*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: Thu, 02 Dec 2010 15:23:25 +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/02/t1291303302t8n00jw4t39t5ri.htm/, Retrieved Thu, 02 Dec 2010 16:21:52 +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/02/t1291303302t8n00jw4t39t5ri.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 «

8 3 3 4 4 4 8 4 3 4 3 4 8 4 4 3 4 3 8 3 3 4 3 2 8 2 3 4 4 4 8 5 4 4 4 5 8 3 2 4 3 4 8 2 3 4 4 4 8 2 4 2 3 2 8 4 3 2 4 2 8 3 3 4 3 4 8 3 4 4 4 4 8 4 2 4 3 5 8 4 2 4 3 5 8 2 3 3 4 4 8 3 2 4 3 3 8 4 4 4 4 4 8 2 2 3 3 4 8 2 1 2 3 2 8 3 3 2 4 4 8 4 4 4 4 4 8 2 2 3 3 4 8 2 3 4 3 4 8 3 3 4 4 4 8 4 4 3 4 4 8 4 3 3 4 4 8 3 3 2 4 3 8 3 4 3 4 3 8 4 4 4 4 4 8 2 4 3 2 3 8 3 3 3 4 4 8 4 4 4 4 4 8 2 2 4 3 4 8 4 4 3 4 4 8 4 3 4 4 4 8 2 2 2 3 3 8 3 4 3 4 4 9 4 4 4 4 4 9 4 4 4 3 4 9 3 4 3 4 3 9 4 2 5 3 5 9 3 2 3 3 4 9 3 3 3 3 4 9 3 4 4 3 4 9 3 5 4 4 4 9 2 2 5 2 5 9 4 3 3 3 4 9 4 3 4 4 4 9 3 3 4 4 4 9 3 2 4 3 4 9 3 4 4 4 5 9 3 3 3 4 4 9 2 3 3 4 3 9 4 4 3 5 3 9 4 1 2 4 4 9 4 4 4 4 4 9 3 2 4 3 4 9 4 4 4 3 4 9 3 4 3 3 3 9 4 4 4 4 3 9 3 2 3 3 3 9 3 4 4 4 4 9 3 2 4 3 4 9 3 4 4 3 4 9 4 4 4 3 4 9 1 1 4 1 5 9 4 4 4 4 3 9 4 4 4 4 4 9 3 3 4 4 3 9 5 3 2 4 2 9 3 3 3 4 4 9 3 3 4 4 4 9 3 3 4 3 5 9 4 3 3 3 2 10 4 4 4 3 4 10 3 1 4 3 4 10 3 3 4 4 4 10 4 3 3 4 4 10 2 3 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation SocialVisible[t] = + 0.258701321270939 + 0.126268884105605Tijd[t] + 0.203513811021763ManyFriends[t] + 0.0129903610844389MakeNewFriends[t] + 0.236770771877937QuiteAccepted[t] + 0.111146519235846IntendMakeNewFriends[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) 0.258701321270939 0.627584 0.4122 0.680802 0.340401 Tijd 0.126268884105605 0.052503 2.405 0.017462 0.008731 ManyFriends 0.203513811021763 0.073339 2.775 0.006264 0.003132 MakeNewFriends 0.0129903610844389 0.096464 0.1347 0.893067 0.446534 QuiteAccepted 0.236770771877937 0.093672 2.5277 0.012577 0.006289 IntendMakeNewFriends 0.111146519235846 0.091801 1.2107 0.228009 0.114005 Multiple Linear Regression - Regression Statistics Multiple R 0.460499502710478 R-squared 0.212059791996598 Adjusted R-squared 0.184315418475351 F-TEST (value) 7.64334403997996 F-TEST (DF numerator) 5 F-TEST (DF denominator) 142 p-value 2.18011195185497e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.697496479514215 Sum Squared Residuals 69.0831901287307 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 3.32302443597393 -0.323024435973929 2 4 3.08625366409602 0.91374633590398 3 4 3.40240136667543 0.59759863332457 4 3 2.86396062562432 0.136039374375675 5 2 3.32302443597395 -1.32302443597395 6 5 3.63768476623156 1.36231523376844 7 3 2.88273985307425 0.117260146925746 8 2 3.32302443597395 -1.32302443597395 9 2 3.04149371447721 -1.04149371447721 10 4 3.07475067533338 0.925249324666617 11 3 3.08625366409602 -0.086253664096017 12 3 3.52653824699572 -0.526538246995716 13 4 2.9938863723101 1.0061136276899 14 4 2.9938863723101 1.0061136276899 15 2 3.31003407488951 -1.31003407488951 16 3 2.77159333383841 0.228406666161592 17 4 3.52653824699572 0.473461753004284 18 2 2.86974949198982 -0.869749491989815 19 2 2.43095228141192 -0.430952281411921 20 3 3.29704371380508 -0.297043713805076 21 4 3.52653824699572 0.473461753004284 22 2 2.86974949198982 -0.869749491989815 23 2 3.08625366409602 -1.08625366409602 24 3 3.32302443597395 -0.323024435973954 25 4 3.51354788591128 0.486452114088723 26 4 3.31003407488951 0.689965925110485 27 3 3.18589719456923 -0.185897194569229 28 3 3.40240136667543 -0.402401366675431 29 4 3.52653824699572 0.473461753004284 