multiple regression: olieprijs en dowjones (seizoenaliteit + lineaire trend)

*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 04:47:19 -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/t1262260109g1bwp2060hkqu9y.htm/, Retrieved Thu, 31 Dec 2009 12:48:41 +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/t1262260109g1bwp2060hkqu9y.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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation olieprijs[t] = -48.6979731359204 + 0.0070117197906449dowjones[t] -1.73792160184585M1[t] -3.42350501392251M2[t] -6.8636609608159M3[t] -10.1170751860686M4[t] -9.07662581132669M5[t] -8.08630606239669M6[t] -4.7462916723035M7[t] -4.60618584590421M8[t] -2.97025370798178M9[t] -0.815979678872401M10[t] + 1.47789919681228M11[t] + 0.55432711653647t + 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) -48.6979731359204 9.563524 -5.0921 2e-06 1e-06 dowjones 0.0070117197906449 0.00085 8.2465 0 0 M1 -1.73792160184585 5.870899 -0.296 0.767845 0.383923 M2 -3.42350501392251 5.871413 -0.5831 0.561192 0.280596 M3 -6.8636609608159 5.868961 -1.1695 0.245074 0.122537 M4 -10.1170751860686 6.030358 -1.6777 0.096627 0.048314 M5 -9.07662581132669 6.026867 -1.506 0.13531 0.067655 M6 -8.08630606239669 6.024562 -1.3422 0.182657 0.091328 M7 -4.7462916723035 6.024539 -0.7878 0.432719 0.21636 M8 -4.60618584590421 6.022221 -0.7649 0.446209 0.223104 M9 -2.97025370798178 6.023589 -0.4931 0.623054 0.311527 M10 -0.815979678872401 6.021978 -0.1355 0.892497 0.446249 M11 1.47789919681228 6.020594 0.2455 0.806608 0.403304 t 0.55432711653647 0.039443 14.0538 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.890326724573104 R-squared 0.792681676489072 Adjusted R-squared 0.764896746534 F-TEST (value) 28.5291947026971 F-TEST (DF numerator) 13 F-TEST (DF denominator) 97 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.7709054395382 Sum Squared Residuals 15820.3144973258 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 27.0220635189905 5.65793648100946 2 31.54 22.1443752221109 9.3956247778891 3 32.43 20.8868079615376 11.5431920384624 4 26.54 18.1942417522267 8.34575824777331 5 25.85 19.9014161117491 5.9485838882509 6 27.6 22.1085302630357 5.49146973696429 7 25.71 20.9220393749683 4.78796062503172 8 25.38 22.7479535405204 2.63204645947958 9 28.57 27.2720637272954 1.29793627270456 10 27.64 31.5259076804036 -3.88590768040363 11 25.36 32.1114316961837 -6.75143169618366 12 25.9 30.2775981526863 -4.37759815268633 13 26.29 20.1742546873017 6.11574531269826 14 21.74 20.2924167412566 1.44758325874341 15 19.2 20.9200905807939 -1.72009058079394 16 19.32 20.0208418251383 -0.700841825138296 17 19.82 21.2319370094726 -1.41193700947263 18 20.36 22.5574676314814 -2.19746763148144 19 24.31 30.7178796931353 -6.40787969313527 20 25.97 29.1571331998059 -3.18713319980592 21 25.61 30.6541437185638 -5.04414371856375 22 24.67 29.2395731585188 -4.56957315851878 23 25.59 25.9458632001245 -0.355863200124529 24 26.09 25.5054687306220 0.584531269377953 25 28.37 20.6426145195676 7.72738548043242 26 27.34 18.7220489271945 8.61795107280552 27 24.46 19.9949412218670 4.46505877813303 28 27.46 16.4924512595386 10.9675487404614 29 30.23 17.7196733993914 12.5103266006086 30 32.33 15.3512196840948 16.9787803159052 31 29.87 19.676852075047 10.1931479249530 32 24.87 22.8741183972535 1.99588160274654 33 25.48 27.0891519756569 -1.60915197565690 34 27.28 33.1260061543282 -5.84600615432817 35 28.24 36.3690420879605 -8.12904208796053 36 29.58 36.3597281511869 -6.7797281511869 37 26.95 36.6328885695819 -9.6828885695819 38 29.08 36.8328773934935 -7.75287739349355 39 28.76 34.5063734508364 -5.7463734508364 40 29.59 34.3491048834268 -4.75910488342678 41 30.7 38.8566900101349 -8.15669001013491 42 30.52 40.8329783459135 -10.3129783459135 43 32.67 42.7789031571187 -10.1089031571187 44 33.19 44.1371356126349 -10.9471356126349 45 37.13 44.0455007784263 -6.91550077842627 46 35.54 48.660939121138 -13.1209391211380 47 37.75 50.0169109075141 -12.2669109075141 48 41.84 48.2569107734123 -6.41691077341226 49 42.94 48.2778596309378 -5.33785963093778 50 49.14 45.7232943350946 3.41670566490543 51 44.61 45.7133223768707 -1.10332237687068 52 40.22 44.8487115169808 -4.62871151698081 53 44.23 45.5048290798856 -1.27482907988561 54 45.85 48.3415255511742 -2.49152555117422 55 53.38 51.9433381081382 1.43666189186183 56 53.26 49.8410063781794 3.41899362182060 57 51.8 52.690297175761 -0.89029717576102 58 55.3 56.1664011696909 -0.86640116969086 59 57.81 59.4264755824145 -1.61647558241448 60 63.96 58.5652376910775 5.39476230892249 61 63.77 57.2292785347174 6.54072146528258 62 59.15 54.6379718271712 4.51202817282878 63 56.12 54.3530703359561 1.76692966404385 64 57.42 52.5834568026877 4.83654319731226 65 63.52 54.4914468170143 9.02855318298573 66 61.71 56.7282205430153 4.98177945698469 67 63.01 61.8458266843209 1.16417331567912 68 68.18 63.1645130402177 5.01548695978226 69 72.03 66.0503348979086 5.97966510209137 70 69.75 66.403629248679 3.34637075132105 71 74.41 69.524731375152 4.885268624848 72 74.33 70.1469630399218 4.18303696007824 73 64.24 70.9002860295801 -6.66028602958013 74 60.03 72.7807737357156 -12.7507737357156 75 59.44 71.4517570504756 -12.0117570504756 76 62.5 70.1022156498648 -7.60221564986477 77 55.04 72.6454674772237 -17.6054674772237 78 58.34 75.0216341926628 -16.6816341926628 79 61.92 76.3710720012779 -14.4510720012779 80 67.65 80.4750939268105 -12.8250939268105 81 67.68 87.243655618571 -19.563655618571 82 70.3 90.460325980247 -20.1603259802470 83 75.26 94.662284712448 -19.4022847124480 84 71.44 90.6986412825422 -19.2586412825422 85 76.36 91.7446334562621 -15.3846334562621 86 81.71 93.0225339635896 -11.3125339635896 87 92.6 85.2235930759278 7.37640692407218 88 90.6 83.9716548148027 6.62834518519726 89 92.23 79.4742985659793 12.7557014340207 90 94.09 80.1877060502648 13.9022939497352 91 102.79 82.4995725173439 20.2904274826562 92 109.65 86.4386787934005 23.2113212065995 93 124.05 89.7217145772315 34.3282854227685 94 132.69 87.13078778791 45.55921221209 95 135.81 84.8303580550585 50.9796419449415 96 116.07 85.3678180275594 30.7021819724406 97 101.42 81.262650257082 20.1573497429180 98 75.73 66.5822972240891 9.1477027759109 99 55.48 59.7195611628742 -4.23956116287422 100 43.8 56.8873214953336 -13.0873214953336 101 45.29 57.084241529149 -11.7942415291490 102 44.01 53.6807177383574 -9.67071773835743 103 47.48 54.3845163886499 -6.90451638864992 104 51.07 60.3843671111771 -9.31436711117714 105 57.84 65.4231375305855 -7.58313753058552 106 69.04 69.4964296990846 -0.456429699084604 107 65.61 72.9529023831442 -7.34290238314422 108 72.87 76.9016341509917 -4.03163415099168 109 68.41 77.5434707959788 -9.13347079597883 110 73.25 77.9714106302843 -4.72141063028433 111 77.43 77.7604827828605 -0.330482782860549 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00748027114777157 0.0149605422955431 0.992519728852228 18 0.00117286362300805 0.00234572724601610 0.998827136376992 19 0.000422764280358312 0.000845528560716625 0.999577235719642 20 0.000174883677174151 0.000349767354348302 0.999825116322826 21 3.44347882798642e-05 6.88695765597284e-05 0.99996556521172 22 1.37833369746501e-05 2.75666739493002e-05 0.999986216663025 23 1.7705925185213e-05 3.5411850370426e-05 0.999982294074815 24 8.4900684745096e-06 1.69801369490192e-05 0.999991509931526 25 4.60809323565757e-06 9.21618647131514e-06 0.999995391906764 26 2.87900832969180e-06 5.75801665938359e-06 0.99999712099167 27 8.77472223482334e-07 1.75494444696467e-06 0.999999122527776 28 1.14399247489017e-06 2.28798494978034e-06 0.999998856007525 29 2.22392175306121e-06 4.44784350612242e-06 0.999997776078247 30 2.68677828932563e-06 5.37355657865125e-06 0.99999731322171 31 1.17773898249539e-06 2.35547796499079e-06 0.999998822261017 32 3.58172832224542e-07 7.16345664449084e-07 0.999999641827168 33 1.03027761958555e-07 2.06055523917111e-07 0.999999896972238 34 3.5115035498484e-08 7.0230070996968e-08 0.999999964884964 