Met seizonaliteit en zonder lineaire trend

*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, 14 Dec 2008 06:44:09 -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/14/t1229262360pr7ynwspmuj1nq6.htm/, Retrieved Sun, 14 Dec 2008 14:46:10 +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/2008/Dec/14/t1229262360pr7ynwspmuj1nq6.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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11554.5 180144 13182.1 173666 14800.1 165688 12150.7 161570 14478.2 156145 13253.9 153730 12036.8 182698 12653.2 200765 14035.4 176512 14571.4 166618 15400.9 158644 14283.2 159585 14485.3 163095 14196.3 159044 15559.1 155511 13767.4 153745 14634 150569 14381.1 150605 12509.9 179612 12122.3 194690 13122.3 189917 13908.7 184128 13456.5 175335 12441.6 179566 12953 181140 13057.2 177876 14350.1 175041 13830.2 169292 13755.5 166070 13574.4 166972 12802.6 206348 11737.3 215706 13850.2 202108 15081.8 195411 13653.3 193111 14019.1 195198 13962 198770 13768.7 194163 14747.1 190420 13858.1 189733 13188 186029 13693.1 191531 12970 232571 11392.8 243477 13985.2 227247 14994.7 217859 13584.7 208679 14257.8 213188 13553.4 216234 14007.3 213586 16535.8 209465 14721.4 204045 13664.6 200237 16805.9 203666 13829.4 241476 13735.6 260307 15870.5 243324 15962.4 244460 15744.1 233575 16083.7 237217 14863.9 235243 15533.1 230354 17473.1 227184 15925.5 221678 15573.7 217142 17495 219452 14155.8 256446 14913.9 265845 17250.4 248624 15879.8 241114 17647.8 229245 17749.9 231805 17111.8 219277 16934.8 219313 20280 212610 16238.2 214771 17896.1 211142 18089.3 211457 15660 240048 16162.4 240636 17850.1 230580 18520.4 208795 18524.7 197922 16843.7 194596

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 invoer[t] = + 7371.69243819927 + 0.0383211999621623werkloosheid[t] -933.426094036105M1[t] -477.97503686183M2[t] + 1564.20482862189M3[t] -213.766242634938M4[t] + 322.29561435927M5[t] + 853.204275156608M6[t] -2374.47138970787M7[t] -3002.76243389198M8[t] -545.897832049348M9[t] + 204.327103683867M10[t] + 413.453664606557M11[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) 7371.69243819927 1297.536623 5.6813 0 0 werkloosheid 0.0383211999621623 0.005893 6.5027 0 0 M1 -933.426094036105 737.984756 -1.2648 0.210068 0.105034 M2 -477.97503686183 738.735671 -0.647 0.519707 0.259853 M3 1564.20482862189 740.555506 2.1122 0.038188 0.019094 M4 -213.766242634938 742.284475 -0.288 0.774198 0.387099 M5 322.29561435927 745.169115 0.4325 0.666679 0.33334 M6 853.204275156608 744.02954 1.1467 0.255341 0.127671 M7 -2374.47138970787 745.674773 -3.1843 0.002155 0.001078 M8 -3002.76243389198 758.780343 -3.9574 0.000178 8.9e-05 M9 -545.897832049348 743.336384 -0.7344 0.46513 0.232565 M10 204.327103683867 738.91242 0.2765 0.782949 0.391474 M11 413.453664606557 737.94481 0.5603 0.577054 0.288527 Multiple Linear Regression - Regression Statistics Multiple R 0.71538090110062 R-squared 0.511769833659534 Adjusted R-squared 0.42925205906678 F-TEST (value) 6.20193450665924 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 2.49468904311989e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1380.37564794128 Sum Squared Residuals 135286021.989481 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 13341.6005901469 -1787.10059014687 2 13182.1 13548.8069139663 -366.706913966309 3 14800.1 15285.2602461519 -485.160246151902 4 12150.7 13349.4824734509 -1198.78247345089 5 14478.2 13677.6518206504 800.548179349631 6 13253.9 14116.0147835391 -862.114783539086 7 12036.8 