Dummie

*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: Thu, 18 Dec 2008 06:54:29 -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/18/t12296085194h5l7b14vmxgmc2.htm/, Retrieved Thu, 18 Dec 2008 14:55:19 +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/18/t12296085194h5l7b14vmxgmc2.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 «

19 0 23 0 22 0 23 0 25 0 25 0 23 0 22 0 21 0 16 0 21 0 21 0 26 0 23 0 22 0 22 0 22 0 12 0 20 0 18 0 23 0 25 0 28 0 28 0 29 0 31 0 33 0 32 0 33 0 35 0 33 0 36 0 30 0 34 0 34 0 35 0 33 0 28 0 27 0 23 0 23 0 24 0 24 0 20 0 16 1 6 1 2 1 12 1 19 1 21 1 22 1 20 1 21 1 20 1 19 1 17 1 17 1 17 1 16 1 12 1 11 1 7 1 2 1 9 1 11 1 10 1 7 1 9 1 15 1 5 1 14 1 14 1 17 1 19 1 17 1 16 1 14 1 20 1 16 1 18 1 18 1 14 1 13 1 14 1 14 1 17 1 18 1 15 1 9 1 9 1 9 1 10 1 6 1 12 1 11 1 15 1 19 1 18 1 15 1 16 1 14 1 18 1 18 1 18 1 18 1 22 1 21 1 12 1 19 1 21 1 19 1 22 1 22 1 21 1 19 1 18 1 18 1 19 1 12 1 16 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Vertrouwen[t] = + 25.6136363636364 -10.5215311004785Aanslag[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) 25.6136363636364 0.778761 32.8902 0 0 Aanslag -10.5215311004785 0.978562 -10.752 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.703474736883576 R-squared 0.494876705433416 Adjusted R-squared 0.490595999547259 F-TEST (value) 115.606331898133 F-TEST (DF numerator) 1 F-TEST (DF denominator) 118 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.1657174025047 Sum Squared Residuals 3148.78708133971 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19 25.6136363636364 -6.61363636363642 2 23 25.6136363636364 -2.61363636363636 3 22 25.6136363636364 -3.61363636363636 4 23 25.6136363636364 -2.61363636363636 5 25 25.6136363636364 -0.613636363636363 6 25 25.6136363636364 -0.613636363636363 7 23 25.6136363636364 -2.61363636363636 8 22 25.6136363636364 -3.61363636363636 9 21 25.6136363636364 -4.61363636363636 10 16 25.6136363636364 -9.61363636363636 11 21 25.6136363636364 -4.61363636363636 12 21 25.6136363636364 -4.61363636363636 13 26 25.6136363636364 0.386363636363637 14 23 25.6136363636364 -2.61363636363636 15 22 25.6136363636364 -3.61363636363636 16 22 25.6136363636364 -3.61363636363636 17 22 25.6136363636364 -3.61363636363636 18 12 25.6136363636364 -13.6136363636364 19 20 25.6136363636364 -5.61363636363636 20 18 25.6136363636364 -7.61363636363636 21 23 25.6136363636364 -2.61363636363636 22 25 25.6136363636364 -0.613636363636363 23 28 25.6136363636364 2.38636363636364 24 28 25.6136363636364 2.38636363636364 25 29 25.6136363636364 3.38636363636364 26 31 25.6136363636364 5.38636363636364 27 33 25.6136363636364 7.38636363636364 28 32 25.6136363636364 6.38636363636364 29 33 25.6136363636364 7.38636363636364 30 35 25.6136363636364 9.38636363636364 31 33 25.6136363636364 7.38636363636364 32 36 25.6136363636364 10.3863636363636 33 30 25.6136363636364 4.38636363636364 34 34 25.6136363636364 8.38636363636364 35 34 25.6136363636364 8.38636363636364 36 35 25.6136363636364 9.38636363636364 37 33 25.6136363636364 7.38636363636364 38 28 25.6136363636364 2.38636363636364 39 27 25.6136363636364 1.38636363636364 40 23 25.6136363636364 -2.61363636363636 41 23 25.6136363636364 -2.61363636363636 42 24 25.6136363636364 -1.61363636363636 43 24 25.6136363636364 -1.61363636363636 44 