Workshop 7

*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: Fri, 20 Nov 2009 08:23:47 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730949tujo67p30qbkpwf.htm/, Retrieved Fri, 20 Nov 2009 16:29:22 +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/2009/Nov/20/t1258730949tujo67p30qbkpwf.htm/}, year = {2009}, } @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 = {2009}, 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 «

33 62 39 64 45 62 46 64 45 64 45 69 49 69 50 65 54 56 59 58 58 53 56 62 48 55 50 60 52 59 53 58 55 53 43 57 42 57 38 53 41 54 41 53 39 57 34 57 27 55 15 49 14 50 31 49 41 54 43 58 46 58 42 52 45 56 45 52 40 59 35 53 36 52 38 53 39 51 32 50 24 56 21 52 12 46 29 48 36 46 31 48 28 48 30 49 38 53 27 48 40 51 40 48 44 50 47 55 45 52 42 53 38 52 46 55 37 53 41 53 40 56 33 54 34 52 36 55 36 54 38 59 42 56 35 56 25 51 24 53 22 52 27 51 17 46 30 49 30 46 34 55 37 57 36 53 33 52 33 53 33 50 37 54 40 53 35 50 37 51 43 52 42 47 33 51 39 49 40 53 37 52 44 45 42 53 43 51 40 48 30 48 30 48 31 48 18 40 24 43 22 40 26 39 28 39 23 36 17 41 12 39 9 40 19 39 21 46 18 40 18 37 15 37 24 44 18 41 19 40 30 36 33 38 35 43 36 42 47 45 46 46

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Spaar[t] = + 36.0876640540603 + 0.426549024778832Alg_E[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) 36.0876640540603 1.63922 22.0151 0 0 Alg_E 0.426549024778832 0.044612 9.5612 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.659133991381146 R-squared 0.434457618594041 Adjusted R-squared 0.429705161607436 F-TEST (value) 91.417475175179 F-TEST (DF numerator) 1 F-TEST (DF denominator) 119 p-value 2.22044604925031e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.21474437315503 Sum Squared Residuals 3236.03350640490 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 62 50.1637818717617 11.8362181282383 2 64 52.7230760204347 11.2769239795653 3 62 55.2823701691077 6.7176298308923 4 64 55.7089191938865 8.29108080611347 5 64 55.2823701691077 8.7176298308923 6 69 55.2823701691077 13.7176298308923 7 69 56.988566268223 12.0114337317770 8 65 57.4151152930019 7.58488470699815 9 56 59.1213113921172 -3.12131139211718 10 58 61.2540565160113 -3.25405651601134 11 53 60.8275074912325 -7.82750749123251 12 62 59.9744094416748 2.02559055832516 13 55 56.5620172434442 -1.56201724344419 14 60 57.4151152930019 2.58488470699815 15 59 58.2682133425595 0.731786657440484 16 58 58.6947623673383 -0.694762367338348 17 53 59.547860416896 -6.54786041689601 18 57 54.42927211955 2.57072788044997 19 57 54.0027230947712 2.9972769052288 20 53 52.2965269956559 0.703473004344129 21 54 53.5761740699924 0.423825930007633 22 53 53.5761740699924 -0.576174069992367 23 57 52.7230760204347 4.2769239795653 24 57 50.5903308965405 6.40966910345946 25 55 47.6044877230887 7.39551227691128 26 49 42.4858994257427 6.51410057425726 27 50 42.0593504009639 7.9406495990361 28 49 49.310683822204 -0.310683822204048 29 54 53.5761740699924 0.423825930007633 30 58 54.42927211955 3.57072788044997 31 58 55.7089191938865 2.29108080611347 32 52 54.0027230947712 -2.0027230947712 33 56 55.2823701691077 0.717629830892306 34 52 55.2823701691077 -3.28237016910769 35 59 53.1496250452135 5.85037495478646 36 53 51.0168799213194 1.98312007868062 37 52 51.4434289460982 0.556571053901792 38 53 52.2965269956559 0.703473004344129 39 51 