WS 4: Personality and friends

*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 16:43:36 +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/t1291308221q85huyy4y49d1oc.htm/, Retrieved Thu, 02 Dec 2010 17:43:42 +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/t1291308221q85huyy4y49d1oc.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 «

13 13 14 13 3 1 1 0 12 12 8 13 5 1 0 0 15 10 12 16 6 0 0 0 12 9 7 12 6 2 0 1 10 10 10 11 5 0 1 2 12 12 7 12 3 0 0 1 15 13 16 18 8 1 1 1 9 12 11 11 4 1 0 0 12 12 14 14 4 4 0 0 11 6 6 9 4 0 0 0 11 5 16 14 6 0 2 1 11 12 11 12 6 2 0 0 15 11 16 11 5 0 2 2 7 14 12 12 4 1 1 1 11 14 7 13 6 0 1 0 11 12 13 11 4 0 0 1 10 12 11 12 6 1 1 0 14 11 15 16 6 2 0 1 10 11 7 9 4 1 0 0 6 7 9 11 4 1 0 0 11 9 7 13 2 0 1 1 15 11 14 15 7 1 2 0 11 11 15 10 5 1 2 1 12 12 7 11 4 2 0 0 14 12 15 13 6 1 0 0 15 11 17 16 6 1 1 0 9 11 15 15 7 1 1 0 13 8 14 14 5 2 2 0 13 9 14 14 6 0 0 2 16 12 8 14 4 1 1 1 13 10 8 8 4 0 1 2 12 10 14 13 7 1 1 1 14 12 14 15 7 1 2 1 11 8 8 13 4 0 2 0 9 12 11 11 4 1 1 0 16 11 16 15 6 2 2 0 12 12 10 15 6 1 1 1 10 7 8 9 5 1 1 2 13 11 14 13 6 1 0 1 16 11 16 16 7 1 3 1 14 12 13 13 6 0 1 2 15 9 5 11 3 1 0 0 5 15 8 12 3 1 0 0 8 11 10 12 4 1 0 0 11 11 8 12 6 0 1 1 16 11 13 14 7 2 0 1 17 11 15 14 5 1 4 4 9 15 6 8 4 0 0 0 9 11 12 13 5 0 0 0 13 12 16 16 6 1 0 1 10 12 5 13 6 1 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = -0.17879188700372 + 0.100252682453213FindingFriends[t] + 0.212183545436264KnowingPeople[t] + 0.382293973272379Liked[t] + 0.592277125614447Celebrity[t] + 0.310366099758153bestfriend[t] -0.0288664388538009secondbestfriend[t] + 0.408723247498028thirdbestfriend[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) -0.17879188700372 1.432707 -0.1248 0.900857 0.450428 FindingFriends 0.100252682453213 0.096695 1.0368 0.301521 0.150761 KnowingPeople 0.212183545436264 0.063597 3.3364 0.001073 0.000537 Liked 0.382293973272379 0.09726 3.9306 0.00013 6.5e-05 Celebrity 0.592277125614447 0.155538 3.8079 0.000205 0.000102 bestfriend 0.310366099758153 0.209436 1.4819 0.14049 0.070245 secondbestfriend -0.0288664388538009 0.200708 -0.1438 0.885836 0.442918 thirdbestfriend 0.408723247498028 0.213014 1.9188 0.05694 0.02847 Multiple Linear Regression - Regression Statistics Multiple R 0.718894523522317 R-squared 0.516809335950379 Adjusted R-squared 0.493955723461546 F-TEST (value) 22.6139012465928 F-TEST (DF numerator) 7 F-TEST (DF denominator) 148 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.08902237966842 Sum Squared Residuals 645.874146407814 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.1232153112844 1.87678468871563 2 12 10.9632820462963 1.03671795370375 3 15 13.0403038088083 1.95969619119169 4 12 11.3794129530986 0.6205870469014 5 10 10.9007697821017 -0.9007697821017 6 12 9.2826074240986 2.71739257590141 7 15 16.8291611440890 -1.82916114408904 8 9 10.2429676104458 -1.24296761044584 9 12 12.9574984658462 -0.95749846584623 10 11 7.50557974224234 3.49442025775766 11 11 12.9741770015330 -1.97417700153297 12 11 12.1201819347053 -1.12018193470527 13 15 12.2452572983187 2.75474270168130 14 7 11.4178073027051 -4.41780730270514 15 11 11.2046484527689 -0.204648452768911 16 11 10.7656918490582 0.234308150941755 17 10 11.7809493960933 -1.78094939609331 18 14 14.8065625745847 -0.806562574584652 19 10 8.52939279970282 1.47060720029718 20 6 9.31733710730725 -3.31733710730725 21 11 8.74299978554309 2.25700021445691 22 15 14.0275399565267 