multiple regression: olieprijs en dowjones (seizoenaliteit)

*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 04:22:21 -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/t12622585994bmqtl775ic03am.htm/, Retrieved Thu, 31 Dec 2009 12:23:30 +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/t12622585994bmqtl775ic03am.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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32,68 10967,87 31,54 10433,56 32,43 10665,78 26,54 10666,71 25,85 10682,74 27,6 10777,22 25,71 10052,6 25,38 10213,97 28,57 10546,82 27,64 10767,2 25,36 10444,5 25,9 10314,68 26,29 9042,56 21,74 9220,75 19,2 9721,84 19,32 9978,53 19,82 9923,81 20,36 9892,56 24,31 10500,98 25,97 10179,35 25,61 10080,48 24,67 9492,44 25,59 8616,49 26,09 8685,4 28,37 8160,67 27,34 8048,1 24,46 8641,21 27,46 8526,63 30,23 8474,21 32,33 7916,13 29,87 7977,64 24,87 8334,59 25,48 8623,36 27,28 9098,03 28,24 9154,34 29,58 9284,73 26,95 9492,49 29,08 9682,35 28,76 9762,12 29,59 10124,63 30,7 10540,05 30,52 10601,61 32,67 10323,73 33,19 10418,4 37,13 10092,96 35,54 10364,91 37,75 10152,09 41,84 10032,8 42,94 10204,59 49,14 10001,6 44,61 10411,75 40,22 10673,38 44,23 10539,51 45,85 10723,78 53,38 10682,06 53,26 10283,19 51,8 10377,18 55,3 10486,64 57,81 10545,38 63,96 10554,27 63,77 10532,54 59,15 10324,31 56,12 10695,25 57,42 10827,81 63,52 10872,48 61,71 10971,19 63,01 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] = -49.8426019672413 + 0.0102961593115195dowjones[t] -4.34264379276595M1[t] -4.93955133821554M2[t] -8.32557403208548M3[t] -14.8814206512382M4[t] -13.0319796676429M5[t] -11.1749570581006M6[t] -6.8268154910034M7[t] -6.74810182753702M8[t] -5.14376833671593M9[t] -2.31247147870193M10[t] + 1.16049694980275M11[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) -49.8426019672413 16.578231 -3.0065 0.003357 0.001678 dowjones 0.0102961593115195 0.001417 7.2651 0 0 M1 -4.34264379276595 10.172416 -0.4269 0.670386 0.335193 M2 -4.93955133821554 10.17666 -0.4854 0.62849 0.314245 M3 -8.32557403208548 10.172531 -0.8184 0.415093 0.207547 M4 -14.8814206512382 10.437386 -1.4258 0.157109 0.078555 M5 -13.0319796676429 10.436467 -1.2487 0.214751 0.107375 M6 -11.1749570581006 10.436919 -1.0707 0.286928 0.143464 M7 -6.8268154910034 10.440676 -0.6539 0.514729 0.257364 M8 -6.74810182753702 10.436467 -0.6466 0.51941 0.259705 M9 -5.14376833671593 10.438741 -0.4928 0.623286 0.311643 M10 -2.31247147870193 10.437758 -0.2215 0.825126 0.412563 M11 1.16049694980275 10.436917 0.1112 0.911692 0.455846 Multiple Linear Regression - Regression Statistics Multiple R 0.608724910362937 R-squared 0.370546016496366 Adjusted R-squared 0.293470018516329 F-TEST (value) 4.80754094928929 F-TEST (DF numerator) 12 F-TEST (DF denominator) 98 p-value 3.72218862221274e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 22.1389830396430 Sum Squared Residuals 48033.1878629007 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 58.7416910680282 -26.0616910680282 2 31.54 52.6434426408408 -21.1034426408408 3 32.43 51.648394062292 -19.218394062292 4 26.54 45.1021228712989 -18.5621228712989 5 25.85 47.1166112886579 -21.2666112886579 6 27.6 49.9464150299526 -22.3464150299526 7 25.71 46.8337536367365 -21.1237536367365 