*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, 22 Nov 2009 08:35:40 -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/22/t12589042715i7zsx6szqwgh34.htm/, Retrieved Sun, 22 Nov 2009 16:38:03 +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/22/t12589042715i7zsx6szqwgh34.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:

ws7m1.1c

Dataseries X:

» Textbox « » Textfile « » CSV «

2756,76 0,86 2849,27 0,88 2921,44 0,88 2981,85 0,88 3080,58 0,87 3106,22 0,88 3119,31 0,87 3061,26 0,85 3097,31 0,84 3161,69 0,83 3257,16 0,86 3277,01 0,87 3295,32 0,85 3363,99 0,89 3494,17 0,98 3667,03 1,01 3813,06 1 3917,96 1,01 3895,51 1,05 3801,06 1 3570,12 0,99 3701,61 1,02 3862,27 1,11 3970,1 1,15 4138,52 1,18 4199,75 1,2 4290,89 1,22 4443,91 1,2 4502,64 1,23 4356,98 1,23 4591,27 1,21 4696,96 1,25 4621,4 1,2 4562,84 1,2 4202,52 1,21 4296,49 1,25 4435,23 1,23 4105,18 1,2 4116,68 1,18 3844,49 1,16 3720,98 1,12 3674,4 1,11 3857,62 1,1 3801,06 1,08 3504,37 1,01 3032,6 1,01 3047,03 0,99 2962,34 1,07 2197,82 1,13 2014,45 1,09 1862,83 0,95 1905,41 0,79 1810,99 0,73 1670,07 0,7 1864,44 0,65 2052,02 0,61 2029,6 0,53 2070,83 0,51 2293,41 0,41 2443,27 0,42

