Model1

*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 15:45:59 -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/t1258757341p9qxbrmgtctl7rf.htm/, Retrieved Fri, 20 Nov 2009 23:49:14 +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/t1258757341p9qxbrmgtctl7rf.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:

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562 13,9 561 15,9 555 18,2 544 19,7 537 20,1 543 19,9 594 20 611 22,6 613 20,6 611 20,1 594 20,2 595 21,8 591 22 589 19,5 584 17,5 573 18,2 567 18,8 569 19,7 621 18,8 629 18,5 628 18,7 612 18,5 595 19,3 597 18,9 593 21,4 590 22,5 580 25 574 22,9 573 22,9 573 21,3 620 22,3 626 20,9 620 19,9 588 20,2 566 19,8 557 17,7 561 18,1 549 17,6 532 18,2 526 16 511 16,3 499 17,3 555 19 565 18,6 542 18 527 17,9 510 17,8 514 18,5 517 17,4 508 19 493 17,4 490 20,6 469 18,5 478 20 528 18,8 534 18,8 518 19,7 506 15,3 502 10,6 516 6,1 528 0,9

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] = + 474.520005774436 + 4.55427432377876X[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) 474.520005774436 26.059968 18.2088 0 0 X 4.55427432377876 1.37548 3.311 0.00159 0.000795 Multiple Linear Regression - Regression Statistics Multiple R 0.395849908365107 R-squared 0.156697149952664 Adjusted R-squared 0.142403881307794 F-TEST (value) 10.9630032042323 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.00158966552042294 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 38.7250875640675 Sum Squared Residuals 88478.312003837 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562 537.824418874961 24.1755811250391 2 561 546.932967522519 14.0670324774813 3 555 557.40779846721 -2.40779846720980 4 544 564.239209952878 -20.2392099528779 5 537 566.06091968239 -29.0609196823895 6 543 565.150064817634 -22.1500648176337 7 594 565.605492250012 28.3945077499884 8 611 577.446605491836 33.5533945081636 9 613 568.338056844279 44.6619431557212 10 611 566.06091968239 44.9390803176105 11 594 566.516347114767 27.4836528852327 12 595 573.803186032813 21.1968139671866 13 591 574.714040897569 16.2859591024309 14 589 563.328355088122 25.6716449118778 15 584 554.219806440565 29.7801935594353 16 573 557.40779846721 15.5922015327902 17 567 560.140363061477 6.85963693852293 18 569 564.239209952878 4.76079004712205 19 621 560.140363061477 60.8596369385229 20 629 558.774080764343 70.2259192356566 21 628 559.684935629099 68.3150643709008 22 612 558.774080764343 53.2259192356566 23 595 562.417500223366 32.5824997766335 24 597 560.595790493855 36.4042095061451 25 593 571.981476303302 21.0185236966982 26 590 576.991178059458 13.0088219405415 27 580 588.376863868905 -8.3768638689054 28 574 578.81288778897 -4.81288778896999 29 573 578.81288778897 -5.81288778896999 30 573 571.526048870924 1.47395112907602 31 620 576.080323194703 43.9196768052973 32 626 569.704339141412 56.2956608585875 33 620 565.150064817634 54.8499351823663 34 588 566.516347114767 21.4836528852327 35 566 564.694637385256 1.30536261474417 36 557 555.13066130532 1.86933869467958 37 561 556.952371034832 4.04762896516806 38 549 554.675233872942 -5.67523387294256 39 532 557.40779846721 -25.4077984672098 40 526 547.388394954897 -21.3883949548965 41 511 548.75467725203 -37.7546772520302 42 499 553.308951575809 -54.3089515758089 43 555 561.051217926233 -6.05121792623282 44 565 559.229508196721 5.77049180327868 45 542 556.496943602454 -14.4969436024541 46 527 556.041516170076 -29.0415161700762 47 510 555.586088737698 -45.5860887376983 48 514 558.774080764343 -44.7740807643434 49 517 553.764379008187 -36.7643790081868 50 508 561.051217926233 -53.0512179262328 51 493 553.764379008187 -60.7643790081868 52 490 568.338056844279 -78.3380568442788 53 469 558.774080764343 -89.7740807643434 54 478 565.605492250012 -87.6054922500116 55 528 560.140363061477 -32.1403630614771 56 534 560.140363061477 -26.1403630614771 57 518 564.239209952878 -46.2392099528779 58 506 544.200402928251 -38.2004029282514 59 502 522.795313606491 -20.7953136064912 60 516 502.301079149487 13.6989208505132 61 528 478.618852665837 49.3811473341628 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00299330295407347 0.00598660590814694 0.997006697045927 6 0.000314382321867891 0.000628764643735783 0.999685617678132 7 0.0667740025018575 0.133548005003715 0.933225997498143 8 0.145755564676377 0.291511129352754 