8 25.38 48.5739585283028 -23.1939585283028 9 28.57 53.6053686459632 -25.0353686459632 10 27.64 58.7057330930499 -31.0657330930499 11 25.36 58.8561309117271 -33.4961309117271 12 25.9 56.3589865601029 -30.4589865601029 13 26.29 38.9183925839667 -12.6283925839668 14 21.74 40.1561576662368 -18.4161576662368 15 19.2 41.9294374417762 -22.7294374417762 16 19.32 38.0165119562974 -18.6965119562974 17 19.82 39.3025471023664 -19.4825471023664 18 20.36 40.8378147334238 -20.4778147334238 19 24.31 51.4503455488356 -27.1403455488356 20 25.97 48.217505492938 -22.247505492938 21 25.61 48.8038577126291 -23.1938577126291 22 24.67 45.5806010490972 -20.9106010490972 23 25.59 40.0346487286764 -14.4446487286764 24 26.09 39.5836601170304 -13.4936601170304 25 28.37 29.8383126487308 -1.46831264873081 26 27.34 28.0823664495835 -0.742366449583471 27 24.46 30.8030988049689 -6.34309880496888 28 27.46 23.0675182519022 4.39248174809779 29 30.23 24.3772345643877 5.85276543561234 30 32.33 20.4881765853572 11.8418234146428 31 29.87 25.4696349117060 4.40036508829404 32 24.87 29.2235626414192 -4.35356264141923 33 25.48 33.8011180566278 -8.32111805662783 34 27.28 41.5196928550408 -14.2396928550408 35 28.24 45.5724380143771 -17.3324380143771 36 29.58 45.7544572772034 -16.1744572772034 37 26.95 43.5509435429987 -16.6009435429987 38 29.08 44.9088648044343 -15.8288648044343 39 28.76 42.3441667388443 -13.5841667388442 40 29.59 39.5207808317104 -9.93078083171041 41 30.7 45.6474523164972 -14.9474523164972 42 30.52 48.1383064932567 -17.6183064932567 43 32.67 49.6253513108688 -16.9553513108688 44 33.19 50.6788023763567 -17.4888023763567 45 37.13 48.9323537808369 -11.8023537808369 46 35.54 54.5636911636186 -19.0236911636186 47 37.75 55.8454309674457 -18.0954309674457 48 41.84 53.4567051733718 -11.6167051733718 49 42.94 50.8828385887318 -7.94283858873181 50 49.14 48.1959136646369 0.944086335363133 51 44.61 49.0328607123867 -4.42286071238667 52 40.22 45.1707982539068 -4.95079825390676 53 44.23 45.641892390469 -1.41189239046896 54 45.85 49.396188276345 -3.54618827634502 55 53.38 53.3147740769656 0.0652259230344052 56 53.26 49.2866586758462 3.97334132415381 57 51.8 51.858728180357 -0.0587281803570034 58 55.3 55.8170426366099 -0.517042636609913 59 57.81 59.8948074630732 -2.08480746307324 60 63.96 58.8258433695499 5.13415663045009 61 63.77 54.2594640349446 9.51053596505536 62 59.15 51.5185872360573 7.63141276394267 63 56.12 51.9518218772025 4.16817812279754 64 57.42 46.7608341363847 10.6591658636153 65 63.52 49.0702045564256 14.4497954435744 66 61.71 51.9435610516081 9.76643894839193 67 63.01 58.0879705721929 4.92202942780706 68 68.18 59.0833512991639 9.09664870083611 69 72.03 61.7090637936877 10.3209362063123 70 69.75 61.0817777773692 8.66822222263082 71 74.41 64.9554727262782 9.45452727372178 72 74.33 66.064867058293 8.26513294170693 73 64.24 64.5664343137413 -0.326434313741267 74 60.03 68.3920360773687 -8.36203607736866 75 59.44 67.2920696354354 -7.8520696354354 76 62.5 62.7179247989708 -0.217924798970818 77 55.04 65.9601272526353 -10.9201272526353 78 58.34 69.0381713949308 -10.6981713949308 79 61.92 69.649321939912 -7.729321939912 80 67.65 74.734748991791 -7.08474899179095 81 67.68 83.0619597050687 -15.3819597050687 