Model 1

*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, 19 Dec 2009 09:00:55 -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/Dec/19/t1261238645uszehl4irauvjnq.htm/, Retrieved Sat, 19 Dec 2009 17:04:17 +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/Dec/19/t1261238645uszehl4irauvjnq.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 «

100,0 100,0 95,3 100,6 90,7 114,2 88,4 91,5 86,0 94,7 86,0 110,6 95,3 71,3 95,3 104,1 88,4 112,3 86,0 110,2 81,4 112,9 83,7 95,1 95,3 103,1 88,4 101,9 86,0 100,4 83,7 106,9 76,7 100,7 79,1 114,3 86,0 73,3 86,0 105,9 79,1 113,9 76,7 112,1 69,8 117,5 69,8 97,5 76,7 112,3 69,8 106,9 67,4 120,9 65,1 92,7 58,1 110,9 60,5 116,5 65,1 77,1 62,8 113,1 55,8 115,9 51,2 123,5 48,8 123,6 48,8 101,5 53,5 121,0 48,8 112,2 46,5 126,0 44,2 101,8 39,5 117,9 41,9 122,2 48,8 82,7 46,5 120,5 41,9 120,3 39,5 134,2 37,2 128,2 37,2 100,5 41,9 126,0 39,5 122,9 39,5 106,1 34,9 130,4 34,9 121,3 34,9 126,1 41,9 88,7 41,9 118,7 39,5 129,3 39,5 136,2 41,9 123,0 46,5 103,5

