multiple regression

*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 10:47:16 -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/t1258739375jrylg4bengkdegl.htm/, Retrieved Fri, 20 Nov 2009 18:49:48 +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/2009/Nov/20/t1258739375jrylg4bengkdegl.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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462 1919 455 1911 461 1870 461 2263 463 1802 462 1863 456 1989 455 2197 456 2409 472 2502 472 2593 471 2598 465 2053 459 2213 465 2238 468 2359 467 2151 463 2474 460 3079 462 2312 461 2565 476 1972 476 2484 471 2202 453 2151 443 1976 442 2012 444 2114 438 1772 427 1957 424 2070 416 1990 406 2182 431 2008 434 1916 418 2397 412 2114 404 1778 409 1641 412 2186 406 1773 398 1785 397 2217 385 2153 390 1895 413 2475 413 1793 401 2308 397 2051 397 1898 409 2142 419 1874 424 1560 428 1808 430 1575 424 1525 433 1997 456 1753 459 1623 446 2251 441 1890

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wkl[t] = + 376.062094076818 + 0.0298370997039684bvg[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) 376.062094076818 22.573269 16.6596 0 0 bvg 0.0298370997039684 0.010765 2.7716 0.007451 0.003726 Multiple Linear Regression - Regression Statistics Multiple R 0.339409919584129 R-squared 0.115199093512105 Adjusted R-squared 0.100202467978412 F-TEST (value) 7.68166766938906 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.00745117171111098 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 24.8730936911148 Sum Squared Residuals 36501.5765962516 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 462 433.319488408733 28.6805115912674 2 455 433.080791611102 21.9192083888985 3 461 431.857470523239 29.1425294767612 4 461 443.583450706898 17.4165492931016 5 463 429.828547743369 33.1714522566311 6 462 431.648610825311 30.351389174689 7 456 435.408085388011 20.591914611989 8 455 441.614202126436 13.3857978735636 9 456 447.939667263678 8.06033273632226 10 472 450.714517536147 21.2854824638532 11 472 453.429693609208 18.5703063907921 12 471 453.578879107728 17.4211208922722 13 465 437.317659769065 27.682340230935 14 459 442.0915957217 16.9084042783001 15 465 442.837523214299 22.1624767857008 16 468 446.447812278479 21.5521877215207 17 467 440.241695540054 26.7583044599461 18 463 449.879078744436 13.1209212555643 19 460 467.930524065337 -7.93052406533657 20 462 445.045468592393 16.9545314076072 21 461 452.594254817497 8.40574518250319 22 476 434.900854693044 41.0991453069564 23 476 450.177449741475 25.8225502585246 24 471 441.763387624956 29.2366123750437 25 453 440.241695540054 12.7583044599461 26 443 435.020203091859 7.97979690814057 27 442 436.094338681202 5.90566131879771 28 444 439.137722851007 4.86227714899293 29 438 428.93343475225 9.06656524775013 30 427 434.453298197484 -7.45329819748403 31 424 437.824890464032 -13.8248904640325 32 416 435.437922487715 -19.4379224877150 33 406 441.166645630877 -35.1666456308769 34 431 435.974990282386 -4.97499028238642 35 434 433.229977109621 0.770022890378676 36 418 447.58162206723 -29.5816220672301 37 412 439.137722851007 -27.1377228510071 38 404 429.112457350474 -25.1124573504737 39 409 425.02477469103 -16.02477469103 40 412 441.285994029693 -29.2859940296928 41 406 428.963271851954 -22.9632718519538 42 398 429.321317048401 -31.3213170484015 43 397 442.210944120516 -45.2109441205158 44 385 440.301369739462 -55.3013697394618 45 390 432.603398015838 -42.603398015838 46 413 449.90891584414 -36.9089158441397 47 413 429.560013846033 -16.5600138460332 48 401 444.926120193577 -43.9261201935769 49 397 437.257985569657 -40.2579855696571 50 397 432.69290931495 -35.6929093149499 51 409 439.973161642718 -30.9731616427182 52 419 431.976818922055 -12.9768189220546 53 424 422.607969615009 1.39203038499142 54 428 430.007570341593 -2.00757034159274 55 430 423.055526110568 6.9444738894319 56 424 421.56367112537 2.43632887463032 57 433 435.646782185643 -2.64678218564276 58 456 428.366529857874 27.6334701421255 59 459 424.487706896359 34.5122931036414 60 446 443.225405510451 2.77459448954926 61 441 432.454212517318 8.54578748268185 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0043710469807148 0.0087420939614296 0.995628953019285 6 0.000607136459360414 0.00121427291872083 0.99939286354064 7 0.000170413221914201 0.000340826443828403 0.999829586778086 8 3.56571163736578e-05 7.13142327473157e-05 