Prof Bach regressie 1 aug 2007 zonder trend

*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: Sun, 14 Dec 2008 10:08:52 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229275121gw0uo401exj6xd0.htm/, Retrieved Sun, 14 Dec 2008 18:18:41 +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/2008/Dec/14/t1229275121gw0uo401exj6xd0.htm/}, year = {2008}, } @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 = {2008}, 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 «

13363 0 12530 0 11420 0 10948 0 10173 0 10602 0 16094 0 19631 0 17140 0 14345 0 12632 0 12894 0 11808 0 10673 0 9939 0 9890 0 9283 0 10131 0 15864 0 19283 0 16203 0 13919 0 11937 0 11795 0 11268 0 10522 0 9929 0 9725 0 9372 0 10068 0 16230 0 19115 0 18351 0 16265 0 14103 0 14115 0 13327 0 12618 0 12129 0 11775 0 11493 0 12470 0 20792 0 22337 0 21325 0 18581 0 16475 0 16581 0 15745 0 14453 0 13712 0 13766 0 13336 0 15346 0 24446 0 26178 0 24628 0 21282 0 18850 0 18822 0 18060 0 17536 0 16417 0 15842 0 15188 0 16905 0 25430 0 27962 0 26607 0 23364 0 20827 0 20506 0 19181 0 18016 0 17354 0 16256 0 15770 0 17538 0 26899 0 28915 0 25247 0 22856 0 19980 0 19856 0 16994 0 16839 0 15618 0 15883 0 15513 0 17106 0 25272 0 26731 0 22891 0 19583 0 16939 0 16757 0 15435 0 14786 0 13680 0 13208 0 12707 0 14277 0 22436 0 23229 1 18241 1 16145 1 13994 1 14780 1 13100 1 12329 1 12463 1 11532 1 10784 1 13106 1 19491 1 20418 1 16094 1 14491 1 13067 1

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 8 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation NWWZPB[t] = + 16490.9198691742 -2312.27882256746Dummy[t] -1431.59198691742M1[t] -2229.49198691742M2[t] -2993.59198691742M3[t] -3377.19198691742M4[t] -3897.79198691741M5[t] -2504.79198691742M6[t] + 5035.70801308258M7[t] + 7351.43589533933M8[t] + 4644.23589533933M9[t] + 2054.63589533933M10[t] -148.06410466067M11[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) 16490.9198691742 1040.711795 15.8458 0 0 Dummy -2312.27882256746 843.512355 -2.7413 0.007185 0.003593 M1 -1431.59198691742 1428.723908 -1.002 0.318622 0.159311 M2 -2229.49198691742 1428.723908 -1.5605 0.121628 0.060814 M3 -2993.59198691742 1428.723908 -2.0953 0.038527 0.019264 M4 -3377.19198691742 1428.723908 -2.3638 0.019911 0.009956 M5 -3897.79198691741 1428.723908 -2.7282 0.007457 0.003728 M6 -2504.79198691742 1428.723908 -1.7532 0.082464 0.041232 M7 5035.70801308258 1428.723908 3.5246 0.000628 0.000314 M8 7351.43589533933 1430.659287 5.1385 1e-06 1e-06 M9 4644.23589533933 1430.659287 3.2462 0.001566 0.000783 M10 2054.63589533933 1430.659287 1.4361 0.153905 0.076953 M11 -148.06410466067 1430.659287 -0.1035 0.917767 0.458883 Multiple Linear Regression - Regression Statistics Multiple R 0.775783195581086 R-squared 0.601839566546002 Adjusted R-squared 0.556764800494606 F-TEST (value) 13.3520286241699 F-TEST (DF numerator) 12 F-TEST (DF denominator) 106 p-value 2.22044604925031e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3109.44888314795 Sum Squared Residuals 1024879269.83246 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13363 15059.3278822567 -1696.32788225675 2 12530 14261.4278822567 -1731.42788225674 3 11420 13497.3278822567 -2077.32788225674 4 10948 13113.7278822567 -2165.72788225674 5 10173 12593.1278822567 -2420.12788225675 6 10602 13986.1278822567 -3384.12788225675 7 16094 21526.6278822567 -5432.62788225674 8 19631 23842.3557645135 -4211.35576451349 9 17140 21135.1557645135 -3995.15576451349 10 14345 18545.5557645135 -4200.5557645135 11 12632 16342.8557645135 -3710.85576451349 12 12894 16490.9198691742 -3596.91986917416 13 11808 15059.3278822567 -3251.32788225675 14 10673 14261.4278822567 -3588.42788225675 15 9939 13497.3278822567 -3558.32788225675 16 9890 13113.7278822567 -3223.72788225675 17 9283 12593.1278822567 -3310.12788225675 18 10131 13986.1278822567 -3855.12788225675 19 15864 21526.6278822567 -5662.62788225674 20 19283 23842.3557645135 -4559.35576451349 21 16203 21135.1557645135 -4932.15576451349 22 13919 18545.5557645135 -4626.55576451349 23 11937 16342.8557645135 -4405.85576451349 24 11795 16490.9198691742 -4695.91986917416 25 11268 15059.3278822567 -3791.32788225674 26 10522 14261.4278822567 -3739.42788225675 27 9929 13497.3278822567 -3568.32788225675 28 9725 13113.7278822567 -3388.72788225675 29 9372 12593.1278822567 -3221.12788225675 30 10068 13986.1278822567 -3918.12788225675 31 16230 21526.6278822567 -5296.62788225674 32 19115 23842.3557645135 -4727.35576451349 33 18351 21135.1557645135 -2784.15576451349 34 16265 18545.5557645135 -2280.55576451349 35 14103 16342.8557645135 -2239.85576451349 36 14115 16490.9198691742 -2375.91986917416 37 13327 15059.3278822567 -1732.32788225675 38 12618 14261.4278822567 -1643.42788225675 39 12129 13497.3278822567 -1368.32788225675 40 11775 13113.7278822567 -1338.72788225675 41 11493 12593.1278822567 -1100.12788225675 42 12470 13986.1278822567 -1516.12788225675 43 20792 21526.6278822567 -734.627882256746 44 22337 23842.3557645135 -1505.35576451349 45 21325 21135.1557645135 189.844235486509 46 18581 18545.5557645135 35.4442354865088 47 16475 16342.8557645135 132.144235486509 48 16581 16490.9198691742 90.0801308258388 49 15745 15059.3278822567 685.672117743254 50 14453 14261.4278822567 191.572117743255 51 13712 13497.3278822567 214.672117743253 52 13766 13113.7278822567 652.272117743254 53 13336 12593.1278822567 742.872117743254 54 15346 13986.1278822567 1359.87211774325 55 24446 21526.6278822567 2919.37211774325 56 26178 23842.3557645135 2335.64423548651 57 24628 21135.1557645135 3492.84423548651 58 21282 18545.5557645135 2736.44423548651 59 18850 16342.8557645135 2507.14423548651 60 18822 16490.9198691742 2331.08013082584 61 18060 15059.3278822567 3000.67211774325 62 17536 14261.4278822567 3274.57211774325 63 16417 13497.3278822567 2919.67211774325 64 15842 13113.7278822567 2728.27211774325 65 15188 12593.1278822567 2594.87211774325 66 16905 13986.1278822567 2918.87211774325 67 25430 21526.6278822567 3903.37211774325 68 27962 23842.3557645135 4119.64423548651 69 26607 21135.1557645135 5471.84423548651 70 23364 18545.5557645135 4818.44423548651 71 20827 16342.8557645135 4484.14423548651 72 20506 16490.9198691742 4015.08013082584 73 19181 15059.3278822567 4121.67211774325 74 18016 14261.4278822567 3754.57211774325 75 17354 13497.3278822567 3856.67211774326 76 16256 13113.7278822567 3142.27211774325 77 15770 12593.1278822567 3176.87211774325 78 17538 13986.1278822567 3551.87211774325 79 26899 21526.6278822567 5372.37211774326 80 28915 23842.3557645135 5072.64423548651 81 25247 21135.1557645135 4111.84423548651 82 22856 18545.5557645135 4310.44423548651 83 19980 16342.8557645135 3637.14423548651 84 19856 16490.9198691742 3365.08013082584 85 16994 15059.3278822567 1934.67211774325 86 16839 14261.4278822567 2577.57211774325 87 15618 13497.3278822567 2120.67211774325 88 15883 13113.7278822567 2769.27211774325 89 15513 12593.1278822567 2919.87211774325 90 17106 13986.1278822567 3119.87211774325 91 25272 