*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, 09 Dec 2010 09:37:57 +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/09/t1291887520nt6njjok3jdwigd.htm/, Retrieved Thu, 09 Dec 2010 10:38:44 +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/09/t1291887520nt6njjok3jdwigd.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 «

-0.0326433382 0.0117006692 -0.0066969042 0.0407013064 -0.0128665773 -0.0778003889 0.0173917427 0.2208242615 -0.1696576949 -0.0125923592 0.0869788752 0.044072034 0.039110383 0.0117006692 -0.0066969042 0.0407013064 -0.0128665773 -0.0326433382 0.0173917427 0.2208242615 -0.1696576949 -0.0125923592 0.0882361169 0.0423550366 0.039110383 0.0117006692 -0.0066969042 0.0407013064 0.044072034 -0.0326433382 0.0173917427 0.2208242615 -0.1696576949 -0.0355066885 -0.0356327003 0.0423550366 0.039110383 0.0117006692 -0.0066969042 0.0882361169 0.044072034 -0.0326433382 0.0173917427 0.2208242615 0.0278273388 0.004227877 -0.0356327003 0.0423550366 0.039110383 0.0117006692 -0.0355066885 0.0882361169 0.044072034 -0.0326433382 0.0173917427 -0.2004308914 0.0210328888 0.004227877 -0.0356327003 0.0423550366 0.039110383 0.0278273388 -0.0355066885 0.0882361169 0.044072034 -0.0326433382 -0.0263424279 -0.031774003 0.0210328888 0.004227877 -0.0356327003 0.0423550366 -0.2004308914 0.0278273388 -0.0355066885 0.0882361169 0.044072034 0 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation (1-B)lnYt[t] = + 0.00668792198944815 + 0.564381448485195`(1-B)lnX_[t-1]`[t] + 0.189102494116169`(1-B)lnX_[t-2]`[t] -0.0904226553207113`(1-B)lnX_[t-3]`[t] + 0.152712556338415`(1-B)lnX_[t-4]`[t] -0.057034187147676`(1-B)lnX_[t-5]`[t] + 0.258408373637674`(1-B)lnY_[t-1]`[t] -0.0554449120219212`(1-B)lnY_[t-2]`[t] -0.0247153522663553`(1-B)lnY_[t-3]`[t] -0.01140685785384`(1-B)lnY_[t-4]`[t] + 0.000229808193820455`(1-B)lnY_[t-5]`[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 0.00668792198944815 0.008595 0.7781 0.438506 0.219253 `(1-B)lnX_[t-1]` 0.564381448485195 0.173012 3.2621 0.001552 0.000776 `(1-B)lnX_[t-2]` 0.189102494116169 0.190671 0.9918 0.32391 0.161955 `(1-B)lnX_[t-3]` -0.0904226553207113 0.188738 -0.4791 0.633011 0.316505 `(1-B)lnX_[t-4]` 0.152712556338415 0.227204 0.6721 0.50318 0.25159 `(1-B)lnX_[t-5]` -0.057034187147676 0.219839 -0.2594 0.795878 0.397939 `(1-B)lnY_[t-1]` 0.258408373637674 0.105682 2.4452 0.016383 0.008191 `(1-B)lnY_[t-2]` -0.0554449120219212 0.105496 -0.5256 0.600457 0.300228 `(1-B)lnY_[t-3]` -0.0247153522663553 0.102569 -0.241 0.81012 0.40506 `(1-B)lnY_[t-4]` -0.01140685785384 0.108939 -0.1047 0.916835 0.458418 `(1-B)lnY_[t-5]` 0.000229808193820455 0.105671 0.0022 0.99827 0.499135 Multiple Linear Regression - Regression Statistics Multiple R 0.50209976555684 R-squared 0.252104174572234 Adjusted R-squared 0.170811150069216 F-TEST (value) 3.10117843583092 F-TEST (DF numerator) 10 F-TEST (DF denominator) 92 p-value 0.00190991425640519 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0834335845071244 Sum Squared Residuals 0.640426998181088 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -0.0326433382 0.00742460276233826 -0.0400679409623383 2 0.044072034 0.0256037319028793 0.0184683020971207 3 0.0882361169 0.0437968639091833 0.0444392529908167 4 -0.0355066885 0.0142357920576378 -0.0497424805576378 5 0.0278273388 -0.0109690921582041 