statistiek Multiple Regression.

*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, 18 Dec 2010 19:03:25 +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/18/t1292699168dm9w06zjgiqs6aq.htm/, Retrieved Sat, 18 Dec 2010 20:06:19 +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/18/t1292699168dm9w06zjgiqs6aq.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 «

6.5 8.9 9 6.3 8.4 11 5.9 8.1 13 5.5 8.3 12 5.2 8.1 13 4.9 8 15 5.4 8.7 13 5.8 9.2 16 5.7 9 10 5.6 8.9 14 5.5 8.5 14 5.4 8.1 15 5.4 7.5 13 5.4 7.1 8 5.5 6.9 7 5.8 7.1 3 5.7 7 3 5.4 6.7 4 5.6 7 4 5.8 7.3 0 6.2 7.7 -4 6.8 8.4 -14 6.7 8.4 -18 6.7 8.8 -8 6.4 9.1 -1 6.3 9 1 6.3 8.6 2 6.4 7.9 0 6.3 7.7 1 6 7.8 0 6.3 9.2 -1 6.3 9.4 -3 6.6 9.2 -3 7.5 8.7 -3 7.8 8.4 -4 7.9 8.6 -8 7.8 9 -9 7.6 9.1 -13 7.5 8.7 -18 7.6 8.2 -11 7.5 7.9 -9 7.3 7.9 -10 7.6 9.1 -13 7.5 9.4 -11 7.6 9.4 -5 7.9 9.1 -15 7.9 9 -6 8.1 9.3 -6 8.2 9.9 -3 8 9.8 -1 7.5 9.3 -3 6.8 8.3 -4 6.5 8 -6 6.6 8.5 0 7.6 10.4 -4 8 11.1 -2 8.1 10.9 -2 7.7 10 -6 7.5 9.2 -7 7.6 9.2 -6 7.8 9.5 -6 7.8 9.6 -3 7.8 9.5 -2 7.5 9.1 -5 7.5 8.9 -11 7.1 9 -11 7.5 10.1 -11 7.5 10.3 -10 7.6 10.2 -14 7.7 9.6 -8 7.7 9.2 -9 7.9 9.3 -5 8.1 9.4 -1 8.2 9.4 -2 8.2 9.2 -5 8.2 9 -4 7.9 9 -6 7.3 9 -2 6.9 9.8 -2 6.6 10 -2 6.7 9.8 -2 6.9 9.3 2 7 9 1 7.1 9 -8 7.2 9.1 -1 7.1 9.1 1 6.9 9.1 -1 7 9.2 2 6.8 8. 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 Multiple Linear Regression - Estimated Regression Equation Mannen[t] = + 2.18740827895666 + 0.523700805492581Vrouwen[t] -0.055791873033909Consumvertr[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) 2.18740827895666 0.457705 4.7791 5e-06 3e-06 Vrouwen 0.523700805492581 0.05283 9.9129 0 0 Consumvertr -0.055791873033909 0.005352 -10.4237 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.79967583384841 R-squared 0.639481439241151 Adjusted R-squared 0.633318728800829 F-TEST (value) 103.766264119285 F-TEST (DF numerator) 2 F-TEST (DF denominator) 117 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.52329703002799 Sum Squared Residuals 32.0392544514255 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.5 6.34621859053545 0.153781409464553 2 6.3 5.97278444172134 0.327215558278656 3 5.9 5.70409045400575 0.195909545994253 4 5.5 5.86462248813817 -0.364622488138174 5 5.2 5.70409045400575 -0.504090454005748 6 4.9 5.54013662738867 -0.640136627388672 7 5.4 6.01831093730130 -0.618310937301296 8 5.8 6.11278572094586 -0.31278572094586 9 5.7 6.3427967980508 -0.642796798050798 10 5.6 6.06725922536590 -0.467259225365905 11 5.5 5.85777890316887 -0.357778903168871 12 5.4 5.59250670793793 -0.192506707937930 13 5.4 5.3898699707102 0.0101300292898011 14 5.4 5.45934901368271 -0.0593490136827114 15 5.5 5.4104007256181 0.0895992743818949 16 5.8 5.73830837885226 0.0616916211477428 17 5.7 5.685938298303 0.0140617016970011 18 5.4 5.47303618362132 -0.0730361836213155 19 5.6 5.63014642526909 -0.0301464252690904 20 5.8 6.0104241590525 -0.210424159052501 21 6.2 6.44307197338517 -0.243071973385169 22 6.8 7.36758126756907 -0.567581267569067 23 6.7 7.5907487597047 -0.890748759704703 24 6.7 7.24231035156265 -0.542310351562645 25 6.4 7.00887748197306 -0.608877481973056 26 6.3 6.84492365535598 -0.54492365535598 27 6.3 6.57965146012504 -0.279651460125038 28 6.4 6.32464464234805 0.0753553576519507 29 6.3 6.16411260821562 0.135887391784376 30 6 6.27227456179879 -0.272274561798791 31 6.3 7.06124756252231 -0.761247562522314 32 6.3 7.27757146968865 -0.97757146968865 33 6.6 7.17283130859013 -0.572831308590132 34 7.5 6.91098090584384 0.589019094156159 35 7.8 6.80966253722998 0.990337462770023 36 7.9 7.13757019046413 0.762429809535872 37 7.8 7.40284238569507 0.397157614304929 38 7.6 7.67837995837996 -0.0783799583799649 39 7.5 7.74785900135248 -0.247859001352477 40 7.6 7.09546548736882 0.504534512631177 41 7.5 6.82677149965323 