Interactie effecten

*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: Wed, 29 Dec 2010 11:10:13 +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/29/t1293620878w3v19bc33za40be.htm/, Retrieved Wed, 29 Dec 2010 12:08:10 +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/29/t1293620878w3v19bc33za40be.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 14 182 13 169 3 39 2 26 12 12 8 96 13 156 5 60 1 12 15 10 12 120 16 160 6 60 0 0 12 9 7 63 12 108 6 54 3 27 10 10 10 100 11 110 5 50 3 30 12 12 7 84 12 144 3 36 1 12 15 13 16 208 18 234 8 104 3 39 9 12 11 132 11 132 4 48 1 12 12 12 14 168 14 168 4 48 4 48 11 6 6 36 9 54 4 24 0 0 11 5 16 80 14 70 6 30 3 15 11 12 11 132 12 144 6 72 2 24 15 11 16 176 11 121 5 55 4 44 7 14 12 168 12 168 4 56 3 42 11 14 7 98 13 182 6 84 1 14 11 12 13 156 11 132 4 48 1 12 10 12 11 132 12 144 6 72 2 24 14 11 15 165 16 176 6 66 3 33 10 11 7 77 9 99 4 44 1 11 6 7 9 63 11 77 4 28 1 7 11 9 7 63 13 117 2 18 2 18 15 11 14 154 15 165 7 77 3 33 11 11 15 165 10 110 5 55 4 44 12 12 7 84 11 132 4 48 2 24 14 12 15 180 13 156 6 72 1 12 15 11 17 187 16 176 6 66 2 22 9 11 15 165 15 165 7 77 2 22 13 8 14 112 14 112 5 40 4 32 13 9 14 126 14 126 6 54 2 18 16 12 8 96 14 168 4 48 3 36 13 10 8 80 8 80 4 40 3 30 12 10 14 140 13 130 7 70 3 30 14 12 14 168 15 180 7 84 4 48 11 8 8 64 13 104 4 32 2 16 9 12 11 132 11 13 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = + 7.13071247390499 -0.501068230469585FindingFriends[t] + 0.0144399681786234KnowingPeople[t] + 0.0195920449423157friends_knowning[t] + 0.453370355865953Liked[t] -0.0119296940515746friends_liked[t] -1.04257015127670Celebrity[t] + 0.146732923963822friends_celeb[t] + 1.14069077304853Sum[t] -0.0845908632946572friends_sum[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) 7.13071247390499 5.618027 1.2693 0.20637 0.103185 FindingFriends -0.501068230469585 0.493294 -1.0158 0.311425 0.155712 KnowingPeople 0.0144399681786234 0.39969 0.0361 0.97123 0.485615 friends_knowning 0.0195920449423157 0.035777 0.5476 0.584794 0.292397 Liked 0.453370355865953 0.502072 0.903 0.368014 0.184007 friends_liked -0.0119296940515746 0.047077 -0.2534 0.800308 0.400154 Celebrity -1.04257015127670 1.209949 -0.8617 0.390285 0.195143 friends_celeb 0.146732923963822 0.107187 1.3689 0.173121 0.086561 Sum 1.14069077304853 0.841424 1.3557 0.177298 0.088649 friends_sum -0.0845908632946572 0.075323 -1.123 0.263263 0.131631 Multiple Linear Regression - Regression Statistics Multiple R 0.722001202058945 R-squared 0.521285735774561 Adjusted R-squared 0.491775952363404 F-TEST (value) 17.6648445199185 F-TEST (DF numerator) 9 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.09351674679716 Sum Squared Residuals 639.890605891545 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 10.9393262245388 2.06067377546121 2 12 10.8637572173315 1.13624278266855 3 15 12.5380843562029 2.46191564379707 4 12 10.9147903176924 1.08520968230764 5 10 10.9065837761927 -0.906583776192673 6 12 8.86754881001966 3.13245118998034 7 15 17.3348198468565 -2.33481984685653 8 9 10.2737377490074 -1.27373774900741 9 12 12.3298145937458 -0.329814593745768 10 11 7.70369581211604 3.29630418788396 11 11 12.2356569872936 -1.23565698729358 12 11 12.1460020623454 -1.14600206234543 13 15 12.5400074425859 2.45999255741406 14 7 10.9327752831474 -3.93277528314742 15 11 11.8860311300231 -0.886031130023053 16 11 10.7728267639802 0.227173236019762 17 10 12.1460020623454 -2.14600206234543 18 14 14.2820743220530 -0.282074322053038 19 10 8.62408199434457 1.37591800565543 20 6 9.54277687401176 -3.54277687401176 21 11 9.76931576610676 1.23068423389324 22 15 14.3014701505356 0.698529849464355 23 11 11.9879112587432 -0.987911258743211 24 12 9.4011601325744 2.59883986742559 25 14 13.3287937060255 0.671206293974508 26 