0.038796430958204 6 -0.2004308914 0.0332859874451499 -0.23371687884515 7 -0.0263424279 -0.0689616192575965 0.0426191913575965 8 0.0655051718 0.000581368829487838 0.0649238029705122 9 -0.0709357514 0.0354942962446731 -0.106430047644673 10 -0.012918559 -0.0485124156658897 0.0355938566658898 11 0.118395754 -0.00125926957940772 0.119655023579408 12 -0.0330932173 0.0351899338116709 -0.0682831511116709 13 -0.0860908693 -0.00999288721735508 -0.0760979820826449 14 0.0210698391 0.00629646729281062 0.0147733718071894 15 0.01494567 0.0117344937352451 0.00321117626475487 16 -0.190034757 -0.0471452574182717 -0.142889499581728 17 -0.1242436027 -0.0512533013620111 -0.0729903013379889 18 0.0062305498 -0.0020293918843038 0.0082599416843038 19 0.0255507001 -0.000391830071781112 0.0259425301717811 20 0.0268806628 0.0414166688200596 -0.0145360060200596 21 0.1773155966 0.0254607071672257 0.151854889432774 22 0.0660542373 0.0455878529621985 0.0204663843378015 23 -0.0139591255 0.0320022137876948 -0.0459613392876948 24 -0.0373949697 -0.00252298120481733 -0.0348719884951827 25 0.0366137196 -0.0617592124190525 0.0983729320190525 26 0.019350449 -0.0527915610467443 0.0721420100467443 27 0.0837801497 -0.0252031845963922 0.108983334296392 28 -0.0369814175 -0.0244574164228798 -0.0125240010771202 29 -0.111311701 -0.0638558400290544 -0.0474558609709456 30 0.1156912701 0.000732851867971882 0.114958418232028 31 0.0961044085 0.0366088038257556 0.0594956046742444 32 0.0671607829 -0.000165274469563978 0.067326057369564 33 -0.0791409595 -0.0291430667184888 -0.0499978927815112 34 -0.1831923744 -0.0872395777001208 -0.0959527966998792 35 0.0242315729 0.0174048093158261 0.00682676358417388 36 0.0682600023 0.0626323839702264 0.00562761832977362 37 0.0345855796 0.0340533287782749 0.000532250821725067 38 0.0463590447 0.0391097027804558 0.00724934191954416 39 -0.0931151599 0.038871541539403 -0.131986701439403 40 0.0760673553 -0.0031614540978178 0.0792288093978178 41 -0.0110651198 0.0354414863335174 -0.0465066061335174 42 0.0284509336 0.0232307528854118 0.00522018071458824 43 0.0368261882 0.022878329738047 0.013947858461953 44 -0.0058804482 0.0529467109696441 -0.0588271591696441 45 0.0680750192 0.0386792336427438 0.0293957855572562 46 0.0157914001 0.0171071252501236 -0.00131572515012358 47 0.1121766425 0.0254597158027679 0.086716926697232 48 -0.0437664455 0.0261296140135815 -0.0698960595135815 49 0.060326653 -0.0126544176264942 0.0729810706264942 50 0.1028673441 0.0310867880087919 0.0717805560912081 51 0.0259509727 0.0332576836204554 -0.00730671092045543 52 0.1348695746 0.0476965870213942 0.0871729875786058 53 -0.096715318 0.0676181102466701 -0.16433342824667 54 -0.1035936648 -0.00314465458577151 -0.100449010214228 55 0.0950389075 0.00594439892486115 0.0890945085751389 56 0.0359719068 0.0541497175885549 -0.0181778107885549 57 0.1520609453 0.0370952321129508 0.114965713187049 58 -0.0022505636 0.0537223114719809 -0.0559728750719809 59 -0.0277954311 -0.00162728509546373 -0.0261681460045363 60 0.0653827593 -0.0112657615971325 0.0766485208971325 61 0.0443888626 0.0255366471408066 0.0188522154591934 62 0.1010961169 0.0309736449664816 0.0701224719335184 63 -0.0029750276 0.0455643614523336 -0.0485393890523336 64 -0.0752062699 0.00851828136081939 -0.0837245512608194 65 -0.0525843352 -0.0114933901245308 -0.0410909450754692 66 0.0229004195 0.0117140711288426 0.0111863483711574 67 0.1009621422 0.0414929820187099 0.05946916018129 68 -0.0289088246 0.066735641994235 -0.095644466594235 69 0.0208474516 0.0242051126621739 -0.00335766106217393 70 0.0788578228 0.0338662846897368 0.0449915381102632 71 0.0549314322 0.0290439337738492 0.0258874984261508 72 -0.0321652782 0.0021643582108228 -0.0343296364108228 73 0.0646729207 -0.0444433705235865 0.109116291223587 74 -0.0010757027 0.0312847667960109 -0.0323604694960109 75 -0.1458885691 0.0360764937015289 -0.181965062801529 76 -0.0677816324 -0.0200375151453304 -0.0477441172546696 77 -0.0098770369 0.0304548760408192 -0.0403319129408192 78 0.0501991563 0.0296077380603701 0.0205914182396299 79 -0.1271063631 0.0364657824036765 -0.163572145503676 80 0.0582277708 -0.000674676828966304 0.0589024476289663 81 0.0595552648 0.0396063768992841 0.0199488879007159 82 0.0885041253 0.00461141937734148 0.0838927059226585 83 0.0004433607 0.0525601631729873 -0.0521168024729873 84 0.0379810835 0.0254594506161646 0.0125216328838354 85 0.0681769863 -0.00173151862694734 0.0699085049269473 86 -0.0520908486 0.0187450143702702 -0.0708358629702702 87 0.0666010623 -0.0583596277258016 0.124960690025802 88 0.0677173945 0.0189716519233726 0.0487457425766274 89 0.1251127483 0.0495466861805223 0.0755660621194777 90 -0.0218349286 -0.0172409202966318 -0.00459400830336819 91 0.0178312442 -0.0161860210778354 0.0340172652778354 92 0.0199663138 -0.0188366701504755 0.0388029839504755 93 0.088436301 -0.0352529926101967 0.123689293610197 94 0.0646054028 0.0260092250118762 0.0385961777881238 95 0.1233912353 0.0352048540009179 0.088186381299082 96 0.0673308704 0.0335013926817026 0.0338294777182974 97 0.0232412696 -0.0384769172460675 0.0617181868460675 98 -0.1570633927 -0.0822930036807366 -0.0747703890192634 99 -0.134923147 -0.0605896045706095 -0.0743335424293905 100 -0.2920959272 -0.0342882639927804 -0.25780766320722 101 -0.3111517877 -0.249359850585182 -0.0617919371148185 102 -0.2363887781 -0.146487540930086 -0.0899012371699145 103 0.0334524402 -0.0666429742329426 0.100095414432943 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 14 0.544372879426283 0.911254241147434 0.455627120573717 15 0.436107728632696 0.872215457265391 0.563892271367304 16 0.675038700044968 0.649922599910064 0.324961299955032 17 0.558526551361708 0.882946897276584 0.441473448638292 18 0.501839578371871 0.996320843256257 0.498160421628129 19 0.487560562912407 0.975121125824814 0.512439437087593 20 0.405620115918283 0.811240231836567 0.594379884081717 21 0.397243211886381 0.794486423772763 0.602756788113619 22 0.313157782889508 0.626315565779016 0.686842217110492 23 0.279548109220749 0.559096218441498 0.720451890779251 24 0.20796618928138 0.41593237856276 0.79203381071862 25 0.223406105744827 0.446812211489655 0.776593894255173 26 0.464938927700134 0.929877855400269 0.535061072299866 27 0.640045334580562 0.719909330838876 0.359954665419438 28 0.649416419553251 0.701167160893498 0.350583580446749 29 0.682460124378885 0.635079751242231 0.317539875621115 30 0.753527089116503 0.492945821766994 0.246472910883497 31 0.730761338294289 0.538477323411422 0.269238661705711 32 0.6934234670952 0.6131530658096 0.3065765329048 33 0.644598456561629 0.710803086876743 0.355401543438371 34 0.68134196776673 