paper - multiple regression - geen motivatie

*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, 19 Dec 2010 10:30:11 +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/19/t1292754496g7hdg1lsvu60nk3.htm/, Retrieved Sun, 19 Dec 2010 11:28:16 +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/19/t1292754496g7hdg1lsvu60nk3.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:

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46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Geen_Motivatie[t] = + 9.03233509669632 -0.0540534078257883Carrièremogelijkheden[t] -0.00254630971406542Leermogelijkheden[t] + 0.118165545509410Persoonlijke_redenen[t] + 0.0169605531731199Ouders[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) 9.03233509669632 2.327646 3.8805 0.000159 8e-05 Carrièremogelijkheden -0.0540534078257883 0.041893 -1.2903 0.199071 0.099535 Leermogelijkheden -0.00254630971406542 0.034064 -0.0747 0.94052 0.47026 Persoonlijke_redenen 0.118165545509410 0.044575 2.6509 0.008945 0.004473 Ouders 0.0169605531731199 0.042173 0.4022 0.688168 0.344084 Multiple Linear Regression - Regression Statistics Multiple R 0.254003140632359 R-squared 0.064517595451102 Adjusted R-squared 0.0379790875206367 F-TEST (value) 2.4310935498012 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 0.0503716627248137 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.69065140618395 Sum Squared Residuals 1020.78430353355 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11 9.87586713780508 1.12413286219492 2 8 9.76322600884514 -1.76322600884514 3 10 10.3877960636140 -0.387796063613952 4 12 10.5772808582452 1.42271914175482 5 12 10.0210965011074 1.9789034988926 6 10 11.3078212679092 -1.30782126790918 7 8 9.75462664887725 -1.75462664887725 8 10 9.93575027088591 0.064249729114086 9 11 9.54297701441022 1.45702298558978 10 7 9.97370674036392 -2.97370674036392 11 10 10.4138549158192 -0.413854915819208 12 9 9.7637546520198 -0.76375465201981 13 9 8.96499250266804 0.0350074973319553 14 11 11.1108763232602 -0.110876323260239 15 12 10.2573159248917 1.74268407510833 16 5 9.67316669566112 -4.67316669566112 17 10 9.8311727823724 0.168827217627608 18 11 10.2536826753535 0.74631732464652 19 12 9.9409545575486 2.05904544245141 20 9 10.6265540617482 -1.62655406174819 21 3 9.6241762142654 -6.62417621426539 22 10 11.7427425701035 -1.7427425701035 23 7 8.26960923477113 -1.26960923477113 24 9 11.3761992001231 -2.37619920012314 25 9 10.6787911592870 -1.67879115928704 26 10 11.2237033377444 -1.22370333774444 27 9 10.4600460600337 -1.46004606003371 28 19 11.6170794462142 7.3829205537858 29 14 9.71315569402507 4.28684430597493 30 5 10.2562586685953 -5.25625866859531 31 13 11.0772153618933 1.92278463810670 32 7 10.2575392593608 -3.25753925936077 33 8 9.38106980892971 -1.38106980892971 34 11 11.3130926870445 -0.313092687044458 35 11 10.7184825937798 0.281517406220169 36 12 10.0112017086102 1.98879829138984 37 9 10.2384196588330 -1.23841965883297 38 13 11.5537863980643 1.44621360193572 39 12 10.8124953164418 1.18750468355819 40 11 11.1325196469038 -0.132519646903811 41 18 9.68804244035193 8.31195755964807 42 8 9.25937493628225 -1.25937493628225 43 14 10.7098458054498 3.29015419455024 44 10 9.75992775154029 0.240072248459714 45 13 10.1155261791268 2.88447382087317 46 13 9.73359330421019 3.26640669578981 47 8 8.83814561348395 -0.838145613483954 48 10 10.6511838660259 -0.6511838660259 49 8 10.0658056983040 -2.06580569830397 50 9 10.1828326422449 -1.18283264224487 51 10 10.6263533138774 -0.626353313877385 52 9 10.2666375489226 -1.26663754892260 53 9 9.29762958748339 -0.297629587483387 