21526.6278822567 3745.37211774325 92 26731 23842.3557645135 2888.64423548651 93 22891 21135.1557645135 1755.84423548651 94 19583 18545.5557645135 1037.44423548651 95 16939 16342.8557645135 596.144235486508 96 16757 16490.9198691742 266.080130825839 97 15435 15059.3278822567 375.672117743254 98 14786 14261.4278822567 524.572117743254 99 13680 13497.3278822567 182.672117743253 100 13208 13113.7278822567 94.2721177432538 101 12707 12593.1278822567 113.872117743254 102 14277 13986.1278822567 290.872117743255 103 22436 21526.6278822567 909.372117743253 104 23229 21530.0769419460 1698.92305805397 105 18241 18822.8769419460 -581.876941946035 106 16145 16233.2769419460 -88.276941946034 107 13994 14030.5769419460 -36.5769419460345 108 14780 14178.6410466067 601.358953393296 109 13100 12747.0490596893 352.950940310712 110 12329 11949.1490596893 379.850940310712 111 12463 11185.0490596893 1277.95094031071 112 11532 10801.4490596893 730.550940310712 113 10784 10280.8490596893 503.150940310711 114 13106 11673.8490596893 1432.15094031071 115 19491 19214.3490596893 276.650940310712 116 20418 21530.0769419460 -1112.07694194603 117 16094 18822.8769419460 -2728.87694194603 118 14491 16233.2769419460 -1742.27694194603 119 13067 14030.5769419460 -963.576941946035 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0842173844441576 0.168434768888315 0.915782615555842 17 0.0332020351485685 0.066404070297137 0.966797964851432 18 0.0115666367633507 0.0231332735267015 0.98843336323665 19 0.00406646101582907 0.00813292203165814 0.99593353898417 20 0.00136399785128429 0.00272799570256858 0.998636002148716 21 0.000603146225897794 0.00120629245179559 0.999396853774102 22 0.000215171003464908 0.000430342006929816 0.999784828996535 23 8.61034912603937e-05 0.000172206982520787 0.99991389650874 24 4.90826036308051e-05 9.81652072616103e-05 0.99995091739637 25 4.1190603799295e-05 8.238120759859e-05 0.9999588093962 26 2.71556793382321e-05 5.43113586764641e-05 0.999972844320662 27 1.39652609587655e-05 2.79305219175310e-05 0.999986034739041 28 7.0086490159231e-06 1.40172980318462e-05 0.999992991350984 29 3.00055633973898e-06 6.00111267947796e-06 0.99999699944366 30 1.52140151437362e-06 3.04280302874725e-06 0.999998478598486 31 1.37675420479531e-06 2.75350840959062e-06 0.999998623245795 32 1.30538462101237e-06 2.61076924202473e-06 0.999998694615379 33 3.58449616437575e-06 7.1689923287515e-06 0.999996415503836 34 1.63801712482232e-05 3.27603424964463e-05 0.999983619828752 35 3.69827948991034e-05 7.39655897982067e-05 0.9999630172051 36 7.89113463016479e-05 0.000157822692603296 0.999921088653698 37 8.96279764984394e-05 0.000179255952996879 0.999910372023502 38 0.000123422310830857 0.000246844621661715 0.99987657768917 39 0.000218172141035415 0.00043634428207083 0.999781827858965 40 0.000335080803770206 0.000670161607540412 0.99966491919623 41 0.000625505394039386 0.00125101078807877 0.99937449460596 42 0.00186371417969099 0.00372742835938199 0.998136285820309 43 0.0475192682304791 0.0950385364609581 0.952480731769521 44 0.138044464037190 0.276088928074379 0.86195553596281 45 0.309907076415534 0.619814152831067 0.690092923584466 46 0.475525164285641 0.951050328571281 0.524474835714359 47 0.610173712198805 0.779652575602389 0.389826287801195 48 0.730268941287852 0.539462117424296 0.269731058712148 49 0.792729291755012 0.414541416489976 0.207270708244988 50 0.84040363193957 0.319192736120860 0.159596368060430 51 0.878125553303876 0.243748893392248 0.121874446696124 