WS 7 (4)

*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: Tue, 23 Nov 2010 10:06:44 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290506730fixmk3j74232bop.htm/, Retrieved Tue, 23 Nov 2010 11:05:33 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290506730fixmk3j74232bop.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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0 1.3954 1.0685 0 1.4790 1.1010 0 1.4619 1.0996 0 1.4670 1.0978 0 1.4799 1.0893 0 1.4508 1.1018 0 1.4678 1.0931 0 1.4824 1.0842 0 1.5189 1.0409 0 1.5348 1.0245 0 1.5666 0.9994 0 1.5446 1.0090 0 1.5803 0.9947 0 1.5718 1.0080 0 1.5832 0.9986 0 1.5801 1.0184 0 1.5605 1.0357 0 1.5416 1.0556 0 1.5479 1.0409 0 1.5580 1.0474 0 1.5790 1.0219 0 1.5554 1.0427 0 1.5761 1.0205 0 1.5360 1.0490 0 1.5621 1.0344 0 1.5773 1.0193 0 1.5710 1.0238 0 1.5925 1.0165 0 1.5844 1.0218 0 1.5696 1.0370 0 1.5540 1.0508 0 1.5012 1.0813 0 1.4676 1.0970 0 1.4770 1.0989 0 1.4660 1.1018 0 1.4241 1.1166 0 1.4214 1.1319 1 1.4469 1.1020 1 1.4618 1.0884 1 1.3834 1.1263 1 1.3412 1.1345 1 1.3437 1.1337 1 1.2630 1.1660 1 1.2759 1.1550 1 1.2743 1.1782 1 1.2797 1.1856 1 1.2573 1.2219 1 1.2705 1.2130 1 1.2680 1.2230 1 1.3371 1.1767 1 1.3885 1.1077 1 1.4060 1.0672 1 1.3855 1.0840 1 1.3431 1.1154 1 1.3 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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation eu/us[t] = + 2.8265990970667 -0.0835405630382506Crisis[t] -1.23598378810765`us/ch`[t] -3.73098538767963e-05t + 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) 2.8265990970667 0.062464 45.252 0 0 Crisis -0.0835405630382506 0.012012 -6.9547 0 0 `us/ch` -1.23598378810765 0.057024 -21.6749 0 0 t -3.73098538767963e-05 0.000174 -0.214 0.830961 0.415481 Multiple Linear Regression - Regression Statistics Multiple R 0.9734892777905 R-squared 0.947681373973069 Adjusted R-squared 0.94612735537821 F-TEST (value) 609.826276923566 F-TEST (DF numerator) 3 F-TEST (DF denominator) 101 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0223779855757989 Sum Squared Residuals 0.050578198081497 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3954 1.50591310961979 -0.110513109619787 2 1.479 1.46570632665242 0.0132936733475817 3 1.4619 1.46739939410189 -0.00549939410189227 4 1.467 1.46958685506661 -0.00258685506660896 5 1.4799 1.48005540741165 -0.000155407411647522 6 1.4508 1.46456830020643 -0.013768300206425 7 1.4678 1.47528404930908 -0.00748404930908482 8 1.4824 1.48624699516937 -0.00384699516936607 9 1.5189 1.53972778334055 -0.0208277833405508 10 1.5348 1.55996060761164 -0.0251606076116395 11 1.5666 1.59094649083926 -0.0243464908392647 12 1.5446 1.57904373661955 -0.0344437366195546 13 1.5803 1.59668099493562 -0.016380994935617 14 1.5718 1.58020510069991 -0.00840510069990837 15 1.5832 1.59178603845424 -0.00858603845424362 16 1.5801 1.56727624959584 0.0128237504041648 17 1.5605 1.5458564202077 0.014643579792304 18 1.5416 1.52122303297048 0.0203769670295232 19 1.5479 1.53935468480178 0.00854531519821726 20 1.558 1.53128348032521 0.026716519674794 21 1.579 1.56276375706807 0.0162362429319255 22 1.5554 1.53701798442156 0.0183820155784413 23 1.5761 1.56441951466367 0.0116804853363284 24 1.536 1.52915666684873 0.00684333315127321 25 1.5621 1.54716472030122 