0.999964342883626 9 4.42641435673939e-06 8.85282871347877e-06 0.999995573585643 10 5.63982604130106e-05 0.000112796520826021 0.999943601739587 11 2.87778107006901e-05 5.75556214013802e-05 0.9999712221893 12 8.41973385408472e-06 1.68394677081694e-05 0.999991580266146 13 2.72780317336709e-06 5.45560634673418e-06 0.999997272196827 14 8.09601159597028e-07 1.61920231919406e-06 0.99999919039884 15 2.08441530095982e-07 4.16883060191965e-07 0.99999979155847 16 6.2371434721413e-08 1.24742869442826e-07 0.999999937628565 17 2.55685487675522e-08 5.11370975351044e-08 0.999999974431451 18 7.739704615356e-09 1.5479409230712e-08 0.999999992260295 19 1.03288548295657e-08 2.06577096591313e-08 0.999999989671145 20 3.17205737465386e-09 6.34411474930771e-09 0.999999996827943 21 1.19497548782806e-09 2.38995097565612e-09 0.999999998805025 22 2.03041065623680e-08 4.06082131247361e-08 0.999999979695893 23 1.35402265226444e-07 2.70804530452887e-07 0.999999864597735 24 4.31548984901247e-07 8.63097969802494e-07 0.999999568451015 25 1.14250759158454e-06 2.28501518316909e-06 0.999998857492408 26 1.13355021755983e-05 2.26710043511966e-05 0.999988664497824 27 6.2677092364401e-05 0.000125354184728802 0.999937322907636 28 0.000225001526417003 0.000450003052834005 0.999774998473583 29 0.000502027325984983 0.00100405465196997 0.999497972674015 30 0.00365138611773903 0.00730277223547806 0.996348613882261 31 0.0172373806285956 0.0344747612571912 0.982762619371404 32 0.0603507168884625 0.120701433776925 0.939649283111538 33 0.221985509407652 0.443971018815304 0.778014490592348 34 0.224511929588257 0.449023859176514 0.775488070411743 35 0.21255999813175 0.4251199962635 0.78744000186825 36 0.310441876942356 0.620883753884712 0.689558123057644 37 0.370912070297485 0.74182414059497 0.629087929702515 38 0.439526457196443 0.879052914392886 0.560473542803557 39 0.442983038430783 0.885966076861567 0.557016961569217 40 0.462130076773653 0.924260153547307 0.537869923226347 41 0.470221180972906 0.940442361945812 0.529778819027094 42 0.546253200229506 0.907493599540988 0.453746799770494 43 0.616429539179472 0.767140921641056 0.383570460820528 44 0.77386111229701 0.45227777540598 0.22613888770299 45 0.871277124574262 0.257445750851475 0.128722875425738 46 0.849671675663854 0.300656648672292 0.150328324336146 47 0.816413481600305 0.367173036799391 0.183586518399695 48 0.81134124759632 0.377317504807361 0.188658752403681 49 0.862582383260108 0.274835233479785 0.137417616739892 50 0.936245482335494 0.127509035329013 0.0637545176645065 51 0.961498665763293 0.0770026684734144 0.0385013342367072 52 0.961031590725645 0.0779368185487103 0.0389684092743552 53 0.938474728870696 0.123050542258608 0.0615252711293042 54 0.905620361263869 0.188759277472262 0.094379638736131 55 0.844618457422148 0.310763085155704 0.155381542577852 56 0.928850272793339 0.142299454413323 0.0711497272066615 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.5 NOK 5% type I error level 27 0.519230769230769 NOK 10% type I error level 29 0.557692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/108ukc1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/108ukc1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/1r3af1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/1r3af1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/2193q1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/2193q1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/3a0d31258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/3a0d31258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/4a9c41258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/4a9c41258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/5xlbk1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/5xlbk1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/62tza1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/62tza1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/7el721258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/7el721258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/83pma1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/83pma1258739231.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/99fhi1258739231.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739375jrylg4bengkdegl/99fhi1258739231.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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