20 25.6136363636364 -5.61363636363636 45 16 15.0921052631579 0.907894736842105 46 6 15.0921052631579 -9.0921052631579 47 2 15.0921052631579 -13.0921052631579 48 12 15.0921052631579 -3.09210526315790 49 19 15.0921052631579 3.90789473684210 50 21 15.0921052631579 5.9078947368421 51 22 15.0921052631579 6.9078947368421 52 20 15.0921052631579 4.90789473684211 53 21 15.0921052631579 5.9078947368421 54 20 15.0921052631579 4.90789473684211 55 19 15.0921052631579 3.90789473684210 56 17 15.0921052631579 1.90789473684211 57 17 15.0921052631579 1.90789473684211 58 17 15.0921052631579 1.90789473684211 59 16 15.0921052631579 0.907894736842105 60 12 15.0921052631579 -3.09210526315790 61 11 15.0921052631579 -4.09210526315789 62 7 15.0921052631579 -8.0921052631579 63 2 15.0921052631579 -13.0921052631579 64 9 15.0921052631579 -6.09210526315789 65 11 15.0921052631579 -4.09210526315789 66 10 15.0921052631579 -5.09210526315789 67 7 15.0921052631579 -8.0921052631579 68 9 15.0921052631579 -6.09210526315789 69 15 15.0921052631579 -0.0921052631578948 70 5 15.0921052631579 -10.0921052631579 71 14 15.0921052631579 -1.09210526315790 72 14 15.0921052631579 -1.09210526315790 73 17 15.0921052631579 1.90789473684211 74 19 15.0921052631579 3.90789473684210 75 17 15.0921052631579 1.90789473684211 76 16 15.0921052631579 0.907894736842105 77 14 15.0921052631579 -1.09210526315790 78 20 15.0921052631579 4.90789473684211 79 16 15.0921052631579 0.907894736842105 80 18 15.0921052631579 2.90789473684211 81 18 15.0921052631579 2.90789473684211 82 14 15.0921052631579 -1.09210526315790 83 13 15.0921052631579 -2.09210526315790 84 14 15.0921052631579 -1.09210526315790 85 14 15.0921052631579 -1.09210526315790 86 17 15.0921052631579 1.90789473684211 87 18 15.0921052631579 2.90789473684211 88 15 15.0921052631579 -0.0921052631578948 89 9 15.0921052631579 -6.09210526315789 90 9 15.0921052631579 -6.09210526315789 91 9 15.0921052631579 -6.09210526315789 92 10 15.0921052631579 -5.09210526315789 93 6 15.0921052631579 -9.0921052631579 94 12 15.0921052631579 -3.09210526315790 95 11 15.0921052631579 -4.09210526315789 96 15 15.0921052631579 -0.0921052631578948 97 19 15.0921052631579 3.90789473684210 98 18 15.0921052631579 2.90789473684211 99 15 15.0921052631579 -0.0921052631578948 100 16 15.0921052631579 0.907894736842105 101 14 15.0921052631579 -1.09210526315790 102 18 15.0921052631579 2.90789473684211 103 18 15.0921052631579 2.90789473684211 104 18 15.0921052631579 2.90789473684211 105 18 15.0921052631579 2.90789473684211 106 22 15.0921052631579 6.9078947368421 107 21 15.0921052631579 5.9078947368421 108 12 15.0921052631579 -3.09210526315790 109 19 15.0921052631579 3.90789473684210 110 21 15.0921052631579 5.9078947368421 111 19 15.0921052631579 3.90789473684210 112 22 15.0921052631579 6.9078947368421 113 22 15.0921052631579 6.9078947368421 114 21 15.0921052631579 5.9078947368421 115 19 15.0921052631579 3.90789473684210 116 18 15.0921052631579 2.90789473684211 117 18 15.0921052631579 2.90789473684211 118 19 15.0921052631579 3.90789473684210 119 12 15.0921052631579 -3.09210526315790 120 16 15.0921052631579 0.907894736842105 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.128516347779105 0.257032695558209 0.871483652220895 6 0.0769170074985324 0.153834014997065 0.923082992501468 7 0.0304993251029407 0.0609986502058814 