0.0149352796987784 26 1.5773 1.56579076564777 0.0115092343522296 27 1.571 1.56019152874741 0.0108084712525908 28 1.5925 1.56917690054672 0.0233230994532817 29 1.5844 1.56258887661587 0.0218111233841292 30 1.5696 1.54376461318276 0.0258353868172422 31 1.554 1.526670727053 0.0273292729470046 32 1.5012 1.48893591166184 0.0122640883381648 33 1.4676 1.46949365633467 -0.00189365633466822 34 1.477 1.46710797728339 0.0098920227166132 35 1.466 1.463486314444 0.00251368555600195 36 1.4241 1.44515644452613 -0.0210564445261279 37 1.4214 1.4262085827142 -0.00480858271420407 38 1.4469 1.3795866250865 0.0673133749135048 39 1.4618 1.39635869475088 0.0654413052491173 40 1.3834 1.34947759932773 0.0339224006722742 41 1.3412 1.33930522241137 0.00189477758863372 42 1.3437 1.34025669958798 0.00344330041202418 43 1.263 1.30029711337822 -0.0372971133782218 44 1.2759 1.31385562519353 -0.0379556251935289 45 1.2743 1.28514349145555 -0.0108434914555548 46 1.2797 1.27595990156968 0.00374009843031882 47 1.2573 1.2310563802075 0.0262436197925034 48 1.2705 1.24201932606778 0.0284806739322221 49 1.268 1.22962217833282 0.0383778216671755 50 1.3371 1.28681091786833 0.0502890821316679 51 1.3885 1.37205648939388 0.0164435106061165 52 1.406 1.42207652295837 -0.0160765229583668 53 1.3855 1.40127468546428 -0.0157746854642812 54 1.3431 1.36242748466382 -0.0193274846638242 55 1.3257 1.35868222344562 -0.0329822234456242 56 1.2978 1.31093593937079 -0.013135939370792 57 1.2793 1.30410071868232 -0.024800718682323 58 1.2945 1.30381621207082 -0.00931621207082485 59 1.289 1.30983522277868 -0.0208352227786758 60 1.2848 1.31535983997128 -0.0305598399712833 61 1.2694 1.29665917491698 -0.0272591749169807 62 1.2636 1.30737492401964 -0.0437749240196407 63 1.29 1.27137048593183 0.0186295140681689 64 1.3559 1.34450341633393 0.0113965836660725 65 1.3305 1.32864551399227 0.00185448600772739 66 1.3482 1.34183323067115 0.00636676932885215 67 1.3146 1.30916594881123 0.00543405118877091 68 1.3027 1.29788118648557 0.00481881351442748 69 1.3247 1.33257502107752 -0.0078750210775208 70 1.3267 1.33674005610321 -0.0100400561032101 71 1.3621 1.37279347286208 -0.0106934728620766 72 1.3479 1.3521152337468 -0.00421523374680203 73 1.4011 1.39991049649269 0.00118950350730856 74 1.4135 1.42076131265783 -0.00726131265783391 75 1.3964 1.39761110596634 -0.00121110596634396 76 1.401 1.40634928100803 -0.00534928100803171 77 1.3955 1.40544678250248 -0.00994678250247943 78 1.4077 1.40318470183001 0.00451529816999115 79 1.3975 1.39672027627797 0.000779723722027607 80 1.3949 1.39989652427318 -0.00499652427317535 81 1.4138 1.41172465878513 0.00207534121486782 82 1.421 1.41613689056844 0.0048631094315571 83 1.4253 1.41894234342721 0.00635765657278631 84 1.4169 1.40160126053983 0.0152987394601703 85 1.4174 1.41169901774844 0.0057009822515643 86 1.4346 1.43366221932288 0.000937780677124924 87 1.4296 1.42966976134705 -6.97613470537839e-05 88 1.4311 1.43049764014485 0.000602359855147532 89 1.4594 1.45802276876578 0.00137723123422367 90 1.4722 1.46812052597438 0.00407947402561763 91 1.4669 1.46721802746883 -0.000318027468829886 92 1.4571 1.46013561002274 -0.00303561002273971 93 1.4709 1.46355905477556 0.00734094522443578 94 1.4893 1.48057832119757 0.0087216788024269 95 1.4997 1.49191206219429 