0.972460043473327 23 11 11.5524226318702 -0.552422631870179 24 12 9.70459952845894 2.29540047154106 25 14 13.0408439899645 0.959156010035451 26 15 14.4829738793472 0.517026120652803 27 9 14.2685899408167 -5.26858994081674 28 13 12.4702997844239 0.529700215576085 29 13 13.4172767656789 -0.417276765678926 30 16 11.1331557025984 4.86684429740159 31 13 8.73724364579759 4.26275635420241 32 12 13.6002890138805 -1.60028901388053 33 14 14.5365158864779 -0.536515886477914 34 11 9.6018952134032 1.39810478659680 35 9 10.2141011715920 -1.21410117159204 36 16 14.1699960215429 1.83000397845709 37 12 13.1243710179722 -1.12437101797221 38 10 9.72142279708293 0.278577202917070 39 13 13.1371310095731 -0.137131009573099 40 16 15.2140578293158 0.785942170684194 41 14 13.0946908554761 0.905309144523878 42 15 8.07683116485417 6.92316883514583 43 5 9.69719186915463 -4.69719186915462 44 8 10.3128253558287 -2.31282535582874 45 11 11.1425032250712 -0.142503225071184 46 16 14.2098846627818 1.79011533721819 47 17 14.2500353897462 2.74996461025383 48 9 8.02555991104887 0.974440088951125 49 9 11.4013974458299 -2.40139744582994 50 13 14.8086327027160 -1.80863270271598 51 10 10.8901420967481 -0.89014209674811 52 6 12.0161222660924 -6.01612226609242 53 12 11.8550776627017 0.144922337298265 54 8 10.4404956295679 -2.44049562956788 55 14 11.8140863561726 2.18591364382737 56 12 12.8958442537491 -0.895844253749078 57 11 11.0437674770327 -0.0437674770327478 58 16 14.1109092390368 1.88909076096322 59 8 10.2174201467094 -2.21742014670938 60 15 14.5793674055268 0.420632594473168 61 7 9.08606772203683 -2.08606772203683 62 16 13.8879547307237 2.11204526927626 63 14 12.8672936124656 1.13270638753437 64 16 13.5913528563597 2.4086471436403 65 9 10.2068533045821 -1.20685330458214 66 14 12.3345658732357 1.66543412676427 67 11 13.2504157773547 -2.25041577735468 68 13 10.3688045275876 2.63119547241239 69 15 12.9674672688824 2.03253273111755 70 5 5.79023616619341 -0.790236166193413 71 15 12.8958442537491 2.10415574625092 72 13 11.9376004569162 1.06239954308383 73 11 13.0096865681784 -2.00968656817842 74 11 14.1073106126889 -3.10731061268895 75 12 12.2155458199278 -0.215545819927803 76 12 13.4207007986088 -1.42070079860878 77 12 12.7717885230907 -0.771788523090728 78 12 12.1014030000488 -0.101403000048823 79 14 11.0636649937123 2.93633500628766 80 6 7.99749563203563 -1.99749563203563 81 7 9.43729486336121 -2.43729486336121 82 14 12.6332558392609 1.36674416073910 83 14 14.0140965062464 -0.0140965062463691 84 10 10.9881669055724 -0.98816690557244 85 13 9.3806153850025 3.6193846149975 86 12 12.4326181625947 -0.432618162594745 87 9 9.09500387955766 -0.095003879557659 88 12 12.3702376121216 -0.37023761212164 89 16 15.0862732073467 0.913726792653251 90 10 10.8197311576487 -0.819731157648661 91 14 12.9976890612934 1.00231093870665 92 10 13.6606689718278 -3.66066897182779 93 16 15.0039444120109 0.996055587989134 94 15 13.3340218313987 1.66597816860126 95 12 11.4971428355237 0.502857164476318 96 10 9.2960508743789 0.703949125621102 97 8 10.3039187256395 -2.30391872563946 98 8 8.49104857341337 -0.491048573413374 99 11 12.5710940967919 -1.57109409679194 100 13 13.0095563032155 -0.00955630321554896 101 16 15.8926974999577 0.107302500042301 102 16 15.186289118266 0.813710881733994 103 14 15.6755434598453 -1.67554345984527 104 11 9.01116250273822 1.98883749726178 105 4 7.39905245048233 -3.39905245048233 106 14 14.6931147771868 -0.693114777186823 