0.673228500346769 42 7.3 6.88256337268714 0.417436627312860 43 7.6 7.67837995837996 -0.0783799583799649 44 7.5 7.72390645395992 -0.223906453959921 45 7.6 7.38915521575647 0.210844784243533 46 7.9 7.78996370444778 0.110036295552218 47 7.9 7.23546676659334 0.664533233406657 48 8.1 7.39257700824112 0.707422991758882 49 8.2 7.53942187243494 0.66057812756506 50 8 7.37546804581786 0.624531954182137 51 7.5 7.22520138913939 0.274798610860609 52 6.8 6.75729245668072 0.0427075433192812 53 6.5 6.71176596110076 -0.211765961100762 54 6.6 6.6388651256436 -0.0388651256435986 55 7.6 7.85706414821514 -0.257064148215140 56 8 8.11207096599213 -0.112070965992127 57 8.1 8.00733080489361 0.0926691951063881 58 7.7 7.75916757208593 -0.0591675720859244 59 7.5 7.39599880072577 0.104001199274232 60 7.6 7.34020692769186 0.259793072308140 61 7.8 7.49731716933963 0.302682830660366 62 7.8 7.38231163078716 0.417688369212835 63 7.8 7.274149677204 0.525850322796002 64 7.5 7.2320449741087 0.267955025891308 65 7.5 7.46205605121363 0.0379439487863695 66 7.1 7.51442613176289 -0.414426131762889 67 7.5 8.09049701780473 -0.590497017804728 68 7.5 8.13944530586934 -0.639445305869335 69 7.6 8.31024271745571 -0.710242717455713 70 7.7 7.66127099595671 0.0387290040432903 71 7.7 7.50758254679359 0.192417453206414 72 7.9 7.33678513520721 0.563214864792791 73 8.1 7.16598772362083 0.934012276379169 74 8.2 7.22177959665474 0.97822040334526 75 8.2 7.28441505465795 0.915584945342049 76 8.2 7.12388302052553 1.07611697947447 77 7.9 7.23546676659334 0.664533233406657 78 7.3 7.0122992744577 0.287700725542293 79 6.9 7.43125991885177 -0.531259918851772 80 6.6 7.53600007995029 -0.936000079950289 81 6.7 7.43125991885177 -0.731259918851772 82 6.9 6.94624202396985 -0.0462420239698452 83 7 6.84492365535598 0.15507634464402 84 7.1 7.34705051266116 -0.247050512661162 85 7.2 7.00887748197306 0.191122518026944 86 7.1 6.89729373590524 0.202706264094762 87 6.9 7.00887748197306 -0.108877481973056 88 7 6.89387194342059 0.106128056579413 89 6.8 6.68439162122356 0.115608378776445 90 6.4 6.47833309151117 -0.078333091511173 91 6.7 6.64228691812825 0.0577130818717508 92 6.6 6.54096854951438 0.059031450485616 93 6.4 6.33148822731735 0.0685117726826489 94 6.3 6.38043651538196 -0.0804365153819589 95 6.2 6.77097962661932 -0.570979626619322 96 6.5 6.60018221503294 -0.100182215032944 97 6.8 6.6593958805515 0.140604119448496 98 6.8 6.334910019802 0.465089980197998 99 6.4 6.07305961705571 0.326940382944289 100 6.1 6.13911686754357 -0.0391168675435737 101 5.8 6.09359037196362 -0.293590371963617 102 6.1 6.41465444022847 -0.314654440228468 103 7.2 7.20704923343664 -0.007049233436641 104 7.3 7.35731589011511 -0.0573158901151138 105 6.9 7.03625182185026 -0.136251821850263 106 6.1 7.2731064839245 -1.17310648392450 107 5.8 7.34258552689702 -1.54258552689702 108 6.2 7.66707138764652 -1.46707138764652 109 7.1 7.64996242522326 -0.549962425223264 110 7.7 7.92549999790816 -0.225499997908157 111 8 7.82075983680964 0.179240163190359 112 7.8 7.39495560744627 0.405044392553726 113 7.4 7.01809966614751 0.381900333852486 114 7.4 7.11941803476138 0.280581965238621 115 7.7 7.69206712831857 0.007932871681432 116 7.8 7.51442613176289 0.285573868237111 117 7.8 7.30494580956586 0.495054190434144 118 8 6.99414711875496 1.00585288124504 119 8.1 6.83019329213788 1.26980670786212 120 8.4 7.31863297950446 1.08136702049554 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.27586473278979 0.55172946557958 0.72413526721021 7 0.154102884338335 0.30820576867667 0.845897115661665 8 0.208779831741633 0.417559663483267 0.791220168258367 9 0.292313454929630 0.584626909859259 0.70768654507037 10 0.199043041728507 0.398086083457014 0.800956958271493 11 0.128741122362153 0.257482244724306 0.871258877637847 12 0.0862710191805094 0.172542038361019 