0.0077879378057134 96 1.4713 1.47160461821544 -0.000304618215444314 97 1.4846 1.48244396569692 0.00215603430308485 98 1.4914 1.48895736992001 0.0024426300799914 99 1.4859 1.48175135409511 0.00414864590489256 100 1.4957 1.49543346428923 0.000266535710774298 101 1.4843 1.48254192303903 0.00175807696097056 102 1.4619 1.4612456920297 0.000654307970299132 103 1.434 1.45094971673453 -0.0169497167345306 104 1.4426 1.45919349826097 -0.0165934982609748 105 1.4318 1.46014497543758 -0.0283449754375845 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.77438339498394 0.451233210032119 0.225616605016059 8 0.757999239574625 0.48400152085075 0.242000760425375 9 0.946334611888813 0.107330776222375 0.0536653881111874 10 0.938708901738134 0.122582196523732 0.0612910982618662 11 0.940003654024696 0.119992691950608 0.0599963459753042 12 0.933967258302975 0.13206548339405 0.0660327416970248 13 0.920980260420663 0.158039479158673 0.0790197395793365 14 0.893288380464766 0.213423239070469 0.106711619535234 15 0.865137266203229 0.269725467593543 0.134862733796771 16 0.818342289229358 0.363315421541285 0.181657710770642 17 0.812516791047574 0.374966417904853 0.187483208952426 18 0.825648242945054 0.348703514109892 0.174351757054946 19 0.829912030467775 0.340175939064451 0.170087969532225 20 0.78752413879075 0.424951722418499 0.21247586120925 21 0.736759433912957 0.526481132174086 0.263240566087043 22 0.715072072567644 0.569855854864711 0.284927927432356 23 0.679674674067779 0.640650651864441 0.320325325932221 24 0.736277859605342 0.527444280789316 0.263722140394658 25 0.703414903762543 0.593170192474915 0.296585096237457 26 0.665310888350662 0.669378223298677 0.334689111649338 27 0.637450353198072 0.725099293603856 0.362549646801928 28 0.573993245972596 0.852013508054808 0.426006754027404 29 0.517034344839749 0.965931310320502 0.482965655160251 30 0.47222676118966 0.94445352237932 0.52777323881034 31 0.448102376815408 0.896204753630816 0.551897623184592 32 0.532036648138522 0.935926703722956 0.467963351861478 33 0.658066022595838 0.683867954808324 0.341933977404162 34 0.658320052900462 0.683359894199076 0.341679947099538 35 0.668280762584099 0.663438474831802 0.331719237415901 36 0.765670556433355 0.46865888713329 0.234329443566645 37 0.746437953725019 0.507124092549963 0.253562046274982 38 0.827831778847125 0.344336442305749 0.172168221152875 39 0.9313940194517 0.137211961096601 0.0686059805483005 40 0.964067767702191 0.071864464595618 0.035932232297809 41 0.98107723751329 0.03784552497342 0.01892276248671 42 0.985537793403855 0.0289244131922908 0.0144622065961454 43 0.997556698286251 0.00488660342749717 0.00244330171374859 44 0.999497116549567 0.00100576690086693 0.000502883450433463 45 0.999322548169708 0.00135490366058366 0.000677451830291829 46 0.998898700191496 0.00220259961700875 0.00110129980850438 47 0.999103213220306 0.00179357355938805 0.000896786779694024 48 0.999388006355366 0.00122398728926885 0.000611993644634427 49 0.999874294501066 0.00025141099786773 0.000125705498933865 50 0.999999084059317 1.83188136571279e-06 9.15940682856395e-07 51 0.999999639536443 7.20927113265874e-07 3.60463556632937e-07 52 0.999999754410521 4.91178957147809e-07 2.45589478573904e-07 53 0.999999759267462 4.81465075259227e-07 2.40732537629614e-07 