35 1.69922453518528e-08 3.39844907037057e-08 0.999999983007755 36 7.3325498653776e-09 1.46650997307552e-08 0.99999999266745 37 1.95668424511597e-09 3.91336849023194e-09 0.999999998043316 38 6.47345202154582e-10 1.29469040430916e-09 0.999999999352655 39 2.13199764205663e-10 4.26399528411327e-10 0.9999999997868 40 8.0940413291743e-11 1.61880826583486e-10 0.99999999991906 41 2.61490466442464e-11 5.22980932884928e-11 0.99999999997385 42 6.5888666333416e-12 1.31777332666832e-11 0.999999999993411 43 2.05462938480733e-12 4.10925876961467e-12 0.999999999997945 44 8.1063920801105e-13 1.6212784160221e-12 0.99999999999919 45 6.33514549189135e-13 1.26702909837827e-12 0.999999999999367 46 3.04477836346505e-13 6.0895567269301e-13 0.999999999999696 47 2.15258131735338e-13 4.30516263470677e-13 0.999999999999785 48 3.16777760324955e-13 6.3355552064991e-13 0.999999999999683 49 1.93750167211857e-13 3.87500334423714e-13 0.999999999999806 50 1.33109026240231e-12 2.66218052480461e-12 0.999999999998669 51 1.2171285881172e-12 2.4342571762344e-12 0.999999999998783 52 4.12507100121560e-13 8.25014200243119e-13 0.999999999999587 53 2.43374841701112e-13 4.86749683402224e-13 0.999999999999757 54 1.18815604551350e-13 2.37631209102701e-13 0.999999999999881 55 3.17623350286591e-13 6.35246700573181e-13 0.999999999999682 56 1.08984283062346e-12 2.17968566124691e-12 0.99999999999891 57 9.6049646407843e-13 1.92099292815686e-12 0.99999999999904 58 1.86282926813895e-12 3.72565853627789e-12 0.999999999998137 59 3.10299089673166e-12 6.20598179346332e-12 0.999999999996897 60 1.13246084396079e-11 2.26492168792159e-11 0.999999999988675 61 1.53375767526321e-11 3.06751535052642e-11 0.999999999984662 62 9.50255529793055e-12 1.90051105958611e-11 0.999999999990497 63 4.37277533661982e-12 8.74555067323964e-12 0.999999999995627 64 3.06596046164466e-12 6.13192092328932e-12 0.999999999996934 65 5.43046032634346e-12 1.08609206526869e-11 0.99999999999457 66 4.07954425146939e-12 8.15908850293877e-12 0.99999999999592 67 1.86171900402458e-12 3.72343800804915e-12 0.999999999998138 68 1.83466651513025e-12 3.6693330302605e-12 0.999999999998165 69 2.61853916250356e-12 5.23707832500713e-12 0.999999999997381 70 2.45967080070759e-12 4.91934160141519e-12 0.99999999999754 71 3.36784700134852e-12 6.73569400269705e-12 0.999999999996632 72 4.19054072309947e-12 8.38108144619894e-12 0.99999999999581 73 3.97358753717037e-12 7.94717507434075e-12 0.999999999996026 74 5.75682874275299e-12 1.15136574855060e-11 0.999999999994243 75 4.58141141670885e-12 9.1628228334177e-12 0.999999999995419 76 2.62868663773777e-12 5.25737327547554e-12 0.999999999997371 77 3.15864417049744e-12 6.31728834099489e-12 0.999999999996841 78 2.42015839886178e-12 4.84031679772355e-12 0.99999999999758 79 1.24885595339557e-12 2.49771190679114e-12 0.999999999998751 80 4.67668385176018e-13 9.35336770352037e-13 0.999999999999532 81 6.77820197227502e-13 1.35564039445500e-12 0.999999999999322 82 7.79957618070229e-12 1.55991523614046e-11 0.9999999999922 83 6.97666569229903e-10 1.39533313845981e-09 0.999999999302333 84 3.68472948182127e-08 7.36945896364253e-08 0.999999963152705 85 2.16892253999585e-06 4.3378450799917e-06 0.99999783107746 86 0.00161424998133087 0.00322849996266173 0.99838575001867 87 0.0301469341578051 0.0602938683156101 0.969853065842195 88 0.0534373034941395 0.106874606988279 0.94656269650586 89 0.068311428182464 0.136622856364928 0.931688571817536 90 0.129241801030599 0.258483602061198 0.8707581989694 91 0.309252599596736 0.618505199193472 0.690747400403264 92 0.586919271167388 0.826161457665225 0.413080728832612 93 0.807050667964522 0.385898664070955 0.192949332035478 94 0.830673835494471 0.338652329011058 0.169326164505529 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 69 0.884615384615385 NOK 5% type I error level 70 0.897435897435897 NOK 10% type I error level 71 0.91025641025641 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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