82 70.3 86.6393162667954 -16.3393162667954 83 75.26 92.1001641735752 -16.8401641735752 84 71.44 86.4755614310769 -15.0355614310769 85 76.36 85.406890376188 -9.04689037618794 86 81.71 88.3476402085833 -6.63764020858335 87 92.6 77.7470986851317 14.8529013148683 88 90.6 73.3162763862834 17.2837236137166 89 92.23 66.219899352065 26.010100647935 90 94.09 66.8563122752267 27.2336877247733 91 102.79 68.880713647307 33.9092863526930 92 109.65 73.7239750321791 35.9260249678209 93 124.05 76.9329649517005 47.1170350482995 94 132.69 71.982321640475 60.7076783595251 95 135.81 67.8949232481239 67.9150767518761 96 116.07 68.8798370140625 47.1901629859375 97 101.42 60.2470925209657 41.1729074790343 98 75.73 39.7544015299014 35.9755984700986 99 55.48 30.5286031977238 24.9513968022762 100 43.8 23.7772325132453 20.0227674867547 101 45.29 23.5740311764960 21.7159688235040 102 44.01 18.1650541598991 25.8449458401009 103 47.48 17.8281343554755 29.6518656445245 104 51.07 25.6974369620031 25.3725630379969 105 57.84 31.484585173129 26.3554148268710 106 69.04 36.3198235179441 32.7201764820559 107 65.61 40.6859837667231 24.9240162332769 108 72.87 46.680081999309 26.189918000691 109 68.41 45.0179403217041 23.3920596782959 110 73.25 46.710589722357 26.5394102776429 111 77.43 47.2524488442387 30.1775511557613 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.009146580580784 0.018293161161568 0.990853419419216 17 0.00147090716314727 0.00294181432629453 0.998529092836853 18 0.000223779013507675 0.000447558027015351 0.999776220986492 19 5.17522555199527e-05 0.000103504511039905 0.99994824774448 20 7.4878383174848e-06 1.49756766349696e-05 0.999992512161683 21 1.02718357881265e-06 2.05436715762530e-06 0.999998972816421 22 2.86881719374637e-07 5.73763438749275e-07 0.99999971311828 23 3.76766971186365e-07 7.5353394237273e-07 0.999999623233029 24 1.49135874604782e-07 2.98271749209564e-07 0.999999850864125 25 4.37063875370267e-08 8.74127750740534e-08 0.999999956293612 26 1.46102698918223e-08 2.92205397836447e-08 0.99999998538973 27 2.75392036014266e-09 5.50784072028532e-09 0.99999999724608 28 1.99928677593953e-09 3.99857355187906e-09 0.999999998000713 29 2.29534387482782e-09 4.59068774965564e-09 0.999999997704656 30 2.06488434914065e-09 4.1297686982813e-09 0.999999997935116 31 5.86145432788705e-10 1.17229086557741e-09 0.999999999413855 32 1.22215994126409e-10 2.44431988252818e-10 0.999999999877784 33 2.72584230868039e-11 5.45168461736079e-11 0.999999999972742 34 6.37914363254206e-12 1.27582872650841e-11 0.999999999993621 35 1.7915220680742e-12 3.5830441361484e-12 0.999999999998209 36 5.51489660238175e-13 1.10297932047635e-12 0.999999999999448 37 1.48981144024938e-13 2.97962288049876e-13 0.999999999999851 38 3.98664929197189e-14 7.97329858394377e-14 0.99999999999996 39 1.23246742230505e-14 2.46493484461009e-14 0.999999999999988 40 5.00169032166593e-15 1.00033806433319e-14 0.999999999999995 41 2.36975764844997e-15 4.73951529689994e-15 0.999999999999998 42 8.24736793413432e-16 1.64947358682686e-15 1 43 5.15921121359214e-16 1.03184224271843e-15 1 44 5.84082464650501e-16 1.16816492930100e-15 1 45 1.68245874868614e-15 3.36491749737227e-15 0.999999999999998 46 3.35104844684771e-15 6.70209689369542e-15 0.999999999999997 