multiple regression: olieprijs en dowjones en dummy

*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 31 Dec 2009 05:41:12 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/31/t1262263345vcd0akdw0fd0ctt.htm/, Retrieved Thu, 31 Dec 2009 13:42:36 +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/2009/Dec/31/t1262263345vcd0akdw0fd0ctt.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

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Dataseries X:

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32,68 10967,87 0 31,54 10433,56 0 32,43 10665,78 0 26,54 10666,71 0 25,85 10682,74 0 27,6 10777,22 0 25,71 10052,6 0 25,38 10213,97 0 28,57 10546,82 0 27,64 10767,2 0 25,36 10444,5 0 25,9 10314,68 0 26,29 9042,56 0 21,74 9220,75 0 19,2 9721,84 0 19,32 9978,53 0 19,82 9923,81 0 20,36 9892,56 0 24,31 10500,98 0 25,97 10179,35 0 25,61 10080,48 0 24,67 9492,44 0 25,59 8616,49 0 26,09 8685,4 0 28,37 8160,67 0 27,34 8048,1 0 24,46 8641,21 0 27,46 8526,63 0 30,23 8474,21 0 32,33 7916,13 0 29,87 7977,64 0 24,87 8334,59 0 25,48 8623,36 0 27,28 9098,03 0 28,24 9154,34 0 29,58 9284,73 0 26,95 9492,49 0 29,08 9682,35 0 28,76 9762,12 0 29,59 10124,63 0 30,7 10540,05 0 30,52 10601,61 0 32,67 10323,73 0 33,19 10418,4 0 37,13 10092,96 0 35,54 10364,91 0 37,75 10152,09 0 41,84 10032,8 0 42,94 10204,59 0 49,14 10001,6 0 44,61 10411,75 0 40,22 10673,38 0 44,23 10539,51 0 45,85 10723,78 0 53,38 10682,06 0 53,26 10283,19 0 51,8 10377,18 0 55,3 10486,64 0 57,81 10545,38 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Olieprijs[t] = -48.065021886369 + 0.00736400267991173DowJones[t] + 13.8791019949137`Dummy(kredietcrisis)`[t] -2.00316998373972M1[t] -3.48228255312318M2[t] -6.82693077863326M3[t] -9.80357951011225M4[t] -8.58665770725735M5[t] -7.41367552842454M6[t] -3.87582979864214M7[t] -3.65260729263648M8[t] -1.93036287787659M9[t] + 0.386228852855002M10[t] + 1.35415373158679M11[t] + 0.40516952938097t + 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) -48.065021886369 9.090277 -5.2875 1e-06 0 DowJones 0.00736400267991173 0.000815 9.0386 0 0 `Dummy(kredietcrisis)` 13.8791019949137 4.10913 3.3776 0.001057 0.000529 M1 -2.00316998373972 5.579747 -0.359 0.720378 0.360189 M2 -3.48228255312318 5.579709 -0.6241 0.534044 0.267022 M3 -6.82693077863326 5.577363 -1.224 0.223932 0.111966 M4 -9.80357951011225 5.731482 -1.7105 0.090407 0.045204 M5 -8.58665770725735 5.729249 -1.4987 0.137222 0.068611 M6 -7.41367552842454 5.728684 -1.2941 0.198724 0.099362 M7 -3.87582979864214 5.730997 -0.6763 0.500481 0.250241 M8 -3.65260729263648 5.729956 -0.6375 0.525344 0.262672 M9 -1.93036287787659 5.732571 -0.3367 0.737051 0.368525 M10 0.386228852855002 5.733824 0.0674 0.946435 0.473218 M11 1.35415373158679 5.721568 0.2367 0.813413 0.406706 t 0.40516952938097 0.057924 6.9949 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.902608410865166 R-squared 0.81470194336454 Adjusted R-squared 0.787679310105203 F-TEST (value) 30.1488731888486 F-TEST (DF numerator) 14 F-TEST (DF denominator) 96 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.1363621682502 Sum Squared Residuals 14139.9635211778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 31.1044017321956 1.57559826780441 2 31.54 26.0957984202895 5.44420157971046 3 