p_Stress_MR2v3

*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: Sun, 05 Dec 2010 16:08:01 +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/05/t1291565306g09bietb3mge5qm.htm/, Retrieved Sun, 05 Dec 2010 17:08:37 +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/05/t1291565306g09bietb3mge5qm.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 Multiple Linear Regression - Estimated Regression Equation PStress[t] = + 7.78523490126505 -0.110074879874858AGE[t] + 0.699382957944335Pstress_M[t] -0.884853431871234Pstress_OKT[t] -0.0337936158398925BelInSprt[t] + 0.202477782082303KunnenRekRel[t] + 0.40160750304506Depressie[t] -0.213401401711012Slaapgebrek[t] + 0.188622682629067ToekZorgen[t] + 0.0134464221743947t + 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) 7.78523490126505 2.124281 3.6649 0.000357 0.000179 AGE -0.110074879874858 0.066625 -1.6521 0.100881 0.050441 Pstress_M 0.699382957944335 0.334342 2.0918 0.038371 0.019185 Pstress_OKT -0.884853431871234 0.545914 -1.6209 0.107433 0.053717 BelInSprt -0.0337936158398925 0.016095 -2.0996 0.037669 0.018834 KunnenRekRel 0.202477782082303 0.106642 1.8987 0.05979 0.029895 Depressie 0.40160750304506 0.061003 6.5834 0 0 Slaapgebrek -0.213401401711012 0.091801 -2.3246 0.02162 0.01081 ToekZorgen 0.188622682629067 0.109684 1.7197 0.087833 0.043917 t 0.0134464221743947 0.006606 2.0355 0.043803 0.021902 Multiple Linear Regression - Regression Statistics Multiple R 0.645940382564006 R-squared 0.417238977826935 Adjusted R-squared 0.377505271769681 F-TEST (value) 10.5008824806252 F-TEST (DF numerator) 9 F-TEST (DF denominator) 132 p-value 3.79685172191557e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.88283492916378 Sum Squared Residuals 467.948892903251 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10 10.0402198850145 -0.0402198850144854 2 6 7.53416040887909 -1.53416040887909 3 13 9.57029838127622 3.42970161872378 4 12 9.72000159896854 2.27999840103146 5 8 11.5239500673814 -3.5239500673814 6 6 8.75139803406387 -2.75139803406387 7 10 13.2624060007997 -3.26240600079965 8 10 9.10515217256379 0.894847827436213 9 9 8.85212860360697 0.147871396393026 10 9 10.1019615634829 -1.10196156348291 11 7 8.2600646371772 -1.26006463717720 12 5 8.96760296292019 -3.96760296292019 13 14 12.6320910057762 1.36790899422382 14 6 8.46237173089294 -2.46237173089294 15 10 11.2533638965480 -1.25336389654803 16 10 10.6099541684759 -0.609954168475865 17 7 8.36224279862053 -1.36224279862053 18 10 9.86886356252428 0.131136437475717 19 8 8.86039306958389 -0.860393069583885 20 6 7.7941250110018 -1.79412501100181 21 10 9.85654404904465 0.143455950955347 22 12 10.1926705416606 1.80732945833940 23 7 9.30611130171264 -2.30611130171264 24 15 8.58724081691981 6.41275918308019 25 8 9.29480647324807 -1.29480647324807 26 10 9.90294242356618 0.0970575764338246 27 13 10.8190457969115 2.18095420308847 28 8 7.93737337894886 0.0626266210511408 29 11 11.7141567678354 -0.714156767835436 30 7 9.12580943691408 -2.12580943691408 31 9 8.22987400440928 0.770125995590721 32 10 9.84860590804863 0.151394091951373 33 8 8.39312208556813 -0.393122085568129 34 15 13.0983950321707 1.90160496782932 35 9 10.3679879686446 -1.36798796864464 36 7 7.78567069387267 -0.785670693872671 37 11 9.8726544402941 1.12734555970590 38 9 8.31193080928604 0.688069190713955 39 8 9.51622217779245 -1.51622217779245 40 8 8.44631302401597 -0.446313024015975 41 12 8.40198896358023 3.59801103641977 42 13 9.6931019105599 3.3068980894401 43 9 9.26124166094825 -0.261241660948248 44 11 10.0017812399735 0.99821876002655 45 8 9.11183782909975 -1.11183782909975 46 10 11.3013677380943 -1.30136773809426 47 13 13.4307325212421 -0.430732521242069 48 12 10.6739157243701 1.32608427562992 49 12 10.6602279437287 1.33977205627133 50 9 8.75235689132237 0.247643108677627 51 8 9.89326899831626 -1.89326899831626 52 9 8.72421962005325 0.275780379946752 53 12 8.83602190108753 3.16397809891247 54 12 