Meervoudige regressie Happiness 1

*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, 21 Nov 2010 13:53:20 +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/Nov/21/t129034778188teu6cc167s7f7.htm/, Retrieved Sun, 21 Nov 2010 14:56:21 +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/Nov/21/t129034778188teu6cc167s7f7.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 «

15 10 77 46 15 12 13 6 11 6 4 15 16 9 20 63 37 12 7 11 4 26 5 4 23 24 12 16 73 45 15 13 14 6 26 20 10 26 22 15 10 76 46 12 11 12 5 15 12 6 19 21 17 8 90 55 14 16 12 5 10 11 5 19 23 14 14 67 40 8 10 6 4 21 12 8 16 23 9 19 69 43 11 15 10 5 27 11 9 23 21 11 23 54 33 4 4 10 2 21 13 8 19 22 13 9 54 33 13 7 12 5 21 9 11 24 20 16 12 76 47 19 15 15 6 22 14 6 19 12 16 14 75 44 10 5 13 6 29 12 8 25 23 15 13 76 47 15 16 18 8 29 18 11 23 23 10 11 80 49 6 15 11 6 29 9 5 31 30 16 11 89 55 7 13 12 3 30 15 10 29 22 12 10 73 43 14 13 13 6 19 12 7 18 21 15 12 74 46 16 15 14 6 19 12 7 17 21 13 18 78 51 16 15 16 7 22 12 13 22 15 18 12 76 47 14 10 16 8 18 15 10 21 22 13 10 69 42 15 17 16 6 28 11 8 24 24 17 15 74 42 14 14 15 7 17 13 6 22 23 14 15 82 48 12 9 13 4 18 10 8 16 15 13 12 77 45 9 6 8 4 20 17 7 22 24 13 9 84 51 12 11 14 2 16 13 5 21 24 15 11 75 46 14 13 15 6 17 17 9 25 21 15 16 79 47 14 10 16 6 25 15 11 22 21 13 17 79 47 10 4 13 6 22 13 11 24 18 13 11 88 55 16 15 15 7 31 17 9 25 19 16 13 57 36 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 10 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Happiness[t] = + 17.3893058309824 -0.369421768925105Depression[t] + 0.0673020022887946Belonging[t] -0.0489287737012476BelongingFinal[t] -0.0937609856736327Popularity[t] -0.0384462079358146KnowingPeople[t] + 0.0656628197343761Liked[t] + 0.130635340805913Celebrity[t] -0.0539467717320201ConcernOverMistakes[t] + 0.097263567422785ParentalExpectations[t] -0.0906357963563662ParentalCriticism[t] + 0.043090642762466PersonalStandards[t] -0.0673752186150563Organization[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) 17.3893058309824 2.51385 6.9174 0 0 Depression -0.369421768925105 0.060753 -6.0807 0 0 Belonging 0.0673020022887946 0.053492 1.2582 0.210692 0.105346 BelongingFinal -0.0489287737012476 0.075322 -0.6496 0.517154 0.258577 Popularity -0.0937609856736327 0.090697 -1.0338 0.30325 0.151625 KnowingPeople -0.0384462079358146 0.069841 -0.5505 0.58298 0.29149 Liked 0.0656628197343761 0.10627 0.6179 0.537784 0.268892 Celebrity 0.130635340805913 0.181069 0.7215 0.471981 0.235991 ConcernOverMistakes -0.0539467717320201 0.03594 -1.501 0.135888 0.067944 ParentalExpectations 0.097263567422785 0.064234 1.5142 0.13252 0.06626 ParentalCriticism -0.0906357963563662 0.083458 -1.086 0.279585 0.139792 PersonalStandards 0.043090642762466 0.051396 0.8384 0.403413 0.201707 Organization -0.0673752186150563 0.051073 -1.3192 0.189532 0.094766 Multiple Linear Regression - Regression Statistics Multiple R 0.598500396625606 R-squared 0.358202724761007 Adjusted R-squared 0.296093311028202 F-TEST (value) 5.76728555677554 F-TEST (DF numerator) 12 F-TEST (DF denominator) 124 p-value 7.55891645942697e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.98907390262861 Sum Squared Residuals 490.595458774659 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 15.5922580224144 -0.592258022414358 2 9 10.3763471898270 -1.37634718982703 3 12 13.2610841337057 -1.26108413370568 4 15 15.6703962274125 -0.670396227412501 5 17 16.6597114733318 0.340288526668162 6 14 13.0004670761162 0.999532923883771 7 9 10.9857534804041 -1.9857534804041 8 11 10.0442590439050 0.955740956094975 9 13 14.4694499681551 -1.46944996815505 10 16 14.8234126846683 1.17658731533166 11 16 14.0250290987119 1.97497090128810 12 15 14.2383308433863 0.761669156613684 13 10 14.8514507853627 -4.85145078536272 14 16 