0.91372898081949 13 0.0526476726969316 0.105295345393863 0.947352327303068 14 0.0365872446375134 0.0731744892750268 0.963412755362487 15 0.0205221646172355 0.0410443292344711 0.979477835382764 16 0.0120438519006234 0.0240877038012467 0.987956148099377 17 0.00682835584400025 0.0136567116880005 0.993171644156 18 0.00407575604658161 0.00815151209316322 0.995924243953418 19 0.00212398226526952 0.00424796453053905 0.99787601773473 20 0.00150416108992658 0.00300832217985315 0.998495838910073 21 0.000890310379374965 0.00178062075874993 0.999109689620625 22 0.000593387187905395 0.00118677437581079 0.999406612812095 23 0.000555859761598426 0.00111171952319685 0.999444140238402 24 0.000300024685232896 0.000600049370465792 0.999699975314767 25 0.000168592529738326 0.000337185059476651 0.999831407470262 26 9.39627936863505e-05 0.000187925587372701 0.999906037206314 27 5.46361428820218e-05 0.000109272285764044 0.999945363857118 28 5.46455234964045e-05 0.000109291046992809 0.999945354476504 29 5.03541628946294e-05 0.000100708325789259 0.999949645837105 30 2.70706713750271e-05 5.41413427500542e-05 0.999972929328625 31 2.20637959159082e-05 4.41275918318165e-05 0.999977936204084 32 2.95286438601444e-05 5.90572877202888e-05 0.99997047135614 33 1.92611639519459e-05 3.85223279038918e-05 0.999980738836048 34 0.00130850397666123 0.00261700795332247 0.99869149602334 35 0.0420403383659617 0.0840806767319233 0.957959661634038 36 0.130434164098636 0.260868328197272 0.869565835901364 37 0.161976288201897 0.323952576403793 0.838023711798103 38 0.132211018877685 0.264422037755371 0.867788981122315 39 0.103828870264961 0.207657740529922 0.896171129735039 40 0.116011355307597 0.232022710615194 0.883988644692403 41 0.142494944364240 0.284989888728481 0.85750505563576 42 0.129878147951503 0.259756295903006 0.870121852048497 43 0.102636155920632 0.205272311841263 0.897363844079368 44 0.0802274316711697 0.160454863342339 0.91977256832883 45 0.0761816042333912 0.152363208466782 0.923818395766609 46 0.0614378391960693 0.122875678392139 0.93856216080393 47 0.0906150857572973 0.181230171514595 0.909384914242703 48 0.13618081167925 0.2723616233585 0.86381918832075 49 0.186941301404580 0.373882602809161 0.81305869859542 50 0.221376379728388 0.442752759456777 0.778623620271612 51 0.194960304105474 0.389920608210948 0.805039695894526 52 0.160463690375952 0.320927380751903 0.839536309624048 53 0.139194166849992 0.278388333699984 0.860805833150008 54 0.114767128487271 0.229534256974541 0.88523287151273 55 0.094428920208571 0.188857840417142 0.905571079791429 56 0.0743122616421878 0.148624523284376 0.925687738357812 57 0.0591620453306366 0.118324090661273 0.940837954669363 58 0.0449944777630426 0.0899889555260853 0.955005522236957 59 0.0339956377752037 0.0679912755504074 0.966004362224796 60 0.0269978750877485 0.0539957501754971 0.973002124912251 61 0.0220385450917634 0.0440770901835269 0.977961454908236 62 0.0198031976165284 0.0396063952330568 0.980196802383472 63 0.0198799943648860 0.0397599887297719 0.980120005635114 64 0.0153347678761911 0.0306695357523822 0.984665232123809 65 0.0109752727213285 0.0219505454426570 0.989024727278671 66 0.0100720730016381 0.0201441460032761 0.989927926998362 67 0.0109467618208409 0.0218935236416818 0.98905323817916 68 0.0124722403054415 0.0249444806108829 0.987527759694559 69 0.0157475830453742 0.0314951660907484 0.984252416954626 70 0.0112245947400436 0.0224491894800872 0.988775405259956 71 0.0081866300946737 0.0163732601893474 0.991813369905326 72 0.00849115103367423 0.0169823020673485 0.991508848966326 73 0.0170733658605655 0.034146731721131 0.982926634139434 74 0.0351234742519956 0.0702469485039911 0.964876525748004 75 0.0611861415447048 0.122372283089410 0.938813858455295 76 0.130300797309975 0.260601594619951 0.869699202690025 77 