Multiple Lineair Regression Paper Finding Friends

*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: Mon, 29 Nov 2010 11:22:54 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215.htm/, Retrieved Mon, 29 Nov 2010 12:23:40 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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66 73 68 5 2 54 58 54 12 1 82 68 41 11 1 61 62 49 6 1 65 65 49 12 1 77 81 72 11 1 66 73 78 12 1 66 64 58 7 2 66 68 58 8 1 48 51 23 13 1 57 68 39 12 1 80 61 63 13 1 60 69 46 12 1 70 73 58 12 1 85 61 39 11 2 59 62 44 12 2 72 63 49 12 1 70 69 57 12 1 74 47 76 11 2 70 66 63 13 2 51 58 18 9 1 70 63 40 11 2 71 69 59 11 1 72 59 62 11 2 50 59 70 9 1 69 63 65 11 2 73 65 56 12 2 66 65 45 12 1 73 71 57 10 2 58 60 50 12 1 78 81 40 12 2 83 67 58 12 1 76 66 49 9 2 77 62 49 9 1 79 63 27 12 1 71 73 51 14 2 79 55 75 12 2 60 59 65 11 1 73 64 47 9 1 70 63 49 11 2 42 64 65 7 1 74 73 61 15 1 68 54 46 11 1 83 76 69 12 1 62 74 55 12 2 79 63 78 9 2 61 73 58 12 2 86 67 34 11 2 64 68 67 11 2 75 66 45 8 1 59 62 68 7 2 82 71 49 12 2 61 63 19 8 1 69 75 72 10 1 60 77 59 12 1 59 62 46 15 2 81 74 56 12 1 65 67 45 12 2 60 56 53 12 2 60 60 67 12 2 45 58 73 8 2 75 65 46 10 1 84 49 70 14 2 77 61 38 10 1 64 66 54 12 2 54 64 46 14 2 72 65 46 6 2 56 46 45 11 1 67 65 47 10 2 81 81 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Vrienden_Vinden[t] = + 9.02573160599013 + 0.0372125888611801Groepsgevoel[t] -0.000142551277212557`Non-verbale_communicatie`[t] -0.0171637167682930Uitingsangst[t] + 0.260679787657076Geslacht[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) 9.02573160599013 1.53429 5.8827 0 0 Groepsgevoel 0.0372125888611801 0.01562 2.3824 0.018537 0.009268 `Non-verbale_communicatie` -0.000142551277212557 0.0208 -0.0069 0.994542 0.497271 Uitingsangst -0.0171637167682930 0.011838 -1.4499 0.149319 0.07466 Geslacht 0.260679787657076 0.305094 0.8544 0.394319 0.197159 Multiple Linear Regression - Regression Statistics Multiple R 0.240728215051863 R-squared 0.057950073522056 Adjusted R-squared 0.0312252529127526 F-TEST (value) 2.16839897147458 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 0.0756037482134682 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.76827713079384 Sum Squared Residuals 440.879365591678 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.8255830626617 -5.82558306266173 2 12 10.3607825125848 1.63921748741521 3 11 11.6244378059135 -0.624437805913516 4 6 10.7065190133457 -4.70651901334566 5 12 10.8549417149587 1.14505828504125 6 11 10.9044464751868 0.0955535248132316 7 12 10.3932661073217 1.60673389267827 8 7 10.9985031918396 -3.99850319183958 9 8 10.7372531990737 -2.73725319907365 10 13 10.6705800581753 2.32941994182472 11 12 10.7284505179206 1.27154948207940 12 13 11.1734087182292 1.82659128177080 13 12 10.7197997158489 1.28020028415113 14 12 10.8853907981323 1.11460920186769 15 11 12.0320806526312 -1.03208065263121 16 12 10.9785922071218 1.02140779287815 17 12 11.1157149395414 0.884285060458567 18 12 10.9031247200095 1.09687527999055 19 11 10.9896803726124 0.0103196273876398 20 13 11.0612498608884 1.93875013911159 21 9 10.8670385496598 -1.86703854965980 22 11 11.4564430003908 -0.456443000390786 23 11 10.9060098753340 0.0939901246659525 24 11 11.1538366143196 -0.153836614319551 25 9 9.93717013757017 -0.937170137570168 26 11 10.9901374923223 0.00986250767771873 27 12 11.2931761961272 0.706823803872786 28 12 10.9608091708931 1.0391908291069 29 10 11.2751571716956 -1.27515717169565 30 12 10.5780026325483 1.42199736745174 31 12 11.7515777882904 0.248422211709599 