15.3497605056874 0.650239494312601 15 12 15.1976467889243 -3.19764678892434 16 15 14.1374767220122 0.8625232779878 17 13 12.1215206615536 0.87847933844641 18 18 15.1743187223615 2.82568127763846 19 13 14.3098080866189 -1.30980808661894 20 17 14.0236885336907 2.97631146630934 21 14 13.8785020845659 0.121497915434081 22 13 15.1811052970486 -2.18110529704858 23 13 16.0910179849196 -3.09101798491963 24 15 15.6619426301443 -0.661942630144332 25 15 13.2794696348127 1.7205303651873 26 13 13.5744007187145 -0.574400718714475 27 13 15.3422262831563 -2.34222628315628 28 16 13.3151082880934 2.68489171190657 29 14 15.9534990870273 -1.9534990870273 30 18 15.9888271007488 2.01117289925119 31 9 10.7837882163600 -1.78378821635997 32 16 16.1255323657905 -0.125532365790519 33 16 15.1857238657002 0.81427613429977 34 17 14.7783285904549 2.2216714095451 35 13 15.1252718748917 -2.12527187489165 36 17 13.9456636102596 3.05433638974035 37 15 13.0959449568281 1.90405504317186 38 14 14.1172809572284 -0.117280957228351 39 10 12.1478420918796 -2.14784209187965 40 13 14.2143000310942 -1.21430003109421 41 11 13.2626123550771 -2.26261235507708 42 11 13.0253576083747 -2.02535760837472 43 15 15.4519651950179 -0.451965195017864 44 15 15.2603954602792 -0.260395460279236 45 12 12.5222703421012 -0.522270342101192 46 17 14.7291894034684 2.27081059653161 47 15 13.7087236167634 1.29127638323656 48 16 15.5727077627709 0.427292237229146 49 14 13.9357182709740 0.0642817290259524 50 17 14.7350332397809 2.26496676021906 51 10 10.3428067365893 -0.342806736589346 52 11 14.0354719963516 -3.03547199635159 53 15 14.0589772901077 0.941022709892318 54 15 14.7352116988952 0.264788301104821 55 7 10.6167900207868 -3.61679002078684 56 17 14.9855898742421 2.01441012575788 57 14 13.1297042512869 0.870295748713117 58 18 15.7815428711431 2.21845712885694 59 14 13.9352181407191 0.0647818592809266 60 14 15.2895859629806 -1.2895859629806 61 9 15.6732330420276 -6.67323304202762 62 14 14.3648153945076 -0.364815394507583 63 11 12.5319859520448 -1.53198595204482 64 16 13.6474258360948 2.35257416390516 65 17 15.0444232461064 1.95557675389359 66 12 15.1335968952848 -3.13359689528478 67 15 14.1446184413009 0.855381558699093 68 15 15.5932417098202 -0.593241709820164 69 16 16.3140717474382 -0.31407174743823 70 16 16.6334281632169 -0.633428163216929 71 11 12.5046700257125 -1.50467002571253 72 12 12.7739898806394 -0.773989880639433 73 14 14.1219496567694 -0.121949656769382 74 15 15.7079336124191 -0.707933612419097 75 17 15.6740037368294 1.32599626317064 76 19 14.6619159333655 4.33808406663445 77 15 13.7595515686571 1.24044843134293 78 16 12.926964014305 3.07303598569499 79 14 14.5226103599700 -0.522610359970031 80 16 11.5087436118892 4.49125638811078 81 15 14.5013675128032 0.498632487196845 82 17 14.4613221404805 2.53867785951953 83 12 14.4616919466267 -2.46169194662671 84 18 14.8058708070347 3.19412919296535 85 13 14.3291907616437 -1.32919076164374 86 14 13.2804662388945 0.719533761105549 87 14 13.7151715010423 0.284828498957662 88 14 14.2258232926222 -0.225823292622172 89 12 14.4414756042756 -2.44147560427561 90 14 13.0583267045283 0.941673295471697 91 12 13.3139957417549 -1.31399574175488 92 15 14.6345865790794 0.365413420920584 93 11 12.543957626866 -1.54395762686600 94 15 15.2746915466298 -0.274691546629762 95 14 14.4082683371557 -0.408268337155737 96 15 13.6449961263209 1.35500387367912 97 16 14.3291513240461 1.67084867595388 98 14 11.3539775482019 2.64602245179807 99 18 15.9306889171102 2.06931108288978 100 14 15.0945360835772 -1.09453608357724 101 13 12.6409821360560 0.359017863944032 102 14 12.5977719529605 1.40222804703946 103 14 14.7185273175246 -0.718527317524611 104 17 15.5900538293438 1.40994617065617 105 12 12.8424499485455 -0.842449948545538 106 16 13.7961072149273 