0.152044082482178 0.304088164964356 0.847955917517822 78 0.130936678098451 0.261873356196903 0.869063321901549 79 0.123484545438615 0.24696909087723 0.876515454561385 80 0.180888377361121 0.361776754722241 0.81911162263888 81 0.223410956152964 0.446821912305928 0.776589043847036 82 0.189456790986471 0.378913581972943 0.810543209013529 83 0.155061625079024 0.310123250158048 0.844938374920976 84 0.133343466717042 0.266686933434085 0.866656533282958 85 0.106181595517138 0.212363191034277 0.893818404482862 86 0.0835678469534269 0.167135693906854 0.916432153046573 87 0.0686883961199022 0.137376792239804 0.931311603880098 88 0.0541058111562857 0.108211622312571 0.945894188843714 89 0.0420642470901613 0.0841284941803226 0.957935752909839 90 0.0349751363064089 0.0699502726128179 0.965024863693591 91 0.0269212320734215 0.053842464146843 0.973078767926578 92 0.0200343608994065 0.040068721798813 0.979965639100594 93 0.0143348688199826 0.0286697376399651 0.985665131180017 94 0.0121650442021884 0.0243300884043769 0.987834955797812 95 0.0174924314285428 0.0349848628570856 0.982507568571457 96 0.0163954412405898 0.0327908824811797 0.98360455875941 97 0.0118348760640229 0.0236697521280458 0.988165123935977 98 0.00825971158531846 0.0165194231706369 0.991740288414682 99 0.00541362517355567 0.0108272503471113 0.994586374826444 100 0.00363053572973667 0.00726107145947335 0.996369464270263 101 0.00324012661195835 0.0064802532239167 0.996759873388042 102 0.0043200336770929 0.0086400673541858 0.995679966322907 103 0.00284654200343380 0.00569308400686759 0.997153457996566 104 0.00221128510881468 0.00442257021762936 0.997788714891185 105 0.00444887710960628 0.00889775421921255 0.995551122890394 106 0.0460936380491606 0.0921872760983213 0.95390636195084 107 0.381017684456587 0.762035368913174 0.618982315543413 108 0.86030020699716 0.27939958600568 0.13969979300284 109 0.93404262635821 0.131914747283579 0.0659573736417893 110 0.8853983979627 0.229203204074601 0.114601602037301 111 0.86110877795547 0.277782444089061 0.138891222044530 112 0.823506264673253 0.352987470653495 0.176493735326747 113 0.735765315201897 0.528469369596205 0.264234684798103 114 0.861192444769053 0.277615110461895 0.138807555230947 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.211009174311927 NOK 5% type I error level 47 0.431192660550459 NOK 10% type I error level 57 0.522935779816514 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/10jnhf1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/10jnhf1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/1um241292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/1um241292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/2um241292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/2um241292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/35v1p1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/35v1p1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/45v1p1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/45v1p1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/55v1p1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/55v1p1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/6ym0r1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/6ym0r1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/7reid1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/7reid1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/8reid1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/8reid1292698995.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/9reid1292698995.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292699168dm9w06zjgiqs6aq/9reid1292698995.ps (open in new window)

Parameters (Session):

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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