paperMR

*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, 19 Dec 2010 15:04:21 +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/Dec/19/t1292771111436g5atlwlppz3v.htm/, Retrieved Sun, 19 Dec 2010 16:05:11 +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/Dec/19/t1292771111436g5atlwlppz3v.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:

» Textbox « » Textfile « » CSV «

15 0 14.4 0 13 0 13.7 0 13.6 0 15.2 0 12.9 0 14 0 14.1 0 13.2 0 11.3 0 13.3 0 14.4 0 13.3 0 11.6 0 13.2 0 13.1 0 14.6 0 14 0 14.3 0 13.8 0 13.7 0 11 0 14.4 0 15.6 0 13.7 0 12.6 0 13.2 0 13.3 0 14.3 0 14 0 13.4 0 13.9 0 13.7 0 10.5 0 14.5 0 15 0 13.5 0 13.5 0 13.2 0 13.8 0 16.2 0 14.7 0 13.9 0 16 0 14.4 0 12.3 0 15.9 0 15.9 0 15.5 0 15.1 0 14.5 0 15.1 0 17.4 0 16.2 0 15.6 0 17.2 0 14.9 0 13.8 0 17.5 0 16.2 0 17.5 0 16.6 0 16.2 0 16.6 0 19.6 0 15.9 0 18 0 18.3 0 16.3 0 14.9 0 18.2 0 18.4 0 18.5 0 16 0 17.4 0 17.2 0 19.6 0 17.2 0 18.3 0 19.3 0 18.1 0 16.2 0 18.4 0 20.5 0 19 0 16.5 0 18.7 0 19 0 19.2 0 20.5 0 19.3 0 20.6 0 20.1 0 16.1 0 20.4 0 19.7 1 15.6 1 14.4 1 13.7 1 14.1 1 15 1 14.2 1 13.6 1 15.4 1 14.8 1 12.5 1 16.2 1 16.1 1 16 1 15.8 1 15.2 1 15.7 1 18.9 1 17.4 1 17 1 19.8 1 17.7 1 16 1 19.6 1 19.7 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 11 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation uitvoercijfer[t] = + 15.6010416666667 + 0.562958333333333X[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) 15.6010416666667 0.237618 65.6559 0 0 X 0.562958333333333 0.52276 1.0769 0.283705 0.141853 Multiple Linear Regression - Regression Statistics Multiple R 0.0982413066289308 R-squared 0.0096513543281596 Adjusted R-squared 0.00132909680150550 F-TEST (value) 1.15970387809422 F-TEST (DF numerator) 1 F-TEST (DF denominator) 119 p-value 0.283705270908207 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.32817506314492 Sum Squared Residuals 645.027495833333 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 15.6010416666666 -0.601041666666647 2 14.4 15.6010416666667 -1.20104166666667 3 13 15.6010416666667 -2.60104166666667 4 13.7 15.6010416666667 -1.90104166666667 5 13.6 15.6010416666667 -2.00104166666667 6 15.2 15.6010416666667 -0.401041666666668 7 12.9 15.6010416666667 -2.70104166666667 8 14 15.6010416666667 -1.60104166666667 9 14.1 15.6010416666667 -1.50104166666667 10 13.2 15.6010416666667 -2.40104166666667 11 11.3 15.6010416666667 -4.30104166666667 12 13.3 15.6010416666667 -2.30104166666667 13 14.4 15.6010416666667 -1.20104166666667 14 13.3 15.6010416666667 -2.30104166666667 15 11.6 15.6010416666667 -4.00104166666667 16 13.2 15.6010416666667 -2.40104166666667 17 13.1 15.6010416666667 -2.50104166666667 18 14.6 15.6010416666667 -1.00104166666667 19 14 15.6010416666667 -1.60104166666667 20 14.3 15.6010416666667 -1.30104166666667 21 13.8 15.6010416666667 -1.80104166666667 22 13.7 15.6010416666667 -1.90104166666667 23 11 15.6010416666667 -4.60104166666667 24 14.4 15.6010416666667 -1.20104166666667 25 15.6 15.6010416666667 -0.00104166666666724 26 13.7 15.6010416666667 -1.90104166666667 27 12.6 15.6010416666667 -3.00104166666667 28 13.2 15.6010416666667 -2.40104166666667 29 13.3 15.6010416666667 -2.30104166666667 30 14.3 15.6010416666667 -1.30104166666667 31 14 15.6010416666667 -1.60104166666667 32 13.4 15.6010416666667 -2.20104166666667 33 13.9 15.6010416666667 -1.70104166666667 34 13.7 15.6010416666667 -1.90104166666667 35 10.5 