54 0.999999779733563 4.40532873125863e-07 2.20266436562932e-07 55 0.999999968194616 6.36107680791894e-08 3.18053840395947e-08 56 0.999999949912205 1.0017559024552e-07 5.00877951227601e-08 57 0.999999968745664 6.250867205792e-08 3.125433602896e-08 58 0.999999937843727 1.24312545035866e-07 6.21562725179329e-08 59 0.999999942542782 1.14914435378617e-07 5.74572176893087e-08 60 0.99999998913661 2.17267800605479e-08 1.0863390030274e-08 61 0.999999996746206 6.5075882414319e-09 3.25379412071595e-09 62 0.999999999999383 1.23290746591488e-12 6.1645373295744e-13 63 0.999999999999896 2.08206348486043e-13 1.04103174243022e-13 64 0.99999999999978 4.39477377520254e-13 2.19738688760127e-13 65 0.999999999999218 1.56399654240196e-12 7.8199827120098e-13 66 0.999999999997818 4.36477102832621e-12 2.18238551416311e-12 67 0.99999999999668 6.6415370923481e-12 3.32076854617405e-12 68 0.999999999998465 3.06931001054293e-12 1.53465500527146e-12 69 0.999999999994537 1.09265163430817e-11 5.46325817154085e-12 70 0.999999999981732 3.6536406329531e-11 1.82682031647655e-11 71 0.999999999972228 5.55440681595651e-11 2.77720340797825e-11 72 0.99999999990068 1.98637474560796e-10 9.93187372803981e-11 73 0.999999999667523 6.64954652279563e-10 3.32477326139781e-10 74 0.999999999853847 2.92305850523509e-10 1.46152925261754e-10 75 0.999999999584464 8.3107224252552e-10 4.1553612126276e-10 76 0.999999999555019 8.89962929685978e-10 4.44981464842989e-10 77 0.999999999914596 1.70807535200674e-10 8.54037676003369e-11 78 0.999999999664378 6.71244236106391e-10 3.35622118053196e-10 79 0.999999998804318 2.39136310554524e-09 1.19568155277262e-09 80 0.999999998386167 3.22766664360217e-09 1.61383332180108e-09 81 0.999999995346982 9.30603576160273e-09 4.65301788080137e-09 82 0.999999983161316 3.36773679416142e-08 1.68386839708071e-08 83 0.99999993509882 1.29802361625884e-07 6.4901180812942e-08 84 0.999999984012312 3.19753762582353e-08 1.59876881291176e-08 85 0.999999976350928 4.72981442113844e-08 2.36490721056922e-08 86 0.999999893003077 2.13993845681972e-07 1.06996922840986e-07 87 0.999999555002997 8.89994006200225e-07 4.44997003100112e-07 88 0.999998726799907 2.54640018587547e-06 1.27320009293774e-06 89 0.999994901216426 1.01975671473276e-05 5.09878357366379e-06 90 0.999980883639132 3.82327217368198e-05 1.91163608684099e-05 91 0.999953535064205 9.29298715898294e-05 4.64649357949147e-05 92 0.999918445500926 0.000163108998148546 8.15544990742728e-05 93 0.999677321768294 0.000645356463412537 0.000322678231706268 94 0.998789084445309 0.00242183110938295 0.00121091555469147 95 0.99604885946725 0.00790228106549928 0.00395114053274964 96 0.990276754478945 0.0194464910421095 0.00972324552105474 97 0.980183056349163 0.0396338873016744 0.0198169436508372 98 0.962686362561575 0.0746272748768499 0.0373136374384249 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 53 0.576086956521739 NOK 5% type I error level 57 0.619565217391304 NOK 10% type I error level 59 0.641304347826087 NOK

Charts produced by software:

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Parameters (Session):

par1 = 0 ; par2 = 36 ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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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