47 1.32826217200103e-14 2.65652434400207e-14 0.999999999999987 48 1.08996296221433e-13 2.17992592442866e-13 0.99999999999989 49 4.9713656953593e-13 9.9427313907186e-13 0.999999999999503 50 1.65285712705091e-11 3.30571425410183e-11 0.999999999983471 51 8.43136769751747e-11 1.68627353950349e-10 0.999999999915686 52 1.25287910204287e-10 2.50575820408574e-10 0.999999999874712 53 3.16050169364343e-10 6.32100338728686e-10 0.99999999968395 54 6.6654936618818e-10 1.33309873237636e-09 0.99999999933345 55 4.52645690657123e-09 9.05291381314245e-09 0.999999995473543 56 3.01320976029855e-08 6.0264195205971e-08 0.999999969867902 57 9.26249992141092e-08 1.85249998428218e-07 0.999999907375 58 4.74441544599144e-07 9.48883089198287e-07 0.999999525558455 59 2.03825563042264e-06 4.07651126084528e-06 0.99999796174437 60 8.30752845583898e-06 1.66150569116780e-05 0.999991692471544 61 2.15488555583156e-05 4.30977111166312e-05 0.999978451144442 62 3.06074466501355e-05 6.1214893300271e-05 0.99996939255335 63 3.90277850860826e-05 7.80555701721652e-05 0.999960972214914 64 5.10374101524801e-05 0.000102074820304960 0.999948962589847 65 8.43531007311745e-05 0.000168706201462349 0.999915646899269 66 9.54476588364232e-05 0.000190895317672846 0.999904552341164 67 9.40644078988214e-05 0.000188128815797643 0.999905935592101 68 0.000111284306137304 0.000222568612274608 0.999888715693863 69 0.000139714296551811 0.000279428593103621 0.999860285703448 70 0.000202781199662715 0.000405562399325429 0.999797218800337 71 0.000272667179995901 0.000545334359991802 0.999727332820004 72 0.000251003114969231 0.000502006229938462 0.999748996885031 73 0.000179864249437191 0.000359728498874383 0.999820135750563 74 0.000132629237701012 0.000265258475402024 0.999867370762299 75 0.000103086717518590 0.000206173435037181 0.999896913282481 76 5.98522847328151e-05 0.000119704569465630 0.999940147715267 77 4.78440068045732e-05 9.56880136091463e-05 0.999952155993195 78 3.8634060779582e-05 7.7268121559164e-05 0.99996136593922 79 3.49334207327396e-05 6.98668414654791e-05 0.999965066579267 80 3.15437531152028e-05 6.30875062304056e-05 0.999968456246885 81 6.97011940333018e-05 0.000139402388066604 0.999930298805967 82 0.00047679936899249 0.00095359873798498 0.999523200631008 83 0.00561880392907851 0.0112376078581570 0.994381196070921 84 0.0383103360170797 0.0766206720341594 0.96168966398292 85 0.138888789523946 0.277777579047892 0.861111210476054 86 0.522864799136931 0.954270401726139 0.477135200863069 87 0.648409922278732 0.703180155442537 0.351590077721268 88 0.673793388894753 0.652413222210493 0.326206611105247 89 0.660332486022294 0.679335027955412 0.339667513977706 90 0.709471747343282 0.581056505313435 0.290528252656718 91 0.806722959024157 0.386554081951687 0.193277040975843 92 0.87825735904926 0.243485281901482 0.121742640950741 93 0.914032473324777 0.171935053350445 0.0859675266752226 94 0.89098371388674 0.218032572226520 0.109016286113260 95 0.92664224539479 0.146715509210421 0.0733577546052104 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 66 0.825 NOK 5% type I error level 68 0.85 NOK 10% type I error level 69 0.8625 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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