15.6010416666667 -5.10104166666667 36 14.5 15.6010416666667 -1.10104166666667 37 15 15.6010416666667 -0.601041666666667 38 13.5 15.6010416666667 -2.10104166666667 39 13.5 15.6010416666667 -2.10104166666667 40 13.2 15.6010416666667 -2.40104166666667 41 13.8 15.6010416666667 -1.80104166666667 42 16.2 15.6010416666667 0.598958333333332 43 14.7 15.6010416666667 -0.901041666666668 44 13.9 15.6010416666667 -1.70104166666667 45 16 15.6010416666667 0.398958333333333 46 14.4 15.6010416666667 -1.20104166666667 47 12.3 15.6010416666667 -3.30104166666667 48 15.9 15.6010416666667 0.298958333333333 49 15.9 15.6010416666667 0.298958333333333 50 15.5 15.6010416666667 -0.101041666666667 51 15.1 15.6010416666667 -0.501041666666667 52 14.5 15.6010416666667 -1.10104166666667 53 15.1 15.6010416666667 -0.501041666666667 54 17.4 15.6010416666667 1.79895833333333 55 16.2 15.6010416666667 0.598958333333332 56 15.6 15.6010416666667 -0.00104166666666724 57 17.2 15.6010416666667 1.59895833333333 58 14.9 15.6010416666667 -0.701041666666667 59 13.8 15.6010416666667 -1.80104166666667 60 17.5 15.6010416666667 1.89895833333333 61 16.2 15.6010416666667 0.598958333333332 62 17.5 15.6010416666667 1.89895833333333 63 16.6 15.6010416666667 0.998958333333335 64 16.2 15.6010416666667 0.598958333333332 65 16.6 15.6010416666667 0.998958333333335 66 19.6 15.6010416666667 3.99895833333333 67 15.9 15.6010416666667 0.298958333333333 68 18 15.6010416666667 2.39895833333333 69 18.3 15.6010416666667 2.69895833333333 70 16.3 15.6010416666667 0.698958333333334 71 14.9 15.6010416666667 -0.701041666666667 72 18.2 15.6010416666667 2.59895833333333 73 18.4 15.6010416666667 2.79895833333333 74 18.5 15.6010416666667 2.89895833333333 75 16 15.6010416666667 0.398958333333333 76 17.4 15.6010416666667 1.79895833333333 77 17.2 15.6010416666667 1.59895833333333 78 19.6 15.6010416666667 3.99895833333333 79 17.2 15.6010416666667 1.59895833333333 80 18.3 15.6010416666667 2.69895833333333 81 19.3 15.6010416666667 3.69895833333333 82 18.1 15.6010416666667 2.49895833333333 83 16.2 15.6010416666667 0.598958333333332 84 18.4 15.6010416666667 2.79895833333333 85 20.5 15.6010416666667 4.89895833333333 86 19 15.6010416666667 3.39895833333333 87 16.5 15.6010416666667 0.898958333333333 88 18.7 15.6010416666667 3.09895833333333 89 19 15.6010416666667 3.39895833333333 90 19.2 15.6010416666667 3.59895833333333 91 20.5 15.6010416666667 4.89895833333333 92 19.3 15.6010416666667 3.69895833333333 93 20.6 15.6010416666667 4.99895833333333 94 20.1 15.6010416666667 4.49895833333333 95 16.1 15.6010416666667 0.498958333333335 96 20.4 15.6010416666667 4.79895833333333 97 19.7 16.164 3.536 98 15.6 16.164 -0.564 99 14.4 16.164 -1.764 100 13.7 16.164 -2.464 101 14.1 16.164 -2.064 102 15 16.164 -1.164 103 14.2 16.164 -1.964 104 13.6 16.164 -2.564 105 15.4 16.164 -0.764 106 14.8 16.164 -1.364 107 12.5 16.164 -3.664 108 16.2 16.164 0.0359999999999995 109 16.1 16.164 -0.0639999999999984 110 16 16.164 -0.164000000000000 111 15.8 16.164 -0.363999999999999 112 15.2 16.164 -0.964 113 15.7 16.164 -0.464000000000000 114 18.9 16.164 2.736 115 17.4 16.164 1.236 116 17 16.164 0.836 117 19.8 16.164 3.636 118 17.7 16.164 1.536 119 16 16.164 -0.164000000000000 120 19.6 16.164 3.436 121 19.7 16.164 3.536 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0671272142031919 0.134254428406384 0.932872785796808 6 0.0453280473949821 0.0906560947899642 