0.63731606446654 0.31865803223327 35 0.619650715486931 0.760698569026139 0.380349284513069 36 0.561493697838192 0.877012604323616 0.438506302161808 37 0.518353757283094 0.963292485433812 0.481646242716906 38 0.457825786004214 0.915651572008428 0.542174213995786 39 0.506411683447337 0.987176633105327 0.493588316552663 40 0.538822602952192 0.922354794095615 0.461177397047808 41 0.488647238241206 0.977294476482413 0.511352761758794 42 0.439263924253481 0.878527848506963 0.560736075746519 43 0.383752513281447 0.767505026562894 0.616247486718553 44 0.339456014518131 0.678912029036262 0.660543985481869 45 0.297905961635409 0.595811923270817 0.702094038364591 46 0.244509527789103 0.489019055578206 0.755490472210897 47 0.265295247154133 0.530590494308267 0.734704752845867 48 0.247014211568559 0.494028423137119 0.75298578843144 49 0.239471012471169 0.478942024942337 0.760528987528831 50 0.230145815500385 0.46029163100077 0.769854184499615 51 0.18625197352903 0.37250394705806 0.81374802647097 52 0.201755040932395 0.40351008186479 0.798244959067605 53 0.325275912524434 0.650551825048869 0.674724087475566 54 0.341331895778425 0.68266379155685 0.658668104221575 55 0.373793012848052 0.747586025696104 0.626206987151948 56 0.330962757135046 0.661925514270091 0.669037242864954 57 0.407779846766507 0.815559693533014 0.592220153233493 58 0.392175363519088 0.784350727038176 0.607824636480912 59 0.370663398504049 0.741326797008098 0.629336601495951 60 0.402110117656041 0.804220235312083 0.597889882343959 61 0.347225362742923 0.694450725485846 0.652774637257077 62 0.390563416745147 0.781126833490295 0.609436583254853 63 0.350834170204941 0.701668340409881 0.649165829795059 64 0.362376267814736 0.724752535629472 0.637623732185264 65 0.316890416782099 0.633780833564198 0.683109583217901 66 0.267496983824472 0.534993967648944 0.732503016175528 67 0.302078660393849 0.604157320787697 0.697921339606152 68 0.324597560843206 0.649195121686412 0.675402439156794 69 0.269049632347527 0.538099264695054 0.730950367652473 70 0.22709081499512 0.45418162999024 0.77290918500488 71 0.183157606098157 0.366315212196314 0.816842393901843 72 0.141766689673578 0.283533379347156 0.858233310326422 73 0.142503147582181 0.285006295164362 0.857496852417819 74 0.117809656874183 0.235619313748366 0.882190343125817 75 0.235840591932843 0.471681183865687 0.764159408067157 76 0.225550071018239 0.451100142036478 0.774449928981761 77 0.188344155853075 0.37668831170615 0.811655844146925 78 0.16056203757898 0.321124075157959 0.83943796242102 79 0.439270438146957 0.878540876293915 0.560729561853043 80 0.374864255209693 0.749728510419385 0.625135744790307 81 0.293378542987813 0.586757085975626 0.706621457012187 82 0.266432434806247 0.532864869612493 0.733567565193753 83 0.369672841303501 0.739345682607002 0.630327158696499 84 0.373410274077501 0.746820548155002 0.626589725922499 85 0.328582120815789 0.657164241631577 0.671417879184211 86 0.712327896122889 0.575344207754222 0.287672103877111 87 0.605140745579228 0.789718508841545 0.394859254420772 88 0.488739518922042 0.977479037844085 0.511260481077958 89 0.598587951967991 0.802824096064018 0.401412048032009 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 0 0 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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