52.7230760204347 -1.72307602043470 40 50 49.7372328469829 0.262767153017120 41 56 46.3248406487522 9.67515935124777 42 52 45.0451935744157 6.95480642558427 43 46 41.2062523514062 4.79374764859376 44 48 48.4575857726464 -0.457585772646385 45 46 51.4434289460982 -5.44342894609821 46 48 49.310683822204 -1.31068382220405 47 48 48.0310367478676 -0.0310367478675531 48 49 48.8841347974252 0.115865202574783 49 53 52.2965269956559 0.703473004344129 50 48 47.6044877230887 0.395512276911279 51 51 53.1496250452135 -2.14962504521354 52 48 53.1496250452135 -5.14962504521354 53 50 54.8558211443289 -4.85582114432886 54 55 56.1354682186654 -1.13546821866536 55 52 55.2823701691077 -3.28237016910769 56 53 54.0027230947712 -1.00272309477120 57 52 52.2965269956559 -0.296526995655871 58 55 55.7089191938865 -0.708919193886526 59 53 51.8699779708770 1.13002202912296 60 53 53.5761740699924 -0.576174069992367 61 56 53.1496250452135 2.85037495478646 62 54 50.1637818717617 3.83621812823829 63 52 50.5903308965405 1.40966910345946 64 55 51.4434289460982 3.55657105390179 65 54 51.4434289460982 2.55657105390179 66 59 52.2965269956559 6.70347300434413 67 56 54.0027230947712 1.9972769052288 68 56 51.0168799213194 4.98312007868062 69 51 46.7513896735311 4.24861032646894 70 53 46.3248406487522 6.67515935124777 71 52 45.4717425991946 6.52825740080544 72 51 47.6044877230887 3.39551227691128 73 46 43.3389974753004 2.6610025246996 74 49 48.8841347974252 0.115865202574783 75 46 48.8841347974252 -2.88413479742522 76 55 50.5903308965405 4.40966910345946 77 57 51.8699779708770 5.13002202912296 78 53 51.4434289460982 1.55657105390179 79 52 50.1637818717617 1.83621812823829 80 53 50.1637818717617 2.83621812823829 81 50 50.1637818717617 -0.163781871761712 82 54 51.8699779708770 2.13002202912296 83 53 53.1496250452135 -0.149625045213535 84 50 51.0168799213194 -1.01687992131938 85 51 51.8699779708770 -0.86997797087704 86 52 54.42927211955 -2.42927211955003 87 47 54.0027230947712 -7.0027230947712 88 51 50.1637818717617 0.836218128238288 89 49 52.7230760204347 -3.7230760204347 90 53 53.1496250452135 -0.149625045213535 91 52 51.8699779708770 0.130022029122961 92 45 54.8558211443289 -9.85582114432886 93 53 54.0027230947712 -1.00272309477120 94 51 54.42927211955 -3.42927211955003 95 48 53.1496250452135 -5.14962504521354 96 48 48.8841347974252 -0.884134797425217 97 48 48.8841347974252 -0.884134797425217 98 48 49.310683822204 -1.31068382220405 99 40 43.7655465000792 -3.76554650007923 100 43 46.3248406487522 -3.32484064875223 101 40 45.4717425991946 -5.47174259919456 102 39 47.1779386983099 -8.17793869830989 103 39 48.0310367478676 -9.03103674786755 104 36 45.8982916239734 -9.8982916239734 105 41 43.3389974753004 -2.33899747530040 106 39 41.2062523514062 -2.20625235140624 107 40 39.9266052770697 0.0733947229302516 108 39 44.1920955248581 -5.19209552485807 109 46 45.0451935744157 0.95480642558427 110 40 43.7655465000792 -3.76554650007923 111 37 43.7655465000792 -6.76554650007923 112 37 42.4858994257427 -5.48589942574274 113 44 46.3248406487522 -2.32484064875223 114 41 43.7655465000792 -2.76554650007923 115 40 44.1920955248581 -4.19209552485807 116 36 48.8841347974252 -12.8841347974252 117 38 50.1637818717617 -12.1637818717617 118 43 51.0168799213194 -8.01687992131938 119 42 51.4434289460982 -9.4434289460982 120 45 56.1354682186654 -11.1354682186654 