 78 0.890770665148064 0.218458669703872 0.109229334851936 79 0.894486270453313 0.211027459093375 0.105513729546687 80 0.872792054919125 0.254415890161749 0.127207945080875 81 0.846977906148545 0.306044187702909 0.153022093851455 82 0.822498741111484 0.355002517777032 0.177501258888516 83 0.813062526441153 0.373874947117695 0.186937473558847 84 0.781496166540126 0.437007666919748 0.218503833459874 85 0.756607161452642 0.486785677094716 0.243392838547358 86 0.739585031262244 0.520829937475512 0.260414968737756 87 0.710158958982883 0.579682082034235 0.289841041017118 88 0.697988563997013 0.604022872005973 0.302011436002987 89 0.720218728150562 0.559562543698877 0.279781271849438 90 0.768871064910916 0.462257870178168 0.231128935089084 91 0.729709910347238 0.540580179305524 0.270290089652762 92 0.745187763786466 0.509624472427067 0.254812236213534 93 0.707404894536794 0.585190210926412 0.292595105463206 94 0.682383938552382 0.635232122895235 0.317616061447618 95 0.641416239601728 0.717167520796545 0.358583760398272 96 0.642818837476366 0.714362325047268 0.357181162523634 97 0.72387463771385 0.552250724572301 0.276125362286150 98 0.806853773836289 0.386292452327423 0.193146226163711 99 0.769056951236136 0.461886097527728 0.230943048763864 100 0.727141385484142 0.545717229031716 0.272858614515858 101 0.78320809067866 0.433583818642679 0.216791909321339 102 0.74493380713415 0.510132385731700 0.255066192865850 103 0.754970621104038 0.490058757791924 0.245029378895962 104 0.79288356975466 0.414232860490679 0.207116430245340 105 0.760751842897627 0.478496314204746 0.239248157102373 106 0.725621531898977 0.548756936202045 0.274378468101023 107 0.677883707975714 0.644232584048571 0.322116292024286 108 0.648865642158395 0.70226871568321 0.351134357841605 109 0.612620407214158 0.774759185571683 0.387379592785842 110 0.559973828521081 0.880052342957838 0.440026171478919 111 0.616603479389773 0.766793041220454 0.383396520610227 112 0.643794887666487 0.712410224667026 0.356205112333513 113 0.630787277707196 0.738425444585608 0.369212722292804 114 0.612704538229719 0.774590923540562 0.387295461770281 115 0.628306385397956 0.743387229204088 0.371693614602044 116 0.761440647838667 0.477118704322666 0.238559352161333 117 0.75373712232401 0.49252575535198 0.24626287767599 118 0.828733206132194 0.342533587735612 0.171266793867806 119 0.789261644650962 0.421476710698076 0.210738355349038 120 0.763185336537162 0.473629326925676 0.236814663462838 121 0.710910352882027 0.578179294235946 0.289089647117973 122 0.68031051712088 0.63937896575824 0.31968948287912 123 0.644910476146118 0.710179047707764 0.355089523853882 124 0.636921494127129 0.726157011745742 0.363078505872871 125 0.569473087452575 0.86105382509485 0.430526912547425 126 0.56146731217799 0.87706537564402 0.43853268782201 127 0.48901222505462 0.97802445010924 0.51098777494538 128 0.542883326123664 0.914233347752672 0.457116673876336 129 0.518786319500513 0.962427360998973 0.481213680499487 130 0.595509196014590 0.808981607970821 0.404490803985410 131 0.648863022723051 0.702273954553898 0.351136977276949 132 0.692869486517902 0.614261026964196 0.307130513482098 133 0.615610593001964 0.768778813996072 0.384389406998036 134 0.726754801249319 0.546490397501362 0.273245198750681 135 0.746652578004773 0.506694843990454 0.253347421995227 136 0.787080596528094 0.425838806943812 0.212919403471906 137 0.71429755902708 0.571404881945838 0.285702440972919 138 0.622387633767318 0.755224732465364 0.377612366232682 139 0.490831376076063 0.981662752152127 0.509168623923937 140 0.476367700116324 0.952735400232648 0.523632299883676 141 0.471436632886288 0.942873265772576 0.528563367113712 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 1 0.00763358778625954 OK 10% type I error level 7 0.0534351145038168 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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