Paper interactiemodel met geslacht

*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 11:56:05 +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/t1291291061q073jab54u0ti76.htm/, Retrieved Thu, 02 Dec 2010 12:57:57 +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/t1291291061q073jab54u0ti76.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 «

66 4818 4488 5 73 68 0 4964 54 3132 2916 12 58 54 1 3132 82 5576 3362 11 68 41 1 2788 61 3782 2989 6 62 49 1 3038 65 4225 3185 12 65 49 1 3185 77 6237 5544 11 81 72 1 5832 66 4818 5148 12 73 78 1 5694 66 4224 3828 7 64 58 0 3712 66 4488 3828 8 68 58 1 3944 48 2448 1104 13 51 23 1 1173 57 3876 2223 12 68 39 1 2652 80 4880 5040 13 61 63 1 3843 60 4140 2760 12 69 46 1 3174 70 5110 4060 12 73 58 1 4234 85 5185 3315 11 61 39 0 2379 59 3658 2596 12 62 44 0 2728 72 4536 3528 12 63 49 1 3087 70 4830 3990 12 69 57 1 3933 74 3478 5624 11 47 76 0 3572 70 4620 4410 13 66 63 0 4158 51 2958 918 9 58 18 1 1044 70 4410 2800 11 63 40 0 2520 71 4899 4189 11 69 59 1 4071 72 4248 4464 11 59 62 0 3658 50 2950 3500 9 59 70 1 4130 69 4347 4485 11 63 65 0 4095 73 4745 4088 12 65 56 0 3640 66 4290 2970 12 65 45 1 2925 73 5183 4161 10 71 57 0 4047 58 3480 2900 12 60 50 1 3000 78 6318 3120 12 81 40 0 3240 83 5561 4814 12 67 58 1 3886 76 5016 3724 9 66 49 0 3234 77 4774 3773 9 62 49 1 3038 79 4977 2133 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Vrienden_vinden[t] = + 16.3447958221408 -0.197146049099005Groepsgevoel[t] + 0.00149711765385549InteractieGR_NV[t] + 0.00253721848070092InteractieGR_U[t] + 0.0378045021275467NVC[t] -0.0261869488673119Uitingsangst[t] -0.203206658726088Geslacht[t] -0.0025083063183694InteractieNV_U[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) 16.3447958221408 8.896113 1.8373 0.068317 0.034159 Groepsgevoel -0.197146049099005 0.136624 -1.443 0.151292 0.075646 InteractieGR_NV 0.00149711765385549 0.001978 0.757 0.450357 0.225178 InteractieGR_U 0.00253721848070092 0.001083 2.3434 0.020537 0.010269 NVC 0.0378045021275467 0.146736 0.2576 0.797072 0.398536 Uitingsangst -0.0261869488673119 0.101626 -0.2577 0.797039 0.398519 Geslacht -0.203206658726088 0.315033 -0.645 0.519977 0.259988 InteractieNV_U -0.0025083063183694 0.001589 -1.5789 0.11664 0.05832 Multiple Linear Regression - Regression Statistics Multiple R 0.31832226596757 R-squared 0.101329065010728 Adjusted R-squared 0.0557443074388085 F-TEST (value) 2.22287164412052 F-TEST (DF numerator) 7 F-TEST (DF denominator) 138 p-value 0.0359099831774408 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.74575675541985 Sum Squared Residuals 420.577997574979 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.4610895472160 -5.46108954721604 2 12 10.5057545890976 1.49424541090236 3 11 11.3575529328106 -0.357552932810554 4 6 10.8020092162753 -4.80200921627535 5 12 10.9186354403370 1.08136455966298 6 11 10.9134673520485 0.0865326479515379 7 12 9.83951398466974 2.16048601533026 8 7 10.9592659436869 -3.95926594368691 9 8 10.7205892882272 -2.72058928822717 10 13 11.5280984991040 1.47190150089603 11 12 11.2447158562459 0.755284143754139 12 13 11.4802961986879 1.51970380131212 13 12 10.9561630555706 1.04383694442937 14 12 10.9134606383625 1.08653936163752 15 11 11.0783388400441 -0.0783388400440663 16 12 11.1252482242370 0.87475177576303 17 12 11.0446876408186 0.955312359181432 18 12 10.9466315438202 1.05336845617981 19 11 12.0592134412493 -1.05921344124933 20 13 11.0661711359114 1.93382886408861 21 9 11.9474054921619 -2.94740549216192 22 11 11.2643467417279 -0.264346741727860 23 11 10.9591729208271 0.0408270791729007 24 11 11.2676696615962 -0.267669661596166 25 9 9.61912257973922 -0.619122579739225 26 11 11.0371313455004 -0.0371313455004201 27 12 11.2896951574196 0.710304842580403 28 12 11.0302075036332 0.969792496366781 29 10 11.3104090312211 -1.31040903122109 30 12 10.7090250743012 1.29097492569875 31 12 12.2300892353863 -0.230089235386269 32 12 11.58488838244 0.415111617559994 33 9 11.4199138767990 -2.41991387679899 34 9 11.1219924321854 -2.12199243218543 35 12 10.8380998342389 1.16190016576113 36 14 11.3800250944513 2.61997490554874 37 12 12.0767165361674 -0.0767165361674068 38 11 10.4167340050598 0.583265994940186 39 9 11.0933739990293 -2.09337399902927 40 11 11.2049021622482 -0.204902162248184 41 7 9.09509598250463 -2.09509598250463 42 15 11.0860520568057 3.91394794319435 43 11 10.7757038294338 0.224296170566164 44 12 11.6656198440118 0.334380155988154 45 12 10.7908758470545 1.20912415294549 46 9 11.8690371585552 -2.86903715855518 47 12 10.5829473934707 1.41705260652932 48 11 11.3640779465228 -0.36407794652277 49 11 10.5108345387015 0.489165461298541 50 8 11.1964949157716 -3.19649491577159 51 7 10.3571030183603 -3.35710301836032 52 12 11.7641441071758 0.235855892824211 53 8 11.6914284737428 -3.69142847374281 54 10 10.2960202683325 -0.296020268332538 55 12 10.1819442762656 1.81805572373441 56 15 11.0612361237472 3.93876387625275 57 12 11.5919480696107 0.408051930389264 58 12 11.2635594629292 0.736440537070776 59 12 10.8991936380386 1.10080636196138 60 12 10.5356278761959 1.46437212380407 61 8 9.37630830245887 -1.37630830245887 62 10 11.1603449004228 -1.16034490042275 63 14 12.2813521391459 1.71864786085406 64 10 10.9139228043237 -0.913922804323727 65 12 10.9612989019072 1.03870109809281 66 14 11.0058331755674 2.99416682443264 67 6 11.3129156136073 -5.31291561360731 68 11 10.7201763813820 0.279823618618013 69 10 11.2092891496837 -1.20928914968373 70 14 12.6645928514371 1.33540714856287 71 12 10.9819146750145 1.0180853249855 72 13 11.2285223710381 1.77147762896191 73 11 10.9150461828765 0.0849538171235077 74 11 10.9020586711964 0.0979413288035835 75 12 11.1503486478775 0.84965135212255 76 13 10.9675258066019 2.0324741933981 77 12 10.2140393297491 1.78596067025092 78 8 10.0654876293763 -2.06548762937633 79 12 11.4103098204603 0.589690179539671 80 11 11.2090531474979 -0.209053147497859 81 10 11.1523426364216 -1.15234263642157 82 12 10.93313511686 1.06686488314000 83 11 11.1285530630626 -0.128553063062625 84 12 11.0191524394601 0.980847560539913 85 12 10.9852653786480 1.01473462135197 86 10 10.600190927754 -0.600190927754004 87 12 11.3892822126529 0.61071778734711 88 12 11.0579917327865 0.942008267213462 89 11 11.3738555422094 -0.373855542209382 90 10 11.0019076418342 -1.00190764183418 91 12 11.2790722686437 0.720927731356286 92 11 10.9459337718571 0.054066228142859 93 12 10.3491715607761 1.65082843922388 94 12 9.54555707566688 2.45444292433312 95 10 9.51019887942858 0.489801120571421 96 11 11.0730925847360 -0.0730925847360255 97 10 10.9088557876422 -0.908855787642202 98 11 11.0951876608556 -0.0951876608555994 99 11 10.6296716021924 0.370328397807585 100 12 11.1933882338027 0.806611766197255 101 11 10.8532847725510 0.146715227448979 102 11 10.6647496566536 0.335250343346360 103 7 8.71540157805907 -1.71540157805907 104 12 10.5729533321225 1.42704666787751 105 8 10.4807030617249 -2.48070306172492 106 10 10.9779031282676 -0.977903128267615 107 12 11.2073701288001 0.792629871199907 108 11 10.991082726884 0.00891727311599877 109 13 11.2744238790258 1.72557612097419 110 9 11.4267762892745 -2.42677628927451 111 11 11.2677461539259 -0.267746153925875 112 13 11.1591193056845 1.84088069431548 113 8 10.6887640660184 -2.6887640660184 114 12 11.0774618918501 0.92253810814987 115 11 10.4530891386404 0.546910861359569 116 11 10.7715797222461 0.228420277753937 117 12 10.7676206412183 1.23237935878171 118 13 11.2488973132639 1.75110268673606 119 11 11.2005361564225 -0.200536156422495 120 10 10.7033667493923 -0.703366749392315 121 10 10.8205647421861 -0.820564742186053 122 10 10.8044356144577 -0.804435614457724 