11.4794310805360 0.520568919463959 55 16 14.6655999636730 1.33440003632697 56 11 10.0715995557749 0.928400444225084 57 13 9.5125244791576 3.48747552084240 58 10 10.3216644783409 -0.321664478340856 59 9 10.2956868329553 -1.29568683295532 60 14 9.49079041078731 4.50920958921269 61 13 10.6067110231196 2.39328897688043 62 12 10.3225851285137 1.67741487148631 63 9 10.0703879502693 -1.07038795026928 64 9 10.5632524747759 -1.56325247477587 65 10 10.486721706027 -0.486721706027003 66 8 9.5793467493503 -1.57934674935030 67 9 9.24578658864099 -0.245786588640989 68 9 8.46573832819215 0.534261671807852 69 11 8.63391537586359 2.36608462413641 70 7 8.80549380432442 -1.80549380432442 71 11 10.9069648827271 0.0930351172728896 72 9 9.3893048960048 -0.389304896004795 73 11 8.97684233642203 2.02315766357797 74 9 9.24736093119316 -0.247360931193159 75 8 10.0592938584649 -2.05929385846488 76 9 8.01594585346084 0.98405414653916 77 8 9.35479694520968 -1.35479694520968 78 9 8.8172989340387 0.182701065961309 79 10 9.07723987951192 0.92276012048808 80 9 9.974479729224 -0.974479729223997 81 17 13.5592990451921 3.44070095480795 82 7 8.78186156016362 -1.78186156016362 83 11 10.6367628140771 0.363237185922887 84 9 9.0231151075045 -0.0231151075044978 85 10 9.32926543002207 0.670734569977929 86 11 8.7175233031564 2.28247669684361 87 8 8.31174248041793 -0.311742480417927 88 12 11.5833769019008 0.416623098099199 89 10 9.40182488758025 0.598175112419747 90 7 9.51884643363178 -2.51884643363178 91 9 8.87702589012856 0.122974109871439 92 7 8.16213537368667 -1.16213537368667 93 12 11.0734379576383 0.926562042361721 94 8 9.4068526894538 -1.40685268945379 95 13 10.5517074299390 2.44829257006099 96 9 11.0392156771744 -2.03921567717440 97 15 12.9883344798035 2.01166552019651 98 8 8.63349779014939 -0.63349779014939 99 14 12.0347918937306 1.96520810626942 100 14 13.6062521829169 0.393747817083087 101 9 10.0253966532017 -1.02539665320169 102 13 11.5068555340163 1.49314446598374 103 11 8.68630697565402 2.31369302434598 104 10 12.0054491327323 -2.00544913273233 105 6 9.98283585525926 -3.98283585525926 106 8 8.68320010245912 -0.683200102459116 107 10 11.1199206128905 -1.11992061289055 108 10 8.0357706550763 1.96422934492371 109 10 9.48495715732456 0.515042842675444 110 12 11.5496635971462 0.450336402853763 111 10 9.4031173448561 0.596882655143907 112 9 9.193585604767 -0.193585604767001 113 9 7.11534116959067 1.88465883040933 114 11 8.97158471875975 2.02841528124025 115 7 8.76192112009075 -1.76192112009075 116 7 8.7191757970052 -1.71917579700521 117 5 8.24361510675914 -3.24361510675914 118 9 9.26109255574654 -0.261092555746537 119 11 12.3310448112959 -1.33104481129592 120 15 12.6073506298585 2.39264937014146 121 9 7.66171453031728 1.33828546968272 122 9 10.0075716097963 -1.00757160979628 123 8 9.4994977946908 -1.49949779469080 124 13 16.2874655991195 -3.28746559911946 125 10 11.1308475217352 -1.13084752173516 126 13 11.7408912172434 1.25910878275656 127 9 8.53962309381247 0.460376906187525 128 11 10.5987612485896 0.401238751410407 129 8 11.1000726017093 -3.10007260170932 130 10 9.23749314033264 0.762506859667358 131 9 9.77517334159996 -0.775173341599965 132 8 8.8399454368709 -0.839945436870903 133 8 8.46435079429643 -0.464350794296430 134 13 11.3356323890762 1.66436761092384 135 11 11.3968288846033 -0.396828884603318 136 8 10.7873593346059 -2.78735933460588 137 12 10.2248195466136 1.77518045338641 138 15 12.9838446421580 2.01615535784203 139 11 11.3897624558432 -0.389762455843214 140 10 10.5791078997774 -0.579107899777426 141 5 8.56632923894461 -3.56632923894462 142 11 7.60879940614919 3.39120059385081 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.49808957897462 0.99617915794924 0.50191042102538 14 0.58611545260407 0.827769094791861 0.413884547395931 15 0.7239606080561 0.5520787838878 0.2760393919439 16 0.784368972787564 0.431262054424872 0.215631027212436 17 0.825248348638568 0.349503302722865 0.174751651361432 