0.954671952605018 7 0.0313584019409592 0.0627168038819184 0.96864159805904 8 0.0116052309763518 0.0232104619527036 0.988394769023648 9 0.00404455712870425 0.00808911425740849 0.995955442871296 10 0.00196752258241489 0.00393504516482977 0.998032477417585 11 0.0125594365042229 0.0251188730084457 0.987440563495777 12 0.00608058834576676 0.0121611766915335 0.993919411654233 13 0.00321281475660756 0.00642562951321511 0.996787185243392 14 0.00151088901429337 0.00302177802858674 0.998489110985707 15 0.00310981341724974 0.00621962683449947 0.99689018658275 16 0.00158012984400427 0.00316025968800853 0.998419870155996 17 0.000813023885942727 0.00162604777188545 0.999186976114057 18 0.000552367979070792 0.00110473595814158 0.99944763202093 19 0.000271640679501232 0.000543281359002464 0.999728359320499 20 0.000146377539998745 0.000292755079997489 0.999853622460001 21 6.71672079802195e-05 0.000134334415960439 0.99993283279202 22 3.02537542887998e-05 6.05075085775995e-05 0.999969746245711 23 0.000228597545655166 0.000457195091310333 0.999771402454345 24 0.000143458610735524 0.000286917221471048 0.999856541389264 25 0.000228631062869575 0.000457262125739151 0.99977136893713 26 0.000124462174145383 0.000248924348290765 0.999875537825855 27 0.000105274549950531 0.000210549099901062 0.99989472545005 28 6.48423979875567e-05 0.000129684795975113 0.999935157602012 29 3.89565490279251e-05 7.79130980558501e-05 0.999961043450972 30 2.40269864221251e-05 4.80539728442503e-05 0.999975973013578 31 1.36987443044038e-05 2.73974886088076e-05 0.999986301255696 32 8.2478660111832e-06 1.64957320223664e-05 0.999991752133989 33 4.71862937478437e-06 9.43725874956874e-06 0.999995281370625 34 2.73571082921501e-06 5.47142165843001e-06 0.99999726428917 35 8.03760486924259e-05 0.000160752097384852 0.999919623951308 36 6.28571159069279e-05 0.000125714231813856 0.999937142884093 37 6.22009551265533e-05 0.000124401910253107 0.999937799044873 38 4.76473465026274e-05 9.52946930052547e-05 0.999952352653497 39 3.77961621806751e-05 7.55923243613501e-05 0.99996220383782 40 3.53028680648163e-05 7.06057361296327e-05 0.999964697131935 41 2.86283862292233e-05 5.72567724584466e-05 0.99997137161377 42 8.84809090444215e-05 0.000176961818088843 0.999911519090956 43 8.0036642757992e-05 0.000160073285515984 0.999919963357242 44 7.0626707831229e-05 0.000141253415662458 0.999929373292169 45 0.000139132662972925 0.000278265325945849 0.999860867337027 46 0.000125092960093797 0.000250185920187594 0.999874907039906 47 0.000357822067471729 0.000715644134943458 0.999642177932528 48 0.000583750190088042 0.00116750038017608 0.999416249809912 49 0.000875408814909549 0.00175081762981910 0.99912459118509 50 0.00104156983291585 0.00208313966583170 0.998958430167084 51 0.00111437427777663 0.00222874855555325 0.998885625722223 52 0.00120660007648276 0.00241320015296551 0.998793399923517 53 0.00135095959358349 0.00270191918716697 0.998649040406417 54 0.00451280611425229 0.00902561222850459 0.995487193885748 55 0.00600331855604835 0.0120066371120967 0.993996681443952 56 0.00673657215065882 0.0134731443013176 0.993263427849341 57 0.0126048284283659 0.0252096568567318 0.987395171571634 58 0.0140371944315864 0.0280743888631728 0.985962805568414 59 0.022535773546667 0.045071547093334 0.977464226453333 60 0.0401417531536665 0.080283506307333 0.959858246846334 61 0.0462013226687212 0.0924026453374424 0.953798677331279 