123 12 10.9941037815806 1.00589621841937 124 12 11.1942336742579 0.805766325742107 125 13 10.8612399071508 2.13876009284922 126 11 11.0093864451284 -0.00938644512836206 127 11 10.0896250243104 0.910374975689612 128 12 11.0097660025763 0.990233997423652 129 9 10.6992855176582 -1.69928551765816 130 11 11.9021591043889 -0.902159104388912 131 12 10.8585958156188 1.14140418438122 132 12 10.8613712389202 1.13862876107980 133 13 10.9373432718480 2.06265672815195 134 6 10.752966216812 -4.752966216812 135 11 11.8801109810361 -0.880110981036071 136 10 11.8887523676348 -1.88875236763478 137 12 11.1408075745305 0.8591924254695 138 11 11.0132255952889 -0.0132255952889118 139 12 12.1418334051205 -0.141833405120458 140 12 11.0552056476720 0.94479435232796 141 7 11.0720461727568 -4.0720461727568 142 12 11.0612259641772 0.938774035822819 143 12 12.1445254574265 -0.144525457426503 144 9 10.9067952306775 -1.90679523067753 145 12 11.0898505908534 0.91014940914661 146 12 11.1847990832062 0.815200916793756 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.99593410661581 0.00813178676838109 0.00406589338419054 12 0.993882368570077 0.0122352628598452 0.00611763142992261 13 0.990215030284142 0.0195699394317164 0.0097849697158582 14 0.983501790950763 0.0329964180984746 0.0164982090492373 15 0.98593793435199 0.0281241312960194 0.0140620656480097 16 0.996552863439132 0.00689427312173524 0.00344713656086762 17 0.993637001976236 0.0127259960475282 0.00636299802376408 18 0.990060000065594 0.0198799998688110 0.00993999993440552 19 0.984428151347286 0.0311436973054287 0.0155718486527143 20 0.994526444087477 0.0109471118250455 0.00547355591252275 21 0.997460895743996 0.00507820851200867 0.00253910425600433 22 0.996099368691779 0.00780126261644238 0.00390063130822119 23 0.993474506375975 0.0130509872480496 0.00652549362402482 24 0.989788247250819 0.0204235054983626 0.0102117527491813 25 0.985007317503461 0.0299853649930777 0.0149926824965389 26 0.978913682815783 0.042172634368435 0.0210863171842175 27 0.973997383287455 0.0520052334250904 0.0260026167125452 28 0.96537485626337 0.0692502874732611 0.0346251437366306 29 0.953668981030211 0.092662037939577 0.0463310189697885 30 0.943761471936555 0.112477056126889 0.0562385280634447 31 0.925469600602639 0.149060798794723 0.0745303993973614 32 0.90345619069504 0.19308761860992 0.09654380930496 33 0.907652817518565 0.184694364962871 0.0923471824814355 34 0.92458101798162 0.150837964036759 0.0754189820183796 35 0.907663564188367 0.184672871623265 0.0923364358116327 36 0.946329441156128 0.107341117687744 0.0536705588438721 37 0.929810109480207 0.140379781039586 0.0701898905197932 38 0.909683017232472 0.180633965535056 0.090316982767528 39 0.918844952261047 0.162310095477906 0.0811550477389528 40 0.897025195938134 0.205949608123733 0.102974804061866 41 0.892043163041012 0.215913673917975 0.107956836958988 42 0.94926383028251 0.101472339434982 0.0507361697174909 43 0.933611790093472 0.132776419813055 0.0663882099065277 44 0.920329428635245 0.159341142729511 0.0796705713647553 45 0.919520598491209 0.160958803017582 0.0804794015087912 46 0.94248742166851 0.115025156662978 0.0575125783314892 47 0.941523503737876 0.116952992524249 0.0584764962621245 48 0.924731367808481 0.150537264383038 0.075268632191519 49 0.908851567724064 0.182296864551873 0.0911484322759363 50 0.948156798616361 0.103686402767278 0.0518432013836388 51 0.970382041830541 0.0592359163389173 0.0296179581694587 52 0.960919681573313 0.0781606368533738 0.0390803184266869 53 0.984138528350387 0.0317229432992259 0.0158614716496130 54 0.978782786711673 0.0424344265766544 0.0212172132883272 55 0.979337440912658 0.0413251181746833 0.0206625590873416 56 0.995152899618698 0.00969420076260425 0.00484710038130212 57 0.993182156590598 0.0136356868188037 0.00681784340940185 58 0.990995480997484 