18 0.779600668443976 0.440798663112047 0.220399331556024 19 0.838845507055879 0.322308985888243 0.161154492944121 20 0.783464178959833 0.433071642080334 0.216535821040167 21 0.846512189543343 0.306975620913314 0.153487810456657 22 0.906531184332027 0.186937631335945 0.0934688156679726 23 0.888448460650766 0.223103078698467 0.111551539349234 24 0.981206466943706 0.0375870661125891 0.0187935330562946 25 0.973475421908367 0.0530491561832656 0.0265245780916328 26 0.961009936813974 0.0779801263720514 0.0389900631860257 27 0.958470764999739 0.0830584700005227 0.0415292350002613 28 0.952246194137226 0.0955076117255472 0.0477538058627736 29 0.96088306525881 0.0782338694823795 0.0391169347411897 30 0.970217674658396 0.0595646506832082 0.0297823253416041 31 0.958497629246697 0.0830047415066052 0.0415023707533026 32 0.950464807099263 0.0990703858014746 0.0495351929007373 33 0.938873544469891 0.122252911060218 0.0611264555301088 34 0.930466409365612 0.139067181268776 0.0695335906343879 35 0.939843940813422 0.120312118373157 0.0601560591865783 36 0.922854480606428 0.154291038787144 0.077145519393572 37 0.899351798792986 0.201296402414029 0.100648201207014 38 0.876591738106196 0.246816523787608 0.123408261893804 39 0.871271869980159 0.257456260039682 0.128728130019841 40 0.846658232064437 0.306683535871126 0.153341767935563 41 0.901309981819516 0.197380036360968 0.0986900181804842 42 0.91012011857653 0.179759762846942 0.0898798814234708 43 0.88891941894696 0.222161162106081 0.111080581053041 44 0.86296754471172 0.274064910576560 0.137032455288280 45 0.865588937149329 0.268822125701343 0.134411062850671 46 0.865073181398905 0.269853637202190 0.134926818601095 47 0.841183618008698 0.317632763982604 0.158816381991302 48 0.813870700042488 0.372258599915024 0.186129299957512 49 0.792729029451627 0.414541941096746 0.207270970548373 50 0.753538639291299 0.492922721417403 0.246461360708701 51 0.774317653536174 0.451364692927653 0.225682346463827 52 0.740121014443465 0.51975797111307 0.259878985556535 53 0.777445222666364 0.445109554667271 0.222554777333636 54 0.7402090346176 0.519581930764799 0.259790965382400 55 0.727490663348034 0.545018673303932 0.272509336651966 56 0.690319627901049 0.619360744197901 0.309680372098951 57 0.74187322213939 0.51625355572122 0.25812677786061 58 0.703239522220574 0.593520955558852 0.296760477779426 59 0.699267469253365 0.60146506149327 0.300732530746635 60 0.745586961893489 0.508826076213021 0.254413038106511 61 0.747924309804372 0.504151380391256 0.252075690195628 62 0.77843790751972 0.443124184960559 0.221562092480280 63 0.825066632779162 0.349866734441675 0.174933367220838 64 0.833942725635326 0.332114548729348 0.166057274364674 65 0.839192494503765 0.321615010992469 0.160807505496235 66 0.834398173081301 0.331203653837398 0.165601826918699 67 0.80535892000806 0.389282159983880 0.194641079991940 68 0.772175789516393 0.455648420967215 0.227824210483607 69 0.793435115363873 0.413129769272254 0.206564884636127 70 0.811362045212823 0.377275909574354 0.188637954787177 71 0.779190933549432 0.441618132901136 0.220809066450568 72 0.77567922555018 0.448641548899642 0.224320774449821 73 0.788409653141613 0.423180693716774 0.211590346858387 74 0.752021675863561 0.495956648272878 0.247978324136439 75 0.75431131261898 0.491377374762039 0.245688687381020 76 0.732928632770288 0.534142734459424 0.267071367229712 77 0.712752565206105 0.57449486958779 0.287247434793895 78 0.675356647674045 0.64928670465191 0.324643352325955 79 0.63085900546771 0.738281989064579 0.369140994532289 80 0.604248362133962 0.791503275732076 0.395751637866038 81 0.674506368592141 0.650987262815717 0.325493631407859 82 0.693562477121753 0.612875045756494 0.306437522878247 83 0.654941116042121 0.690117767915758 0.345058883957879 84 0.611201586992942 0.777596826014115 0.388798413007058 85 0.561673417347205 0.87665316530559 0.438326582652795 86 0.608245612959922 0.783508774080157 0.391754387040078 