54 9 11.4208787438449 -2.42087874384492 55 10 11.6240186978917 -1.62401869789166 56 8 10.4415293209703 -2.44152932097029 57 11 10.4737083353193 0.526291664680694 58 11 10.9182939986123 0.0817060013877064 59 10 11.666315839207 -1.66631583920701 60 23 10.4432268576229 12.5567731423771 61 9 10.2559908088346 -1.25599080883457 62 12 10.8255992856076 1.17440071439236 63 9 9.81025108435722 -0.810251084357217 64 9 10.4908476881996 -1.49084768819957 65 8 9.61012665603774 -1.61012665603774 66 9 9.82526108288087 -0.825261082880872 67 9 10.1413171621010 -1.14131716210104 68 9 9.65133684805865 -0.651336848058655 69 11 10.9048627725772 0.0951372274227718 70 12 10.9518575261269 1.04814247387309 71 8 11.0171166391778 -3.01711663917778 72 9 10.8471754767786 -1.84717547677862 73 10 11.6028142983519 -1.60281429835187 74 8 9.26179473699789 -1.26179473699789 75 9 10.6412664869304 -1.64126648693037 76 9 10.5011594360543 -1.50115943605426 77 13 11.1397119165781 1.86028808342188 78 11 11.6446202454374 -0.644620245437383 79 18 10.0475200290737 7.95247997092625 80 10 7.82172533475595 2.17827466524405 81 14 10.2786842617921 3.72131573820793 82 7 11.6503228232594 -4.65032282325936 83 10 10.4089262242814 -0.408926224281372 84 9 10.5619210463070 -1.56192104630697 85 9 10.0030341568760 -1.00303415687596 86 12 10.6879043000831 1.31209569991694 87 8 10.2541964467113 -2.25419644671130 88 9 10.3809613521886 -1.3809613521886 89 8 10.1064130383308 -2.10641303833081 90 13 10.8538023339334 2.14619766606661 91 6 10.741153490192 -4.74115349019201 92 11 10.4335179712326 0.566482028767416 93 10 10.9086522446946 -0.90865224469458 94 10 11.0592646643137 -1.05926466431375 95 14 10.9134543971810 3.08654560281898 96 13 10.6294727920578 2.37052720794224 97 10 10.3204527805154 -0.320452780515408 98 8 8.50853892621273 -0.508538926212731 99 10 10.8050945635326 -0.805094563532586 100 8 10.4264005582826 -2.42640055828261 101 10 10.7010231110128 -0.701023111012776 102 7 10.5086492695997 -3.50864926959974 103 11 10.7129652836301 0.28703471636986 104 10 10.4812730461718 -0.481273046171791 105 8 8.9035680254608 -0.903568025460796 106 12 10.8488136463757 1.1511863536243 107 12 10.1711357728429 1.82886422715708 108 11 11.0003045431667 -0.000304543166748475 109 11 9.80757053028062 1.19242946971938 110 6 9.30388338560802 -3.30388338560802 111 14 10.9792343879895 3.02076561201051 112 9 10.0407969848829 -1.04079698488294 113 11 11.3406702761952 -0.340670276195176 114 10 10.1351001744866 -0.135100174486613 115 10 11.040629151616 -1.04062915161600 116 8 9.57234896568422 -1.57234896568422 117 9 10.4571273297241 -1.45712732972413 118 10 10.6737656517488 -0.673765651748847 119 10 10.3654227405432 -0.365422740543248 120 12 9.57254971355502 2.42745028644498 121 10 11.1229972750021 -1.12299727500208 122 11 9.0321124512975 1.96788754870251 123 16 11.1654209253159 4.83457907468414 124 12 10.1726248262606 1.82737517373938 125 10 10.3990165900203 -0.399016590020257 126 13 10.8370947987571 2.16290520124288 127 8 9.51892812343323 -1.51892812343323 128 12 10.0755223295287 1.92447767047131 129 10 10.5285434043166 -0.528543404316617 130 8 9.77299429234395 -1.77299429234395 131 14 10.1196958072038 3.88030419279621 132 9 10.1612183937474 -1.16121839374739 133 12 10.1326803737710 1.86731962622902 134 10 10.4836477037438 -0.483647703743817 135 9 9.97133920977434 -0.971339209774336 136 10 11.1204658070519 -1.1204658070519 137 11 10.7656787336349 0.234321266365054 138 11 10.1294492283561 0.870550771643947 139 10 10.6128846594801 -0.612884659480148 140 10 10.3268408324729 -0.326840832472879 141 20 11.0373231589470 8.96267684105296 