WS7.3

*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: Sat, 21 Nov 2009 05:10:07 -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/21/t1258806170f43xt4g21hr7bae.htm/, Retrieved Sat, 21 Nov 2009 13:23:02 +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/21/t1258806170f43xt4g21hr7bae.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:

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269285 8.2 269829 8 270911 7.5 266844 6.8 271244 6.5 269907 6.6 271296 7.6 270157 8 271322 8.1 267179 7.7 264101 7.5 265518 7.6 269419 7.8 268714 7.8 272482 7.8 268351 7.5 268175 7.5 270674 7.1 272764 7.5 272599 7.5 270333 7.6 270846 7.7 270491 7.7 269160 7.9 274027 8.1 273784 8.2 276663 8.2 274525 8.2 271344 7.9 271115 7.3 270798 6.9 273911 6.6 273985 6.7 271917 6.9 273338 7 270601 7.1 273547 7.2 275363 7.1 281229 6.9 277793 7 279913 6.8 282500 6.4 280041 6.7 282166 6.6 290304 6.4 283519 6.3 287816 6.2 285226 6.5 287595 6.8 289741 6.8 289148 6.4 288301 6.1 290155 5.8 289648 6.1 288225 7.2 289351 7.3 294735 6.9 305333 6.1 309030 5.8 310215 6.2 321935 7.1 325734 7.7 320846 7.9 323023 7.7 319753 7.4 321753 7.5 320757 8 324479 8.1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 209353.868069024 + 5735.54723429517X[t] + 3983.78103921857M1[t] + 3985.98987757529M2[t] + 5357.06728336256M3[t] + 3780.10695867444M4[t] + 4567.97996731963M5[t] + 5422.22403977358M6[t] + 1522.45486384059M7[t] + 1953.34861000709M8[t] + 2057.72025932738M9[t] + 1986.24169349609M10[t] + 2914.60840423528M11[t] + 841.58801269033t + 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) 209353.868069024 15462.823805 13.5392 0 0 X 5735.54723429517 1969.645973 2.912 0.005212 0.002606 M1 3983.78103921857 5502.990624 0.7239 0.472233 0.236117 M2 3985.98987757529 5525.544563 0.7214 0.473791 0.236895 M3 5357.06728336256 5482.78641 0.9771 0.332891 0.166446 M4 3780.10695867444 5443.439219 0.6944 0.490387 0.245194 M5 4567.97996731963 5439.253765 0.8398 0.404714 0.202357 M6 5422.22403977358 5454.429364 0.9941 0.32461 0.162305 M7 1522.45486384059 5462.216272 0.2787 0.781521 0.39076 M8 1953.34861000709 5472.586249 0.3569 0.722533 0.361266 M9 2057.72025932738 5680.924276 0.3622 0.718605 0.359302 M10 1986.24169349609 5686.135583 0.3493 0.728213 0.364106 M11 2914.60840423528 5696.571216 0.5116 0.610987 0.305494 t 841.58801269033 61.010651 13.7941 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.888249380073616 R-squared 0.788986961201163 Adjusted R-squared 0.738187525934777 F-TEST (value) 15.5314120533782 F-TEST (DF numerator) 13 F-TEST (DF denominator) 54 p-value 7.84927678409986e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8977.46866398717 Sum Squared Residuals 4352126955.09506 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 269285 261210.724442154 8074.2755578462 2 269829 260907.411846341 8921.58815365855 3 270911 260252.303647672 10658.6963523285 4 266844 255502.048271667 11341.9517283329 5 271244 255410.845122714 15833.1548772859 6 269907 257680.231931288 12226.7680687121 7 271296 260357.598002340 10938.4019976596 8 270157 263924.298654915 6232.70134508474 9 271322 265443.813040355 5878.18695964458 10 267179 263919.703593496 3259.29640650361 11 264101 264542.548870067 -441.548870066871 12 265518 263043.083201951 2474.91679804856 13 269419 269015.561700719 403.438299280631 14 268714 269859.358551766 -1145.35855176644 15 272482 272072.023970244 409.976029755979 16 268351 269615.987487958 -1264.98748795769 17 268175 271245.448509293 -3070.44850929322 18 270674 270647.061700719 26.9382992805758 19 272764 269883.099431195 2880.90056880517 20 272599 271155.581190052 1443.41880994835 21 270333 272675.095575492 -2342.09557549180 22 270846 274018.759745780 -3172.75974578036 23 270491 275788.71446921 -5297.71446920987 24 269160 274862.803524524 -5702.80352452396 25 274027 280835.282023292 -6808.2820232919 26 273784 282252.633597768 -8468.63359776845 27 276663 284465.299016246 -7802.29901624605 28 274525 283729.926704248 -9204.92670424827 29 271344 283638.723555295 -12294.7235552953 30 271115 281893.227299862 -10778.2272998624 31 270798 276540.827242902 -5742.8272429017 32 273911 276092.64483147 -2181.64483146998 33 273985 277612.15921691 -3627.15921691013 34 271917 279529.378110628 -7612.3781106282 35 273338 281872.887557487 -8534.88755748723 36 270601 280373.421889372 -9772.4218893718 37 273547 285772.34566471 -12225.3456647102 38 275363 286042.587792328 -10679.5877923277 39 281229 287108.143763946 -5879.14376394631 40 277793 286946.326175378 -9153.32617537805 41 279913 287428.677749855 -7515.67774985453 42 282500 286830.290941281 -4330.29094128075 43 280041 285492.773948327 -5451.77394832664 44 282166 286191.700983754 -4025.70098375394 45 290304 285990.551198906 4313.44880109446 46 283519 286187.105922335 -2668.10592233506 47 287816 287383.505922335 432.494077664936 48 285226 287031.149701079 -1805.14970107867 49 287595 293577.182923276 -5982.18292327611 50 289741 294420.979774323 -4679.97977432315 51 289148 294339.426299083 -5191.4262990827 52 288301 291883.389816796 -3582.38981679636 53 290155 291792.186667843 -1637.18666784334 54 289648 295208.682923276 -5560.68292327616 55 288225 298459.603717758 -10234.6037177582 56 289351 300305.640200045 -10954.6402000445 57 294735 298957.380968337 -4222.38096833709 58 305333 295139.05262776 10193.94737224 59 309030 295188.343180901 13841.6568190990 60 310215 295409.541683074 14805.4583169259 61 321935 305396.903245849 16538.0967541514 62 325734 309682.028437473 16051.9715625272 63 320846 313041.803302809 7804.19669719057 64 323023 311159.321543953 11863.6784560474 65 319753 311068.118395000 8684.88160500044 66 321753 313337.505203573 8415.49479642665 67 320757 313147.097657478 7609.90234252172 68 324479 314993.134139765 9485.86586023536 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0136127042841586 0.0272254085683172 0.986387295715841 18 0.00314842678082165 0.0062968535616433 0.996851573219178 19 0.000926553539736921 0.00185310707947384 0.999073446460263 20 0.00032315919237547 0.00064631838475094 0.999676840807625 21 0.000134499853789380 0.000268999707578759 0.99986550014621 22 0.000107940942986832 0.000215881885973664 0.999892059057013 23 0.000388741641042712 0.000777483282085423 0.999611258358957 24 0.000168268808250272 0.000336537616500544 0.99983173119175 25 7.98350298557177e-05 0.000159670059711435 0.999920164970144 26 3.0192511435412e-05 6.0385022870824e-05 0.999969807488565 27 1.29134604938512e-05 2.58269209877024e-05 0.999987086539506 28 8.66212765085053e-06 1.73242553017011e-05 0.999991337872349 29 3.65358385010062e-06 7.30716770020125e-06 0.99999634641615 30 1.74393837761806e-06 3.48787675523612e-06 0.999998256061622 31 2.27324846482756e-06 4.54649692965513e-06 0.999997726751535 32 4.13727261753147e-06 8.27454523506293e-06 0.999995862727382 33 2.77690728167555e-06 5.5538145633511e-06 0.999997223092718 34 8.81442547307764e-07 1.76288509461553e-06 0.999999118557453 35 6.8750161209482e-07 1.37500322418964e-06 0.999999312498388 36 2.18355626330723e-07 4.36711252661446e-07 0.999999781644374 37 8.26575429268427e-08 1.65315085853685e-07 0.999999917342457 38 2.78229728818504e-08 5.56459457637008e-08 0.999999972177027 39 3.98039941130153e-08 7.96079882260305e-08 0.999999960196006 40 2.89059402668932e-08 5.78118805337863e-08 0.99999997109406 41 4.6528532325386e-08 9.3057064650772e-08 0.999999953471468 42 4.17072541309245e-07 8.3414508261849e-07 0.999999582927459 43 1.13542918955451e-06 2.27085837910902e-06 0.99999886457081 44 3.41279194283345e-05 6.8255838856669e-05 0.999965872080572 45 0.502661449166827 0.994677101666346 0.497338550833173 46 0.564045836442472 0.871908327115057 0.435954163557528 47 0.811200766853801 0.377598466292398 0.188799233146199 48 0.985606632787531 0.0287867344249369 0.0143933672124685 49 0.969794829765385 0.0604103404692296 0.0302051702346148 50 0.953615149968263 0.0927697000634734 0.0463848500317367 51 0.878020919185846 0.243958161628309 0.121979080814154 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.771428571428571 NOK 5% type I error level 29 0.828571428571429 NOK 10% type I error level 31 0.885714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/1009ix1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/1009ix1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/185v61258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/185v61258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/2c9d21258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/2c9d21258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/3vf3h1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/3vf3h1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/4jhpa1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/4jhpa1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/5jgw41258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/5jgw41258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/6yfcg1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/6yfcg1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/7no0o1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/7no0o1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/8dooo1258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/8dooo1258805402.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/9xt671258805402.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258806170f43xt4g21hr7bae/9xt671258805402.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

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

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