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*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: Fri, 20 Nov 2009 04:27:26 -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/20/t12587165231dd23aviu1w2hz6.htm/, Retrieved Fri, 20 Nov 2009 12:28:56 +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/20/t12587165231dd23aviu1w2hz6.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:

» Textbox « » Textfile « » CSV «

1,58 0,55 1,59 0,55 1,6 0,55 1,6 0,55 1,6 0,55 1,6 0,56 1,61 0,56 1,61 0,56 1,62 0,56 1,63 0,56 1,63 0,55 1,63 0,56 1,63 0,55 1,63 0,55 1,64 0,56 1,64 0,55 1,64 0,55 1,65 0,55 1,65 0,55 1,65 0,53 1,65 0,53 1,65 0,53 1,66 0,53 1,67 0,54 1,68 0,54 1,68 0,54 1,68 0,55 1,68 0,55 1,69 0,54 1,7 0,55 1,7 0,56 1,71 0,58 1,73 0,59 1,73 0,6 1,73 0,6 1,74 0,6 1,74 0,59 1,74 0,6 1,75 0,6 1,78 0,62 1,82 0,65 1,83 0,68 1,84 0,73 1,85 0,78 1,86 0,78 1,86 0,82 1,87 0,82 1,87 0,81 1,87 0,83 1,87 0,85 1,87 0,86 1,87 0,85 1,87 0,85 1,88 0,82 1,88 0,8 1,87 0,81 1,87 0,8 1,87 0,8 1,87 0,8 1,87 0,8 1,87 0,79