11998.4276391785 38.3723608214787 8 12653.2 12062.4857147108 590.714285289198 9 14035.4 13589.9462538711 445.453746128889 10 14571.4 13961.0212371787 610.378762821309 11 15400.9 13864.5745496031 1536.3254503969 12 14283.2 13487.1811341609 796.018865839063 13 14485.3 12688.2624519920 1797.03754800798 14 14196.3 12988.4743281196 1207.82567188042 15 15559.1 14895.2653941370 663.834605863022 16 13767.4 13049.6190837470 717.780916253029 17 14634 13463.9728096614 1170.02719033865 18 14381.1 13996.2610336573 384.838966342672 19 12509.9 11880.1684160953 629.731583904712 20 12122.3 11829.6844249407 292.615575059331 21 13122.3 14103.6419393639 -981.341939363897 22 13908.7 14632.0254485162 -723.325448516153 23 13456.5 14504.1936981715 -1047.69369817155 24 12441.6 14252.8770306049 -1811.27703060490 25 12953 13379.7685053092 -426.768505309241 26 13057.2 13710.139165807 -652.939165807017 27 14350.1 15643.678429398 -1293.57842939801 28 13830.2 13645.3987795587 184.801220441293 29 13755.5 14057.9897302748 -302.48973027483 30 13574.4 14623.4641134380 -1049.06411343804 31 12802.6 12904.7240182837 -102.124018283659 32 11737.3 12635.0427633455 -897.742763345471 33 13850.2 14570.8156881026 -720.615688102616 34 15081.8 15064.4035476892 17.3964523107681 35 13653.3 15185.3913486989 -1532.09134869895 36 14019.1 14851.9140284134 -832.814028413423 37 13962 14055.3712606422 -93.3712606421627 38 13768.7 14334.2765495908 -565.576549590754 39 14747.1 16233.0201636161 -1485.92016361610 40 13858.1 14428.7224279853 -570.622427985267 41 13188 14822.8425603196 -1634.84256031963 42 13693.1 15564.5944633088 -1871.49446330878 43 12970 13909.6208448914 -939.62084489144 44 11392.8 13699.2608074947 -2306.46080749468 45 13985.2 15534.1723339514 -1548.97233395141 46 14994.7 15924.6378444399 -929.93784443985 47 13584.7 15781.9757897099 -2197.27578970989 48 14257.8 15541.3124157327 -1283.51241573272 49 13553.4 14724.6126967814 -1171.21269678137 50 14007.3 15078.5892164558 -1071.28921645583 51 16535.8 16962.8474168955 -427.047416895485 52 14721.4 14977.1754418437 -255.775441843735 53 13664.6 15367.3101693820 -1702.71016938203 54 16805.9 16029.6222248496 776.27777515038 55 13829.4 14250.8711305545 -421.471130554497 56 13735.6 14344.2066028579 -608.606602857872 57 15870.5 16150.2622657431 -279.762265743099 58 15962.4 16944.0200846333 -981.62008463333 59 15744.1 16736.0203839679 -991.920383967882 60 16083.7 16462.1325296235 -378.432529623520 61 14863.9 15453.0603868621 -589.160386862108 62 15533.1 15721.1590974214 -188.059097421370 63 17473.1 17641.8607590250 -168.760759025038 64 15925.5 15652.8931607765 272.606839223458 65 15573.7 16015.1300547424 -441.430054742382 66 17495 16634.5606874523 860.439312547684 67 14155.8 14824.5394939881 -668.739493988066 68 14913.9 14556.4294082483 357.470591751673 69 17250.4 16353.3646255426 897.035374457443 70 15879.8 16815.7973495599 -935.997349559935 71 17647.8 16570.0895881317 1077.71041186828 72 17749.9 16254.7381954283 1495.16180457170 73 17111.8 14841.2241082662 2270.57589173377 74 16934.8 15298.0547286391 1636.74527136086 75 20280 17083.3675907765 3196.63240922352 76 16238.2 15388.2086326379 849.991367362113 77 17896.1 15785.2028549694 2110.89714503059 78 18089.3 16328.1826937548 1761.11730624517 79 15660 14196.1484570085 1463.85154299147 80 16162.4 13590.3902784022 2572.00972159782 81 17850.1 15661.8968934253 2188.20310657470 82 18520.4 15577.2944879828 2943.10551201719 83 18524.7 15369.7546417169 3154.94535828309 84 16843.7 14828.8446660362 2014.8553339638 