Multiple Regression - Totale Uitvoer van Belgie (B)

*The author of this computation has been verified*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 11 Dec 2008 10:20:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/11/t1229016173mskugognogs8k8q.htm/, Retrieved Thu, 11 Dec 2008 17:23:02 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/11/t1229016173mskugognogs8k8q.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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No (this computation is public)

User-defined keywords:

Dataseries X:

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15044.5 1 14944.2 1 16754.8 1 14254 1 15454.9 1 15644.8 1 14568.3 1 12520.2 1 14803 1 15873.2 1 14755.3 1 12875.1 1 14291.1 1 14205.3 1 15859.4 1 15258.9 1 15498.6 1 15106.5 1 15023.6 1 12083 1 15761.3 1 16943 1 15070.3 1 13659.6 1 14768.9 0 14725.1 0 15998.1 0 15370.6 0 14956.9 0 15469.7 0 15101.8 0 11703.7 0 16283.6 0 16726.5 0 14968.9 0 14861 0 14583.3 0 15305.8 0 17903.9 0 16379.4 0 15420.3 0 17870.5 0 15912.8 0 13866.5 0 17823.2 0 17872 0 17420.4 0 16704.4 0 15991.2 0 16583.6 0 19123.5 0 17838.7 0 17209.4 0 18586.5 0 16258.1 0 15141.6 0 19202.1 0 17746.5 0 19090.1 0 18040.3 0 17515.5 0 17751.8 0 21072.4 0 17170 0 19439.5 0 19795.4 0 17574.9 0 16165.4 0 19464.6 0 19932.1 0 19961.2 0 17343.4 0 18924.2 0 18574.1 0 21350.6 0 18594.6 0 19823.1 0 20844.4 0 19640.2 0 17735.4 0 19813.6 0 22160 0 20664.3 0 17877.4 0 21211.2 0 21423.1 0 21688.7 0 23243.2 0 21490.2 0 22925.8 0 23184.8 0 18562.2 0

