paperMR3

*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 15:18:15 +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/t12927718545gn3obv5qtx0q65.htm/, Retrieved Sun, 19 Dec 2010 16:17:45 +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/t12927718545gn3obv5qtx0q65.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 18 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation uitvoercijfer[t] = + 12.7757731042654 -3.94653929699842X[t] + 0.769258521231271M1[t] -0.404615946191776M2[t] -1.6681543515726M3[t] -1.35169275695342M4[t] -1.17523116233424M5[t] + 0.601230432284937M6[t] -0.772307973095888M7[t] -0.80584637847671M8[t] + 0.220615216142469M9[t] -1.00292318923835M10[t] -3.30646159461918M11[t] + 0.0735384053808224t + 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) 12.7757731042654 0.387137 33.0006 0 0 X -3.94653929699842 0.326344 -12.0932 0 0 M1 0.769258521231271 0.451594 1.7034 0.091391 0.045695 M2 -0.404615946191776 0.462012 -0.8758 0.383117 0.191559 M3 -1.6681543515726 0.461716 -3.6129 0.000463 0.000231 M4 -1.35169275695342 0.461452 -2.9292 0.004154 0.002077 M5 -1.17523116233424 0.461218 -2.5481 0.012251 0.006125 M6 0.601230432284937 0.461015 1.3041 0.194983 0.097491 M7 -0.772307973095888 0.460844 -1.6759 0.096686 0.048343 M8 -0.80584637847671 0.460703 -1.7492 0.08313 0.041565 M9 0.220615216142469 0.460594 0.479 0.63293 0.316465 M10 -1.00292318923835 0.460516 -2.1778 0.031615 0.015807 M11 -3.30646159461918 0.460469 -7.1806 0 0 t 0.0735384053808224 0.003792 19.3934 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.908760355834885 R-squared 0.825845384337147 Adjusted R-squared 0.804686412340725 F-TEST (value) 39.0305060414476 F-TEST (DF numerator) 13 F-TEST (DF denominator) 107 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.02960504914899 Sum Squared Residuals 113.429261623941 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15 13.6185700308775 1.38142996912254 2 14.4 12.5182339688353 1.88176603116472 3 13 11.3282339688353 1.67176603116473 4 13.7 11.7182339688353 1.98176603116473 5 13.6 11.9682339688353 1.63176603116474 6 15.2 13.8182339688353 1.38176603116473 7 12.9 12.5182339688353 0.381766031164725 8 14 12.5582339688353 1.44176603116472 9 14.1 13.6582339688353 0.441766031164725 10 13.2 12.5082339688353 0.691766031164726 11 11.3 10.2782339688353 1.02176603116473 12 13.3 13.6582339688353 -0.358233968835272 13 14.4 14.5010308954474 -0.101030895447369 14 13.3 13.4006948334051 -0.100694833405141 15 11.6 12.2106948334051 -0.610694833405144 16 13.2 12.6006948334051 0.599305166594858 17 13.1 12.8506948334051 0.249305166594855 18 14.6 14.7006948334051 -0.100694833405142 19 14 13.4006948334051 0.599305166594859 20 14.3 13.4406948334051 0.859305166594859 21 13.8 14.5406948334051 -0.740694833405141 22 13.7 13.3906948334051 0.309305166594857 23 11 11.1606948334051 -0.160694833405144 24 14.4 14.5406948334051 -0.140694833405141 25 15.6 15.3834917600172 0.216508239982763 26 13.7 14.283155697975 -0.583155697975011 27 12.6 13.0931556979750 -0.493155697975014 28 13.2 13.483155697975 -0.283155697975011 29 13.3 13.7331556979750 -0.433155697975013 30 14.3 15.583155697975 -1.28315569797501 31 14 14.283155697975 -0.28315569797501 32 13.4 14.323155697975 -0.923155697975011 33 13.9 15.423155697975 -1.52315569797501 34 13.7 14.273155697975 -0.573155697975012 35 10.5 12.0431556979750 -1.54315569797501 36 14.5 15.423155697975 -0.92315569797501 37 15 16.2659526245871 -1.26595262458711 38 13.5 15.1656165625449 -1.66561656254488 39 13.5 13.9756165625449 -0.475616562544882 40 13.2 14.3656165625449 -1.16561656254488 41 13.8 14.6156165625449 -0.815616562544882 42 16.2 16.4656165625449 -0.265616562544881 43 14.7 15.1656165625449 -0.465616562544880 44 13.9 15.2056165625449 -1.30561656254488 45 16 16.3056165625449 -0.305616562544881 46 14.4 15.1556165625449 -0.75561656254488 47 12.3 12.9256165625449 -0.62561656254488 48 15.9 16.3056165625449 -0.405616562544879 49 15.9 17.1484134891570 -1.24841348915698 50 15.5 16.0480774271147 -0.548077427114749 51 15.1 14.8580774271148 0.241922572885248 52 14.5 15.2480774271147 -0.748077427114749 53 15.1 15.4980774271148 -0.398077427114752 54 17.4 17.3480774271147 0.0519225728852497 55 