142 10 10.0829973213103 -0.0829973213102895 143 8 10.5344325172098 -2.53443251720985 144 8 10.1647102925233 -2.16471029252328 145 9 9.70408710857366 -0.704087108573664 146 18 9.86900984925175 8.13099015074825 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.235656558544417 0.471313117088834 0.764343441455583 9 0.185436560171114 0.370873120342229 0.814563439828886 10 0.27782985637792 0.55565971275584 0.72217014362208 11 0.187719685229842 0.375439370459684 0.812280314770158 12 0.118701906363781 0.237403812727562 0.881298093636219 13 0.0677456422325624 0.135491284465125 0.932254357767438 14 0.0409701625519397 0.0819403251038794 0.95902983744806 15 0.0364903499098096 0.0729806998196193 0.96350965009019 16 0.0698821248998471 0.139764249799694 0.930117875100153 17 0.0439800589212208 0.0879601178424415 0.95601994107878 18 0.0450840938238962 0.0901681876477925 0.954915906176104 19 0.039820338115251 0.079640676230502 0.960179661884749 20 0.0312334408526720 0.0624668817053441 0.968766559147328 21 0.248168401838493 0.496336803676985 0.751831598161507 22 0.225306699304124 0.450613398608249 0.774693300695876 23 0.174131386995114 0.348262773990228 0.825868613004886 24 0.136683816990033 0.273367633980066 0.863316183009967 25 0.108879165030934 0.217758330061869 0.891120834969066 26 0.0807658450997813 0.161531690199563 0.919234154900219 27 0.05986861431011 0.11973722862022 0.94013138568989 28 0.365413508968292 0.730827017936584 0.634586491031708 29 0.491011500454018 0.982023000908036 0.508988499545982 30 0.581343116510281 0.837313766979437 0.418656883489719 31 0.529651783462315 0.94069643307537 0.470348216537685 32 0.508709146496099 0.982581707007802 0.491290853503901 33 0.456884573821490 0.913769147642979 0.54311542617851 34 0.398657091851706 0.797314183703411 0.601342908148294 35 0.36221387762561 0.72442775525122 0.63778612237439 36 0.436239006648619 0.872478013297239 0.563760993351381 37 0.385146785033639 0.770293570067277 0.614853214966361 38 0.344475660387055 0.68895132077411 0.655524339612945 39 0.302809437213082 0.605618874426163 0.697190562786918 40 0.260607312500596 0.521214625001192 0.739392687499404 41 0.719368138305378 0.561263723389244 0.280631861694622 42 0.689716968057707 0.620566063884586 0.310283031942293 43 0.71969179701898 0.56061640596204 0.28030820298102 44 0.673781959033132 0.652436081933736 0.326218040966868 45 0.692119782076572 0.615760435846857 0.307880217923428 46 0.698559617871046 0.602880764257909 0.301440382128954 47 0.655213787155387 0.689572425689226 0.344786212844613 48 0.608030669403422 0.783938661193156 0.391969330596578 49 0.606341156692955 0.78731768661409 0.393658843307045 50 0.560554411898216 0.878891176203568 0.439445588101784 51 0.511513271749581 0.976973456500837 0.488486728250419 52 0.470737436867006 0.941474873734011 0.529262563132994 53 0.427066095240331 0.854132190480662 0.572933904759669 54 0.430267312401462 0.860534624802924 0.569732687598538 55 0.401749108179788 0.803498216359577 0.598250891820212 56 0.39013848929211 0.78027697858422 0.60986151070789 57 0.349818941628352 0.699637883256705 0.650181058371648 58 0.304537284837323 0.609074569674645 0.695462715162677 59 0.275547301462373 0.551094602924746 0.724452698537627 60 0.971526165752753 0.0569476684944931 0.0284738342472466 61 0.96591092669261 0.0681781466147811 0.0340890733073905 62 0.958508487289934 0.082983025420133 0.0414915127100665 63 0.947707368903884 0.104585262192232 0.0522926310961158 64 0.937841709640861 0.124316580718277 0.0621582903591386 65 0.930452916266247 0.139094167467506 0.0695470837337531 66 0.91729007308009 0.165419853839821 0.0827099269199103 