121 46 55.7089191938865 -9.70891919388653 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0166589971808693 0.0333179943617386 0.983341002819131 6 0.125359571865563 0.250719143731126 0.874640428134437 7 0.107864363297976 0.215728726595952 0.892135636702024 8 0.0686718629853859 0.137343725970772 0.931328137014614 9 0.436705937062484 0.873411874124969 0.563294062937516 10 0.397320311317986 0.794640622635971 0.602679688682014 11 0.52560730428747 0.94878539142506 0.47439269571253 12 0.454187466858273 0.908374933716546 0.545812533141727 13 0.565154991689273 0.869690016621454 0.434845008310727 14 0.487080011238196 0.974160022476392 0.512919988761804 15 0.410524334131582 0.821048668263165 0.589475665868417 16 0.343948499868437 0.687896999736873 0.656051500131563 17 0.394676170967939 0.789352341935879 0.605323829032061 18 0.435074220808018 0.870148441616035 0.564925779191982 19 0.463058536935022 0.926117073870043 0.536941463064978 20 0.64211548247099 0.715769035058019 0.357884517529010 21 0.685258198198807 0.629483603602386 0.314741801801193 22 0.728866281456535 0.54226743708693 0.271133718543465 23 0.700345074400178 0.599309851199644 0.299654925599822 24 0.682818878044044 0.634362243911912 0.317181121955956 25 0.689323412399143 0.621353175201715 0.310676587600857 26 0.748388852913045 0.503222294173911 0.251611147086955 27 0.756748367314758 0.486503265370484 0.243251632685242 28 0.781019679719495 0.437960640561009 0.218980320280505 29 0.75387319535115 0.492253609297699 0.246126804648849 30 0.718351360182755 0.56329727963449 0.281648639817245 31 0.675645422697326 0.648709154605348 0.324354577302674 32 0.675157565338673 0.649684869322655 0.324842434661327 33 0.631705645996477 0.736588708007046 0.368294354003523 34 0.638934206590235 0.72213158681953 0.361065793409765 35 0.631053178554381 0.737893642891238 0.368946821445619 36 0.598552390914294 0.802895218171411 0.401447609085706 37 0.572720405639094 0.854559188721812 0.427279594360906 38 0.538803549341869 0.922392901316261 0.461196450658131 39 0.531018246751319 0.937963506497362 0.468981753248681 40 0.512292277002597 0.975415445994806 0.487707722997403 41 0.569430306612288 0.861139386775423 0.430569693387712 42 0.57233840766456 0.85532318467088 0.42766159233544 43 0.575700387236865 0.84859922552627 0.424299612763135 44 0.572174947679008 0.855650104641985 0.427825052320992 45 0.654344174685202 0.691311650629597 0.345655825314798 46 0.646026186855839 0.707947626288322 0.353973813144161 47 0.625280396068216 0.749439207863567 0.374719603931784 48 0.597373533075935 0.805252933848129 0.402626466924065 49 0.556685134001403 0.886629731997194 0.443314865998597 50 0.528323430281504 0.943353139436992 0.471676569718496 51 0.503250363129967 0.993499273740066 0.496749636870033 52 0.534515660605622 0.930968678788757 0.465484339394378 53 0.544410172278412 0.911179655443176 0.455589827721588 54 0.497827088076007 0.995654176152015 0.502172911923993 55 0.47336814299015 0.94673628598030 0.52663185700985 56 0.429757816297482 0.859515632594964 0.570242183702518 57 0.387479866452297 0.774959732904594 0.612520133547703 58 0.341943180514139 0.683886361028277 0.658056819485861 59 0.303547025353062 0.607094050706124 0.696452974646938 60 0.265220912407403 0.530441824814805 0.734779087592597 61 0.242984095209740 0.485968190419481 0.75701590479026 62 0.230394047209656 