107 9 10.5946298751140 -1.59462987511395 108 14 15.2695903139292 -1.26959031392921 109 8 10.0456509556939 -2.04565095569389 110 8 10.5072804617848 -2.50728046178481 111 11 11.9938048364073 -0.993804836407263 112 12 13.2321103857661 -1.23211038576614 113 11 11.0330095267163 -0.0330095267162853 114 14 13.0418323070248 0.958167692975176 115 15 14.4249520599917 0.575047940008309 116 16 13.3922013962518 2.6077986037482 117 16 12.9005883180425 3.09941168195749 118 11 12.6735746528740 -1.67357465287404 119 14 13.4060293555616 0.593970644438402 120 14 11.0475585464594 2.95244145354057 121 12 11.5995959110711 0.400404088928909 122 14 13.0940073967967 0.905992603203296 123 8 10.8603554276809 -2.86035542768093 124 13 14.3119707018324 -1.31197070183239 125 16 14.5509505579911 1.44904944200886 126 12 10.6108428289380 1.38915717106205 127 16 15.8791859628305 0.120814037169506 128 12 12.7304778902064 -0.730477890206395 129 11 11.3400659417745 -0.340065941774508 130 4 5.79461524078687 -1.79461524078687 131 16 16.1318191848810 -0.131819184881045 132 15 13.1214871661986 1.8785128338014 133 10 11.178654301887 -1.17865430188699 134 13 14.3237444497438 -1.32374444974382 135 15 12.6306718948986 2.36932810510136 136 12 10.2439559275061 1.75604407249388 137 14 12.9259198644642 1.07408013553584 138 7 10.4573515489763 -3.4573515489763 139 19 13.7765654976555 5.22343450234449 140 12 13.1083948356822 -1.10839483568217 141 12 11.9682206017187 0.0317793982812942 142 13 13.2804451266867 -0.280445126686737 143 15 12.4806220133584 2.51937798664163 144 8 9.01775498964167 -1.01775498964167 145 12 11.2040952590524 0.795904740947636 146 10 10.4678451340069 -0.467845134006906 147 8 11.3455760195686 -3.34557601956863 148 10 14.3588232059195 -4.3588232059195 149 15 14.2541581734762 0.745841826523842 150 16 14.0430694516713 1.95693054832875 151 13 13.2761746054612 -0.276174605461218 152 16 15.0615026963005 0.938497303699518 153 9 9.79027571063433 -0.790275710634327 154 14 13.3770991827921 0.622900817207884 155 14 13.4450599447031 0.554940055296899 156 12 10.0761761577271 1.92382384227293 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.261461129222117 0.522922258444234 0.738538870777883 12 0.138882989021840 0.277765978043680 0.86111701097816 13 0.613315968719905 0.77336806256019 0.386684031280095 14 0.864709048614367 0.270581902771267 0.135290951385633 15 0.805918223082224 0.388163553835552 0.194081776917776 16 0.740120266376842 0.519759467246316 0.259879733623158 17 0.663676658246522 0.672646683506957 0.336323341753478 18 0.573374334761589 0.853251330476823 0.426625665238411 19 0.494043379044287 0.988086758088574 0.505956620955713 20 0.744042586032448 0.511914827935103 0.255957413967552 21 0.683772221791443 0.632455556417114 0.316227778208557 22 0.685156950631647 0.629686098736705 0.314843049368353 23 0.615886058067833 0.768227883864334 0.384113941932167 24 0.612940630765622 0.774118738468757 0.387059369234378 25 0.577285707503833 0.845428584992335 0.422714292496167 26 0.516435180888059 0.967129638223883 0.483564819111941 27 0.742561049638454 0.514877900723091 0.257438950361546 28 0.69979835144288 0.60040329711424 0.30020164855712 29 0.638879944191044 0.722240111617912 0.361120055808956 30 0.756608118744544 0.486783762510912 0.243391881255456 31 0.820768691404323 0.358462617191355 0.179231308595677 32 0.78460751272131 0.430784974557380 0.215392487278690 33 0.737828268253584 0.524343463492832 0.262171731746416 