Multiple regression 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: Tue, 21 Dec 2010 16:31:40 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry.htm/, Retrieved Tue, 21 Dec 2010 17:29:48 +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/2010/Dec/21/t1292948977eiwcsbau7wtezry.htm/}, year = {2010}, } @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 = {2010}, 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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1038.00 0 934.00 0 988.00 0 870.00 0 854.00 0 834.00 0 872.00 0 954.00 0 870.00 0 1238.00 0 1082.00 0 1053.00 0 934.00 0 787.00 0 1081.00 0 908.00 0 995.00 0 825.00 0 822.00 0 856.00 0 887.00 0 1094.00 0 990.00 0 936.00 0 1097.00 0 918.00 0 926.00 0 907.00 0 899.00 0 971.00 0 1087.00 0 1000.00 0 1071.00 0 1190.00 0 1116.00 0 1070.00 0 1314.00 0 1068.00 0 1185.00 0 1215.00 0 1145.00 0 1251.00 1 1363.00 1 1368.00 1 1535.00 1 1853.00 1 1866.00 1 2023.00 1 1373.00 1 1968.00 1 1424.00 1 1160.00 1 1243.00 1 1375.00 1 1539.00 1 1773.00 1 1906.00 1 2076.00 1 2004.00 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Asielaanvragen[t] = + 997.09756097561 + 619.569105691057Verandering[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) 997.09756097561 31.446538 31.7077 0 0 Verandering 619.569105691057 56.932806 10.8825 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.821632729798222 R-squared 0.675080342675679 Adjusted R-squared 0.66937999781034 F-TEST (value) 118.427982626194 F-TEST (DF numerator) 1 F-TEST (DF denominator) 57 p-value 1.55431223447522e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 201.356087207142 Sum Squared Residuals 2311023.60975610 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1038 997.097560975608 40.9024390243918 2 934 997.09756097561 -63.09756097561 3 988 997.09756097561 -9.0975609756098 4 870 997.09756097561 -127.097560975610 5 854 997.09756097561 -143.097560975610 6 834 997.09756097561 -163.097560975610 7 872 997.09756097561 -125.097560975610 8 954 997.09756097561 -43.0975609756098 9 870 997.09756097561 -127.097560975610 10 1238 997.09756097561 240.90243902439 11 1082 997.09756097561 84.9024390243902 12 1053 997.09756097561 55.9024390243902 13 934 997.09756097561 -63.0975609756098 14 787 997.09756097561 -210.097560975610 15 1081 997.09756097561 83.9024390243902 16 908 997.09756097561 -89.0975609756098 17 995 997.09756097561 -2.09756097560980 18 825 997.09756097561 -172.097560975610 19 822 997.09756097561 -175.097560975610 20 856 997.09756097561 -141.097560975610 21 887 997.09756097561 -110.097560975610 22 1094 997.09756097561 96.9024390243902 23 990 997.09756097561 -7.0975609756098 24 936 997.09756097561 -61.0975609756098 25 1097 997.09756097561 99.9024390243902 26 918 997.09756097561 -79.0975609756098 27 926 997.09756097561 -71.0975609756098 28 907 997.09756097561 -90.0975609756098 29 899 997.09756097561 -98.0975609756098 30 971 997.09756097561 -26.0975609756098 31 1087 997.09756097561 89.9024390243902 32 1000 997.09756097561 2.90243902439020 33 1071 997.09756097561 73.9024390243902 34 1190 997.09756097561 192.902439024390 35 1116 997.09756097561 118.902439024390 36 1070 997.09756097561 72.9024390243902 37 1314 997.09756097561 316.90243902439 38 1068 997.09756097561 70.9024390243902 39 1185 997.09756097561 187.902439024390 40 1215 997.09756097561 217.902439024390 41 1145 997.09756097561 147.902439024390 42 1251 1616.66666666667 -365.666666666667 43 1363 1616.66666666667 -253.666666666667 44 1368 1616.66666666667 -248.666666666667 45 1535 1616.66666666667 -81.6666666666667 46 1853 1616.66666666667 236.333333333333 47 1866 1616.66666666667 249.333333333333 48 2023 1616.66666666667 406.333333333333 49 1373 1616.66666666667 -243.666666666667 50 1968 1616.66666666667 351.333333333333 51 1424 1616.66666666667 -192.666666666667 52 1160 1616.66666666667 -456.666666666667 53 1243 1616.66666666667 -373.666666666667 54 1375 1616.66666666667 -241.666666666667 55 1539 1616.66666666667 -77.6666666666667 56 1773 1616.66666666667 156.333333333333 57 1906 1616.66666666667 289.333333333333 58 2076 1616.66666666667 459.333333333333 59 2004 1616.66666666667 387.333333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0938819578920843 0.187763915784169 0.906118042107916 6 0.05648005812182 0.11296011624364 0.94351994187818 7 0.0233109275414465 0.046621855082893 0.976689072458554 8 0.00882129243637188 