2.20389278507266 107 10 12.3287532128499 -2.32875321284993 108 13 14.5643015234240 -1.56430152342403 109 15 15.3301671929391 -0.330167192939076 110 16 14.9937588594251 1.00624114057488 111 14 12.8242893787846 1.17571062121543 112 13 12.4388900869289 0.561109913071106 113 17 14.7659087707156 2.23409122928439 114 14 13.9804249905477 0.0195750094522665 115 16 12.9947584531866 3.00524154681336 116 12 14.0065703068552 -2.00657030685520 117 16 14.3235182657491 1.67648173425089 118 8 10.5607830856912 -2.56078308569116 119 9 12.1883336587029 -3.18833365870288 120 13 11.8831289165083 1.11687108349165 121 19 15.2176278196193 3.78237218038072 122 11 12.4318895916441 -1.43188959164413 123 15 14.8735806538499 0.126419346150068 124 11 13.4466997710899 -2.44669977108987 125 15 15.8492974720167 -0.849297472016677 126 16 16.0832409374489 -0.0832409374488868 127 15 12.8115158499849 2.18848415001507 128 12 12.9738247901357 -0.973824790135663 129 16 15.1167461618508 0.88325383814922 130 15 13.0787541659559 1.92124583404405 131 13 14.6048003186335 -1.60480031863350 132 14 13.9855981904439 0.0144018095560894 133 11 13.3938277336665 -2.39382773366646 134 15 14.6630824662544 0.336917533745566 135 14 12.7238953692018 1.27610463079824 136 13 16.0418341374656 -3.04183413746564 137 15 16.0687584843007 -1.06875848430071 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.135225341751821 0.270450683503642 0.864774658248179 17 0.188489119323522 0.376978238647045 0.811510880676478 18 0.124162170137085 0.248324340274169 0.875837829862915 19 0.130489120306253 0.260978240612506 0.869510879693747 20 0.57566792116835 0.8486641576633 0.42433207883165 21 0.648215523008188 0.703568953983624 0.351784476991812 22 0.676624134040649 0.646751731918702 0.323375865959351 23 0.678371908962623 0.643256182074755 0.321628091037377 24 0.596519325383009 0.806961349233982 0.403480674616991 25 0.519980029586543 0.960039940826914 0.480019970413457 26 0.53115270111009 0.93769459777982 0.46884729888991 27 0.514694917284793 0.970610165430414 0.485305082715207 28 0.669988268863748 0.660023462272503 0.330011731136252 29 0.65055940951569 0.69888118096862 0.34944059048431 30 0.662576190535191 0.674847618929618 0.337423809464809 31 0.643259962431207 0.713480075137586 0.356740037568793 32 0.577854281905808 0.844291436188384 0.422145718094192 33 0.515158417682542 0.969683164634916 0.484841582317458 34 0.542480555175864 0.915038889648273 0.457519444824136 35 0.51750781088488 0.96498437823024 0.48249218911512 36 0.615938502000904 0.768122995998191 0.384061497999096 37 0.688405092364436 0.623189815271127 0.311594907635564 38 0.633981838947292 0.732036322105415 0.366018161052708 39 0.613716987111302 0.772566025777396 0.386283012888698 40 0.588939700281741 0.822120599436519 0.411060299718259 41 0.588344533003722 0.823310933992555 0.411655466996277 42 0.61417724962695 0.7716455007461 0.38582275037305 43 0.555433296582456 0.889133406835088 0.444566703417544 44 0.49602944243664 0.99205888487328 0.50397055756336 45 0.44597681344324 0.89195362688648 0.55402318655676 46 0.507905628490039 0.984188743019923 0.492094371509961 47 0.51630297276382 0.96739405447236 0.48369702723618 48 0.472474317887539 0.944948635775078 0.527525682112461 49 0.416702437310555 0.83340487462111 0.583297562689445 50 0.416421932135589 0.832843864271179 0.58357806786441 51 0.370810533470863 0.741621066941725 0.629189466529137 52 0.450885009420593 0.901770018841186 0.549114990579407 53 0.41689085790562 0.83378171581124 0.58310914209438 54 0.363559236378611 0.727118472757223 0.636440763621389 55 0.494488131380527 0.988976262761054 0.505511868619473 56 0.483355072257885 0.96671014451577 0.516644927742115 57 0.465959333757322 0.931918667514644 0.534040666242678 58 0.475615808385850 0.951231616771701 0.52438419161415 59 