15 14.5317879703339 0.468212029666071 27 9 14.3212313362724 -5.32123133627244 28 13 13.0420161354698 -0.04201613546983 29 13 12.5628021417156 0.437197858284439 30 16 10.7069471353146 5.29305286468536 31 13 9.05888360657697 3.94111639342303 32 12 13.2656904540232 -1.26569045402323 33 14 14.7947034298603 -0.794703429860286 34 11 10.5978043918972 0.402195608102839 35 9 10.3993381625201 -1.39933816252006 36 16 14.4000743401058 1.59992565989420 37 12 12.9527000501128 -0.952700050112836 38 10 10.2816921026549 -0.281692102654904 39 13 12.8754994188057 0.124500581194257 40 16 15.5039013505371 0.496098649462928 41 14 13.0809055180780 0.919094481922042 42 15 8.59442596298534 6.40557403701466 43 5 8.72145244464664 -3.72145244464664 44 8 10.2803705458728 -2.28037054587275 45 11 11.1736409222425 -0.173640922242532 46 16 13.7493739666929 2.25062603330708 47 17 14.3274425279742 2.67255747202575 48 9 8.29370726420254 0.706292735797464 49 9 11.4237199277776 -2.42371992777761 50 13 14.6345807087657 -1.63458070876572 51 10 10.9589490446740 -0.958949044674015 52 6 12.0949341895663 -6.0949341895663 53 12 12.1721492745242 -0.172149274524156 54 8 10.2711326945549 -2.27113269455494 55 14 12.3180862921800 1.68191370781998 56 12 12.9345812369023 -0.934581236902313 57 11 10.9332068487977 0.0667931512023159 58 16 14.3950911126313 1.60490888736871 59 8 10.5242002387326 -2.52420023873258 60 15 14.4723407949832 0.52765920501683 61 7 8.99149290399343 -1.99149290399343 62 16 14.2653534812969 1.73464651870310 63 14 12.4452647828127 1.55473521718729 64 16 13.3095955845957 2.69040441540429 65 9 10.8253687840318 -1.82536878403182 66 14 12.2370801682762 1.76291983172384 67 11 13.3953809193164 -2.39538091931637 68 13 10.3039986825980 2.69600131740202 69 15 13.2065059315906 1.79349406840940 70 5 4.63866377481478 0.361336225185218 71 15 12.9345812369023 2.06541876309769 72 13 12.4882809953014 0.511719004698555 73 11 12.3566762937614 -1.35667629376135 74 11 14.0222084053630 -3.02220840536303 75 12 12.7149926705601 -0.714992670560117 76 12 13.5799945330508 -1.57999453305078 77 12 12.4119671620672 -0.411967162067232 78 12 12.0489431376081 -0.0489431376080512 79 14 10.581346721802 3.41865327819800 80 6 7.58956533899704 -1.58956533899704 81 7 9.58577374630882 -2.58577374630882 82 14 12.6144008030286 1.38559919697143 83 14 14.0323606737721 -0.0323606737721461 84 10 11.1562354690427 -1.15623546904270 85 13 8.61966062207213 4.38033937792787 86 12 12.6976423672448 -0.697642367244768 87 9 9.3863694549946 -0.386369454994597 88 12 11.8585104760371 0.141489523962873 89 16 14.6627708860537 1.33722911394632 90 10 10.4708006369433 -0.470800636943259 91 14 12.9221207325253 1.07787926747475 92 10 13.5000256756662 -3.50002567566621 93 16 15.6040064720802 0.395993527919832 94 15 13.2616522008653 1.73834779913472 95 12 11.5021975743568 0.497802425643212 96 10 9.39950381303553 0.600496186964467 97 8 9.49370108808755 -1.49370108808755 98 8 8.16051735795122 -0.160517357951225 99 11 12.4720212055406 -1.47202120554064 100 13 12.5818165031051 0.418183496894866 101 16 15.5236625362739 0.476337463726132 102 16 15.0442479373467 0.955752062653301 103 14 16.2050902994530 -2.20509029945303 104 11 9.3786282020592 1.62137179794081 105 4 7.74128845004528 -3.74128845004528 106 14 15.1234318658932 -1.12343186589319 107 9 10.9355728477718 -1.93557284777184 108 14 15.1029146254051 -1.10291462540508 109 8 10.4697215653163 -2.46972156531635 110 8 11.0222268640315 -3.02222686403148 111 11 11.7399605772429 -0.739960577242933 112 12 11.7259545405275 0.274045459472457 113 11 11.2068372326216 -0.20683723262155 114 14 13.4501327455468 0.549867254453217 115 15 14.4802280286219 0.519771971378125 116 16 13.4078344169117 2.59216558308832 117 16 13.2638628122735 2.73613718772651 118 11 12.6382246488744 -1.63822464887442 119 14 14.1414093873231 -0.141409387323128 120 14 10.9504667477682 3.04953325223175 121 12 11.3949111380132 0.60508886198677 122 14 12.6519598870773 1.34804011292273 123 8 10.8911831905579 -2.89118319055786 124 13 14.1836397913579 -1.18363979135789 125 16 14.0158089738105 1.98419102618952 126 12 10.5492818160930 1.45071818390696 127 16 15.6975419798839 0.302458020116144 128 12 13.2031932925128 -1.20319329251285 129 11 11.4293830912187 -0.429383091218686 130 4 6.10612877200011 -2.10612877200011 131 16 16.0743432204218 -0.0743432204217903 132 15 12.6257857705249 2.37421422947515 133 10 11.0761743627835 -1.07617436278347 134 13 13.4873970152744 -0.487397015274438 135 15 13.2655191318124 1.73448086818763 136 12 10.3950767885287 1.60492321147130 137 14 13.6377868794558 0.362213120544226 138 7 10.3648158549381 -3.36481585493814 139 19 14.0748221740323 4.92517782596775 140 12 13.0450278327524 -1.04502783275237 141 12 11.9822290424360 0.017770957564037 142 13 13.5150636392988 -0.515063639298782 143 15 12.4478409941893 2.55215900581071 144 8 9.40102189128404 -1.40102189128404 145 12 10.7702217095278 1.22977829047224 146 10 10.8018247307924 -0.80182473079244 147 8 11.1739712445785 -3.17397124457851 148 10 14.7937263715450 -4.79372637154504 149 15 13.8677393419999 1.13226065800013 150 16 14.5114376971260 1.48856230287396 151 13 13.1972777828502 -0.197277782850188 152 16 15.2288615510811 0.771138448918888 153 9 9.84022680652135 -0.840226806521354 154 14 12.7935752210566 1.20642477894339 155 14 13.1839295279290 0.816070472070984 156 12 10.2218177031707 1.77818229682926 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.789856754638046 0.420286490723907 0.210143245361953 14 0.794290257201504 0.411419485596992 0.205709742798496 15 0.720281079680346 0.559437840639308 0.279718920319654 16 0.652360434596922 0.695279130806156 0.347639565403078 17 0.559647021224976 0.880705957550048 0.440352978775024 18 0.461611528974334 0.923223057948668 0.538388471025666 19 0.366800294452807 0.733600588905615 0.633199705547193 20 0.7874859103077 0.425028179384601 0.212514089692301 21 0.746649252642219 0.506701494715563 0.253350747357781 22 0.715504873072459 0.568990253855082 0.284495126927541 23 0.649591316169457 0.700817367661086 0.350408683830543 24 0.677632001947583 0.644735996104833 0.322367998052417 25 0.61206367099689 0.775872658006222 0.387936329003111 26 0.542401760210631 0.915196479578738 0.457598239789369 27 0.779451156916408 0.441097686167184 0.220548843083592 28 0.722935064379629 0.554129871240742 0.277064935620371 29 0.673630398785584 0.652739202428833 0.326369601214416 30 0.82041456995533 0.359170860089339 0.179585430044670 31 0.86360228890417 0.272795422191660 0.136397711095830 32 0.829536882497178 0.340926235005643 0.170463117502821 33 0.793887141927808 0.412225716144383 0.206112858072192 34 0.74801120861961 0.503977582760781 0.251988791380391 35 0.726702639941764 0.546594720116472 0.273297360058236 36 0.729559521730714 0.540880956538572 0.270440478269286 37 0.696963145270283 0.606073709459435 0.303036854729717 38 0.714191519632903 0.571616960734194 0.285808480367097 39 0.670387563368332 0.659224873263335 0.329612436631668 40 0.631597275384731 0.736805449230539 0.368402724615269 41 0.599846621122815 0.80030675775437 0.400153378877185 42 0.858905884951662 0.282188230096676 0.141094115048338 43 0.944000158658084 0.111999682683831 0.0559998413419157 44 0.949119846429745 0.101760307140511 0.0508801535702555 45 0.933842776755845 0.132314446488309 0.0661572232441546 46 0.93994727887907 0.120105442241860 0.0600527211209302 47 0.934796900723957 0.130406198552087 0.0652030992760433 48 0.920770403555792 0.158459192888415 0.0792295964442076 49 0.919012806743867 0.161974386512267 0.0809871932561333 50 0.905959821703128 