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0741736131168315 0.148347226233663 0.925826386883168 17 0.0313353382182023 0.0626706764364046 0.968664661781798 18 0.0157500493159197 0.0315000986318395 0.98424995068408 19 0.00513745470236607 0.0102749094047321 0.994862545297634 20 0.00637965902802705 0.0127593180560541 0.993620340971973 21 0.00280485084714107 0.00560970169428215 0.99719514915286 22 0.00210203130564584 0.00420406261129169 0.997897968694354 23 0.00081844569490836 0.00163689138981672 0.999181554305092 24 0.000296030292637191 0.000592060585274383 0.999703969707363 25 0.000190526405323752 0.000381052810647505 0.999809473594676 26 7.18662392613262e-05 0.000143732478522652 0.999928133760739 27 2.86129131400833e-05 5.72258262801665e-05 0.99997138708686 28 0.000166320501912835 0.000332641003825669 0.999833679498087 29 6.23463179348832e-05 0.000124692635869766 0.999937653682065 30 3.7650310426389e-05 7.5300620852778e-05 0.999962349689574 31 0.000170645215508417 0.000341290431016835 0.999829354784492 32 7.1249335935969e-05 0.000142498671871938 0.999928750664064 33 5.75289329440695e-05 0.000115057865888139 0.999942471067056 34 8.3479845603591e-05 0.000166959691207182 0.999916520154396 35 4.08804985937089e-05 8.17609971874178e-05 0.999959119501406 36 4.71599670497653e-05 9.43199340995307e-05 0.99995284003295 37 5.86602239743347e-05 0.000117320447948669 0.999941339776026 38 3.21720331839964e-05 6.43440663679929e-05 0.999967827966816 39 2.14625255823343e-05 4.29250511646686e-05 0.999978537474418 40 1.53486788233930e-05 3.06973576467860e-05 0.999984651321177 41 1.14429923981878e-05 2.28859847963756e-05 0.999988557007602 42 1.49623585264583e-05 2.99247170529166e-05 0.999985037641474 43 1.18562195488710e-05 2.37124390977420e-05 0.99998814378045 44 1.97025779468890e-05 3.94051558937779e-05 0.999980297422053 45 3.07672464943769e-05 6.15344929887539e-05 0.999969232753506 46 2.74269670281574e-05 5.48539340563148e-05 0.999972573032972 47 0.000112249971440955 0.00022449994288191 0.99988775002856 48 0.000259267608218124 0.000518535216436247 0.999740732391782 49 0.000444476638543089 0.000888953277086179 0.999555523361457 50 0.000796652974985232 0.00159330594997046 0.999203347025015 51 0.00435970115404395 0.0087194023080879 0.995640298845956 52 0.00773914247443273 0.0154782849488655 0.992260857525567 53 0.068881899057065 0.13776379811413 0.931118100942935 54 0.232239983827588 0.464479967655175 0.767760016172413 55 0.260345970477885 0.52069194095577 0.739654029522115 56 0.305792022073568 0.611584044147135 0.694207977926432 57 0.383707529107244 0.767415058214488 0.616292470892756 58 0.311210298467466 0.622420596934932 0.688789701532534 59 0.33343227769306 0.66686455538612 0.66656772230694 60 0.291230962474115 0.58246192494823 0.708769037525885 61 0.301820338487695 0.60364067697539 0.698179661512305 62 0.265998937815347 0.531997875630695 0.734001062184653 63 0.432023897066941 0.864047794133883 0.567976102933059 64 0.34708238338008 0.69416476676016 0.65291761661992 65 0.516554528248581 0.966890943502838 0.483445471751419 66 0.441268999049167 0.882537998098334 0.558731000950833 67 0.373625665129375 0.747251330258749 0.626374334870625 68 0.270049374188658 0.540098748377316 0.729950625811342 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.584905660377358 NOK 5% type I error level 35 0.660377358490566 NOK 10% type I error level 36 0.679245283018868 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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