52 0.905377566458597 0.189244867082806 0.094622433541403 53 0.926261708817234 0.147476582365533 0.0737382911827663 54 0.95880128549857 0.0823974290028589 0.0411987145014295 55 0.991901667987756 0.0161966640244877 0.00809833201224383 56 0.99721640641189 0.00556718717621946 0.00278359358810973 57 0.998976853118502 0.00204629376299568 0.00102314688149784 58 0.999377300224539 0.00124539955092254 0.00062269977546127 59 0.99952750990752 0.000944980184958844 0.000472490092479422 60 0.999615494589687 0.000769010820625778 0.000384505410312889 61 0.999682378077186 0.000635243845627407 0.000317621922813703 62 0.999758776165533 0.000482447668933284 0.000241223834466642 63 0.999777027260413 0.000445945479174286 0.000222972739587143 64 0.999765776721922 0.000468446556155983 0.000234223278077991 65 0.999736387315592 0.000527225368816337 0.000263612684408168 66 0.999737174151319 0.000525651697362076 0.000262825848681038 67 0.9998167352401 0.000366529519800920 0.000183264759900460 68 0.99985739718463 0.000285205630738263 0.000142602815369131 69 0.999960637219128 7.87255617441137e-05 3.93627808720568e-05 70 0.999980262247566 3.947550486721e-05 1.9737752433605e-05 71 0.999987577400387 2.4845199225049e-05 1.24225996125245e-05 72 0.999988386059664 2.32278806713434e-05 1.16139403356717e-05 73 0.999991020198089 1.79596038227008e-05 8.97980191135042e-06 74 0.999990307199061 1.93856018777517e-05 9.69280093887587e-06 75 0.999989845395117 2.03092097664312e-05 1.01546048832156e-05 76 0.999984909337672 3.01813246556691e-05 1.50906623278345e-05 77 0.99997780266688 4.43946662409892e-05 2.21973331204946e-05 78 0.999968850477936 6.22990441282865e-05 3.11495220641432e-05 79 0.999984691231448 3.06175371050798e-05 1.53087685525399e-05 80 0.999989264498023 2.14710039541850e-05 1.07355019770925e-05 81 0.999995039056623 9.92188675414077e-06 4.96094337707038e-06 82 0.999998275536761 3.44892647754887e-06 1.72446323877444e-06 83 0.999998940690182 2.11861963694115e-06 1.05930981847057e-06 84 0.999998889875895 2.22024821008485e-06 1.11012410504243e-06 85 0.999997414177276 5.17164544897424e-06 2.58582272448712e-06 86 0.999995412693583 9.17461283409436e-06 4.58730641704718e-06 87 0.999989190667025 2.16186659506241e-05 1.08093329753121e-05 88 0.999983608119127 3.2783761746893e-05 1.63918808734465e-05 89 0.999979573699174 4.0852601652346e-05 2.0426300826173e-05 90 0.999969648677574 6.07026448519516e-05 3.03513224259758e-05 91 0.999980378090914 3.92438181725552e-05 1.96219090862776e-05 92 0.9999748871299 5.0225740200059e-05 2.51128701000295e-05 93 0.999992589111588 1.48217768244930e-05 7.41088841224648e-06 94 0.999991988125181 1.60237496375266e-05 8.01187481876329e-06 95 0.999983307157402 3.33856851966503e-05 1.66928425983251e-05 96 0.99993485332408 0.000130293351839154 6.51466759195769e-05 97 0.999766919974158 0.000466160051683566 0.000233080025841783 98 0.99923711744862 0.00152576510275972 0.000762882551379858 99 0.997613900425057 0.00477219914988665 0.00238609957494332 100 0.992504426516567 0.0149911469668666 0.00749557348343329 101 0.977820213303464 0.0443595733930725 0.0221797866965362 102 0.948635309195994 0.102729381608012 0.0513646908040058 103 0.867320927083304 0.265358145833392 0.132679072916696 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 0.772727272727273 NOK 5% type I error level 72 0.818181818181818 NOK 10% type I error level 75 0.852272727272727 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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