0.96950067489706 8 0.0120259624354612 0.0240519248709224 0.987974037564539 9 0.00582121604510197 0.0116424320902039 0.994178783954898 10 0.0395865745957582 0.0791731491915163 0.960413425404242 11 0.0211499762694138 0.0422999525388276 0.978850023730586 12 0.0109316250436917 0.0218632500873835 0.989068374956308 13 0.0127168208231681 0.0254336416463362 0.987283179176832 14 0.00664903612499462 0.0132980722499892 0.993350963875005 15 0.00330232057980343 0.00660464115960687 0.996697679420197 16 0.00160151962930831 0.00320303925861663 0.998398480370692 17 0.000761253060748332 0.00152250612149666 0.999238746939252 18 0.0409003128341948 0.0818006256683895 0.959099687165805 19 0.0316622882515475 0.0633245765030951 0.968337711748452 20 0.0351878260701993 0.0703756521403986 0.9648121739298 21 0.0264917016398876 0.0529834032797751 0.973508298360112 22 0.0247966776030582 0.0495933552061163 0.975203322396942 23 0.0422000751753169 0.0844001503506338 0.957799924824683 24 0.0597156534561263 0.119431306912253 0.940284346543874 25 0.0902424592225537 0.180484918445107 0.909757540777446 26 0.166825889117740 0.333651778235480 0.83317411088226 27 0.323116546511203 0.646233093022406 0.676883453488797 28 0.428069620975694 0.856139241951388 0.571930379024306 29 0.551002134626943 0.897995730746114 0.448997865373057 30 0.717430355565543 0.565139288868914 0.282569644434457 31 0.776809876264548 0.446380247470905 0.223190123735452 32 0.883232492728524 0.233535014542952 0.116767507271476 33 0.873733660619395 0.252532678761210 0.126266339380605 34 0.909688886356285 0.180622227287430 0.0903111136437151 35 0.936545650570275 0.126908698859449 0.0634543494297246 36 0.96460339423605 0.0707932115278981 0.0353966057639491 37 0.974364105651587 0.0512717886968257 0.0256358943484128 38 0.96835694836414 0.0632861032717213 0.0316430516358607 39 0.960091156157765 0.0798176876844697 0.0399088438422349 40 0.949226837335485 0.101546325329030 0.0507731626645149 41 0.936051481220081 0.127897037559838 0.0639485187799191 42 0.91921707790622 0.161565844187561 0.0807829220937804 43 0.90122310213981 0.197553795720381 0.0987768978601906 44 0.892506879673445 0.214986240653110 0.107493120326555 45 0.86564545606387 0.268709087872260 0.134354543936130 46 0.900726338055028 0.198547323889943 0.0992736619449717 47 0.956334865784864 0.0873302684302711 0.0436651342151356 48 0.949013956803381 0.101972086393238 0.0509860431966188 49 0.95824615059739 0.0835076988052185 0.0417538494026093 50 0.969572550079327 0.0608548998413466 0.0304274499206733 51 0.978757003971637 0.0424859920567269 0.0212429960283634 52 0.978693228789056 0.0426135424218874 0.0213067712109437 53 0.98056056771274 0.0388788645745217 0.0194394322872609 54 0.97945592804169 0.0410881439166213 0.0205440719583107 55 0.975908923792365 0.0481821524152693 0.0240910762076346 56 0.968377555727974 0.0632448885440523 0.0316224442720262 57 0.95902132010006 0.08195735979988 0.04097867989994 58 0.947568792248112 0.104862415503775 0.0524312077518877 59 0.932371534232435 0.13525693153513 0.067628465767565 60 0.922222959715213 0.155554080569573 0.0777770402847867 61 0.916250368199835 0.16749926360033 0.083749631800165 62 0.943232147525667 0.113535704948666 0.0567678524743329 63 0.990087798163347 0.0198244036733062 0.00991220183665308 64 0.99159972363014 0.0168005527397206 