0.854244435323623 9 0.180519849332671 0.361039698665342 0.819480150667329 10 0.184933350353786 0.369866700707572 0.815066649646214 11 0.130239759137240 0.260479518274480 0.86976024086276 12 0.0826842738850585 0.165368547770117 0.917315726114942 13 0.0493985558999449 0.0987971117998897 0.950601444100055 14 0.0310960078744596 0.0621920157489192 0.96890399212554 15 0.0208907221296325 0.0417814442592650 0.979109277870368 16 0.0114322989793561 0.0228645979587121 0.988567701020644 17 0.00620090904245518 0.0124018180849104 0.993799090957545 18 0.00334334176185157 0.00668668352370314 0.996656658238148 19 0.00871659126913478 0.0174331825382696 0.991283408730865 20 0.0279014305250720 0.0558028610501441 0.972098569474928 21 0.0615776613444012 0.123155322688802 0.938422338655599 22 0.0753034798717058 0.150606959743412 0.924696520128294 23 0.0628692845740217 0.125738569148043 0.937130715425978 24 0.0566738966209028 0.113347793241806 0.943326103379097 25 0.044301957064568 0.088603914129136 0.955698042935432 26 0.0336421861491779 0.0672843722983558 0.966357813850822 27 0.0260873439701704 0.0521746879403409 0.97391265602983 28 0.0195362963008291 0.0390725926016582 0.980463703699171 29 0.0143400482439805 0.0286800964879611 0.98565995175602 30 0.0104762727372787 0.0209525454745573 0.989523727262721 31 0.0224935745379435 0.0449871490758869 0.977506425462057 32 0.0851535323272297 0.170307064654459 0.91484646767277 33 0.285685153093215 0.57137030618643 0.714314846906785 34 0.414129076528555 0.82825815305711 0.585870923471445 35 0.484801502510684 0.969603005021367 0.515198497489316 36 0.536716600997166 0.926566798005667 0.463283399002834 37 0.621673294422018 0.756653411155965 0.378326705577982 38 0.671867052195312 0.656265895609375 0.328132947804688 39 0.705188553571087 0.589622892857826 0.294811446428913 40 0.702476896801277 0.595046206397446 0.297523103198723 41 0.717574790302602 0.564850419394797 0.282425209697399 42 0.773674835324355 0.45265032935129 0.226325164675645 43 0.831524957763732 0.336950084472537 0.168475042236268 44 0.945691693087483 0.108616613825034 0.0543083069125169 45 0.966918497185705 0.0661630056285898 0.0330815028142949 46 0.967874566137706 0.0642508677245883 0.0321254338622942 47 0.959018795662236 0.0819624086755287 0.0409812043377643 48 0.94906788786092 0.101864224278158 0.0509321121390792 49 0.932312552468493 0.135374895063014 0.0676874475315071 50 0.911964508335428 0.176070983329143 0.0880354916645716 51 0.887019524495885 0.22596095100823 0.112980475504115 52 0.873996066000006 0.252007867999988 0.126003933999994 53 0.947309124613964 0.105381750772072 0.0526908753860359 54 0.988857825983778 0.0222843480324437 0.0111421740162219 55 0.971641875378966 0.0567162492420685 0.0283581246210342 56 0.97348787288619 0.0530242542276203 0.0265121271138101 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0576923076923077 NOK 5% type I error level 12 0.230769230769231 NOK 10% type I error level 23 0.442307692307692 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/10pebh1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/10pebh1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/1d4rs1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/1d4rs1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/2bwit1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/2bwit1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/3m4la1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/3m4la1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/47dgp1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/47dgp1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/513441258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/513441258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/682oe1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/682oe1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/7ugym1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/7ugym1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/8b64t1258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/8b64t1258757154.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/9bsv91258757154.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258757341p9qxbrmgtctl7rf/9bsv91258757154.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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