32.43 24.8663884264896 7.56361157351043 4 26.54 22.3017577468839 4.23824225311613 5 25.85 24.0418940420787 1.80810595792127 6 27.6 26.3157967234906 1.28420327650942 7 25.71 24.9227083607363 0.7872916392637 8 25.38 26.7394295085803 -1.35942950858028 9 28.57 31.3179517447298 -2.74795174472977 10 27.64 35.6625919154413 -8.02259191544131 11 25.36 34.6593226587465 -9.29932265874653 12 25.9 32.7543436286346 -6.85434362863457 13 26.29 21.7884480851065 4.50155191489349 14 21.74 22.0266966826375 -0.286696682637501 15 19.2 22.7772460893853 -3.57724608938532 16 19.32 22.0960327351939 -2.77603273519387 17 19.82 23.3151658407850 -3.49516584078496 18 20.36 24.6631924652515 -4.3031924652515 19 24.31 33.0866142349268 -8.77661423492676 20 25.97 31.3465220883734 -5.3765220883734 21 25.61 32.7458570875514 -7.13585708755137 22 24.67 31.1372902117686 -6.46729021176864 23 25.59 26.0598864724127 -0.469886472412717 24 26.09 25.6183556948796 0.471644305120389 25 28.37 20.1562421142908 8.21375788570922 26 27.34 18.2533332926106 9.08666670738936 27 24.46 19.6815182259640 4.77848177403605 28 27.46 16.2662715968017 11.1937284031983 29 30.23 17.5023419085565 12.7276580914435 30 32.33 14.9707910011652 17.3592089988348 31 29.87 19.3667660651699 10.5032339348301 32 24.87 22.6237388571511 2.24626114284894 33 25.48 26.8776558551700 -1.39765585517003 34 27.28 33.0948882673563 -5.8148882673563 35 28.24 34.8826496663749 -6.64264966637488 36 29.58 34.8938577736027 -5.31385777360275 37 26.95 34.8258025160225 -7.87580251602247 38 29.08 35.149989024828 -6.06998902482803 39 28.76 32.7979368224755 -4.03793682247547 40 29.59 32.8959822318722 -3.30598223187225 41 30.7 37.5772275573970 -6.87722755739705 42 30.52 39.6087072705862 -9.0887072705862 43 32.67 41.5054134650557 -8.83541346505568 44 33.19 42.8309556341496 -9.64095563414957 45 37.13 42.5618285461399 -5.43182854613994 46 35.54 47.2862303350545 -11.7462303350545 47 37.75 47.0921176928285 -9.34211769282845 48 41.84 45.2646816109360 -3.42468161093595 49 42.94 44.9317431769593 -1.99174317695926 50 49.14 42.3629812329615 6.77701876703852 51 44.61 42.4438482359982 2.16615176400184 52 40.22 41.7990130550454 -1.57901305504545 53 44.23 42.4352853485215 1.79471465147846 54 45.85 45.3704018305627 0.479598169437341 55 53.38 49.0061908979201 4.3738091020799 56 53.26 46.6973031843703 6.56269681562965 57 51.8 49.5168597403961 2.28314025960389 58 55.3 53.0446847338518 2.2553152661482 59 57.81 54.8503406593826 2.95965934061743 60 63.96 53.9668224410012 9.99317755899883 61 63.77 52.208802208408 11.5611977915921 62 59.15 49.6014528903674 9.54854710963257 63 56.12 49.3935773483248 6.72642265167522 64 57.42 47.7982703414759 9.62172965852414 65 63.52 49.7493116734234 13.7706883265766 66 61.71 52.0543640861713 9.65563591382874 67 63.01 57.282103252872 5.72789674712798 68 68.18 58.5661124468512 9.6138875531488 69 72.03 61.4240354568393 10.6059645431607 70 69.75 61.6721545767427 8.07784542325729 71 74.41 63.3318559691576 11.0781440308424 72 74.33 64.0063397977652 10.3236602022348 73 64.24 64.4425714437052 -0.202571443705235 74 60.03 66.5316884748052 -6.50168847480523 75 59.44 65.2272392936969 -5.78723929369691 76 62.5 64.0731096874015 -1.57310968740151 77 55.04 66.691329662149 -11.6513296621490 78 58.34 