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 16804.1742511521 -3134.00987903226X[t] + 520.565718605996M1[t] + 668.453218605987M2[t] + 2698.25321860599M3[t] + 1243.00321860599M4[t] + 1390.94071860599M5[t] + 2259.77821860599M6[t] + 1137.39071860599M7[t] -1298.42178139400M8[t] + 1684.31428571429M9[t] + 2270.3M10[t] + 1509.9M11[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) 16804.1742511521 788.289613 21.3173 0 0 X -3134.00987903226 487.729315 -6.4257 0 0 M1 520.565718605996 1062.55315 0.4899 0.625549 0.312774 M2 668.453218605987 1062.55315 0.6291 0.531098 0.265549 M3 2698.25321860599 1062.55315 2.5394 0.013067 0.006533 M4 1243.00321860599 1062.55315 1.1698 0.245588 0.122794 M5 1390.94071860599 1062.55315 1.3091 0.194311 0.097155 M6 2259.77821860599 1062.55315 2.1267 0.036562 0.018281 M7 1137.39071860599 1062.55315 1.0704 0.287685 0.143842 M8 -1298.42178139400 1062.55315 -1.222 0.225348 0.112674 M9 1684.31428571429 1097.252704 1.535 0.128772 0.064386 M10 2270.3 1097.252704 2.0691 0.041809 0.020904 M11 1509.9 1097.252704 1.3761 0.172687 0.086344 Multiple Linear Regression - Regression Statistics Multiple R 0.673559567976199 R-squared 0.453682491612284 Adjusted R-squared 0.37069755362934 F-TEST (value) 5.46704622115316 F-TEST (DF numerator) 12 F-TEST (DF denominator) 79 p-value 1.10064691827283e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2052.77184252911 Sum Squared Residuals 332895906.76095 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15044.5 14190.7300907258 853.769909274233 2 14944.2 14338.6175907258 605.58240927418 3 16754.8 16368.4175907258 386.382409274194 4 14254 14913.1675907258 -659.167590725803 5 15454.9 15061.1050907258 393.794909274206 6 15644.8 15929.9425907258 -285.142590725805 7 14568.3 14807.5550907258 -239.25509072581 8 12520.2 12371.7425907258 148.457409274193 9 14803 15354.4786578341 -551.478657834102 10 15873.2 15940.4643721198 -67.2643721198227 11 14755.3 15180.0643721198 -424.76437211982 12 12875.1 13670.1643721198 -795.06437211982 13 14291.1 14190.7300907258 100.369909274186 14 14205.3 14338.6175907258 -133.317590725806 15 15859.4 16368.4175907258 -509.017590725809 16 15258.9 14913.1675907258 345.73240927419 17 15498.6 15061.1050907258 437.494909274191 18 15106.5 15929.9425907258 -823.442590725808 19 15023.6 14807.5550907258 216.044909274193 20 12083 12371.7425907258 -288.742590725808 21 15761.3 15354.4786578341 406.821342165897 22 16943 15940.4643721198 1002.53562788018 23 15070.3 15180.0643721198 -109.764372119817 24 13659.6 13670.1643721198 -10.5643721198168 25 14768.9 17324.7399697581 -2555.83996975807 26 14725.1 17472.6274697581 -2747.52746975806 27 15998.1 19502.4274697581 -3504.32746975806 28 15370.6 18047.1774697581 -2676.57746975806 29 14956.9 18195.1149697581 -3238.21496975806 30 15469.7 19063.9524697581 -3594.25246975806 31 15101.8 17941.5649697581 -2839.76496975806 32 11703.7 15505.7524697581 -3802.05246975806 33 16283.6 18488.4885368664 -2204.88853686636 34 16726.5 19074.4742511521 -2347.97425115207 35 14968.9 18314.0742511521 -3345.17425115207 36 14861 16804.1742511521 -1943.17425115207 37 14583.3 17324.7399697581 -2741.43996975807 38 15305.8 17472.6274697581 -2166.82746975806 39 17903.9 19502.4274697581 -1598.52746975806 40 16379.4 18047.1774697581 -1667.77746975807 41 15420.3 18195.1149697581 -2774.81496975807 42 17870.5 19063.9524697581 -1193.45246975806 43 15912.8 17941.5649697581 -2028.76496975806 44 13866.5 15505.7524697581 -1639.25246975806 45 17823.2 18488.4885368664 -665.288536866357 46 17872 19074.4742511521 -1202.47425115207 47 17420.4 18314.0742511521 -893.67425115207 48 16704.4 16804.1742511521 -99.774251152072 49 15991.2 17324.7399697581 -1333.53996975807 50 16583.6 17472.6274697581 -889.027469758063 51 19123.5 19502.4274697581 -378.927469758065 52 17838.7 18047.1774697581 -208.477469758065 53 17209.4 18195.1149697581 -985.714969758065 54 18586.5 19063.9524697581 -477.452469758065 55 16258.1 17941.5649697581 -1683.46496975806 56 15141.6 15505.7524697581 -364.152469758064 57 19202.1 18488.4885368664 713.61146313364 58 17746.5 19074.4742511521 -1327.97425115207 59 19090.1 18314.0742511521 776.025748847926 60 18040.3 16804.1742511521 1236.12574884793 61 17515.5 17324.7399697581 190.760030241930 62 17751.8 17472.6274697581 279.172530241937 63 21072.4 19502.4274697581 1569.97253024194 64 17170 18047.1774697581 -877.177469758066 65 19439.5 18195.1149697581 1244.38503024193 66 19795.4 19063.9524697581 731.447530241937 67 17574.9 17941.5649697581 -366.664969758062 68 16165.4 15505.7524697581 659.647530241936 69 19464.6 18488.4885368664 976.11146313364 70 19932.1 19074.4742511521 857.625748847927 71 19961.2 18314.0742511521 1647.12574884793 72 17343.4 16804.1742511521 539.225748847928 73 18924.2 17324.7399697581 1599.46003024193 74 18574.1 17472.6274697581 1101.47253024194 75 21350.6 