0.0180090380050311 0.00900451900251553 59 0.989329752823633 0.0213404943527341 0.0106702471763671 60 0.988084104446273 0.0238317911074532 0.0119158955537266 61 0.987172784759458 0.0256544304810838 0.0128272152405419 62 0.984364050637372 0.0312718987252568 0.0156359493626284 63 0.98369471749434 0.032610565011318 0.016305282505659 64 0.979303003377989 0.0413939932440228 0.0206969966220114 65 0.974617598519877 0.0507648029602469 0.0253824014801235 66 0.985103471330042 0.0297930573399164 0.0148965286699582 67 0.999393561334202 0.001212877331596 0.000606438665798 68 0.99911198586007 0.00177602827986179 0.000888014139930897 69 0.998962736124056 0.00207452775188795 0.00103726387594397 70 0.998565950954332 0.00286809809133615 0.00143404904566807 71 0.998190078174244 0.00361984365151149 0.00180992182575574 72 0.99813468817569 0.003730623648618 0.001865311824309 73 0.997240235093497 0.00551952981300545 0.00275976490650272 74 0.99600420378973 0.0079915924205388 0.0039957962102694 75 0.994735941704665 0.0105281165906707 0.00526405829533536 76 0.995322424723809 0.00935515055238276 0.00467757527619138 77 0.995790209045614 0.00841958190877218 0.00420979095438609 78 0.996216250129352 0.00756749974129602 0.00378374987064801 79 0.994667823577313 0.0106643528453747 0.00533217642268733 80 0.99240059463452 0.0151988107309595 0.00759940536547977 81 0.991357154925048 0.0172856901499048 0.00864284507495242 82 0.989441928306079 0.021116143387842 0.010558071693921 83 0.985547380196753 0.0289052396064942 0.0144526198032471 84 0.982050494863489 0.0358990102730225 0.0179495051365112 85 0.978075851723821 0.0438482965523571 0.0219241482761785 86 0.972992666358537 0.0540146672829268 0.0270073336414634 87 0.965840534889053 0.0683189302218931 0.0341594651109466 88 0.956921130785606 0.0861577384287882 0.0430788692143941 89 0.946294168139039 0.107411663721922 0.0537058318609612 90 0.93742103669295 0.125157926614099 0.0625789633070496 91 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0.739647519878742 0.369823759939371 109 0.602393094238014 0.795213811523973 0.397606905761986 110 0.592254591556951 0.815490816886097 0.407745408443049 111 0.538178328292682 0.923643343414636 0.461821671707318 112 0.525996678308492 0.948006643383016 0.474003321691508 113 0.614446856985742 0.771106286028516 0.385553143014258 114 0.570468984964943 0.859062030070114 0.429531015035057 115 0.507751811502074 0.984496376995852 0.492248188497926 116 0.446119403108117 0.892238806216233 0.553880596891883 117 0.421242290292168 0.842484580584337 0.578757709707832 118 0.461994368488242 0.923988736976484 0.538005631511758 119 0.397944648727415 0.79588929745483 0.602055351272585 120 0.347024613231764 0.694049226463527 0.652975386768236 121 0.302114989960879 0.604229979921757 0.697885010039121 122 0.255638826557768 0.511277653115537 0.744361173442232 123 0.211095824050385 0.42219164810077 0.788904175949615 124 0.164166767752735 0.328333535505469 0.835833232247265 125 0.217930499080628 0.435860998161255 0.782069500919372 126 0.165231458208003 0.330462916416007 0.834768541791997 127 0.139810082293012 0.279620164586023 0.860189917706988 128 0.121203847159102 0.242407694318204 0.878796152840898 129 0.1236598791052 0.2473197582104 0.8763401208948 130 0.085522861178076 0.171045722356152 0.914477138821924 131 0.0705281839489308 0.141056367897862 0.92947181605107 132 0.052780001853264 0.105560003706528 0.947219998146736 133 0.0305111959484059 0.0610223918968117 0.969488804051594 134 0.360948951890361 0.721897903780723 0.639051048109639 135 0.395605680732811 0.791211361465622 0.604394319267189 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.128 NOK 5% type I error level 48 0.384 NOK 10% type I error level 58 0.464 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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