69.1427784481735 -10.8027784481735 79 61.92 70.413028934663 -8.49302893466295 80 67.65 74.6223145532103 -6.97231455321025 81 67.68 81.5580540471995 -13.8780540471995 82 70.3 84.8134109414984 -14.5134109414985 83 75.26 101.487375341935 -26.2273753419355 84 71.44 97.3455804978003 -25.9055804978003 85 76.36 98.0891856156 -21.7291856155999 86 81.71 99.5454402563883 -17.8354402563883 87 92.6 91.446004882445 1.15399511755497 88 90.6 90.394382193454 0.205617806545992 89 92.23 85.6182597972486 6.61174020275144 90 94.09 86.3234089877588 7.7665910122412 91 102.79 88.6044424820929 14.1855575179071 92 109.65 92.6405267576087 17.0094732423913 93 124.05 95.9156205194138 28.1343794805862 94 132.69 93.0715949140223 39.6184050859777 95 135.81 89.0373757943026 46.7726242056974 96 116.07 89.62282883051 26.4471711694900 97 101.42 84.9564693795124 16.4635306204876 98 75.73 69.6526957609825 6.07730423901748 99 55.48 62.536502024861 -7.05650202486107 100 43.8 59.8251804118715 -16.0251804118715 101 45.29 59.9791841698402 -14.6891841698402 102 44.01 56.3605591868403 -12.3505591868403 103 47.48 56.9527323065634 -9.4727323065634 104 51.07 63.1530969697052 -12.0830969697052 105 57.84 68.2721370025602 -10.4321370025602 106 69.04 72.427154104264 -3.38715410426402 107 65.61 74.4390757448591 -8.82907574485912 108 72.87 78.6071897248704 -5.73718972487035 109 68.41 78.9263337281998 -10.5163337281998 110 73.25 79.4899239641293 -6.23992396412933 111 77.43 79.3597386503597 -1.92973865035973 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0089292857799715 0.017858571559943 0.991070714220029 19 0.00304970246336840 0.00609940492673679 0.996950297536632 20 0.00121047893439922 0.00242095786879844 0.9987895210656 21 0.000265080712289585 0.00053016142457917 0.99973491928771 22 0.000107338017443766 0.000214676034887532 0.999892661982556 23 0.000121141702687201 0.000242283405374403 0.999878858297313 24 5.85644218498711e-05 0.000117128843699742 0.99994143557815 25 3.12109074298041e-05 6.24218148596083e-05 0.99996878909257 26 1.88815187929852e-05 3.77630375859705e-05 0.999981118481207 27 5.98614926523927e-06 1.19722985304785e-05 0.999994013850735 28 7.0467352895457e-06 1.40934705790914e-05 0.99999295326471 29 1.19633399265786e-05 2.39266798531572e-05 0.999988036660073 30 1.29866254156402e-05 2.59732508312803e-05 0.999987013374584 31 5.56529028532856e-06 1.11305805706571e-05 0.999994434709715 32 1.73052282468474e-06 3.46104564936948e-06 0.999998269477175 33 5.17104257624557e-07 1.03420851524911e-06 0.999999482895742 34 1.87370558489981e-07 3.74741116979963e-07 0.999999812629441 35 9.45482283772267e-08 1.89096456754453e-07 0.999999905451772 36 4.26122856362063e-08 8.52245712724127e-08 0.999999957387714 37 1.22088889440564e-08 2.44177778881128e-08 0.99999998779111 38 4.25082423144342e-09 8.50164846288685e-09 0.999999995749176 39 1.46108180457159e-09 2.92216360914319e-09 0.999999998538918 40 5.67806227570994e-10 1.13561245514199e-09 0.999999999432194 41 1.96785414615179e-10 3.93570829230358e-10 0.999999999803215 42 5.52711938328375e-11 1.10542387665675e-10 0.999999999944729 43 1.97517558534990e-11 3.95035117069979e-11 0.999999999980248 44 9.36512987035085e-12 1.87302597407017e-11 0.999999999990635 45 8.35756007045417e-12 1.67151201409083e-11 