16.2 16.0480774271147 0.151922572885251 56 15.6 16.0880774271148 -0.48807742711475 57 17.2 17.1880774271147 0.0119225728852496 58 14.9 16.0380774271148 -1.13807742711475 59 13.8 13.8080774271147 -0.0080774271147488 60 17.5 17.1880774271147 0.311922572885252 61 16.2 18.0308743537268 -1.83087435372685 62 17.5 16.9305382916846 0.569461708315383 63 16.6 15.7405382916846 0.859461708315381 64 16.2 16.1305382916846 0.0694617083153818 65 16.6 16.3805382916846 0.219461708315381 66 19.6 18.2305382916846 1.36946170831538 67 15.9 16.9305382916846 -1.03053829168462 68 18 16.9705382916846 1.02946170831538 69 18.3 18.0705382916846 0.229461708315383 70 16.3 16.9205382916846 -0.620538291684617 71 14.9 14.6905382916846 0.209461708315382 72 18.2 18.0705382916846 0.129461708315382 73 18.4 18.9133352182967 -0.513335218296715 74 18.5 17.8129991562545 0.687000843745514 75 16 16.6229991562545 -0.622999156254489 76 17.4 17.0129991562545 0.387000843745511 77 17.2 17.2629991562545 -0.06299915625449 78 19.6 19.1129991562545 0.487000843745513 79 17.2 17.8129991562545 -0.612999156254487 80 18.3 17.8529991562545 0.447000843745514 81 19.3 18.9529991562545 0.347000843745514 82 18.1 17.8029991562545 0.297000843745514 83 16.2 15.5729991562545 0.627000843745511 84 18.4 18.9529991562545 -0.552999156254488 85 20.5 19.7957960828666 0.704203917133417 86 19 18.6954600208244 0.304539979175645 87 16.5 17.5054600208244 -1.00546002082436 88 18.7 17.8954600208244 0.804539979175643 89 19 18.1454600208244 0.854539979175641 90 19.2 19.9954600208244 -0.795460020824357 91 20.5 18.6954600208244 1.80453997917564 92 19.3 18.7354600208244 0.564539979175646 93 20.6 19.8354600208244 0.764539979175643 94 20.1 18.6854600208244 1.41453997917564 95 16.1 16.4554600208244 -0.355460020824355 96 20.4 19.8354600208244 0.564539979175643 97 19.7 16.7317176504380 2.96828234956197 98 15.6 15.6313815883958 -0.0313815883958056 99 14.4 14.4413815883958 -0.0413815883958076 100 13.7 14.8313815883958 -1.13138158839581 101 14.1 15.0813815883958 -0.981381588395809 102 15 16.9313815883958 -1.93138158839581 103 14.2 15.6313815883958 -1.43138158839581 104 13.6 15.6713815883958 -2.07138158839581 105 15.4 16.7713815883958 -1.37138158839581 106 14.8 15.6213815883958 -0.821381588395806 107 12.5 13.3913815883958 -0.891381588395806 108 16.2 16.7713815883958 -0.571381588395806 109 16.1 17.6141785150079 -1.5141785150079 110 16 16.5138424529657 -0.513842452965674 111 15.8 15.3238424529657 0.476157547034324 112 15.2 15.7138424529657 -0.513842452965675 113 15.7 15.9638424529657 -0.263842452965678 114 18.9 17.8138424529657 1.08615754703432 115 17.4 16.5138424529657 0.886157547034325 116 17 16.5538424529657 0.446157547034325 117 19.8 17.6538424529657 2.14615754703433 118 17.7 16.5038424529657 1.19615754703432 119 16 14.2738424529657 1.72615754703432 120 19.6 17.6538424529657 1.94615754703433 121 19.7 18.4966393795778 1.20336062042223 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0581178846970316 0.116235769394063 0.941882115302968 18 0.0188892310772802 0.0377784621545603 0.98111076892272 19 0.183252228139517 0.366504456279034 0.816747771860483 20 0.152313449843593 0.304626899687186 0.847686550156407 21 0.0864896136677097 0.172979227335419 0.91351038633229 22 0.0767844779543315 0.153568955908663 0.923215522045668 23 0.0436221792183892 0.0872443584367784 0.95637782078161 24 0.0681279134286964 0.136255826857393 0.931872086571304 25 0.085849397127 0.171698794254 0.914150602873 26 0.0537478275447692 0.107495655089538 0.94625217245523 27 0.0349203496749854 0.0698406993499708 0.965079650325015 28 0.0222950454571158 0.0445900909142316 0.977704954542884 29 0.0131119947538031 0.0262239895076062 0.986888005246197 30 0.00901707857984824 0.0180341571596965 0.990982921420152 31 0.00711184276769986 0.0142236855353997 0.9928881572323 32 0.00566531134945472 0.0113306226989094 0.994334688650545 33 0.00318739226039923 0.00637478452079846 0.9968126077396 34 0.00190702951539058 0.00381405903078116 0.99809297048461 35 0.00134994552477535 0.00269989104955069 0.998650054475225 36 0.00113225673558876 0.00226451347117752 0.998867743264411 37 0.0005911840050404 0.0011823680100808 0.99940881599496 38 0.000344352432161998 0.000688704864323997 