0.00840027636986032 65 0.990521209832831 0.0189575803343377 0.00947879016716886 66 0.990710041026746 0.0185799179465081 0.00928995897325404 67 0.995219814091583 0.00956037181683394 0.00478018590841697 68 0.996342672157405 0.00731465568519035 0.00365732784259518 69 0.994636672194676 0.0107266556106480 0.00536332780532402 70 0.998948771981873 0.00210245603625331 0.00105122801812666 71 0.998453728519714 0.0030925429605717 0.00154627148028585 72 0.99775682559942 0.00448634880115966 0.00224317440057983 73 0.996777356912048 0.00644528617590309 0.00322264308795155 74 0.996183659131953 0.00763268173609446 0.00381634086804723 75 0.994552038151625 0.0108959236967500 0.00544796184837501 76 0.99203317900736 0.0159336419852806 0.00796682099264029 77 0.988948647342995 0.0221027053140094 0.0110513526570047 78 0.988506921256987 0.0229861574860255 0.0114930787430127 79 0.983568094802553 0.0328638103948937 0.0164319051974468 80 0.978744838165492 0.0425103236690165 0.0212551618345083 81 0.97269517831768 0.0546096433646411 0.0273048216823206 82 0.963502892425296 0.0729942151494088 0.0364971075747044 83 0.954622164045408 0.0907556719091837 0.0453778359545918 84 0.940988586613678 0.118022826772645 0.0590114133863225 85 0.924361542907806 0.151276914184388 0.0756384570921942 86 0.902527246928862 0.194945506142277 0.0974727530711383 87 0.880581551260855 0.238836897478289 0.119418448739145 88 0.848384789551788 0.303230420896423 0.151615210448212 89 0.881337334787159 0.237325330425683 0.118662665212841 90 0.914359079021176 0.171281841957648 0.0856409209788242 91 0.94517201166867 0.109655976662659 0.0548279883313293 92 0.962117134231076 0.0757657315378486 0.0378828657689243 93 0.99642809197766 0.00714381604468149 0.00357190802234074 94 0.997597440754279 0.00480511849144218 0.00240255924572109 95 0.999205521892827 0.00158895621434622 0.00079447810717311 96 0.998969832145603 0.00206033570879309 0.00103016785439654 97 0.998248071640088 0.00350385671982316 0.00175192835991158 98 0.996891225434716 0.00621754913056877 0.00310877456528439 99 0.996063551974913 0.0078728960501744 0.0039364480250872 100 0.994134958254338 0.0117300834913248 0.00586504174566238 101 0.99490285986389 0.0101942802722197 0.00509714013610987 102 0.991056858560945 0.0178862828781094 0.0089431414390547 103 0.984681041901671 0.0306379161966573 0.0153189580983287 104 0.97442180408544 0.0511563918291198 0.0255781959145599 105 0.95843626742356 0.0831274651528813 0.0415637325764407 106 0.95375366805334 0.0924926638933212 0.0462463319466606 107 0.939424228035977 0.121151543928045 0.0605757719640225 108 0.97516919989262 0.04966160021476 0.02483080010738 109 0.954399300066467 0.091201399867067 0.0456006999335335 110 0.933892998965918 0.132214002068163 0.0661070010340815 111 0.885470424260103 0.229059151479794 0.114529575739897 112 0.871901567209041 0.256196865581918 0.128098432790959 113 0.873738921067163 0.252522157865675 0.126261078932837 114 0.859808900146174 0.280382199707651 0.140191099853826 115 0.768724693586917 0.462550612826166 0.231275306413083 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.153153153153153 NOK 5% type I error level 45 0.405405405405405 NOK 10% type I error level 69 0.621621621621622 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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