30 2 2.92885982291956 -0.928859822919558 31 3 3.31003407488951 -0.310034074889515 32 4 3.52653824699572 0.473461753004284 33 2 2.88273985307425 -0.882739853074254 34 4 3.51354788591128 0.486452114088723 35 4 3.32302443597395 0.676975564026046 36 2 2.74561261166953 -0.74561261166953 37 3 3.51354788591128 -0.513547885911277 38 4 3.65280713110132 0.347192868898679 39 4 3.41603635922338 0.583963640776615 40 3 3.52867025078104 -0.528670250781036 41 4 3.13314561750014 0.866854382499855 42 3 2.99601837609542 0.00398162390457978 43 3 3.19953218711718 -0.199532187117183 44 3 3.41603635922338 -0.416036359223384 45 3 3.85632094212308 -0.856320942123084 46 2 2.89637484562221 -0.896374845622208 47 4 3.19953218711718 0.800467812882817 48 4 3.44929332007956 0.550706679920441 49 3 3.44929332007956 -0.449293320079558 50 3 3.00900873717986 -0.00900873717985918 51 3 3.76395365033717 -0.763953650337168 52 3 3.43630295899512 -0.436302958995120 53 2 3.32515643975927 -1.32515643975927 54 4 3.76544102265897 0.234558977341028 55 4 3.01628497586716 0.983715024132845 56 4 3.65280713110132 0.347192868898679 57 3 3.00900873717986 -0.00900873717985918 58 4 3.41603635922338 0.583963640776615 59 3 3.2918994789031 -0.291899478903099 60 4 3.54166061186547 0.458339388134525 61 3 2.88487185685957 0.115128143140426 62 3 3.65280713110132 -0.652807131101321 63 3 3.00900873717986 -0.00900873717985918 64 3 3.41603635922338 -0.416036359223384 65 4 3.41603635922338 0.583963640776615 66 1 2.44309990163807 -1.44309990163807 67 4 3.54166061186547 0.458339388134525 68 4 3.65280713110132 0.347192868898679 69 3 3.33814680084371 -0.338146800843712 70 5 3.20101955943899 1.79898044056101 71 3 3.43630295899512 -0.436302958995120 72 3 3.44929332007956 -0.449293320079558 73 3 3.32366906743747 -0.323669067437468 74 4 2.97723914864549 1.02276085135451 75 4 3.54230524332899 0.457694756671011 76 3 2.9317638102637 0.0682361897362986 77 3 3.57556220418516 -0.575562204185163 78 4 3.56257184310072 0.437428156899276 79 2 3.45142532386488 -1.45142532386488 80 4 3.29254411036661 0.707455889633386 81 3 3.44993795154307 -0.449937951543073 82 2 2.91298458281377 -0.912984582813771 83 4 3.32728844354459 0.672711556455407 84 4 3.77907601520693 0.220923984793074 85 3 3.56257184310072 -0.562571843100724 86 4 3.66792949597108 0.332070504028920 87 4 3.56257184310072 0.437428156899276 88 4 3.77907601520693 0.220923984793074 89 3 3.76608565412249 -0.766085654122487 90 3 3.32580107122279 -0.325801071222788 91 4 3.13527762128546 0.864722378714536 92 5 2.45970963882963 2.54029036117037 93 3 2.89850684940753 0.101493150592473 94 4 3.34606767099452 0.653932329005477 95 4 3.33879143230723 0.661208567692773 96 4 3.77907601520693 0.220923984793074 97 4 3.77907601520693 0.220923984793074 98 5 3.92347949529895 1.07652050470105 99 4 3.33879143230723 0.661208567692773 100 3 2.44523190542339 0.55476809457661 101 4 3.57556220418516 0.424437795814837 102 4 3.21465455198694 0.785345448013059 103 4 3.77907601520693 0.220923984793074 104 4 3.35905803207896 0.640941967921038 105 4 3.56257184310072 0.437428156899276 106 3 3.43843496278044 -0.438434962780439 107 4 3.33879143230723 0.661208567692773 108 4 3.77907601520693 0.220923984793074 109 4 3.77907601520693 0.220923984793074 110 4 3.61448067931152 0.385519320688481 111 4 3.54230524332899 0.457694756671011 112 4 3.49831727726901 0.501682722730995 113 4 3.70183108829077 0.298168911709232 114 3 3.65558376635016 -0.655583766350155 115 4 3.46506031641283 0.534939683587168 116 3 3.66857412743459 -0.668574127434594 117 3 3.48532691618457 -0.485326916184567 118 4 3.90534489931253 0.0946551006874692 119 4 3.66857412743459 0.331425872565406 120 4 3.46506031641283 0.534939683587168 121 4 3.90534489931253 0.0946551006874692 122 3 3.70183108829077 -0.701831088290768 123 3 3.57769420797048 -0.577694207970483 124 1 2.23806123182145 -1.23806123182145 125 4 3.90534489931253 0.0946551006874692 126 3 3.90534489931253 -0.90534489931253 127 4 3.49831727726901 0.501682722730995 128 4 3.70183108829077 0.298168911709232 129 3 3.90534489931253 -0.90534489931253 130 4 3.70183108829077 0.298168911709232 131 4 3.90534489931253 0.0946551006874692 132 2 3.49831727726901 -1.49831727726901 133 4 4.10885871033429 -0.108858710334293 134 3 3.57769420797048 -0.577694207970483 135 3 3.89235453822809 -0.892354538228092 136 4 3.70183108829077 0.298168911709232 137 4 3.90534489931253 0.0946551006874692 138 3 3.70183108829077 -0.701831088290768 139 3 3.70183108829077 -0.701831088290768 140 3 3.49831727726901 -0.498317277269006 141 4 3.90534489931253 0.0946551006874692 142 4 3.90534489931253 0.0946551006874692 143 3 3.70183108829077 -0.701831088290768 144 4 4.14211567119047 -0.142115671190467 145 3 3.15039998615522 -0.150399986155222 146 4 3.79419838007668 0.205801619923316 147 4 3.66857412743459 0.331425872565406 148 4 3.35391379717699 0.646086202823015 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.924079379789117 0.151841240421765 0.0759206202108825 10 0.997887234666541 0.00422553066691724 0.00211276533345862 11 0.995191038716915 0.0096179225661697 0.00480896128308485 12 0.991934769735718 0.0161304605285636 0.0080652302642818 13 0.988234794316622 0.0235304113667561 0.0117652056833780 14 0.982090737993658 0.035818524012684 0.017909262006342 15 0.99381545464438 0.0123690907112379 0.00618454535561896 16 0.989360386989122 0.0212792260217553 0.0106396130108776 17 0.98648215475882 0.0270356904823613 0.0135178452411806 18 0.990072239058844 0.0198555218823121 0.00992776094115604 19 0.984465299087008 0.0310694018259844 0.0155347009129922 20 0.97587085180526 0.0482582963894817 0.0241291481947409 21 0.967662774337405 0.0646744513251896 0.0323372256625948 22 0.970118346285353 0.0597633074292935 0.0298816537146467 23 0.983889564353653 0.0322208712926935 0.0161104356463468 24 0.976758041477196 0.046483917045608 0.023241958522804 25 0.970829427027763 0.0583411459444745 0.0291705729722373 26 0.971181947510335 0.0576361049793308 0.0288180524896654 27 0.959527840964973 0.080944318070054 0.040472159035027 28 0.947830794569855 0.104338410860289 0.0521692054301445 29 0.934201477721242 0.131597044557516 0.0657985222787581 30 0.940086182154888 0.119827635690225 0.0599138178451125 31 0.923732915921732 0.152534168156536 0.076267084078268 32 0.905944179663389 0.188111640673222 0.0940558203366112 33 0.912030766085392 0.175938467829216 0.0879692339146078 34 0.894733675559694 0.210532648880612 0.105266324440306 35 0.884962187018022 0.230075625963956 0.115037812981978 36 0.874360493309387 0.251279013381226 0.125639506690613 37 0.86486492767924 0.270270144641520 0.135135072320760 38 0.833547345561244 0.332905308877512 0.166452654438756 39 0.807275285302225 0.38544942939555 0.192724714697775 40 0.803102829316993 0.393794341366014 0.196897170683007 41 0.799876643367622 0.400246713264757 0.200123356632378 42 0.759812459655079 0.480375080689842 0.240187540344921 43 0.72216820109099 0.55566359781802 0.27783179890901 44 0.704444087012165 0.591111825975671 0.295555912987835 45 0.752138910169101 0.495722179661797 0.247861089830899 46 0.773671176624728 0.452657646750544 0.226328823375272 47 0.79845091526574 