0.421813924301254 0.843627848602509 0.578186075698746 60 0.380029288107141 0.760058576214282 0.619970711892859 61 0.856998076163661 0.286003847672678 0.143001923836339 62 0.828279868338503 0.343440263322994 0.171720131661497 63 0.805231179665707 0.389537640668586 0.194768820334293 64 0.812186392203146 0.375627215593708 0.187813607796854 65 0.825757908954612 0.348484182090777 0.174242091045388 66 0.903826171087978 0.192347657824044 0.0961738289120219 67 0.883221817497143 0.233556365005715 0.116778182502857 68 0.861807682647077 0.276384634705845 0.138192317352923 69 0.834233238744365 0.331533522511270 0.165766761255635 70 0.810822174363054 0.378355651273892 0.189177825636946 71 0.815118751910661 0.369762496178677 0.184881248089339 72 0.782704458795738 0.434591082408525 0.217295541204262 73 0.74730294401625 0.505394111967501 0.252697055983750 74 0.730550627671696 0.538898744656608 0.269449372328304 75 0.741746220286154 0.516507559427693 0.258253779713846 76 0.877734118893478 0.244531762213045 0.122265881106522 77 0.858025777134151 0.283948445731697 0.141974222865849 78 0.892374044091445 0.215251911817109 0.107625955908554 79 0.866678988021975 0.266642023956051 0.133321011978025 80 0.961737044918586 0.076525910162827 0.0382629550814135 81 0.949988993807943 0.100022012384113 0.0500110061920566 82 0.94912215209866 0.101755695802679 0.0508778479013397 83 0.950483972388882 0.0990320552222351 0.0495160276111176 84 0.96436040658688 0.071279186826242 0.035639593413121 85 0.957064770816636 0.0858704583667271 0.0429352291833635 86 0.945457140118224 0.109085719763553 0.0545428598817765 87 0.927810822048675 0.144378355902650 0.0721891779513251 88 0.910149425047385 0.179701149905229 0.0898505749526147 89 0.911000715764373 0.177998568471254 0.088999284235627 90 0.887263692858206 0.225472614283587 0.112736307141793 91 0.89288479697606 0.214230406047880 0.107115203023940 92 0.870303199813004 0.259393600373992 0.129696800186996 93 0.848017117781784 0.303965764436431 0.151982882218216 94 0.809652075567523 0.380695848864954 0.190347924432477 95 0.775912989234809 0.448174021530382 0.224087010765191 96 0.759459589996565 0.481080820006869 0.240540410003435 97 0.807305306888774 0.385389386222452 0.192694693111226 98 0.8117618604802 0.3764762790396 0.1882381395198 99 0.787553202092248 0.424893595815503 0.212446797907752 100 0.751437257691304 0.497125484617392 0.248562742308696 101 0.700832119942947 0.598335760114107 0.299167880057053 102 0.668207538206628 0.663584923586744 0.331792461793372 103 0.609291556099695 0.78141688780061 0.390708443900305 104 0.560524567934433 0.878950864131134 0.439475432065567 105 0.498815315954068 0.997630631908136 0.501184684045932 106 0.798569315185051 0.402861369629898 0.201430684814949 107 0.814965084691482 0.370069830617037 0.185034915308518 108 0.847499530615663 0.305000938768674 0.152500469384337 109 0.793456628966256 0.413086742067489 0.206543371033744 110 0.74742133634924 0.505157327301521 0.252578663650760 111 0.755105640543061 0.489788718913877 0.244894359456939 112 0.690949686470773 0.618100627058453 0.309050313529227 113 0.617927940584889 0.764144118830222 0.382072059415111 114 0.5307416187765 0.938516762447 0.4692583812235 115 0.547955092365335 0.90408981526933 0.452044907634665 116 0.578792516520892 0.842414966958216 0.421207483479108 117 0.585975048492105 0.82804990301579 0.414024951507895 118 0.604258113474572 0.791483773050856 0.395741886525428 119 0.675933483582528 0.648133032834944 0.324066516417472 120 0.67925066681918 0.641498666361641 0.320749333180821 121 0.540203070775981 0.919593858448039 0.459796929224019 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 4 0.0377358490566038 OK

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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