0.460788094419311 0.769605952790344 63 0.202531007218153 0.405062014436307 0.797468992781847 64 0.192049936779375 0.384099873558749 0.807950063220625 65 0.174555407669478 0.349110815338955 0.825444592330522 66 0.225174890105401 0.450349780210802 0.774825109894599 67 0.208970640898786 0.417941281797572 0.791029359101214 68 0.2310978176451 0.4621956352902 0.7689021823549 69 0.233367130297664 0.466734260595329 0.766632869702336 70 0.288120197667943 0.576240395335887 0.711879802332057 71 0.354643432950748 0.709286865901496 0.645356567049252 72 0.365666767231214 0.731333534462428 0.634333232768786 73 0.372719101765479 0.745438203530957 0.627280898234521 74 0.352536412697341 0.705072825394682 0.647463587302659 75 0.342888362294365 0.68577672458873 0.657111637705635 76 0.397596563777422 0.795193127554845 0.602403436222578 77 0.500472165416822 0.999055669166356 0.499527834583178 78 0.508335255658251 0.983329488683498 0.491664744341749 79 0.526248909854504 0.947502180290993 0.473751090145496 80 0.579231730805955 0.841536538388091 0.420768269194045 81 0.573151055955456 0.853697888089088 0.426848944044544 82 0.62839763235793 0.74320473528414 0.37160236764207 83 0.639126116522401 0.721747766955197 0.360873883477599 84 0.635926357715513 0.728147284568973 0.364073642284487 85 0.640295346045684 0.719409307908633 0.359704653954316 86 0.632815850599041 0.734368298801918 0.367184149400959 87 0.639701868346309 0.720596263307382 0.360298131653691 88 0.685294599562411 0.629410800875178 0.314705400437589 89 0.669069578901144 0.661860842197711 0.330930421098856 90 0.72723088738494 0.545538225230119 0.272769112615060 91 0.79730827922422 0.405383441551559 0.202691720775779 92 0.823396795819962 0.353206408360077 0.176603204180038 93 0.88274962582505 0.2345007483499 0.11725037417495 94 0.90926136810055 0.181477263798901 0.0907386318994506 95 0.914691774485852 0.170616451028296 0.0853082255141479 96 0.944851095538117 0.110297808923765 0.0551489044618827 97 0.971945728193902 0.0561085436121963 0.0280542718060982 98 0.990204946110206 0.0195901077795874 0.0097950538897937 99 0.987061594278358 0.0258768114432850 0.0129384057216425 100 0.985797737726839 0.0284045245463228 0.0142022622731614 101 0.981132775812897 0.0377344483742067 0.0188672241871033 102 0.979129923259595 0.04174015348081 0.020870076740405 103 0.9776972861822 0.0446054276355982 0.0223027138177991 104 0.987441552218117 0.0251168955637651 0.0125584477818825 105 0.980028801251227 0.0399423974975469 0.0199711987487734 106 0.966702429071365 0.0665951418572693 0.0332975709286346 107 0.949745355151685 0.100509289696631 0.0502546448483155 108 0.924379765523179 0.151240468953642 0.075620234476821 109 0.970923776192606 0.0581524476147886 0.0290762238073943 110 0.951833157256124 0.0963336854877511 0.0481668427438756 111 0.9291993415247 0.141601316950600 0.0708006584753002 112 0.892500654228638 0.214998691542724 0.107499345771362 113 0.897797397186259 0.204405205627482 0.102202602813741 114 0.883523359388522 0.232953281222957 0.116476640611478 115 0.95192589609694 0.0961482078061198 0.0480741039030599 116 0.933430226420272 0.133139547159457 0.0665697735797285 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 9 0.0803571428571429 NOK 10% type I error level 14 0.125 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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