0.403098169468518 0.201549084734259 48 0.773249013472454 0.453501973055092 0.226750986527546 49 0.760421861459676 0.479156277080647 0.239578138540324 50 0.718892280492197 0.562215439015606 0.281107719507803 51 0.746207152750241 0.507585694499518 0.253792847249759 52 0.723644385423187 0.552711229153626 0.276355614576813 53 0.818948566291862 0.362102867416276 0.181051433708138 54 0.792803044707436 0.414393910585128 0.207196955292564 55 0.820983576419078 0.358032847161843 0.179016423580922 56 0.792692069643208 0.414615860713585 0.207307930356792 57 0.756169461101492 0.487661077797016 0.243830538898508 58 0.748219311141782 0.503561377716436 0.251780688858218 59 0.72495010962798 0.550099780744041 0.275049890372020 60 0.697881359581483 0.604237280837034 0.302118640418517 61 0.66158861542508 0.676822769149841 0.338411384574920 62 0.676228380140267 0.647543239719466 0.323771619859733 63 0.632782255848937 0.734435488302126 0.367217744151063 64 0.615405619821521 0.769188760356958 0.384594380178479 65 0.597535963722337 0.804928072555326 0.402464036277663 66 0.761891101128046 0.476217797743907 0.238108898871953 67 0.730410700318177 0.539178599363645 0.269589299681823 68 0.691173759871609 0.617652480256782 0.308826240128391 69 0.686231672594378 0.627536654811244 0.313768327405622 70 0.858044816857215 0.283910366285570 0.141955183142785 71 0.859986449478296 0.280027101043408 0.140013550521704 72 0.873525321079652 0.252949357840697 0.126474678920348 73 0.892129039479917 0.215741921040165 0.107870960520083 74 0.900591949262101 0.198816101475797 0.0994080507378987 75 0.88237472311353 0.235250553772941 0.117625276886471 76 0.862867785786407 0.274264428427187 0.137132214213593 77 0.879218578981682 0.241562842036636 0.120781421018318 78 0.85702847089225 0.285943058215502 0.142971529107751 79 0.949925690370997 0.100148619258005 0.0500743096290025 80 0.948665615478739 0.102668769042523 0.0513343845212614 81 0.95625357637729 0.087492847245422 0.043746423622711 82 0.979997727972718 0.0400045440545634 0.0200022720272817 83 0.98011585919655 0.0397682816068993 0.0198841408034497 84 0.973773862614427 0.052452274771145 0.0262261373855725 85 0.977355396091672 0.0452892078166556 0.0226446039083278 86 0.970113078147812 0.0597738437043765 0.0298869218521883 87 0.961914306066576 0.0761713878668477 0.0380856939334238 88 0.951421772001139 0.0971564559977222 0.0485782279988611 89 0.966298156056883 0.067403687886235 0.0337018439431175 90 0.96776295638997 0.0644740872200603 0.0322370436100301 91 0.96463036081128 0.070739278377442 0.035369639188721 92 0.999716924673442 0.00056615065311601 0.000283075326558005 93 0.999696068443962 0.000607863112075815 0.000303931556037908 94 0.999681046703362 0.000637906593275789 0.000318953296637894 95 0.999519193145854 0.00096161370829147 0.000480806854145735 96 0.999294326253535 0.00141134749292986 0.000705673746464929 97 0.998995410590966 0.00200917881806825 0.00100458940903413 98 0.998913368573276 0.00217326285344796 0.00108663142672398 99 0.998381935328818 0.00323612934236473 0.00161806467118236 100 0.997642584819793 0.00471483036041493 0.00235741518020746 101 0.996429945995571 0.00714010800885722 0.00357005400442861 102 0.99659314047669 0.0068137190466204 0.0034068595233102 103 0.995152181567303 0.00969563686539422 0.00484781843269711 104 0.994193461146886 0.0116130777062283 0.00580653885311417 105 0.9921422260556 0.0157155478888009 0.00785777394440044 106 0.989338104410996 0.0213237911780073 0.0106618955890037 107 0.985109286062092 0.0297814278758168 0.0148907139379084 108 0.979115859995435 