67 0.901448570549018 0.197102858901965 0.0985514294509825 68 0.881115175801793 0.237769648396415 0.118884824198207 69 0.855557701023573 0.288884597952854 0.144442298976427 70 0.833666772267549 0.332666455464902 0.166333227732451 71 0.842446128270764 0.315107743458471 0.157553871729235 72 0.827599888419274 0.344800223161452 0.172400111580726 73 0.804866297535008 0.390267404929983 0.195133702464992 74 0.780409248762202 0.439181502475596 0.219590751237798 75 0.757754850084903 0.484490299830194 0.242245149915097 76 0.735829218712198 0.528341562575604 0.264170781287802 77 0.71609197121454 0.567816057570919 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0.905606981801883 0.188786036396234 0.0943930181981172 96 0.9031462187288 0.193707562542400 0.0968537812711999 97 0.879852851994461 0.240294296011077 0.120147148005539 98 0.856739217638572 0.286521564722855 0.143260782361428 99 0.829051061331179 0.341897877337642 0.170948938668821 100 0.872426169049661 0.255147661900678 0.127573830950339 101 0.852469624680287 0.295060750639426 0.147530375319713 102 0.857234709953011 0.285530580093978 0.142765290046989 103 0.823872564724051 0.352254870551898 0.176127435275949 104 0.799277968157546 0.401444063684908 0.200722031842454 105 0.761555446470334 0.476889107059332 0.238444553529666 106 0.7273573633666 0.5452852732668 0.2726426366334 107 0.688843987218354 0.622312025563293 0.311156012781646 108 0.63823354430408 0.723532911391841 0.361766455695920 109 0.587444556445029 0.825110887109942 0.412555443554971 110 0.56818015586279 0.86363968827442 0.43181984413721 111 0.56044833273986 0.87910333452028 0.43955166726014 112 0.533034842996947 0.933930314006107 0.466965157003053 113 0.524912899389269 0.950174201221462 0.475087100610731 114 0.479677569580341 0.959355139160683 0.520322430419659 115 0.426701107388578 0.853402214777156 0.573298892611422 116 0.452906175919664 0.905812351839329 0.547093824080336 117 0.410828140410511 0.821656280821022 0.589171859589489 118 0.355892393934301 0.711784787868601 0.644107606065699 119 0.305666239157093 0.611332478314187 0.694333760842907 120 0.271152272114799 0.542304544229599 0.7288477278852 121 0.258583364540526 0.517166729081052 0.741416635459474 122 0.267667243718723 0.535334487437446 0.732332756281277 123 0.269219599098851 0.538439198197701 0.73078040090115 124 0.218782420894668 0.437564841789335 0.781217579105332 125 0.215905824819102 0.431811649638205 0.784094175180898 126 0.174974813680620 0.349949627361241 0.82502518631938 127 0.148199915597983 0.296399831195966 0.851800084402017 128 0.110248146497742 0.220496292995485 0.889751853502258 129 0.176893714202571 0.353787428405143 0.823106285797429 130 0.132997135628892 0.265994271257783 0.867002864371108 131 0.217411737248461 0.434823474496923 0.782588262751539 132 0.206316927827260 0.412633855654519 0.79368307217274 133 0.147127476402308 0.294254952804617 0.852872523597692 134 0.142043432351484 0.284086864702968 0.857956567648516 135 0.519618504379345 0.96076299124131 0.480381495620655 136 0.46286576609792 0.92573153219584 0.53713423390208 137 0.795634488451696 0.408731023096608 0.204365511548304 138 0.7090209786546 0.581958042690801 0.290979021345400 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 0 0 OK 10% type I error level 10 0.0763358778625954 OK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

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

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

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Software written by Ed van Stee & Patrick Wessa

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