32 12 11.3700097609909 0.629990239009073 33 9 11.5248174288116 -2.52481742881159 34 9 11.3019204351245 -2.30192043512455 35 12 11.7538048304721 0.246195169527861 36 14 11.3034291920286 2.69657080797138 37 12 11.1917666234689 0.808233376531147 38 11 10.3951146100234 0.604885389976566 39 9 11.1871124106620 -2.18711241066199 40 11 11.3019695494761 -0.301969549476149 41 7 9.72457525413613 -2.72457525413613 42 15 10.9827500032722 4.01724999672785 43 11 11.0196386958965 -0.0196386958965047 44 12 11.1799259150448 0.82007408495521 45 12 10.8997184739276 1.10028152607239 46 9 11.1391350629463 -2.13913506294627 47 12 10.8111572860388 1.18884271396123 48 11 12.1542565176706 -1.15425651767058 49 11 10.7690343580937 0.230965641906268 50 8 11.2955799193665 -3.29557991936651 51 7 10.5666630046828 -3.56666300468281 52 12 11.7473802055926 0.25261979440739 53 8 11.2212879651172 -3.22128796511724 54 10 10.6076010719606 -0.607601071960603 55 12 10.4955309876434 1.50446901235663 56 15 10.9442647735853 4.05573522641474 57 12 11.3289141578647 0.671085842135335 58 12 11.1839912671346 0.81600873286543 59 12 10.8621866527317 1.13781334726834 60 12 10.6213244128667 1.37867558713329 61 8 9.96043838189368 -1.96043838189368 62 10 11.2785587538754 -1.27855875387543 63 14 11.4645034592795 2.53549654072051 64 10 11.4908638708530 -1.49086387085298 65 12 10.9924477786360 1.00755222136403 66 14 10.7579167267249 3.24208327327507 67 6 11.4276007749490 -5.42760077494896 68 11 10.5913917565483 0.408608243451663 69 10 11.2243741138748 -1.22437411387477 70 14 12.1206713063983 1.87932869360166 71 12 10.9113525321516 1.08864746784840 72 13 11.2415378306431 1.75846216935694 73 11 11.0315523277833 -0.0315523277833113 74 11 11.1076296786765 -0.107629678676501 75 12 11.0776470430170 0.922352956983023 76 13 11.7348075955734 1.26519240442660 77 12 10.5050417612804 1.49495823871958 78 8 10.4324057791145 -2.4324057791145 79 12 11.4329434317665 0.567056568233486 80 11 11.3842836585326 -0.384283658532567 81 10 11.1847040235206 -1.18470402352063 82 12 10.8372077930816 1.16279220691840 83 11 11.1617746600052 -0.161774660005211 84 12 11.2674457864087 0.732554213591312 85 12 11.1216231375658 0.878376862434226 86 10 10.4858926473004 -0.485892647300355 87 12 11.6365486323207 0.363451367679258 88 12 11.2141164540713 0.785883545928694 89 11 11.4261457962986 -0.426145796298554 90 10 10.6997120500736 -0.699712050073586 91 12 11.1170320308167 0.882967969183311 92 11 10.7971100488958 0.202889951104171 93 12 10.3258848739394 1.67411512606065 94 12 10.2271048199294 1.77289518007056 95 10 10.0509803030922 -0.0509803030922393 96 11 11.0676698218659 -0.06766982186588 97 10 10.9906539191775 -0.99065391917747 98 11 11.1593171585123 -0.159317158512255 99 11 10.6813284778682 0.318671522131839 100 12 11.0229902987351 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11.1211113746126 0.878888625387354 125 13 10.8318698001661 2.16813019983389 126 11 10.8505922531796 0.149407746820375 127 11 10.5197970909074 0.480202909092637 128 12 11.0645088917910 0.935491108209021 129 9 11.2254607458388 -2.22546074583881 130 11 11.5437305545888 -0.543730554588838 131 12 11.2084442442498 0.791555755750213 132 12 11.0490557903492 0.950944209650763 133 13 11.3260393232093 1.67396067679073 134 6 10.6716213747002 -4.67162137470023 135 11 11.4464647863746 -0.446464786374606 136 10 11.3508699748150 -1.35086997481498 137 12 11.7916414026958 0.208358597304168 138 11 10.9453948631338 0.0546051368662412 139 12 11.1303571837694 0.869642816230557 140 12 10.9897939476121 1.01020605238787 141 