62 0.0686256529362493 0.137251305872499 0.93137434706375 63 0.0782461875299714 0.156492375059943 0.921753812470029 64 0.0855178370633664 0.171035674126733 0.914482162936634 65 0.0954623653422118 0.190924730684424 0.904537634657788 66 0.226290275179962 0.452580550359924 0.773709724820038 67 0.237218900399985 0.47443780079997 0.762781099600015 68 0.279008603101634 0.558017206203268 0.720991396898366 69 0.329279010684921 0.658558021369842 0.670720989315079 70 0.335060448861612 0.670120897723224 0.664939551138388 71 0.396899032110829 0.793798064221658 0.603100967889171 72 0.434643411556105 0.86928682311221 0.565356588443895 73 0.473306008657117 0.946612017314235 0.526693991342883 74 0.508125937444318 0.983748125111364 0.491874062555682 75 0.529176189578484 0.941647620843033 0.470823810421516 76 0.53140656729761 0.93718686540478 0.46859343270239 77 0.533476312838979 0.933047374322043 0.466523687161021 78 0.6039451899399 0.792109620120201 0.396054810060101 79 0.600948260803267 0.798103478393465 0.399051739196732 80 0.602971830952107 0.794056338095785 0.397028169047892 81 0.63241452854633 0.73517094290734 0.36758547145367 82 0.623036668698247 0.753926662603505 0.376963331301753 83 0.650498479672624 0.699003040654751 0.349501520327376 84 0.644105298902283 0.711789402195433 0.355894701097717 85 0.709192765647666 0.581614468704668 0.290807234352334 86 0.702915022300268 0.594169955399465 0.297084977699732 87 0.722159873006938 0.555680253986123 0.277840126993062 88 0.708903554034256 0.582192891931489 0.291096445965744 89 0.69615496128404 0.607690077431921 0.303845038715960 90 0.683045072392565 0.63390985521487 0.316954927607435 91 0.70571532253524 0.588569354929519 0.294284677464760 92 0.685949204451001 0.628101591097998 0.314050795548999 93 0.709092826993051 0.581814346013897 0.290907173006949 94 0.716481872377153 0.567036255245693 0.283518127622847 95 0.744738129913246 0.510523740173509 0.255261870086754 96 0.73126767053227 0.53746465893546 0.26873232946773 97 0.793693917226616 0.412612165546768 0.206306082773384 98 0.755212046181547 0.489575907636907 0.244787953818453 99 0.735889761392684 0.528220477214631 0.264110238607316 100 0.747269705693487 0.505460588613026 0.252730294306513 101 0.739815213318276 0.520369573363449 0.260184786681724 102 0.696175476767866 0.607649046464269 0.303824523232134 103 0.690104582355877 0.619790835288245 0.309895417644123 104 0.736156528985728 0.527686942028543 0.263843471014272 105 0.688145326666178 0.623709346667643 0.311854673333822 106 0.669448221037538 0.661103557924924 0.330551778962462 107 0.885538237908694 0.228923524182611 0.114461762091306 108 0.848050304753437 0.303899390493125 0.151949695246563 109 0.807144842378554 0.385710315242893 0.192855157621446 110 0.76720632199041 0.465587356019179 0.232793678009589 111 0.74245846995316 0.51508306009368 0.25754153004684 112 0.794973525147145 0.410052949705711 0.205026474852855 113 0.836365466633641 0.327269066732717 0.163634533366359 114 0.75517284146298 0.48965431707404 0.24482715853702 115 0.643634616759145 0.71273076648171 0.356365383240855 116 0.546453415564103 0.907093168871793 0.453546584435897 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 44 0.392857142857143 NOK 5% type I error level 52 0.464285714285714 NOK 10% type I error level 56 0.5 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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