0.999999999991642 46 5.95070500939431e-12 1.19014100187886e-11 0.99999999999405 47 5.46016315808192e-12 1.09203263161638e-11 0.99999999999454 48 9.31237055766658e-12 1.86247411153332e-11 0.999999999990688 49 6.5031511772535e-12 1.3006302354507e-11 0.999999999993497 50 3.85629514230069e-11 7.71259028460139e-11 0.999999999961437 51 3.68286676871447e-11 7.36573353742895e-11 0.999999999963171 52 1.42732511269439e-11 2.85465022538879e-11 0.999999999985727 53 8.98315462060916e-12 1.79663092412183e-11 0.999999999991017 54 4.86262833871509e-12 9.72525667743019e-12 0.999999999995137 55 1.27819819187019e-11 2.55639638374037e-11 0.999999999987218 56 4.18779569824077e-11 8.37559139648155e-11 0.999999999958122 57 4.51134016959788e-11 9.02268033919577e-11 0.999999999954887 58 1.23343867762668e-10 2.46687735525337e-10 0.999999999876656 59 2.40075316809323e-10 4.80150633618647e-10 0.999999999759925 60 8.02400298799177e-10 1.60480059759835e-09 0.9999999991976 61 9.41239854073834e-10 1.88247970814767e-09 0.99999999905876 62 5.09833019781512e-10 1.01966603956302e-09 0.999999999490167 63 2.24449658746475e-10 4.4889931749295e-10 0.99999999977555 64 1.38669244190789e-10 2.77338488381578e-10 0.99999999986133 65 1.92785980373221e-10 3.85571960746442e-10 0.999999999807214 66 1.24069391204693e-10 2.48138782409387e-10 0.99999999987593 67 5.42249872131889e-11 1.08449974426378e-10 0.999999999945775 68 4.40236905780247e-11 8.80473811560495e-11 0.999999999955976 69 4.69239881400671e-11 9.38479762801341e-11 0.999999999953076 70 3.6422040868204e-11 7.2844081736408e-11 0.999999999963578 71 4.70369852615475e-11 9.4073970523095e-11 0.999999999952963 72 5.56743226945559e-11 1.11348645389112e-10 0.999999999944326 73 5.10246624400561e-11 1.02049324880112e-10 0.999999999948975 74 7.74919495364524e-11 1.54983899072905e-10 0.999999999922508 75 7.21765906144348e-11 1.44353181228870e-10 0.999999999927823 76 8.79820890507566e-11 1.75964178101513e-10 0.999999999912018 77 1.29950100750796e-10 2.59900201501593e-10 0.99999999987005 78 1.10513614118723e-10 2.21027228237446e-10 0.999999999889486 79 6.12815310524155e-11 1.22563062104831e-10 0.999999999938718 80 3.79126419666595e-11 7.58252839333189e-11 0.999999999962087 81 1.86097054790063e-11 3.72194109580126e-11 0.99999999998139 82 6.26878316441579e-12 1.25375663288316e-11 0.999999999993731 83 5.05768323440502e-10 1.01153664688100e-09 0.999999999494232 84 2.21293587080508e-08 4.42587174161016e-08 0.99999997787064 85 1.24748046718671e-06 2.49496093437341e-06 0.999998752519533 86 0.00103197232121132 0.00206394464242264 0.998968027678789 87 0.0207638590607589 0.0415277181215178 0.97923614093924 88 0.033962514431007 0.067925028862014 0.966037485568993 89 0.0367859405262961 0.0735718810525921 0.963214059473704 90 0.0628719554003769 0.125743910800754 0.937128044599623 91 0.15414852909021 0.30829705818042 0.84585147090979 92 0.344463712089148 0.688927424178296 0.655536287910852 93 0.559030836851219 0.881938326297562 0.440969163148781 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 0.894736842105263 NOK 5% type I error level 70 0.921052631578947 NOK 10% type I error level 72 0.947368421052632 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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