87 0.562839841243618 0.874320317512764 0.437160158756382 88 0.51547955458658 0.96904089082684 0.48452044541342 89 0.477723874643947 0.955447749287893 0.522276125356053 90 0.54120202854557 0.91759594290886 0.45879797145443 91 0.487142276513988 0.974284553027975 0.512857723486012 92 0.475980976779234 0.951961953558469 0.524019023220766 93 0.427833308310593 0.855666616621186 0.572166691689407 94 0.428116805596634 0.856233611193268 0.571883194403366 95 0.488011014633238 0.976022029266477 0.511988985366762 96 0.516471509057534 0.967056981884931 0.483528490942466 97 0.518722309766572 0.962555380466856 0.481277690233428 98 0.474151721876672 0.948303443753344 0.525848278123328 99 0.5055380107187 0.9889239785626 0.4944619892813 100 0.474050899587931 0.948101799175862 0.525949100412069 101 0.462118774281004 0.924237548562007 0.537881225718996 102 0.457310918282967 0.914621836565935 0.542689081717032 103 0.524632780872415 0.95073443825517 0.475367219127585 104 0.52525694233047 0.94948611533906 0.47474305766953 105 0.708508490968007 0.582983018063986 0.291491509031993 106 0.687035869783788 0.625928260432425 0.312964130216212 107 0.714660081881894 0.570679836236212 0.285339918118106 108 0.686735283889838 0.626529432220324 0.313264716110162 109 0.631765592923737 0.736468814152525 0.368234407076263 110 0.63089807781717 0.73820384436566 0.36910192218283 111 0.601555444154826 0.796889111690348 0.398444555845174 112 0.537038508258182 0.925922983483636 0.462961491741818 113 0.478458737608933 0.956917475217866 0.521541262391067 114 0.480922112333467 0.961844224666935 0.519077887666533 115 0.44121729539143 0.88243459078286 0.55878270460857 116 0.38333329934863 0.76666659869726 0.61666670065137 117 0.496119100484644 0.992238200969287 0.503880899515356 118 0.425033570800244 0.850067141600489 0.574966429199756 119 0.413100250735972 0.826200501471944 0.586899749264028 120 0.78400441791994 0.431991164160118 0.215995582080059 121 0.72420518527694 0.551589629446119 0.275794814723060 122 0.653876769353493 0.692246461293015 0.346123230646507 123 0.584765001752251 0.830469996495499 0.415234998247749 124 0.510124564613772 0.979750870772456 0.489875435386228 125 0.413359319736043 0.826718639472086 0.586640680263957 126 0.342772183199736 0.685544366399472 0.657227816800264 127 0.306433997366762 0.612867994733523 0.693566002633238 128 0.208467454954484 0.416934909908968 0.791532545045516 129 0.237933796999716 0.475867593999432 0.762066203000284 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 0.00854700854700855 OK 10% type I error level 9 0.0769230769230769 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/1053hn1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/1053hn1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/19ujw1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/19ujw1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/29ujw1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/29ujw1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/39ujw1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/39ujw1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/4jl0h1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/4jl0h1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/5jl0h1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/5jl0h1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/6jl0h1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/6jl0h1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/7uczk1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/7uczk1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/853hn1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/853hn1291565270.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/953hn1291565270.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291565306g09bietb3mge5qm/953hn1291565270.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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