*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: Thu, 26 Nov 2009 08:09:05 -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/26/t125924840984hsfqgyo71myi1.htm/, Retrieved Thu, 26 Nov 2009 16:13:41 +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/26/t125924840984hsfqgyo71myi1.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:

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Dataseries X:

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15991.2 0 16704.4 17420.4 17872 17823.2 15583.6 0 15991.2 16704.4 17420.4 17872 19123.5 0 15583.6 15991.2 16704.4 17420.4 17838.7 0 19123.5 15583.6 15991.2 16704.4 17209.4 0 17838.7 19123.5 15583.6 15991.2 18586.5 0 17209.4 17838.7 19123.5 15583.6 16258.1 0 18586.5 17209.4 17838.7 19123.5 15141.6 0 16258.1 18586.5 17209.4 17838.7 19202.1 0 15141.6 16258.1 18586.5 17209.4 17746.5 0 19202.1 15141.6 16258.1 18586.5 19090.1 1 17746.5 19202.1 15141.6 16258.1 18040.3 1 19090.1 17746.5 19202.1 15141.6 17515.5 1 18040.3 19090.1 17746.5 19202.1 17751.8 1 17515.5 18040.3 19090.1 17746.5 21072.4 1 17751.8 17515.5 18040.3 19090.1 17170 1 21072.4 17751.8 17515.5 18040.3 19439.5 1 17170 21072.4 17751.8 17515.5 19795.4 1 19439.5 17170 21072.4 17751.8 17574.9 1 19795.4 19439.5 17170 21072.4 16165.4 1 17574.9 19795.4 19439.5 17170 19464.6 1 16165.4 17574.9 19795.4 19439.5 19932.1 1 19464.6 16165.4 17574.9 19795.4 19961.2 1 19932.1 19464.6 16165.4 17574.9 17343.4 1 19961.2 19932.1 19464.6 16165.4 1892 etc...