0.0176425848727438 0.991178707563628 9 0.00344320224278191 0.00688640448556381 0.996556797757218 10 0.0671917305262391 0.134383461052478 0.932808269473761 11 0.0521191644794571 0.104238328958914 0.947880835520543 12 0.0333661863602574 0.0667323727205149 0.966633813639743 13 0.0177683978250942 0.0355367956501883 0.982231602174906 14 0.0197129793140922 0.0394259586281844 0.980287020685908 15 0.0146989890361809 0.0293979780723619 0.98530101096382 16 0.00821369557401359 0.0164273911480272 0.991786304425986 17 0.00422076132923138 0.00844152265846277 0.995779238670769 18 0.0035112015603212 0.0070224031206424 0.996488798439679 19 0.00289935932707816 0.00579871865415632 0.997100640672922 20 0.00189895282601823 0.00379790565203647 0.998101047173982 21 0.00106775823354265 0.00213551646708530 0.998932241766457 22 0.000929130328589528 0.00185826065717906 0.99907086967141 23 0.00046489688221698 0.00092979376443396 0.999535103117783 24 0.000225434061125698 0.000450868122251397 0.999774565938874 25 0.000184692072002824 0.000369384144005648 0.999815307927997 26 9.30603273763517e-05 0.000186120654752703 0.999906939672624 27 4.54648106969175e-05 9.0929621393835e-05 0.999954535189303 28 2.39040700737934e-05 4.78081401475869e-05 0.999976095929926 29 1.33633315097571e-05 2.67266630195142e-05 0.99998663666849 30 6.2260742487436e-06 1.24521484974872e-05 0.999993773925751 31 4.60034550324888e-06 9.20069100649776e-06 0.999995399654497 32 2.17587515360138e-06 4.35175030720276e-06 0.999997824124846 33 1.34816970237509e-06 2.69633940475018e-06 0.999998651830298 34 2.60830042896943e-06 5.21660085793885e-06 0.999997391699571 35 1.92021073006487e-06 3.84042146012974e-06 0.99999807978927 36 1.04063733986296e-06 2.08127467972592e-06 0.99999895936266 37 7.31604522236722e-06 1.46320904447344e-05 0.999992683954778 38 3.67519543911415e-06 7.35039087822831e-06 0.99999632480456 39 3.29438796740728e-06 6.58877593481456e-06 0.999996705612033 40 3.46322326126837e-06 6.92644652253674e-06 0.99999653677674 41 2.02442411176888e-06 4.04884822353776e-06 0.999997975575888 42 2.28036223255080e-06 4.56072446510159e-06 0.999997719637767 43 1.87887817153372e-06 3.75775634306743e-06 0.999998121121829 44 1.59180058852883e-06 3.18360117705766e-06 0.999998408199412 45 1.26002286395255e-06 2.5200457279051e-06 0.999998739977136 46 9.37072596379606e-06 1.87414519275921e-05 0.999990629274036 47 2.33752940848357e-05 4.67505881696714e-05 0.999976624705915 48 0.000207476766261680 0.000414953532523361 0.999792523233738 49 0.000222778022852694 0.000445556045705388 0.999777221977147 50 0.000612906506635482 0.00122581301327096 0.999387093493365 51 0.000435507564193776 0.000871015128387552 0.999564492435806 52 0.00480186833653764 0.00960373667307528 0.995198131663462 53 0.0343659523209757 0.0687319046419513 0.965634047679024 54 0.156311327696848 0.312622655393697 0.843688672303152 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 37 0.74 NOK 5% type I error level 43 0.86 NOK 10% type I error level 45 0.9 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/10dsqh1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/10dsqh1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/1orb51292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/1orb51292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/2orb51292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/2orb51292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/3hiaq1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/3hiaq1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/4hiaq1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/4hiaq1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/5hiaq1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/5hiaq1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/6as9t1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/6as9t1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/7kj9w1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/7kj9w1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/8kj9w1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/8kj9w1292949091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/9kj9w1292949091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292948977eiwcsbau7wtezry/9kj9w1292949091.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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