0.041768280009129 0.0208841400045645 109 0.972540765455543 0.0549184690889138 0.0274592345444569 110 0.976596007461913 0.0468079850761739 0.0234039925380870 111 0.96704752292209 0.0659049541558209 0.0329524770779105 112 0.968768960532875 0.0624620789342498 0.0312310394671249 113 0.96287826195303 0.0742434760939382 0.0371217380469691 114 0.954064148474993 0.0918717030500142 0.0459358515250071 115 0.95181586330152 0.0963682733969617 0.0481841366984808 116 0.961114620462947 0.0777707590741069 0.0388853795370535 117 0.959372975410948 0.081254049178104 0.040627024589052 118 0.942938468174465 0.114123063651069 0.0570615318255347 119 0.922742507715698 0.154514984568605 0.0772574922843023 120 0.927857477795104 0.144285044409792 0.0721425222048959 121 0.902211955639256 0.195576088721487 0.0977880443607437 122 0.893077931751044 0.213844136497911 0.106922068248956 123 0.869990704802754 0.260018590394493 0.130009295197247 124 0.90629148641028 0.187417027179440 0.0937085135897199 125 0.873816638361049 0.252366723277902 0.126183361638951 126 0.901731080876599 0.196537838246802 0.0982689191234012 127 0.953601326801245 0.0927973463975092 0.0463986731987546 128 0.960670446982887 0.0786591060342252 0.0393295530171126 129 0.98108979631907 0.0378204073618593 0.0189102036809297 130 0.988236134652764 0.0235277306944717 0.0117638653472359 131 0.978594433422418 0.0428111331551638 0.0214055665775819 132 0.99212005085522 0.0157598982895587 0.00787994914477935 133 0.989586643471422 0.0208267130571569 0.0104133565285785 134 0.980333508682488 0.039332982635024 0.019666491317512 135 0.959652193615263 0.0806956127694741 0.0403478063847371 136 0.980801919693745 0.0383961606125095 0.0191980803062548 137 0.954241556513941 0.0915168869721177 0.0457584434860588 138 0.9209977869039 0.158004426192199 0.0790022130960995 139 0.877867117480574 0.244265765038851 0.122132882519426 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.106870229007634 NOK 5% type I error level 41 0.312977099236641 NOK 10% type I error level 66 0.50381679389313 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/10gl451291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/10gl451291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/1927b1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/1927b1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/22c6e1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/22c6e1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/32c6e1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/32c6e1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/42c6e1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/42c6e1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/5d3oh1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/5d3oh1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/6d3oh1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/6d3oh1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/7ncnk1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/7ncnk1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/8ncnk1291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/8ncnk1291303394.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/9gl451291303394.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291303302t8n00jw4t39t5ri/9gl451291303394.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

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

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Software written by Ed van Stee & Patrick Wessa

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