34 0.698741073649531 0.602517852700938 0.301258926350469 35 0.673536449192064 0.652927101615872 0.326463550807936 36 0.704543700821158 0.590912598357684 0.295456299178842 37 0.682052180767615 0.63589563846477 0.317947819232385 38 0.634364540725974 0.731270918548052 0.365635459274026 39 0.584939035906339 0.830121928187323 0.415060964093661 40 0.541809562294046 0.916380875411907 0.458190437705954 41 0.493389910925422 0.986779821850844 0.506610089074578 42 0.820151022344117 0.359697955311766 0.179848977655883 43 0.957503192576703 0.0849936148465945 0.0424968074232972 44 0.961509395146503 0.0769812097069945 0.0384906048534972 45 0.950626429353704 0.0987471412925916 0.0493735706462958 46 0.954359048717966 0.0912819025640688 0.0456409512820344 47 0.955288137241416 0.089423725517168 0.044711862758584 48 0.946072145045743 0.107855709908513 0.0539278549542566 49 0.946745920298258 0.106508159403484 0.0532540797017418 50 0.941048545847936 0.117902908304128 0.0589514541520638 51 0.93303090217069 0.133938195658619 0.0669690978293093 52 0.989153719690974 0.0216925606180521 0.0108462803090260 53 0.985245002705137 0.0295099945897256 0.0147549972948628 54 0.990190054539324 0.0196198909213529 0.00980994546067645 55 0.992416640799626 0.0151667184007484 0.00758335920037418 56 0.990310672631011 0.0193786547379774 0.00968932736898872 57 0.987202682552208 0.0255946348955836 0.0127973174477918 58 0.986679971496181 0.0266400570076375 0.0133200285038188 59 0.990472872210774 0.0190542555784517 0.00952712778922585 60 0.988569772169275 0.0228604556614509 0.0114302278307254 61 0.988998880057266 0.0220022398854682 0.0110011199427341 62 0.99016365069662 0.0196726986067603 0.00983634930338016 63 0.988980276454835 0.0220394470903309 0.0110197235451655 64 0.990236495293778 0.0195270094124442 0.00976350470622212 65 0.988800098337982 0.0223998033240362 0.0111999016620181 66 0.987368312001295 0.025263375997409 0.0126316879987045 67 0.988633085175611 0.0227338296487779 0.0113669148243890 68 0.99060364039371 0.0187927192125801 0.00939635960629004 69 0.99066992991873 0.0186601401625411 0.00933007008127055 70 0.987728182581703 0.0245436348365936 0.0122718174182968 71 0.988379041652139 0.0232419166957227 0.0116209583478613 72 0.98566468443591 0.0286706311281782 0.0143353155640891 73 0.985591140776001 0.0288177184479975 0.0144088592239987 74 0.990015022867939 0.0199699542641230 0.00998497713206149 75 0.986601826772714 0.0267963464545719 0.0133981732272859 76 0.984343274103383 0.0313134517932351 0.0156567258966175 77 0.9797632039356 0.0404735921288005 0.0202367960644003 78 0.9736679342668 0.0526641314664009 0.0263320657332004 79 0.980061998484135 0.039876003031729 0.0199380015158645 80 0.979643363499448 0.0407132730011045 0.0203566365005523 81 0.98126414528746 0.0374717094250785 0.0187358547125393 82 0.97918775414938 0.041624491701239 0.0208122458506195 83 0.97234998802768 0.0553000239446415 0.0276500119723208 84 0.965491770195112 0.0690164596097759 0.0345082298048880 85 0.985548477816903 0.0289030443661936 0.0144515221830968 86 0.980943974632421 0.0381120507351575 0.0190560253675788 87 0.97478094419514 0.0504381116097199 0.0252190558048600 88 0.967650775406632 0.0646984491867356 0.0323492245933678 89 0.959427414529498 0.0811451709410032 0.0405725854705016 90 0.949263356319127 0.101473287361745 0.0507366436808727 91 0.940297991627452 0.119404016745097 0.0597020083725485 92 0.966256364634164 0.0674872707316718 