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] = + 3086.07012968317 + 1029.87773295181X[t] + 0.349104238294711Y1[t] + 0.244365087497713Y2[t] + 0.335553698868969Y3[t] -0.331113011041976Y4[t] + 3112.6595004969M1[t] + 3772.75130059336M2[t] + 6074.5867318478M3[t] + 3017.52005227921M4[t] + 3327.18125048565M5[t] + 4366.06517737135M6[t] + 3452.46890980666M7[t] + 1033.45678168423M8[t] + 5183.76502098402M9[t] + 5750.95578491961M10[t] + 3520.34574469702M11[t] + 8.64931994290336t + 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) 3086.07012968317 2743.367215 1.1249 0.267678 0.133839 X 1029.87773295181 530.111595 1.9428 0.059482 0.029741 Y1 0.349104238294711 0.154231 2.2635 0.029398 0.014699 Y2 0.244365087497713 0.157967 1.5469 0.130167 0.065083 Y3 0.335553698868969 0.149786 2.2402 0.031004 0.015502 Y4 -0.331113011041976 0.158431 -2.0899 0.043368 0.021684 M1 3112.6595004969 1032.343729 3.0151 0.00456 0.00228 M2 3772.75130059336 1128.104194 3.3443 0.001864 0.000932 M3 6074.5867318478 1043.812331 5.8196 1e-06 1e-06 M4 3017.52005227921 915.16174 3.2973 0.002124 0.001062 M5 3327.18125048565 860.224078 3.8678 0.000417 0.000209 M6 4366.06517737135 832.942349 5.2417 6e-06 3e-06 M7 3452.46890980666 1027.151733 3.3612 0.001779 0.00089 M8 1033.45678168423 861.254795 1.1999 0.237589 0.118795 M9 5183.76502098402 1064.699008 4.8688 2e-05 1e-05 M10 5750.95578491961 1254.265328 4.5851 4.8e-05 2.4e-05 M11 3520.34574469702 1065.262711 3.3047 0.002081 0.001041 t 8.64931994290336 11.298399 0.7655 0.448682 0.224341 Multiple Linear Regression - Regression Statistics Multiple R 0.900217706946649 R-squared 0.810391919900283 Adjusted R-squared 0.725567252487252 F-TEST (value) 9.55372941168509 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 5.256585167146e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1119.19048751641 Sum Squared Residuals 47598319.1991945 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15991.2 16391.4156463212 -400.215646321166 2 15583.6 16468.5158556123 -884.9158556123 3 19123.5 18371.6987262737 751.801273726272 4 17838.7 16457.2322679961 1381.46773200386 5 17209.4 17291.4197458337 -82.0197458337296 6 18586.5 19128.0896330134 -541.58963301338 7 16258.1 16946.8888422906 -688.788842290593 8 15141.6 14274.4369415472 867.163058452755 9 19202.1 18145.1003655654 1056.99963443461 10 17746.5 18628.3656288960 -881.86562889595 11 19090.1 18316.6887802137 773.41121978631 12 18040.3 16650.5544597565 1389.74554024352 13 17515.5 17900.7862369868 -385.286236986817 14 17751.8 19062.6010325871 -1310.80103258706 15 21072.4 20530.188602666 542.211397333997 16 17170 18870.2541047229 -1700.25410472288 17 19439.5 18890.7184001334 548.78159986659 18 19795.4 20812.9310062759 -1017.53100627586 19 17574.9 18177.8582042070 -602.958204206977 20 16165.4 17132.9535034078 -967.553503407808 21 19464.6 19625.1985448531 -160.598544853142 22 19932.1 20145.4306319171 -213.330631917135 23 19961.2 19155.1589421756 806.041057824427 24 17343.4 17341.6246815332 1.77531846682331 25 18924.2 18620.6227592028 303.577240797171 26 18574.1 19056.4982130619 -482.398213061923 27 21350.6 20743.0060392282 607.593960771834 28 18594.6 19975.5553075725 -1380.95530757255 29 19832.1 19369.3134125899 462.786587410122 30 20844.4 21222.9804832400 -378.580483239975 31 19640.2 19129.7122825814 510.487717418572 32 17735.4 17874.1240895034 -138.724089503373 33 19813.6 21003.7721154782 -1190.17211547817 34 22160 21100.3945434593 1059.60545654071 35 20664.3 19964.9751350432 699.324864956807 36 17877.4 17832.5535027985 44.8464972014501 37 20906.5 19714.6709996432 1191.82900035679 38 21164.1 19481.0514690465 1683.0485309535 39 21374.4 22181.7628858054 -807.362885805437 40 22952.3 21208.9151537494 1743.38484625058 41 21343.5 21212.9314384861 130.568561513853 42 23899.3 22069.6816895366 1829.61831046341 43 22392.9 22123.6779166053 269.222083394673 44 18274.1 18749.6707636218 -475.570763621771 45 22786.7 22492.9289741033 293.771025896705 46 22321.5 22285.9091957276 35.5908042723698 47 17842.2 20120.9771425675 -2278.77714256754 48 16373.5 17809.8673559118 -1436.36735591179 49 15933.8 16643.7043578460 -709.904357845976 50 16446.1 15451.0334296922 995.066570307783 51 17729 18823.2437460267 -1094.24374602666 52 16643 16686.643165959 -43.6431659590128 53 16196.7 17256.8170029568 -1060.11700295684 54 18252.1 18144.0171879342 108.082812065807 55 17570.4 17058.3627543157 512.037245684324 56 15836.8 15122.1147019198 714.685298080195 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.420664951862492 0.841329903724983 0.579335048137508 22 0.262872972420708 0.525745944841415 0.737127027579292 23 0.151471466131326 0.302942932262652 0.848528533868674 24 0.115600914293722 0.231201828587445 0.884399085706278 25 0.0598230685468692 0.119646137093738 0.940176931453131 26 0.0589303983150285 0.117860796630057 0.941069601684972 27 0.0468391483303059 0.0936782966606119 0.953160851669694 28 0.0659403331639868 0.131880666327974 0.934059666836013 29 0.0376295636488461 0.0752591272976922 0.962370436351154 30 0.0286847219264654 0.0573694438529307 0.971315278073535 31 0.0432394102091016 0.0864788204182033 0.956760589790898 32 0.047001246202308 0.094002492404616 0.952998753797692 33 0.0850206272734412 0.170041254546882 0.914979372726559 34 0.166914436203308 0.333828872406615 0.833085563796692 35 0.116950349147859 0.233900698295717 0.883049650852141 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 5 0.333333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/10radi1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/10radi1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/1cmt71259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/1cmt71259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/2j14r1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/2j14r1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/341231259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/341231259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/4pesa1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/4pesa1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/5wfwr1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/5wfwr1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/6r5mz1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/6r5mz1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/7uent1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/7uent1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/8nxwz1259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/8nxwz1259248140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/9w4l91259248140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125924840984hsfqgyo71myi1/9w4l91259248140.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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