7 11.5361570566770 -4.53615705667703 142 12 11.1901354533617 0.809864546638266 143 12 11.5248024442403 0.475197555759668 144 9 10.8623142194376 -1.86231421943762 145 12 10.6596282975940 1.34037170240601 146 12 11.1581229700409 0.841877029959108 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.954603281913009 0.0907934361739826 0.0453967180869913 9 0.95690439202649 0.086191215947018 0.043095607973509 10 0.988319249262117 0.0233615014757661 0.0116807507378830 11 0.97878637412762 0.0424272517447588 0.0212136258723794 12 0.978856781018064 0.0422864379638729 0.0211432189819364 13 0.970329294602587 0.059341410794827 0.0296707053974135 14 0.958173054054128 0.0836538918917434 0.0418269459458717 15 0.967612866407414 0.0647742671851722 0.0323871335925861 16 0.989004092643731 0.0219918147125371 0.0109959073562686 17 0.982168981236138 0.0356620375277231 0.0178310187638615 18 0.974454386921366 0.0510912261572684 0.0255456130786342 19 0.96584338093801 0.0683132381239783 0.0341566190619892 20 0.988020876891245 0.0239582462175108 0.0119791231087554 21 0.989600474257962 0.0207990514840750 0.0103995257420375 22 0.985410384011071 0.0291792319778577 0.0145896159889289 23 0.97791731427538 0.0441653714492409 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0.242870423440539 0.485740846881079 0.75712957655946 127 0.187151945913958 0.374303891827915 0.812848054086043 128 0.15854513832324 0.31709027664648 0.84145486167676 129 0.163146590064839 0.326293180129679 0.83685340993516 130 0.118847095955125 0.23769419191025 0.881152904044875 131 0.0880643039140018 0.176128607828004 0.911935696085998 132 0.0660033447213557 0.132006689442711 0.933996655278644 133 0.0526991118611983 0.105398223722397 0.947300888138802 134 0.316019911815304 0.632039823630609 0.683980088184696 135 0.231304397781750 0.462608795563499 0.76869560221825 136 0.216917624577706 0.433835249155413 0.783082375422294 137 0.134044155165076 0.268088310330153 0.865955844834924 138 0.0719877132952196 0.143975426590439 0.92801228670478 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.106870229007634 NOK 5% type I error level 42 0.320610687022901 NOK 10% type I error level 59 0.450381679389313 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/102fk31291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/102fk31291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/1vena1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/1vena1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/26nmc1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/26nmc1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/36nmc1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/36nmc1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/46nmc1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/46nmc1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/5hx4x1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/5hx4x1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/6hx4x1291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/6hx4x1291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/796301291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/796301291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/896301291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/896301291029762.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/92fk31291029762.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291029806rydptbu51ady215/92fk31291029762.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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