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 Werkloosheid[t] = + 147.452825345231 -0.766966107144957Productie[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) 147.452825345231 18.032791 8.1769 0 0 Productie -0.766966107144957 0.162586 -4.7173 1.5e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.526577470275251 R-squared 0.277283832201483 Adjusted R-squared 0.264823208618750 F-TEST (value) 22.2528054362962 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.54792737370180e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 17.9423454241922 Sum Squared Residuals 18671.8100406197 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 70.756214630735 29.2437853692649 2 95.3 70.2960349664482 25.0039650335518 3 90.7 59.8652959092769 30.8347040907231 4 88.4 77.2754265414674 11.1245734585326 5 86 74.8211349986035 11.1788650013965 6 86 62.6263738949987 23.3736261050013 7 95.3 92.7681419057955 2.53185809420445 8 95.3 67.611653591441 27.6883464085590 9 88.4 61.3225315128523 27.0774684871477 10 86 62.9331603378567 23.0668396621433 11 81.4 60.8623518485653 20.5376481514347 12 83.7 74.5143485557456 9.18565144425444 13 95.3 68.3786196985859 26.9213803014141 14 88.4 69.2989790271598 19.1010209728402 15 86 70.4494281878773 15.5505718121227 16 83.7 65.4641484914351 18.2358515085649 17 76.7 70.2193383557338 6.4806616442662 18 79.1 59.7885992985624 19.3114007014376 19 86 91.2342096915056 -5.23420969150563 20 86 66.23111459858 19.7688854014200 21 79.1 60.0953857414204 19.0046142585796 22 76.7 61.4759247342813 15.2240752657187 23 69.8 57.3343077556985 12.4656922443015 24 69.8 72.6736298985977 -2.87362989859767 25 76.7 61.3225315128523 15.3774684871477 26 69.8 65.4641484914351 4.33585150856493 27 67.4 54.7266229914057 12.6733770085943 28 65.1 76.3550672128935 -11.2550672128935 29 58.1 62.3962840628552 -4.29628406285524 30 60.5 58.1012738628435 2.39872613715651 31 65.1 88.3197384843548 -23.2197384843548 32 62.8 60.7089586271363 2.09104137286365 33 55.8 58.5614535271305 -2.76145352713046 34 51.2 52.7325111128288 -1.53251111282878 35 48.8 52.6558145021143 -3.8558145021143 36 48.8 69.6057654700178 -20.8057654700178 37 53.5 54.6499263806912 -1.14992638069118 38 48.8 61.3992281235668 -12.5992281235668 39 46.5 50.8150958449664 -4.31509584496639 40 44.2 69.3756756378744 -25.1756756378743 41 39.5 57.0275213128405 -17.5275213128405 42 41.9 53.7295670521172 -11.8295670521172 43 48.8 84.024728284343 -35.224728284343 44 46.5 55.0334094342637 -8.53340943426366 45 41.9 55.1868026556927 -13.2868026556927 46 39.5 44.5259737663778 -5.02597376637775 47 37.2 49.1277704092475 -11.9277704092475 48 37.2 70.3727315771628 -33.1727315771628 49 41.9 50.8150958449664 -8.9150958449664 50 39.5 53.1926907771158 -13.6926907771158 51 39.5 66.0777213771511 -26.5777213771510 52 34.9 47.4404449735286 -12.5404449735286 53 34.9 54.4198365485477 -19.5198365485477 54 34.9 50.7383992342519 -15.8383992342519 55 41.9 79.4229316414733 -37.5229316414733 56 41.9 56.4139484271246 -14.5139484271246 57 39.5 48.284107691388 -8.78410769138803 58 39.5 42.9920415520878 -3.49204155208784 59 41.9 53.1159941664013 -11.2159941664013 60 46.5 68.0718332557279 -21.5718332557279 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0601791254152831 0.120358250830566 0.939820874584717 6 0.0312021199802574 0.0624042399605148 0.968797880019743 7 0.0101345060613526 0.0202690121227052 0.989865493938647 8 0.00444894968361483 0.00889789936722966 0.995551050316385 9 0.0017206790198681 0.0034413580397362 0.998279320980132 10 0.000837254328531323 0.00167450865706265 0.999162745671469 11 0.000790557322948697 0.00158111464589739 0.99920944267705 12 0.00063579377681244 0.00127158755362488 0.999364206223188 13 0.000541330492686747 0.00108266098537349 0.999458669507313 14 0.000280304723678276 0.000560609447356552 0.999719695276322 15 0.000177880265723603 0.000355760531447205 0.999822119734276 16 0.000149363726783377 0.000298727453566753 0.999850636273217 17 0.000532585050485208 0.00106517010097042 0.999467414949515 18 0.000679788530244582 0.00135957706048916 0.999320211469755 19 0.000583703464855733 0.00116740692971147 0.999416296535144 20 0.000742248538249532 0.00148449707649906 0.99925775146175 21 0.00148853449448395 0.0029770689889679 0.998511465505516 22 0.00388832210379440 0.00777664420758881 0.996111677896206 23 0.0160388910701496 0.0320777821402992 0.98396110892985 24 0.0607043187221544 0.121408637444309 0.939295681277846 25 0.143998665995901 0.287997331991802 0.856001334004099 26 0.31375633316477 0.62751266632954 0.68624366683523 27 0.571337324736338 0.857325350527323 0.428662675263662 28 0.817795953201965 0.36440809359607 0.182204046798035 29 0.93407281214796 0.131854375704082 0.0659271878520409 30 0.979444860358079 0.0411102792838428 0.0205551396419214 31 0.995949161214997 0.00810167757000591 0.00405083878500296 32 0.999755743358166 0.000488513283667132 0.000244256641833566 33 0.999973619509203 5.27609815948624e-05 2.63804907974312e-05 34 0.99999409106147 1.18178770614830e-05 5.90893853074151e-06 35 0.99999769751898 4.60496203963822e-06 2.30248101981911e-06 36 0.999999078005395 1.84398921055317e-06 9.21994605276584e-07 37 0.999999955010964 8.99780723324759e-08 4.49890361662379e-08 38 0.99999998512879 2.97424183071219e-08 1.48712091535610e-08 39 0.999999993390318 1.32193639439577e-08 6.60968197197883e-09 40 0.999999991820252 1.63594966251375e-08 8.17974831256877e-09 41 0.999999982457516 3.50849681954027e-08 1.75424840977013e-08 42 0.999999952828186 9.43436281033805e-08 4.71718140516903e-08 43 0.999999962861936 7.42761288201526e-08 3.71380644100763e-08 44 0.999999979941111 4.01177777049984e-08 2.00588888524992e-08 45 0.99999993668441 1.26631179211957e-07 6.33155896059784e-08 46 0.999999717693757 5.64612485211656e-07 2.82306242605828e-07 47 0.99999881801197 2.36397606110245e-06 1.18198803055122e-06 48 0.999998384347466 3.23130506733471e-06 1.61565253366736e-06 49 0.999994633363043 1.07332739143379e-05 5.36663695716896e-06 50 0.999973135673706 5.37286525871199e-05 2.68643262935599e-05 51 0.999898830226206 0.000202339547588456 0.000101169773794228 52 0.999660546339949 0.00067890732010263 0.000339453660051315 53 0.999422190342988 0.00115561931402391 0.000577809657011953 54 0.999457998059345 0.00108400388130924 0.00054200194065462 55 0.999875336966088 0.000249326067825026 0.000124663033912513 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 40 0.784313725490196 NOK 5% type I error level 43 0.843137254901961 NOK 10% type I error level 44 0.862745098039216 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/10dofm1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/10dofm1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/175fb1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/175fb1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/206cu1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/206cu1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/3y5xc1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/3y5xc1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/4odf51261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/4odf51261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/52ux91261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/52ux91261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/6t3vj1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/6t3vj1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/7catn1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/7catn1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/8ibey1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/8ibey1261238451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/9uo4i1261238451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261238645uszehl4irauvjnq/9uo4i1261238451.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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