0.188080356593743 0.0940401782968716 51 0.894811508700092 0.210376982599816 0.105188491299908 52 0.978157470213162 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0.0334825176286586 0.0167412588143293 70 0.978464810038453 0.0430703799230934 0.0215351899615467 71 0.97843875569584 0.043122488608318 0.021561244304159 72 0.971736232525735 0.0565275349485301 0.0282637674742651 73 0.96722932069742 0.0655413586051578 0.0327706793025789 74 0.983171346943996 0.0336573061120082 0.0168286530560041 75 0.978631496071622 0.0427370078567551 0.0213685039283776 76 0.97635682845968 0.0472863430806391 0.0236431715403196 77 0.969335964613115 0.0613280707737694 0.0306640353868847 78 0.959907761006203 0.080184477987593 0.0400922389937965 79 0.974872137223659 0.0502557255526825 0.0251278627763412 80 0.973524285597229 0.0529514288055429 0.0264757144027715 81 0.979794345741267 0.0404113085174653 0.0202056542587327 82 0.97938721677699 0.0412255664460185 0.0206127832230093 83 0.972650243757004 0.0546995124859916 0.0273497562429958 84 0.967176595754993 0.0656468084900135 0.0328234042450067 85 0.989707413712195 0.0205851725756105 0.0102925862878052 86 0.98649381468275 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0.248407040581598 0.124203520290799 104 0.914359596350102 0.171280807299797 0.0856404036498984 105 0.925043542953849 0.149912914092302 0.0749564570461511 106 0.909435769665577 0.181128460668845 0.0905642303344224 107 0.905858722900243 0.188282554199513 0.0941412770997566 108 0.910164860487339 0.179670279025322 0.089835139512661 109 0.908865267778052 0.182269464443895 0.0911347322219477 110 0.901221164039377 0.197557671921246 0.0987788359606229 111 0.886025292846051 0.227949414307898 0.113974707153949 112 0.899915057077616 0.200169885844768 0.100084942922384 113 0.871777571593192 0.256444856813617 0.128222428406808 114 0.841065216834467 0.317869566331067 0.158934783165533 115 0.802814400797231 0.394371198405538 0.197185599202769 116 0.792288192023945 0.415423615952111 0.207711807976055 117 0.801063213911621 0.397873572176757 0.198936786088379 118 0.792850862305772 0.414298275388456 0.207149137694228 119 0.745725907062652 0.508548185874696 0.254274092937348 120 0.81587134627801 0.368257307443979 0.184128653721989 121 0.78347260470419 0.433054790591622 0.216527395295811 122 0.739600669094388 0.520798661811224 0.260399330905612 123 0.731701256823724 0.536597486352553 0.268298743176276 124 0.677187246593199 0.645625506813603 0.322812753406801 125 0.682576594490703 0.634846811018594 0.317423405509297 126 0.6522920413469 0.6954159173062 0.3477079586531 127 0.588959466234945 0.82208106753011 0.411040533765055 128 0.552166421609131 0.895667156781738 0.447833578390869 129 0.49903524125999 0.99807048251998 0.50096475874001 130 0.44494380864256 0.88988761728512 0.55505619135744 131 0.374981421696104 0.749962843392208 0.625018578303896 132 0.375366110833356 0.750732221666712 0.624633889166644 133 0.37547158393236 0.75094316786472 0.62452841606764 134 0.335005100297721 0.670010200595443 0.664994899702279 135 0.299849971717293 0.599699943434586 0.700150028282707 136 0.301040174722991 0.602080349445982 0.698959825277009 137 0.226501992443876 0.453003984887752 0.773498007556124 138 0.351490911221509 0.702981822443019 0.64850908877849 139 0.618000476480684 0.763999047038632 0.381999523519316 140 0.813317324653412 0.373365350693175 0.186682675346588 141 0.74603262934877 0.507934741302459 0.253967370651229 142 0.719534248872319 0.560931502255363 0.280465751127681 143 0.610057764065747 0.779884471868506 0.389942235934253 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 22 0.16793893129771 NOK 10% type I error level 44 0.33587786259542 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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