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation BEL20[t] = + 217.853883931972 + 3217.76011165303`Depositorente `[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) 217.853883931972 308.328354 0.7066 0.482666 0.241333 `Depositorente ` 3217.76011165303 307.584131 10.4614 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.808460902097025 R-squared 0.653609030219535 Adjusted R-squared 0.647636772119872 F-TEST (value) 109.440854583360 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 5.6621374255883e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 504.128264402531 Sum Squared Residuals 14740427.8042315 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2756.76 2985.12757995361 -228.367579953613 2 2849.27 3049.48278218664 -200.21278218664 3 2921.44 3049.48278218664 -128.042782186639 4 2981.85 3049.48278218664 -67.6327821866392 5 3080.58 3017.30518107011 63.2748189298911 6 3106.22 3049.48278218664 56.7372178133607 7 3119.31 3017.30518107011 102.004818929891 8 3061.26 2952.94997883705 108.310021162952 9 3097.31 2920.77237772052 176.537622279482 10 3161.69 2888.59477660399 273.095223396012 11 3257.16 2985.12757995358 272.032420046421 12 3277.01 3017.30518107011 259.704818929891 13 3295.32 2952.94997883705 342.370021162952 14 3363.99 3081.66038330317 282.329616696830 15 3494.17 3371.25879335194 122.911206648059 16 3667.03 3467.79159670153 199.238403298468 17 3813.06 3435.613995585 377.446004414998 18 3917.96 3467.79159670153 450.168403298468 19 3895.51 3596.50200116765 299.007998832347 20 3801.06 3435.613995585 365.446004414998 21 3570.12 3403.43639446847 166.683605531528 22 3701.61 3499.96919781806 201.640802181937 23 3862.27 3789.56760786684 72.7023921331647 24 3970.1 3918.27801233296 51.8219876670442 25 4138.52 4014.81081568255 123.709184317454 26 4199.75 4079.16601791561 120.583982084393 27 4290.89 4143.52122014867 147.368779851333 28 4443.91 4079.16601791561 364.743982084393 29 4502.64 4175.6988212652 326.941178734802 30 4356.98 4175.6988212652 181.281178734802 31 4591.27 4111.34361903214 479.926380967863 32 4696.96 4240.05402349826 456.905976501742 33 4621.4 4079.16601791561 542.233982084393 34 4562.84 4079.16601791561 483.673982084393 35 4202.52 4111.34361903214 91.176380967863 36 4296.49 4240.05402349826 56.4359765017413 37 4435.23 4175.6988212652 259.531178734802 38 4105.18 4079.16601791561 26.0139820843932 39 4116.68 4014.81081568255 101.869184317454 40 3844.49 3950.45561344949 -105.965613449486 41 3720.98 3821.74520898337 -100.765208983365 42 3674.4 3789.56760786684 -115.167607866835 43 3857.62 3757.39000675031 100.229993249695 44 3801.06 3693.03480451724 108.025195482756 45 3504.37 3467.79159670153 36.5784032984675 46 3032.6 3467.79159670153 -435.191596701533 47 3047.03 3403.43639446847 -356.406394468472 48 2962.34 3660.85720340071 -698.517203400714 49 2197.82 3853.9228100999 -1656.10281009989 50 2014.45 3725.21240563377 -1710.76240563377 51 1862.83 3274.72599000235 -1411.89599000235 52 1905.41 2759.88437213787 -854.474372137867 53 1810.99 2566.81876543869 -755.828765438685 54 1670.07 2470.28596208909 -800.215962089094 55 1864.44 2309.39795650644 -444.957956506443 56 2052.02 2180.68755204032 -128.667552040322 57 2029.6 1923.26674310808 106.333256891920 58 2070.83 1858.91154087502 211.91845912498 59 2293.41 1537.13552970972 756.274470290283 60 2443.27 1569.31313082625 873.956869173752 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0215406111855296 0.0430812223710592 0.97845938881447 6 0.00784797276341012 0.0156959455268202 0.99215202723659 7 0.00418923637557117 0.00837847275114234 0.99581076362443 8 0.00144367754060340 0.00288735508120681 0.998556322459397 9 0.000388673318353047 0.000777346636706095 0.999611326681647 10 0.000100338167156563 0.000200676334313126 0.999899661832844 11 8.19307435356172e-05 0.000163861487071234 0.999918069256464 12 7.67413700354303e-05 0.000153482740070861 0.999923258629965 13 4.09035299078307e-05 8.18070598156613e-05 0.999959096470092 14 7.08848603603402e-05 0.000141769720720680 0.99992911513964 15 4.8737334234757e-05 9.7474668469514e-05 0.999951262665765 16 2.00915820389891e-05 4.01831640779781e-05 0.99997990841796 17 1.28554557469427e-05 2.57109114938853e-05 0.999987144544253 18 7.997143236592e-06 1.5994286473184e-05 0.999992002856763 19 2.59974841886399e-06 5.19949683772799e-06 0.99999740025158 20 9.9169558632252e-07 1.98339117264504e-06 0.999999008304414 21 3.25875191564507e-07 6.51750383129015e-07 0.999999674124809 22 1.03405069918391e-07 2.06810139836782e-07 0.99999989659493 23 7.10877104598248e-08 1.42175420919650e-07 0.99999992891229 24 3.935311629854e-08 7.870623259708e-08 0.999999960646884 25 1.31886252679384e-08 2.63772505358768e-08 0.999999986811375 26 4.10756446128977e-09 8.21512892257953e-09 0.999999995892435 27 1.16580087724456e-09 2.33160175448912e-09 0.999999998834199 28 5.45172499191597e-10 1.09034499838319e-09 0.999999999454827 29 1.94410382653584e-10 3.88820765307168e-10 0.99999999980559 30 5.6644484066548e-11 1.13288968133096e-10 0.999999999943356 31 5.64217814987622e-11 1.12843562997524e-10 0.999999999943578 32 4.0610527753013e-11 8.1221055506026e-11 0.99999999995939 33 6.88593998951903e-11 1.37718799790381e-10 0.99999999993114 34 7.71948819115255e-11 1.54389763823051e-10 0.999999999922805 35 5.6058924593981e-11 1.12117849187962e-10 0.99999999994394 36 5.5014855234977e-11 1.10029710469954e-10 0.999999999944985 37 4.48856746474370e-11 8.97713492948739e-11 0.999999999955114 38 5.34049879731766e-11 1.06809975946353e-10 0.999999999946595 39 6.14793434596873e-11 1.22958686919375e-10 0.99999999993852 40 1.56930259873502e-10 3.13860519747003e-10 0.99999999984307 41 3.39373215722216e-10 6.78746431444433e-10 0.999999999660627 42 9.10237329605562e-10 1.82047465921112e-09 0.999999999089763 43 5.01985577535529e-09 1.00397115507106e-08 0.999999994980144 44 1.28255858960495e-07 2.5651171792099e-07 0.99999987174414 45 5.77539604271518e-06 1.15507920854304e-05 0.999994224603957 46 0.000239785221561518 0.000479570443123036 0.999760214778438 47 0.0103146625209057 0.0206293250418114 0.989685337479094 48 0.645433433260342 0.709133133479317 0.354566566739658 49 0.97517052843075 0.0496589431385007 0.0248294715692504 50 0.997702628180168 0.00459474363966458 0.00229737181983229 51 0.999309604248353 0.00138079150329469 0.000690395751647347 52 0.999665661874225 0.000668676251550858 0.000334338125775429 53 0.999131586895686 0.00173682620862758 0.000868413104313788 54 0.996722011780947 0.00655597643810526 0.00327798821905263 55 0.982848000419177 0.0343039991616463 0.0171519995808231 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 45 0.88235294117647 NOK 5% type I error level 50 0.980392156862745 NOK 10% type I error level 50 0.980392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/1053dm1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/1053dm1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/1b51u1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/1b51u1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/24zmk1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/24zmk1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/3oe4e1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/3oe4e1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/4kl1d1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/4kl1d1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/50i4p1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/50i4p1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/6hotv1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/6hotv1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/7oniu1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/7oniu1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/8stlj1258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/8stlj1258904136.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/9pew91258904136.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589042715i7zsx6szqwgh34/9pew91258904136.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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