0.999655647567838 39 0.000661724605389077 0.00132344921077815 0.99933827539461 40 0.000353186311884523 0.000706372623769046 0.999646813688115 41 0.000219398337813669 0.000438796675627338 0.999780601662186 42 0.000699328625376725 0.00139865725075345 0.999300671374623 43 0.000723343353757784 0.00144668670751557 0.999276656646242 44 0.000414943027681808 0.000829886055363617 0.999585056972318 45 0.00215278011555304 0.00430556023110608 0.997847219884447 46 0.00150105812656514 0.00300211625313028 0.998498941873435 47 0.00155724389663343 0.00311448779326687 0.998442756103367 48 0.00258924484237056 0.00517848968474112 0.99741075515763 49 0.00177979690537142 0.00355959381074283 0.998220203094629 50 0.00208094437664923 0.00416188875329847 0.99791905562335 51 0.00504060830527053 0.0100812166105411 0.99495939169473 52 0.00335589296673665 0.0067117859334733 0.996644107033263 53 0.00275730853211499 0.00551461706422997 0.997242691467885 54 0.00402059099720872 0.00804118199441744 0.995979409002791 55 0.00529857596043378 0.0105971519208676 0.994701424039566 56 0.00395877276003333 0.00791754552006666 0.996041227239967 57 0.00634807583359035 0.0126961516671807 0.99365192416641 58 0.00442220640575288 0.00884441281150576 0.995577793594247 59 0.005164993672172 0.010329987344344 0.994835006327828 60 0.00850189717018081 0.0170037943403616 0.99149810282982 61 0.0101572138084447 0.0203144276168895 0.989842786191555 62 0.0175467049465285 0.0350934098930571 0.982453295053471 63 0.0317965139372781 0.0635930278745563 0.968203486062722 64 0.0279215944538576 0.0558431889077151 0.972078405546142 65 0.026533742413565 0.05306748482713 0.973466257586435 66 0.0774106705446989 0.154821341089398 0.922589329455301 67 0.0609795647630154 0.121959129526031 0.939020435236985 68 0.107978131925328 0.215956263850656 0.892021868074672 69 0.105741472326038 0.211482944652077 0.894258527673962 70 0.0825120682505766 0.165024136501153 0.917487931749423 71 0.0776784451566054 0.155356890313211 0.922321554843395 72 0.07075215782986 0.14150431565972 0.92924784217014 73 0.0599242427480752 0.119848485496150 0.940075757251925 74 0.063339936447363 0.126679872894726 0.936660063552637 75 0.047038816507948 0.094077633015896 0.952961183492052 76 0.0427148002604955 0.085429600520991 0.957285199739504 77 0.0319908231180247 0.0639816462360493 0.968009176881975 78 0.0337895498309944 0.0675790996619888 0.966210450169006 79 0.0247344503611982 0.0494689007223965 0.975265549638802 80 0.0246106720942958 0.0492213441885916 0.975389327905704 81 0.0204430355273095 0.0408860710546189 0.97955696447269 82 0.0163033676999387 0.0326067353998775 0.983696632300061 83 0.0166553368316846 0.0333106736633692 0.983344663168315 84 0.0113744223961148 0.0227488447922296 0.988625577603885 85 0.0103230306616578 0.0206460613233156 0.989676969338342 86 0.00688720826611452 0.0137744165322290 0.993112791733886 87 0.00869491828053847 0.0173898365610769 0.991305081719462 88 0.00721604971422916 0.0144320994284583 0.99278395028577 89 0.00596506732816876 0.0119301346563375 0.994034932671831 90 0.00468724604344377 0.00937449208688754 0.995312753956556 91 0.00814984334661282 0.0162996866932256 0.991850156653387 92 0.00636992409953875 0.0127398481990775 0.99363007590046 93 0.00440620474532420 0.00881240949064839 0.995593795254676 94 0.0053213276246266 0.0106426552492532 0.994678672375373 95 0.0031270976874507 0.0062541953749014 0.99687290231255 96 0.00185203608558584 0.00370407217117168 0.998147963914414 97 0.420677802324111 0.841355604648223 0.579322197675889 98 0.720948949175836 0.558102101648329 0.279051050824164 99 0.804874101598092 0.390251796803815 0.195125898401908 100 0.911327288047899 0.177345423904203 0.0886727119521014 101 0.991866888203036 0.0162662235939274 0.00813311179696372 102 0.983149146099818 0.0337017078003645 0.0168508539001822 103 0.958593726767032 0.0828125464659367 0.0414062732329684 104 0.893783357032130 0.212433285935741 0.106216642967870 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.306818181818182 NOK 5% type I error level 56 0.636363636363636 NOK 10% type I error level 66 0.75 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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