0.0337436353658359 93 0.95770964114874 0.0845807177025204 0.0422903588512602 94 0.95316076757293 0.0936784648541402 0.0468392324270701 95 0.941540002333498 0.116919995333003 0.0584599976665017 96 0.927696148657647 0.144607702684706 0.0723038513423532 97 0.924170536424758 0.151658927150484 0.075829463575242 98 0.90578151832675 0.188436963346500 0.0942184816732498 99 0.897308907354435 0.205382185291131 0.102691092645565 100 0.872699781340802 0.254600437318395 0.127300218659198 101 0.84525211771284 0.30949576457432 0.15474788228716 102 0.816108349970765 0.36778330005847 0.183891650029235 103 0.84401597281025 0.311968054379501 0.155984027189751 104 0.884448452137034 0.231103095725931 0.115551547862966 105 0.89180242268617 0.216395154627662 0.108197577313831 106 0.867174934472917 0.265650131054167 0.132825065527083 107 0.845696087573755 0.308607824852489 0.154303912426245 108 0.840845629590787 0.318308740818426 0.159154370409213 109 0.832676320552864 0.334647358894273 0.167323679447136 110 0.854595467445651 0.290809065108698 0.145404532554349 111 0.84220023346248 0.315599533075039 0.157799766537519 112 0.88616038795857 0.227679224082860 0.113839612041430 113 0.856450737082678 0.287098525834644 0.143549262917322 114 0.825650166885159 0.348699666229682 0.174349833114841 115 0.787821694841171 0.424356610317658 0.212178305158829 116 0.799328243706175 0.401343512587651 0.200671756293825 117 0.808361487041376 0.383277025917249 0.191638512958624 118 0.790836706763782 0.418326586472436 0.209163293236218 119 0.748676543938378 0.502646912123243 0.251323456061622 120 0.829517887299593 0.340964225400814 0.170482112700407 121 0.798913804738225 0.402172390523550 0.201086195261775 122 0.753178065840193 0.493643868319614 0.246821934159807 123 0.753511796991328 0.492976406017344 0.246488203008672 124 0.722134836882699 0.555730326234603 0.277865163117301 125 0.697977220986737 0.604045558026527 0.302022779013263 126 0.681689325333196 0.636621349333607 0.318310674666804 127 0.619085066737338 0.761829866525324 0.380914933262662 128 0.60409832040882 0.79180335918236 0.39590167959118 129 0.59508712788076 0.80982574423848 0.40491287211924 130 0.58391677672224 0.83216644655552 0.41608322327776 131 0.517032869680371 0.965934260639258 0.482967130319629 132 0.488146002922837 0.976292005845674 0.511853997077163 133 0.453610614218248 0.907221228436496 0.546389385781752 134 0.409441783959971 0.818883567919942 0.590558216040029 135 0.360971276955665 0.72194255391133 0.639028723044335 136 0.337252872026252 0.674505744052505 0.662747127973748 137 0.304213898931773 0.608427797863547 0.695786101068227 138 0.335276433501748 0.670552867003497 0.664723566498252 139 0.729826853073804 0.540346293852392 0.270173146926196 140 0.727597419912 0.544805160175999 0.272402580088000 141 0.63596998235342 0.728060035293161 0.364030017646581 142 0.634713512865379 0.730572974269242 0.365286487134621 143 0.512819813794224 0.974360372411551 0.487180186205776 144 0.395603929558432 0.791207859116864 0.604396070441568 145 0.350774998743705 0.70154999748741 0.649225001256295 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 32 0.237037037037037 NOK 10% type I error level 46 0.340740740740741 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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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Software written by Ed van Stee & Patrick Wessa

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