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] = + 1.2035843418843 + 0.823909896346055X[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) 1.2035843418843 0.026673 45.1243 0 0 X 0.823909896346055 0.040904 20.1424 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.934366697063404 R-squared 0.873041124581175 Adjusted R-squared 0.870889279235094 F-TEST (value) 405.717411881327 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0376961676966682 Sum Squared Residuals 0.0838390624819047 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.58 1.65673478487464 -0.0767347848746352 2 1.59 1.65673478487463 -0.0667347848746309 3 1.6 1.65673478487463 -0.0567347848746306 4 1.6 1.65673478487463 -0.0567347848746306 5 1.6 1.65673478487463 -0.0567347848746306 6 1.6 1.66497388383809 -0.0649738838380912 7 1.61 1.66497388383809 -0.0549738838380912 8 1.61 1.66497388383809 -0.0549738838380912 9 1.62 1.66497388383809 -0.0449738838380911 10 1.63 1.66497388383809 -0.0349738838380914 11 1.63 1.65673478487463 -0.0267347848746308 12 1.63 1.66497388383809 -0.0349738838380914 13 1.63 1.65673478487463 -0.0267347848746308 14 1.63 1.65673478487463 -0.0267347848746308 15 1.64 1.66497388383809 -0.0249738838380913 16 1.64 1.65673478487463 -0.0167347848746308 17 1.64 1.65673478487463 -0.0167347848746308 18 1.65 1.65673478487463 -0.00673478487463078 19 1.65 1.65673478487463 -0.00673478487463078 20 1.65 1.64025658694771 0.00974341305229031 21 1.65 1.64025658694771 0.00974341305229031 22 1.65 1.64025658694771 0.00974341305229031 23 1.66 1.64025658694771 0.0197434130522903 24 1.67 1.64849568591117 0.0215043140888298 25 1.68 1.64849568591117 0.0315043140888298 26 1.68 1.64849568591117 0.0315043140888298 27 1.68 1.65673478487463 0.0232652151253692 28 1.68 1.65673478487463 0.0232652151253692 29 1.69 1.64849568591117 0.0415043140888298 30 1.7 1.65673478487463 0.0432652151253693 31 1.7 1.66497388383809 0.0350261161619087 32 1.71 1.68145208176501 0.0285479182349877 33 1.73 1.68969118072847 0.0403088192715272 34 1.73 1.69793027969193 0.0320697203080666 35 1.73 1.69793027969193 0.0320697203080666 36 1.74 1.69793027969193 0.0420697203080666 37 1.74 1.68969118072847 0.0503088192715272 38 1.74 1.69793027969193 0.0420697203080666 39 1.75 1.69793027969193 0.0520697203080666 40 1.78 1.71440847761885 0.0655915223811455 41 1.82 1.73912577450924 0.080874225490764 42 1.83 1.76384307139962 0.0661569286003823 43 1.84 1.80503856621692 0.0349614337830796 44 1.85 1.84623406103422 0.00376593896577684 45 1.86 1.84623406103422 0.0137659389657768 46 1.86 1.87919045688807 -0.0191904568880653 47 1.87 1.87919045688807 -0.0091904568880653 48 1.87 1.87095135792460 -0.000951357924604825 49 1.87 1.88742955585153 -0.0174295558515258 50 1.87 1.90390775377845 -0.033907753778447 51 1.87 1.91214685274191 -0.0421468527419075 52 1.87 1.90390775377845 -0.033907753778447 53 1.87 1.90390775377845 -0.033907753778447 54 1.88 1.87919045688807 0.000809543111934496 55 1.88 1.86271225896114 0.0172877410388555 56 1.87 1.87095135792460 -0.000951357924604825 57 1.87 1.86271225896114 0.00728774103885575 58 1.87 1.86271225896114 0.00728774103885575 59 1.87 1.86271225896114 0.00728774103885575 60 1.87 1.86271225896114 0.00728774103885575 61 1.87 1.85447315999768 0.0155268400023163 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0373098577562783 0.0746197155125565 0.962690142243722 6 0.0109667062701586 0.0219334125403172 0.989033293729841 7 0.00436461841112589 0.00872923682225177 0.995635381588874 8 0.00154471177885839 0.00308942355771678 0.998455288221142 9 0.00125935963169759 0.00251871926339518 0.998740640368302 10 0.00243007065199096 0.00486014130398192 0.99756992934801 11 0.0215886639376187 0.0431773278752373 0.978411336062381 12 0.0209184810960350 0.0418369621920699 0.979081518903965 13 0.0452408045643361 0.0904816091286723 0.954759195435664 14 0.0705979784736973 0.141195956947395 0.929402021526303 15 0.0976378243746222 0.195275648749244 0.902362175625378 16 0.178394979161777 0.356789958323553 0.821605020838223 17 0.27144872340234 0.54289744680468 0.72855127659766 18 0.418865231142182 0.837730462284364 0.581134768857818 19 0.557884616371058 0.884230767257884 0.442115383628942 20 0.58089152655073 0.83821694689854 0.41910847344927 21 0.584340539719559 0.831318920560881 0.415659460280441 22 0.60019965360673 0.79960069278654 0.39980034639327 23 0.606994969007861 0.786010061984279 0.393005030992139 24 0.700140329725125 0.599719340549751 0.299859670274875 25 0.789775179576204 0.420449640847593 0.210224820423796 26 0.844123731895051 0.311752536209898 0.155876268104949 27 0.93234746472215 0.135305070555701 0.0676525352778506 28 0.97330616716585 0.053387665668301 0.0266938328341505 29 0.981025543155336 0.0379489136893272 0.0189744568446636 30 0.993155647106122 0.0136887057877565 0.00684435289387826 31 0.998611526377696 0.00277694724460823 0.00138847362230411 32 0.99986372114551 0.000272557708979529 0.000136278854489765 33 0.999955067976108 8.9864047784517e-05 4.49320238922585e-05 34 0.99997445517949 5.10896410206127e-05 2.55448205103063e-05 35 0.999986581392286 2.68372154272965e-05 1.34186077136483e-05 36 0.999989639625238 2.07207495246542e-05 1.03603747623271e-05 37 0.999992914049517 1.41719009667598e-05 7.08595048337989e-06 38 0.999998989893654 2.02021269216572e-06 1.01010634608286e-06 39 0.999999981653216 3.66935686114807e-08 1.83467843057403e-08 40 0.999999999640754 7.18490896025335e-10 3.59245448012667e-10 41 0.999999998787959 2.42408250501001e-09 1.21204125250500e-09 42 0.999999996977052 6.04589568283019e-09 3.02294784141510e-09 43 0.999999999225947 1.5481066279839e-09 7.7405331399195e-10 44 0.999999999967446 6.51081611684099e-11 3.25540805842050e-11 45 0.999999999976119 4.77624147245039e-11 2.38812073622519e-11 46 0.999999999995584 8.831264032374e-12 4.415632016187e-12 47 0.999999999958654 8.26929746931182e-11 4.13464873465591e-11 48 0.99999999960035 7.99298770669757e-10 3.99649385334879e-10 49 0.999999996291506 7.41698701143813e-09 3.70849350571907e-09 50 0.999999967393059 6.52138825249516e-08 3.26069412624758e-08 51 0.99999972301037 5.53979261390577e-07 2.76989630695289e-07 52 0.999997763063497 4.47387300538111e-06 2.23693650269056e-06 53 0.999993115092083 1.37698158349266e-05 6.88490791746328e-06 54 0.99996247498694 7.50500261204738e-05 3.75250130602369e-05 55 1 8.27832263769462e-56 4.13916131884731e-56 56 1 0 0 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.576923076923077 NOK 5% type I error level 35 0.673076923076923 NOK 10% type I error level 38 0.730769230769231 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/10crtd1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/10crtd1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/1b30j1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/1b30j1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/2utiu1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/2utiu1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/3g5vy1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/3g5vy1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/4sfv11258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/4sfv11258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/5shci1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/5shci1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/64o7r1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/64o7r1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/7ycky1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/7ycky1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/8e2hu1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/8e2hu1258716442.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/92e8u1258716442.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587165231dd23aviu1w2hz6/92e8u1258716442.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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