19502.4274697581 1848.17253024193 76 18594.6 18047.1774697581 547.422530241933 77 19823.1 18195.1149697581 1627.98503024193 78 20844.4 19063.9524697581 1780.44753024194 79 19640.2 17941.5649697581 1698.63503024194 80 17735.4 15505.7524697581 2229.64753024194 81 19813.6 18488.4885368664 1325.11146313364 82 22160 19074.4742511521 3085.52574884793 83 20664.3 18314.0742511521 2350.22574884793 84 17877.4 16804.1742511521 1073.22574884793 85 21211.2 17324.7399697581 3886.46003024193 86 21423.1 17472.6274697581 3950.47253024194 87 21688.7 19502.4274697581 2186.27253024194 88 23243.2 18047.1774697581 5196.02253024194 89 21490.2 18195.1149697581 3295.08503024194 90 22925.8 19063.9524697581 3861.84753024193 91 23184.8 17941.5649697581 5243.23503024193 92 18562.2 15505.7524697581 3056.44753024194 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0365749405611739 0.0731498811223477 0.963425059438826 17 0.00892492897765452 0.0178498579553090 0.991075071022345 18 0.00247159134789120 0.00494318269578240 0.997528408652109 19 0.000622486049224929 0.00124497209844986 0.999377513950775 20 0.000150364281843376 0.000300728563686751 0.999849635718157 21 6.894792383523e-05 0.00013789584767046 0.999931052076165 22 3.71089640482922e-05 7.42179280965844e-05 0.999962891035952 23 8.59958714250845e-06 1.71991742850169e-05 0.999991400412858 24 2.98216650446004e-06 5.96433300892008e-06 0.999997017833496 25 6.87256741068541e-07 1.37451348213708e-06 0.999999312743259 26 1.60155098673788e-07 3.20310197347576e-07 0.9999998398449 27 5.04465843871377e-08 1.00893168774275e-07 0.999999949553416 28 1.82191762578066e-08 3.64383525156132e-08 0.999999981780824 29 7.74640078067364e-09 1.54928015613473e-08 0.9999999922536 30 2.53412833394138e-09 5.06825666788277e-09 0.999999997465872 31 7.49536174428712e-10 1.49907234885742e-09 0.999999999250464 32 4.96298480228037e-10 9.92596960456074e-10 0.999999999503701 33 4.8670063702303e-10 9.7340127404606e-10 0.9999999995133 34 1.38698376470782e-10 2.77396752941563e-10 0.999999999861302 35 5.84787621433693e-11 1.16957524286739e-10 0.999999999941521 36 2.40151833271822e-10 4.80303666543644e-10 0.999999999759848 37 1.17446495005209e-10 2.34892990010417e-10 0.999999999882554 38 6.72261600901493e-11 1.34452320180299e-10 0.999999999932774 39 4.10783445267536e-10 8.21566890535072e-10 0.999999999589217 40 7.16170960578697e-10 1.43234192115739e-09 0.99999999928383 41 5.48783035982954e-10 1.09756607196591e-09 0.999999999451217 42 1.43548714190027e-08 2.87097428380054e-08 0.999999985645129 43 1.32000795291745e-08 2.64001590583490e-08 0.99999998679992 44 3.22789679012773e-08 6.45579358025547e-08 0.999999967721032 45 9.31150063634523e-08 1.86230012726905e-07 0.999999906884994 46 7.13699004592825e-08 1.42739800918565e-07 0.9999999286301 47 3.10062998557695e-07 6.2012599711539e-07 0.999999689937001 48 1.30963053764777e-06 2.61926107529554e-06 0.999998690369462 49 1.65759123585991e-06 3.31518247171982e-06 0.999998342408764 50 2.45731817003661e-06 4.91463634007322e-06 0.99999754268183 51 6.6209444607318e-06 1.32418889214636e-05 0.99999337905554 52 1.34776125874113e-05 2.69552251748226e-05 0.999986522387413 53 2.35102842487766e-05 4.70205684975531e-05 0.999976489715751 54 5.81673311023006e-05 0.000116334662204601 0.999941832668898 55 0.000137544464515300 0.000275088929030599 0.999862455535485 56 0.000293731117670433 0.000587462235340866 0.99970626888233 57 0.000460729946535534 0.000921459893071068 0.999539270053464 58 0.000700796155529409 0.00140159231105882 0.99929920384447 59 0.00156511950527490 0.00313023901054980 0.998434880494725 60 0.00244877301379792 0.00489754602759583 0.997551226986202 61 0.00401187058606415 0.00802374117212829 0.995988129413936 62 0.00561800968851951 0.0112360193770390 0.99438199031148 63 0.00917253429240064 0.0183450685848013 0.9908274657076 64 0.0202319229867375 0.040463845973475 0.979768077013263 65 0.0268261651133162 0.0536523302266325 0.973173834886684 66 0.0359522059379969 0.0719044118759939 0.964047794062003 67 0.0939984124106352 0.187996824821270 0.906001587589365 68 0.110957414738257 0.221914829476514 0.889042585261743 69 0.0851376153902979 0.170275230780596 0.914862384609702 70 0.0914390179273155 0.182878035854631 0.908560982072685 71 0.0787063883090191 0.157412776618038 0.921293611690981 72 0.0509658001627754 0.101931600325551 0.949034199837225 73 0.0573670992874812 0.114734198574962 0.942632900712519 74 0.0718050865475964 0.143610173095193 0.928194913452404 75 0.0464269301162701 0.0928538602325402 0.95357306988373 76 0.186064441000988 0.372128882001976 0.813935558999012 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 44 0.721311475409836 NOK 5% type I error level 48 0.78688524590164 NOK 10% type I error level 52 0.852459016393443 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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